From 75aff2046d409a9bc1c87a7d7a126c9ae87b619a Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Fri, 1 Dec 2023 11:53:14 -0600 Subject: [PATCH 001/178] Update transform_detect_color_card.md add link to tutorial page --- docs/transform_detect_color_card.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/transform_detect_color_card.md b/docs/transform_detect_color_card.md index e57d34744..8674432b8 100644 --- a/docs/transform_detect_color_card.md +++ b/docs/transform_detect_color_card.md @@ -15,6 +15,8 @@ Automatically detects a color card and creates a labeled mask. - radius - Radius of circle to make the color card labeled mask (default = 20). - **Returns** - labeled_mask - Labeled color card mask (useful downstream of this step in `pcv.transform.get_color_matrix` and `pcv.transform.correct_color`) +- **Example use:** + - [Color Correction Tutorial](tutorials/transform_color_correction_tutorial.md) !!! note Color chip size can only be used reasonably as a scaling factor (converting pixels to a known real world scale like cms) From bef9964b376f7680fb5031dcd24281952efa13ae Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Fri, 1 Dec 2023 11:53:50 -0600 Subject: [PATCH 002/178] Update transform_affine_color_correction.md --- docs/transform_affine_color_correction.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/transform_affine_color_correction.md b/docs/transform_affine_color_correction.md index 6320e64af..3ccfb5b11 100644 --- a/docs/transform_affine_color_correction.md +++ b/docs/transform_affine_color_correction.md @@ -15,7 +15,8 @@ Euclidean distance between the transformed source color values and the target co - rgb_img - an RGB image with color chips visualized - source_matrix - array of RGB color values (intensity in the range [0-1]) from the image to be corrected where each row is one color reference and the columns are organized as index,R,G,B - target_matrix - array of target RGB color values (intensity in the range [0-1]) where each row is one color reference and the columns are organized as index,R,G,B - +- **Example use:** + - [Color Correction Tutorial](tutorials/transform_color_correction_tutorial.md) **Reference Images** From ab3b01fe265c799f7edf5d931a1d404565c9dda5 Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Fri, 1 Dec 2023 15:03:46 -0600 Subject: [PATCH 003/178] Update analyze_grayscale.md typo of trait variable name --- docs/analyze_grayscale.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/analyze_grayscale.md b/docs/analyze_grayscale.md index 82a986a94..90f093999 100644 --- a/docs/analyze_grayscale.md +++ b/docs/analyze_grayscale.md @@ -18,7 +18,7 @@ the values out to the [Outputs class](outputs.md). Can also return/plot/print ou - **Example use:** * [Grayscale Image Tutorial](tutorials/grayscale_tutorial.md) -- **Output data stored:** Data ('gray_frequencies', 'gray_mean', 'gray_median', 'nir_stdev') automatically gets stored to +- **Output data stored:** Data ('gray_frequencies', 'gray_mean', 'gray_median', 'gray_stdev') automatically gets stored to the [`Outputs` class](outputs.md) when this function is ran. These data can always get accessed during a workflow (example below). For more detail about data output see [Summary of Output Observations](output_measurements.md#summary-of-output-observations) From e0a2ddb542f6a97c02a0e41a4452b1fd7fa953dc Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Fri, 1 Dec 2023 15:09:49 -0600 Subject: [PATCH 004/178] Update get_color_matrix.md add link to detect_color_card doc page --- docs/get_color_matrix.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/get_color_matrix.md b/docs/get_color_matrix.md index 8a15b70e4..6cac913e0 100644 --- a/docs/get_color_matrix.md +++ b/docs/get_color_matrix.md @@ -8,7 +8,7 @@ Computes the average *R*, *G*, *B* values for each region in the RGB image denot - **Parameters** - rgb_img - RGB image with color chips visualized - - mask - a gray-scale img with unique values for each segmented space, representing unique, discrete color chips. + - mask - a gray-scale img with unique values for each segmented space, representing unique, discrete color chips. Likely created with [`pcv.transform.detect_color_card`](transform_detect_color_card.md). - **Returns** - color_matrix - a *n* x 4 matrix containing the average red value, average green value, and average blue value for each color chip. From 39e8e9a5d4bca0d84210a71189e3cb70a264f9e7 Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Fri, 1 Dec 2023 15:25:48 -0600 Subject: [PATCH 005/178] Update reassign_frame_labels.py add file description --- plantcv/plantcv/photosynthesis/reassign_frame_labels.py | 1 + 1 file changed, 1 insertion(+) diff --git a/plantcv/plantcv/photosynthesis/reassign_frame_labels.py b/plantcv/plantcv/photosynthesis/reassign_frame_labels.py index 0d427594a..ee7e725ea 100644 --- a/plantcv/plantcv/photosynthesis/reassign_frame_labels.py +++ b/plantcv/plantcv/photosynthesis/reassign_frame_labels.py @@ -1,3 +1,4 @@ +"""Reassign PSII frame labels based on induction curve""" import numpy as np from plantcv.plantcv import fatal_error from plantcv.plantcv.classes import PSII_data From 948979b8d72bbd9d53068119b56390e6e675105f Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Fri, 1 Dec 2023 15:44:10 -0600 Subject: [PATCH 006/178] Update output_measurements.md add spaces for readability and add missing measurements --- docs/output_measurements.md | 41 ++++++++++++++++++++----------------- 1 file changed, 22 insertions(+), 19 deletions(-) diff --git a/docs/output_measurements.md b/docs/output_measurements.md index f15c0016d..92dde927b 100644 --- a/docs/output_measurements.md +++ b/docs/output_measurements.md @@ -147,32 +147,35 @@ suggestions for additional metadata we should track that would be useful to you, Functions that automatically store data to the [`Outputs` class](outputs.md) are [analyze.bound_horizontal](analyze_bound_horizontal2.md), -[analyze.bound_vertical](analyze_bound_vertical2.md), -[analyze.color](analyze_color2.md), -[analyze.grayscale](analyze_grayscale.md), -[analyze.size](analyze_size.md), -[analyze.spectral_index](analyze_spectral_index.md), -[analyze.spectral_reflectance](analyze_spectral_reflectance.md), -[analyze.thermal](analyze_thermal.md), +[analyze.bound_vertical](analyze_bound_vertical2.md), +[analyze.color](analyze_color2.md), +[analyze.grayscale](analyze_grayscale.md), +[analyze.size](analyze_size.md), +[analyze.spectral_index](analyze_spectral_index.md), +[analyze.spectral_reflectance](analyze_spectral_reflectance.md), +[analyze.thermal](analyze_thermal.md), [analyze.yii](analyze_yii.md), -[analyze.npq](analyze_npq.md), -[homology.landmark_reference_pt_dist](homology_landmark_reference_pt_dist.md), +[analyze.npq](analyze_npq.md), +[homology.acute](homology_landmark_reference_pt_dist.md), +[homology.landmark_reference_pt_dist](homology_landmark_reference_pt_dist.md), [homology.x_axis_pseudolandmarks](homology_x_axis_pseudolandmarks.md), -[homology.y_axis_pseudolandmarks](homology_y_axis_pseudolandmarks.md), -[morphology.check_cycles](check_cycles.md), +[homology.y_axis_pseudolandmarks](homology_y_axis_pseudolandmarks.md), +[morphology.analyze_stem](analyze_stem.md), +[morphology.check_cycles](check_cycles.md), [morphology.fill_segments](fill_segments.md), -[morphology.find_tips](find_tips.md), +[morphology.find_tips](find_tips.md), [morphology.find_branch_pts](find_branch_pts.md), -[morphology.segment_angle](segment_angle.md), +[morphology.segment_angle](segment_angle.md), [morphology.segment_curvature](segment_curvature.md), -[morphology.segment_euclidean_length](segment_euclidean_length.md), +[morphology.segment_euclidean_length](segment_euclidean_length.md), [morphology.segment_insertion_angle](segment_insertion_angle.md), -[morphology.segment_path_length](segment_pathlength.md), -[morphology.segment_tangent_angle](segment_tangent_angle.md), -[report_size_marker_area](report_size_marker.md), -[transform.find_color_card](find_color_card.md), +[morphology.segment_path_length](segment_pathlength.md), +[morphology.segment_tangent_angle](segment_tangent_angle.md), +[report_size_marker_area](report_size_marker.md), +[transform.find_color_card](find_color_card.md), +[transform.detect_color_card](transform_detect_color_card.md) [watershed_segmentation](watershed.md), and -[within_frame](within_frame.md) +[within_frame](within_frame.md). All of these functions include an optional `label` parameter that allows users to append custom prefixes to the unique variable identifier. From 565f0799b8108441070cfa34db4b7a87d39da934 Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Mon, 4 Dec 2023 11:07:38 -0600 Subject: [PATCH 007/178] Update transform_detect_color_card.md extra examples of grabbing outputs --- docs/transform_detect_color_card.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/transform_detect_color_card.md b/docs/transform_detect_color_card.md index 8674432b8..0bc669979 100644 --- a/docs/transform_detect_color_card.md +++ b/docs/transform_detect_color_card.md @@ -33,6 +33,8 @@ rgb_img, path, filename = pcv.readimage("target_img.png") cc_mask = pcv.transform.detect_color_card(rgb_img=rgb_img) avg_chip_size = pcv.outputs.observations['default']['median_color_chip_size']['value'] +avg_chip_w = pcv.outputs.observations['default']['median_color_chip_width']['value'] +avg_chip_h = pcv.outputs.observations['default']['median_color_chip_height']['value'] ``` From dd1b73d84e847768ba0cae52d50e8823076d9803 Mon Sep 17 00:00:00 2001 From: Noah Fahlgren Date: Tue, 16 Jan 2024 10:32:26 -0600 Subject: [PATCH 008/178] Update PyPi deploy workflow to use OIDC authentication --- .github/workflows/publish-package.yml | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/.github/workflows/publish-package.yml b/.github/workflows/publish-package.yml index 6a67c1355..914e31a45 100644 --- a/.github/workflows/publish-package.yml +++ b/.github/workflows/publish-package.yml @@ -33,7 +33,11 @@ jobs: deploy: needs: build runs-on: ubuntu-latest - + environment: + name: pypi + url: https://pypi.org/p/plantcv + permissions: + id-token: write steps: - uses: actions/checkout@main with: @@ -46,10 +50,8 @@ jobs: run: | python -m pip install --upgrade pip pip install setuptools wheel twine - - name: Build and publish - env: - TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} - TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + - name: Build run: | python setup.py sdist bdist_wheel - twine upload dist/* + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 From fdc3c7f045e854a9a349218a47179753c3403b3f Mon Sep 17 00:00:00 2001 From: Malia Gehan Date: Mon, 2 Oct 2023 09:27:58 -0500 Subject: [PATCH 009/178] watershed update altered watershed output so it is the labeled image rather than the matplotlib figure. 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z^%C{BnUy&&zS6Yo4c6!BRc&nOG(WwA%}+5uH1c;{ikK;k=944kd@Att`%sbw?Xo#8 zI%m?yqjU=?jVed)@!aUuFWErynY~@CT89;w35@lvo~gJon4i!(v3$UPQqv8>%MAML z8J@|wct{+kt8QF4(~|nPKsCA7w8BE*?2pAYKu+x*q3|z(=zR{PqRjeN9iaSxI9vky z?>;uAp?y**){vTJ_FZw11fZUKgaKXLZ9y+oY7b$`Al-ALusK*JNBQj#i@aw zO4i<5;TgD^-5uP3c=*J{e_2-+_>80)i{{(^bS|V-m*BqtRPTR=r;^lvh~9p113g#n h>77BZgWNc`o-VKVis}`~heAkoE#xh=auus*{{!+0xZeN( literal 0 HcmV?d00001 diff --git a/docs/watershed.md b/docs/watershed.md index 8287966be..060bc260e 100644 --- a/docs/watershed.md +++ b/docs/watershed.md @@ -41,6 +41,7 @@ analysis_image = pcv.watershed_segmentation(rgb_img=crop_img, mask=bin_mask, dis **Watershed Segmentation** +![Screenshot](img/documentation_images/watershed/watershed-labels.png) ![Screenshot](img/documentation_images/watershed/watershed.jpg) **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/main/plantcv/plantcv/watershed.py) diff --git a/plantcv/plantcv/watershed.py b/plantcv/plantcv/watershed.py index 3c380a6b2..fb3f0f963 100755 --- a/plantcv/plantcv/watershed.py +++ b/plantcv/plantcv/watershed.py @@ -68,9 +68,11 @@ def watershed_segmentation(rgb_img, mask, distance=10, label=None): # Reset debug mode params.debug = debug - _debug(visual=dist_transform, - filename=os.path.join(params.debug_outdir, str(params.device) + '_watershed_dist_img.png'), - cmap='gray') + #_debug(visual=dist_transform, + # filename=os.path.join(params.debug_outdir, str(params.device) + '_watershed_dist_img.png'), + # cmap='gray') + _debug(visual=labels, + filename=os.path.join(params.debug_outdir, str(params.device) + '_watershed__labels_img.png')) _debug(visual=joined, filename=os.path.join(params.debug_outdir, str(params.device) + '_watershed_img.png')) @@ -79,6 +81,6 @@ def watershed_segmentation(rgb_img, mask, distance=10, label=None): value=estimated_object_count, label='none') # Store images - outputs.images.append([dist_transform, joined]) + outputs.images.append([labels, joined]) - return joined + return labels From 6e5b008e662e59881572be329708918676a7fadf Mon Sep 17 00:00:00 2001 From: JorgeGtz Date: Tue, 13 Apr 2021 17:20:19 -0500 Subject: [PATCH 010/178] add detect_discs function and update init --- plantcv/plantcv/__init__.py | 2 ++ plantcv/plantcv/detect_discs.py | 46 +++++++++++++++++++++++++++++++++ 2 files changed, 48 insertions(+) create mode 100644 plantcv/plantcv/detect_discs.py diff --git a/plantcv/plantcv/__init__.py b/plantcv/plantcv/__init__.py index 637fa9284..dce46cbe2 100644 --- a/plantcv/plantcv/__init__.py +++ b/plantcv/plantcv/__init__.py @@ -74,6 +74,7 @@ from plantcv.plantcv.create_labels import create_labels from plantcv.plantcv.floodfill import floodfill from plantcv.plantcv import analyze +from plantcv.plantcv.detect_discs import detect_discs # add new functions to end of lists # Auto versioning @@ -151,4 +152,5 @@ "create_labels", "analyze", "floodfill" + "detect_discs" ] diff --git a/plantcv/plantcv/detect_discs.py b/plantcv/plantcv/detect_discs.py new file mode 100644 index 000000000..f5aac1383 --- /dev/null +++ b/plantcv/plantcv/detect_discs.py @@ -0,0 +1,46 @@ +import os +import numpy as np +from skimage.measure import label, regionprops +from plantcv.plantcv import params +from plantcv.plantcv._debug import _debug + +def detect_discs(bin_img, ecc_thresh=0): + """ Detect disc-shaped regions in a binary image based on eccentricity. + A value of eccentricity between 0 and 1 correspond to an ellipse. + The closer the value to 0 the closer the shape is to a circle. + + Inputs: + bin_img = Binary image containing the connected regions to consider + ecc_thresh = Eccentricity threshold below which a region is kept + + + Returns: + discs_mask = Binary image that contains only the detected discs + discs_coor = List of coordinates (as row,column) of the centroids of the + detected discs + + :param bin_img: numpy.ndarray + :param ecc_thresh: float + :return discs_mask: numpy.ndarray + :return discs_coor: list + """ + + # label connected regions + labeled_img = label(bin_img) + # measure regions + obj_measures = regionprops(labeled_img) + + # Check the eccentricity of each region. + # A value closer to 0 keeps only the most circular objects + discs_mask = np.zeros(labeled_img.shape, dtype=np.int32) + # Store the list of coordinates (row,col) for the objects that pass the + discs_coor = [] + for i,obj in enumerate(obj_measures): + if obj.eccentricity < ecc_thresh: + discs_coor.append(obj.centroid) + discs_mask = discs_mask + (labeled_img == i+1) + + _debug(visual=255*discs_mask, filename=os.path.join(params.debug_outdir, + str(params.device) + "_discs_mask.png")) + + return discs_mask, discs_coor From 3857e9ae637abc20a1b7e74ef02becdffb675d4e Mon Sep 17 00:00:00 2001 From: JorgeGtz Date: Tue, 13 Apr 2021 18:30:42 -0500 Subject: [PATCH 011/178] add test for detect_discs --- tests/testdata/discs_binary.png | Bin 0 -> 1363 bytes tests/tests.py | 6439 +++++++++++++++++++++++++++++++ 2 files changed, 6439 insertions(+) create mode 100644 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os.path.join(os.path.dirname(os.path.abspath(__file__)), "parallel_data") +TEST_TMPDIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", ".cache") +TEST_IMG_DIR = "images" +TEST_IMG_DIR2 = "images_w_date" +TEST_SNAPSHOT_DIR = "snapshots" +TEST_PIPELINE = os.path.join(PARALLEL_TEST_DATA, "plantcv-script.py") +META_FIELDS = {"imgtype": 0, "camera": 1, "frame": 2, "zoom": 3, "lifter": 4, "gain": 5, "exposure": 6, "id": 7} +VALID_META = { + # Camera settings + "camera": { + "label": "camera identifier", + "datatype": "", + "value": "none" + }, + "imgtype": { + "label": "image type", + "datatype": "", + "value": "none" + }, + "zoom": { + "label": "camera zoom setting", + "datatype": "", + "value": "none" + }, + "exposure": { + "label": "camera exposure setting", + "datatype": "", + "value": "none" + }, + "gain": { + "label": "camera gain setting", + "datatype": "", + "value": "none" + }, + "frame": { + "label": "image series frame identifier", + "datatype": "", + "value": "none" + }, + "lifter": { + "label": "imaging platform height setting", + "datatype": "", + "value": "none" + }, + # Date-Time + "timestamp": { + "label": "datetime of image", + "datatype": "", + "value": None + }, + # Sample attributes + "id": { + "label": "image identifier", + "datatype": "", + "value": "none" + }, + "plantbarcode": { + "label": "plant barcode identifier", + "datatype": "", + "value": "none" + }, + "treatment": { + "label": "treatment identifier", + "datatype": "", + "value": "none" + }, + "cartag": { + "label": "plant carrier identifier", + "datatype": "", + "value": "none" + }, + # Experiment attributes + "measurementlabel": { + "label": "experiment identifier", + "datatype": "", + "value": "none" + }, + # Other + "other": { + "label": "other identifier", + "datatype": "", + "value": "none" + } +} + +METADATA_COPROCESS = { + 'VIS_SV_0_z1_h1_g0_e82_117770.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'snapshots', 'snapshot57383', 'VIS_SV_0_z1_h1_g0_e82_117770.jpg'), + 'camera': 'SV', + 'imgtype': 'VIS', + 'zoom': 'z1', + 'exposure': 'e82', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': '2014-10-22 17:49:35.187', + 'id': '117770', + 'plantbarcode': 'Ca031AA010564', + 'treatment': 'none', + 'cartag': '2143', + 'measurementlabel': 'C002ch_092214_biomass', + 'other': 'none', + 'coimg': 'NIR_SV_0_z1_h1_g0_e65_117779.jpg' + }, + 'NIR_SV_0_z1_h1_g0_e65_117779.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'snapshots', 'snapshot57383', 'NIR_SV_0_z1_h1_g0_e65_117779.jpg'), + 'camera': 'SV', + 'imgtype': 'NIR', + 'zoom': 'z1', + 'exposure': 'e65', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': '2014-10-22 17:49:35.187', + 'id': '117779', + 'plantbarcode': 'Ca031AA010564', + 'treatment': 'none', + 'cartag': '2143', + 'measurementlabel': 'C002ch_092214_biomass', + 'other': 'none' + } +} +METADATA_VIS_ONLY = { + 'VIS_SV_0_z1_h1_g0_e82_117770.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'snapshots', 'snapshot57383', 'VIS_SV_0_z1_h1_g0_e82_117770.jpg'), + 'camera': 'SV', + 'imgtype': 'VIS', + 'zoom': 'z1', + 'exposure': 'e82', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': '2014-10-22 17:49:35.187', + 'id': '117770', + 'plantbarcode': 'Ca031AA010564', + 'treatment': 'none', + 'cartag': '2143', + 'measurementlabel': 'C002ch_092214_biomass', + 'other': 'none' + } +} +METADATA_NIR_ONLY = { + 'NIR_SV_0_z1_h1_g0_e65_117779.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'snapshots', 'snapshot57383', 'NIR_SV_0_z1_h1_g0_e65_117779.jpg'), + 'camera': 'SV', + 'imgtype': 'NIR', + 'zoom': 'z1', + 'exposure': 'e65', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': '2014-10-22 17:49:35.187', + 'id': '117779', + 'plantbarcode': 'Ca031AA010564', + 'treatment': 'none', + 'cartag': '2143', + 'measurementlabel': 'C002ch_092214_biomass', + 'other': 'none' + } +} +# Set the temp directory for dask +dask.config.set(temporary_directory=TEST_TMPDIR) + + +# ########################## +# Tests setup function +# ########################## +def setup_function(): + if not os.path.exists(TEST_TMPDIR): + os.mkdir(TEST_TMPDIR) + + +# ############################## +# Tests for the parallel subpackage +# ############################## +def test_plantcv_parallel_workflowconfig_save_config_file(): + # Create a test tmp directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_workflowconfig_save_config_file") + os.mkdir(cache_dir) + # Define output path/filename + template_file = os.path.join(cache_dir, "config.json") + # Create config instance + config = plantcv.parallel.WorkflowConfig() + # Save template file + config.save_config(config_file=template_file) + + assert os.path.exists(template_file) + + +def test_plantcv_parallel_workflowconfig_import_config_file(): + # Define input path/filename + config_file = os.path.join(PARALLEL_TEST_DATA, "workflow_config_template.json") + # Create config instance + config = plantcv.parallel.WorkflowConfig() + # import config file + config.import_config(config_file=config_file) + + assert config.cluster == "LocalCluster" + + +def test_plantcv_parallel_workflowconfig_validate_config(): + # Create a test tmp directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_workflowconfig_validate_config") + os.mkdir(cache_dir) + # Create config instance + config = plantcv.parallel.WorkflowConfig() + # Set valid values in config + config.input_dir = os.path.join(PARALLEL_TEST_DATA, "images") + config.json = os.path.join(cache_dir, "valid_config.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.img_outdir = cache_dir + # Validate config + assert config.validate_config() + + +def test_plantcv_parallel_workflowconfig_invalid_startdate(): + # Create a test tmp directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_workflowconfig_invalid_startdate") + os.mkdir(cache_dir) + # Create config instance + config = plantcv.parallel.WorkflowConfig() + # Set valid values in config + config.input_dir = os.path.join(PARALLEL_TEST_DATA, "images") + config.json = os.path.join(cache_dir, "valid_config.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.img_outdir = cache_dir + config.start_date = "2020-05-10" + # Validate config + assert not config.validate_config() + + +def test_plantcv_parallel_workflowconfig_invalid_enddate(): + # Create a test tmp directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_workflowconfig_invalid_enddate") + os.mkdir(cache_dir) + # Create config instance + config = plantcv.parallel.WorkflowConfig() + # Set valid values in config + config.input_dir = os.path.join(PARALLEL_TEST_DATA, "images") + config.json = os.path.join(cache_dir, "valid_config.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.img_outdir = cache_dir + config.end_date = "2020-05-10" + config.timestampformat = "%Y%m%d" + # Validate config + assert not config.validate_config() + + +def test_plantcv_parallel_workflowconfig_invalid_metadata_terms(): + # Create a test tmp directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_workflowconfig_invalid_metadata_terms") + os.mkdir(cache_dir) + # Create config instance + config = plantcv.parallel.WorkflowConfig() + # Set invalid values in config + # input_dir and json are not defined by default, but are required + # Set an incorrect metadata term + config.filename_metadata.append("invalid") + # Validate config + assert not config.validate_config() + + +def test_plantcv_parallel_workflowconfig_invalid_filename_metadata(): + # Create a test tmp directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_workflowconfig_invalid_filename_metadata") + os.mkdir(cache_dir) + # Create config instance + config = plantcv.parallel.WorkflowConfig() + # Set invalid values in config + # input_dir and json are not defined by default, but are required + # Do not set required filename_metadata + # Validate config + assert not config.validate_config() + + +def test_plantcv_parallel_workflowconfig_invalid_cluster(): + # Create a test tmp directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_workflowconfig_invalid_cluster") + os.mkdir(cache_dir) + # Create config instance + config = plantcv.parallel.WorkflowConfig() + # Set invalid values in config + # input_dir and json are not defined by default, but are required + # Set invalid cluster type + config.cluster = "MyCluster" + # Validate config + assert not config.validate_config() + + +def test_plantcv_parallel_metadata_parser_snapshots(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_snapshots", "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": "VIS", "camera": "SV"} + config.start_date = "2014-10-21 00:00:00.0" + config.end_date = "2014-10-23 00:00:00.0" + config.timestampformat = '%Y-%m-%d %H:%M:%S.%f' + config.imgformat = "jpg" + + meta = plantcv.parallel.metadata_parser(config=config) + assert meta == METADATA_VIS_ONLY + + +def test_plantcv_parallel_metadata_parser_snapshots_coimg(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_snapshots_coimg", "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": "VIS"} + config.start_date = "2014-10-21 00:00:00.0" + config.end_date = "2014-10-23 00:00:00.0" + config.timestampformat = '%Y-%m-%d %H:%M:%S.%f' + config.imgformat = "jpg" + config.coprocess = "FAKE" + + meta = plantcv.parallel.metadata_parser(config=config) + assert meta == METADATA_VIS_ONLY + + +def test_plantcv_parallel_metadata_parser_images(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_IMG_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_images", "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": "VIS"} + config.start_date = "2014" + config.end_date = "2014" + config.timestampformat = '%Y' # no date in filename so check date range and date_format are ignored + config.imgformat = "jpg" + + meta = plantcv.parallel.metadata_parser(config=config) + expected = { + 'VIS_SV_0_z1_h1_g0_e82_117770.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'images', 'VIS_SV_0_z1_h1_g0_e82_117770.jpg'), + 'camera': 'SV', + 'imgtype': 'VIS', + 'zoom': 'z1', + 'exposure': 'e82', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': None, + 'id': '117770', + 'plantbarcode': 'none', + 'treatment': 'none', + 'cartag': 'none', + 'measurementlabel': 'none', + 'other': 'none'} + } + assert meta == expected + config.include_all_subdirs = False + meta = plantcv.parallel.metadata_parser(config=config) + assert meta == expected + +def test_plantcv_parallel_metadata_parser_regex(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_IMG_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_images", "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": "VIS"} + config.start_date = "2014-10-21 00:00:00.0" + config.end_date = "2014-10-23 00:00:00.0" + config.timestampformat = '%Y-%m-%d %H:%M:%S.%f' + config.imgformat = "jpg" + config.delimiter = r'(VIS)_(SV)_(\d+)_(z1)_(h1)_(g0)_(e82)_(\d+)' + + meta = plantcv.parallel.metadata_parser(config=config) + expected = { + 'VIS_SV_0_z1_h1_g0_e82_117770.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'images', 'VIS_SV_0_z1_h1_g0_e82_117770.jpg'), + 'camera': 'SV', + 'imgtype': 'VIS', + 'zoom': 'z1', + 'exposure': 'e82', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': None, + 'id': '117770', + 'plantbarcode': 'none', + 'treatment': 'none', + 'cartag': 'none', + 'measurementlabel': 'none', + 'other': 'none'} + } + assert meta == expected + + +def test_plantcv_parallel_metadata_parser_images_outside_daterange(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_IMG_DIR2) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_images_outside_daterange", + "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "timestamp"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": "NIR"} + config.start_date = "1970-01-01 00_00_00" + config.end_date = "1970-01-01 00_00_00" + config.timestampformat = "%Y-%m-%d %H_%M_%S" + config.imgformat = "jpg" + config.delimiter = r"(NIR)_(SV)_(\d)_(z1)_(h1)_(g0)_(e65)_(\d{4}-\d{2}-\d{2} \d{2}_\d{2}_\d{2})" + + meta = plantcv.parallel.metadata_parser(config=config) + assert meta == {} + + +def test_plantcv_parallel_metadata_parser_no_default_dates(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_no_default_dates", "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": "VIS", "camera": "SV", "id": "117770"} + config.start_date = None + config.end_date = None + config.timestampformat = '%Y-%m-%d %H:%M:%S.%f' + config.imgformat = "jpg" + + meta = plantcv.parallel.metadata_parser(config=config) + assert meta == METADATA_VIS_ONLY + + +def test_plantcv_parallel_check_date_range_wrongdateformat(): + start_date = 10 + end_date = 10 + img_time = '2010-10-10' + + with pytest.raises(SystemExit, match=r'does not match format'): + date_format = '%Y%m%d' + _ = plantcv.parallel.check_date_range( + start_date, end_date, img_time, date_format) + + +def test_plantcv_parallel_metadata_parser_snapshot_outside_daterange(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_snapshot_outside_daterange", + "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": "VIS"} + config.start_date = "1970-01-01 00:00:00.0" + config.end_date = "1970-01-01 00:00:00.0" + config.timestampformat = '%Y-%m-%d %H:%M:%S.%f' + config.imgformat = "jpg" + + meta = plantcv.parallel.metadata_parser(config=config) + + assert meta == {} + + +def test_plantcv_parallel_metadata_parser_fail_images(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_fail_images", "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"cartag": "VIS"} + config.start_date = "1970-01-01 00:00:00.0" + config.end_date = "1970-01-01 00:00:00.0" + config.timestampformat = '%Y-%m-%d %H:%M:%S.%f' + config.imgformat = "jpg" + config.coprocess = "NIR" + + meta = plantcv.parallel.metadata_parser(config=config) + assert meta == METADATA_NIR_ONLY + + +def test_plantcv_parallel_metadata_parser_images_with_frame(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_images_with_frame", "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": "VIS"} + config.start_date = "2014-10-21 00:00:00.0" + config.end_date = "2014-10-23 00:00:00.0" + config.timestampformat = '%Y-%m-%d %H:%M:%S.%f' + config.imgformat = "jpg" + config.coprocess = "NIR" + + meta = plantcv.parallel.metadata_parser(config=config) + + assert meta == { + 'VIS_SV_0_z1_h1_g0_e82_117770.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'snapshots', 'snapshot57383', 'VIS_SV_0_z1_h1_g0_e82_117770.jpg'), + 'camera': 'SV', + 'imgtype': 'VIS', + 'zoom': 'z1', + 'exposure': 'e82', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': '2014-10-22 17:49:35.187', + 'id': '117770', + 'plantbarcode': 'Ca031AA010564', + 'treatment': 'none', + 'cartag': '2143', + 'measurementlabel': 'C002ch_092214_biomass', + 'other': 'none', + 'coimg': 'NIR_SV_0_z1_h1_g0_e65_117779.jpg' + }, + 'NIR_SV_0_z1_h1_g0_e65_117779.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'snapshots', 'snapshot57383', 'NIR_SV_0_z1_h1_g0_e65_117779.jpg'), + 'camera': 'SV', + 'imgtype': 'NIR', + 'zoom': 'z1', + 'exposure': 'e65', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': '2014-10-22 17:49:35.187', + 'id': '117779', + 'plantbarcode': 'Ca031AA010564', + 'treatment': 'none', + 'cartag': '2143', + 'measurementlabel': 'C002ch_092214_biomass', + 'other': 'none' + } + } + + +def test_plantcv_parallel_metadata_parser_images_no_frame(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_images_no_frame", + "output.json") + config.filename_metadata = ["imgtype", "camera", "X", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": "VIS"} + config.start_date = "2014-10-21 00:00:00.0" + config.end_date = "2014-10-23 00:00:00.0" + config.timestampformat = '%Y-%m-%d %H:%M:%S.%f' + config.imgformat = "jpg" + config.coprocess = "NIR" + + meta = plantcv.parallel.metadata_parser(config=config) + + assert meta == { + 'VIS_SV_0_z1_h1_g0_e82_117770.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'snapshots', 'snapshot57383', 'VIS_SV_0_z1_h1_g0_e82_117770.jpg'), + 'camera': 'SV', + 'imgtype': 'VIS', + 'zoom': 'z1', + 'exposure': 'e82', + 'gain': 'g0', + 'frame': 'none', + 'lifter': 'h1', + 'timestamp': '2014-10-22 17:49:35.187', + 'id': '117770', + 'plantbarcode': 'Ca031AA010564', + 'treatment': 'none', + 'cartag': '2143', + 'measurementlabel': 'C002ch_092214_biomass', + 'other': 'none', + 'coimg': 'NIR_SV_0_z1_h1_g0_e65_117779.jpg' + }, + 'NIR_SV_0_z1_h1_g0_e65_117779.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'snapshots', 'snapshot57383', 'NIR_SV_0_z1_h1_g0_e65_117779.jpg'), + 'camera': 'SV', + 'imgtype': 'NIR', + 'zoom': 'z1', + 'exposure': 'e65', + 'gain': 'g0', + 'frame': 'none', + 'lifter': 'h1', + 'timestamp': '2014-10-22 17:49:35.187', + 'id': '117779', + 'plantbarcode': 'Ca031AA010564', + 'treatment': 'none', + 'cartag': '2143', + 'measurementlabel': 'C002ch_092214_biomass', + 'other': 'none' + } + } + + +def test_plantcv_parallel_metadata_parser_images_no_camera(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_images_no_frame", "output.json") + config.filename_metadata = ["imgtype", "X", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": "VIS"} + config.start_date = "2014-10-21 00:00:00.0" + config.end_date = "2014-10-23 00:00:00.0" + config.timestampformat = '%Y-%m-%d %H:%M:%S.%f' + config.imgformat = "jpg" + config.coprocess = "NIR" + + meta = plantcv.parallel.metadata_parser(config=config) + + assert meta == { + 'VIS_SV_0_z1_h1_g0_e82_117770.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'snapshots', 'snapshot57383', 'VIS_SV_0_z1_h1_g0_e82_117770.jpg'), + 'camera': 'none', + 'imgtype': 'VIS', + 'zoom': 'z1', + 'exposure': 'e82', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': '2014-10-22 17:49:35.187', + 'id': '117770', + 'plantbarcode': 'Ca031AA010564', + 'treatment': 'none', + 'cartag': '2143', + 'measurementlabel': 'C002ch_092214_biomass', + 'other': 'none', + 'coimg': 'NIR_SV_0_z1_h1_g0_e65_117779.jpg' + }, + 'NIR_SV_0_z1_h1_g0_e65_117779.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'snapshots', 'snapshot57383', 'NIR_SV_0_z1_h1_g0_e65_117779.jpg'), + 'camera': 'none', + 'imgtype': 'NIR', + 'zoom': 'z1', + 'exposure': 'e65', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': '2014-10-22 17:49:35.187', + 'id': '117779', + 'plantbarcode': 'Ca031AA010564', + 'treatment': 'none', + 'cartag': '2143', + 'measurementlabel': 'C002ch_092214_biomass', + 'other': 'none' + } + } + + +def test_plantcv_parallel_job_builder_single_image(): + # Create cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_job_builder_single_image") + os.mkdir(cache_dir) + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + config.json = os.path.join(cache_dir, "output.json") + config.tmp_dir = cache_dir + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.img_outdir = cache_dir + config.metadata_filters = {"imgtype": "VIS", "camera": "SV"} + config.start_date = "2014-10-21 00:00:00.0" + config.end_date = "2014-10-23 00:00:00.0" + config.timestampformat = '%Y-%m-%d %H:%M:%S.%f' + config.imgformat = "jpg" + config.other_args = ["--other", "on"] + config.writeimg = True + + jobs = plantcv.parallel.job_builder(meta=METADATA_VIS_ONLY, config=config) + + image_name = list(METADATA_VIS_ONLY.keys())[0] + result_file = os.path.join(cache_dir, image_name + '.txt') + + expected = ['python', TEST_PIPELINE, '--image', METADATA_VIS_ONLY[image_name]['path'], '--outdir', + cache_dir, '--result', result_file, '--writeimg', '--other', 'on'] + + if len(expected) != len(jobs[0]): + assert False + else: + assert all([i == j] for i, j in zip(jobs[0], expected)) + + +def test_plantcv_parallel_job_builder_coprocess(): + # Create cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_job_builder_coprocess") + os.mkdir(cache_dir) + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + config.json = os.path.join(cache_dir, "output.json") + config.tmp_dir = cache_dir + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.img_outdir = cache_dir + config.metadata_filters = {"imgtype": "VIS", "camera": "SV"} + config.start_date = "2014-10-21 00:00:00.0" + config.end_date = "2014-10-23 00:00:00.0" + config.timestampformat = '%Y-%m-%d %H:%M:%S.%f' + config.imgformat = "jpg" + config.other_args = ["--other", "on"] + config.writeimg = True + config.coprocess = "NIR" + + jobs = plantcv.parallel.job_builder(meta=METADATA_COPROCESS, config=config) + + img_names = list(METADATA_COPROCESS.keys()) + vis_name = img_names[0] + vis_path = METADATA_COPROCESS[vis_name]['path'] + result_file = os.path.join(cache_dir, vis_name + '.txt') + nir_name = img_names[1] + coresult_file = os.path.join(cache_dir, nir_name + '.txt') + + expected = ['python', TEST_PIPELINE, '--image', vis_path, '--outdir', cache_dir, '--result', result_file, + '--coresult', coresult_file, '--writeimg', '--other', 'on'] + + if len(expected) != len(jobs[0]): + assert False + else: + assert all([i == j] for i, j in zip(jobs[0], expected)) + + +def test_plantcv_parallel_multiprocess_create_dask_cluster_local(): + client = plantcv.parallel.create_dask_cluster(cluster="LocalCluster", cluster_config={}) + status = client.status + client.shutdown() + assert status == "running" + + +def test_plantcv_parallel_multiprocess_create_dask_cluster(): + client = plantcv.parallel.create_dask_cluster(cluster="HTCondorCluster", cluster_config={"cores": 1, + "memory": "1GB", + "disk": "1GB"}) + status = client.status + client.shutdown() + assert status == "running" + + +def test_plantcv_parallel_multiprocess_create_dask_cluster_invalid_cluster(): + with pytest.raises(ValueError): + _ = plantcv.parallel.create_dask_cluster(cluster="Skynet", cluster_config={}) + + +def test_plantcv_parallel_convert_datetime_to_unixtime(): + unix_time = plantcv.parallel.convert_datetime_to_unixtime(timestamp_str="1970-01-01", date_format="%Y-%m-%d") + assert unix_time == 0 + + +def test_plantcv_parallel_convert_datetime_to_unixtime_bad_strptime(): + with pytest.raises(SystemExit): + _ = plantcv.parallel.convert_datetime_to_unixtime(timestamp_str="1970-01-01", date_format="%Y-%m") + + +def test_plantcv_parallel_multiprocess(): + image_name = list(METADATA_VIS_ONLY.keys())[0] + image_path = os.path.join(METADATA_VIS_ONLY[image_name]['path'], image_name) + result_file = os.path.join(TEST_TMPDIR, image_name + '.txt') + jobs = [['python', TEST_PIPELINE, '--image', image_path, '--outdir', TEST_TMPDIR, '--result', result_file, + '--writeimg', '--other', 'on']] + # Create a dask LocalCluster client + client = Client(n_workers=1) + plantcv.parallel.multiprocess(jobs, client=client) + assert os.path.exists(result_file) + + +def test_plantcv_parallel_process_results(): + # Create a test tmp directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_process_results") + os.mkdir(cache_dir) + plantcv.parallel.process_results(job_dir=os.path.join(PARALLEL_TEST_DATA, "results"), + json_file=os.path.join(cache_dir, 'appended_results.json')) + plantcv.parallel.process_results(job_dir=os.path.join(PARALLEL_TEST_DATA, "results"), + json_file=os.path.join(cache_dir, 'appended_results.json')) + # Assert that the output JSON file matches the expected output JSON file + result_file = open(os.path.join(cache_dir, "appended_results.json"), "r") + results = json.load(result_file) + result_file.close() + expected_file = open(os.path.join(PARALLEL_TEST_DATA, "appended_results.json")) + expected = json.load(expected_file) + expected_file.close() + assert results == expected + + +def test_plantcv_parallel_process_results_new_output(): + # Create a test tmp directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_process_results_new_output") + os.mkdir(cache_dir) + plantcv.parallel.process_results(job_dir=os.path.join(PARALLEL_TEST_DATA, "results"), + json_file=os.path.join(cache_dir, 'new_result.json')) + # Assert output matches expected values + result_file = open(os.path.join(cache_dir, "new_result.json"), "r") + results = json.load(result_file) + result_file.close() + expected_file = open(os.path.join(PARALLEL_TEST_DATA, "new_result.json")) + expected = json.load(expected_file) + expected_file.close() + assert results == expected + + +def test_plantcv_parallel_process_results_valid_json(): + # Test when the file is a valid json file but doesn't contain expected keys + with pytest.raises(RuntimeError): + plantcv.parallel.process_results(job_dir=os.path.join(PARALLEL_TEST_DATA, "results"), + json_file=os.path.join(PARALLEL_TEST_DATA, "valid.json")) + + +def test_plantcv_parallel_process_results_invalid_json(): + # Create a test tmp directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_process_results_invalid_json") + os.mkdir(cache_dir) + # Move the test data to the tmp directory + shutil.copytree(os.path.join(PARALLEL_TEST_DATA, "bad_results"), os.path.join(cache_dir, "bad_results")) + with pytest.raises(RuntimeError): + plantcv.parallel.process_results(job_dir=os.path.join(cache_dir, "bad_results"), + json_file=os.path.join(cache_dir, "bad_results", "invalid.txt")) + + +# #################################################################################################################### +# ########################################### PLANTCV MAIN PACKAGE ################################################### +matplotlib.use('Template') + +TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data") +HYPERSPECTRAL_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "hyperspectral_data") +HYPERSPECTRAL_DATA = "darkReference" +HYPERSPECTRAL_WHITE = "darkReference_whiteReference" +HYPERSPECTRAL_DARK = "darkReference_darkReference" +HYPERSPECTRAL_HDR = "darkReference.hdr" +HYPERSPECTRAL_MASK = "darkReference_mask.png" +HYPERSPECTRAL_DATA_NO_DEFAULT = "darkReference2" +HYPERSPECTRAL_HDR_NO_DEFAULT = "darkReference2.hdr" +HYPERSPECTRAL_DATA_APPROX_PSEUDO = "darkReference3" +HYPERSPECTRAL_HDR_APPROX_PSEUDO = "darkReference3.hdr" +HYPERSPECTRAL_HDR_SMALL_RANGE = {'description': '{[HEADWALL Hyperspec III]}', 'samples': '800', 'lines': '1', + 'bands': '978', 'header offset': '0', 'file type': 'ENVI Standard', + 'interleave': 'bil', 'sensor type': 'Unknown', 'byte order': '0', + 'default bands': '159,253,520', 'wavelength units': 'nm', + 'wavelength': ['379.027', '379.663', '380.3', '380.936', '381.573', '382.209']} +FLUOR_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "photosynthesis_data") +FLUOR_IMG = "PSII_PSD_supopt_temp_btx623_22_rep1.DAT" +TEST_COLOR_DIM = (2056, 2454, 3) +TEST_GRAY_DIM = (2056, 2454) +TEST_BINARY_DIM = TEST_GRAY_DIM +TEST_INPUT_COLOR = "input_color_img.jpg" +TEST_INPUT_GRAY = "input_gray_img.jpg" +TEST_INPUT_GRAY_SMALL = "input_gray_img_small.jpg" +TEST_INPUT_BINARY = "input_binary_img.png" +# Image from http://www.libpng.org/pub/png/png-OwlAlpha.html +# This image may be used, edited and reproduced freely. +TEST_INPUT_RGBA = "input_rgba.png" +TEST_INPUT_BAYER = "bayer_img.png" +TEST_INPUT_ROI_CONTOUR = "input_roi_contour.npz" +TEST_INPUT_ROI_HIERARCHY = "input_roi_hierarchy.npz" +TEST_INPUT_CONTOURS = "input_contours.npz" +TEST_INPUT_OBJECT_CONTOURS = "input_object_contours.npz" +TEST_INPUT_OBJECT_HIERARCHY = "input_object_hierarchy.npz" +TEST_VIS = "VIS_SV_0_z300_h1_g0_e85_v500_93054.png" +TEST_NIR = "NIR_SV_0_z300_h1_g0_e15000_v500_93059.png" +TEST_VIS_TV = "VIS_TV_0_z300_h1_g0_e85_v500_93054.png" +TEST_NIR_TV = "NIR_TV_0_z300_h1_g0_e15000_v500_93059.png" +TEST_INPUT_MASK = "input_mask_binary.png" +TEST_INPUT_MASK_OOB = "mask_outbounds.png" +TEST_INPUT_MASK_RESIZE = "input_mask_resize.png" +TEST_INPUT_NIR_MASK = "input_nir.png" +TEST_INPUT_FDARK = "FLUO_TV_dark.png" +TEST_INPUT_FDARK_LARGE = "FLUO_TV_DARK_large" +TEST_INPUT_FMIN = "FLUO_TV_min.png" +TEST_INPUT_FMAX = "FLUO_TV_max.png" +TEST_INPUT_FMASK = "FLUO_TV_MASK.png" +TEST_INPUT_GREENMAG = "input_green-magenta.jpg" +TEST_INPUT_MULTI = "multi_ori_image.jpg" +TEST_INPUT_MULTI_MASK = "multi_ori_mask.jpg" +TEST_INPUT_MULTI_OBJECT = "roi_objects.npz" +TEST_INPUT_MULTI_CONTOUR = "multi_contours.npz" +TEST_INPUT_ClUSTER_CONTOUR = "clusters_i.npz" +TEST_INPUT_MULTI_HIERARCHY = "multi_hierarchy.npz" +TEST_INPUT_VISUALIZE_CONTOUR = "roi_objects_visualize.npz" +TEST_INPUT_VISUALIZE_HIERARCHY = "roi_obj_hierarchy_visualize.npz" +TEST_INPUT_VISUALIZE_CLUSTERS = "clusters_i_visualize.npz" +TEST_INPUT_VISUALIZE_BACKGROUND = "visualize_background_img.png" +TEST_INPUT_GENOTXT = "cluster_names.txt" +TEST_INPUT_GENOTXT_TOO_MANY = "cluster_names_too_many.txt" +TEST_INPUT_CROPPED = 'cropped_img.jpg' +TEST_INPUT_CROPPED_MASK = 'cropped-mask.png' +TEST_INPUT_MARKER = 'seed-image.jpg' +TEST_INPUT_SKELETON = 'input_skeleton.png' +TEST_INPUT_SKELETON_PRUNED = 'input_pruned_skeleton.png' +TEST_FOREGROUND = "TEST_FOREGROUND.jpg" +TEST_BACKGROUND = "TEST_BACKGROUND.jpg" +TEST_PDFS = "naive_bayes_pdfs.txt" +TEST_PDFS_BAD = "naive_bayes_pdfs_bad.txt" +TEST_VIS_SMALL = "setaria_small_vis.png" +TEST_MASK_SMALL = "setaria_small_mask.png" +TEST_VIS_COMP_CONTOUR = "setaria_composed_contours.npz" +TEST_ACUTE_RESULT = np.asarray([[[119, 285]], [[151, 280]], [[168, 267]], [[168, 262]], [[171, 261]], [[224, 269]], + [[246, 271]], [[260, 277]], [[141, 248]], [[183, 194]], [[188, 237]], [[173, 240]], + [[186, 260]], [[147, 244]], [[163, 246]], [[173, 268]], [[170, 272]], [[151, 320]], + [[195, 289]], [[228, 272]], [[210, 272]], [[209, 247]], [[210, 232]]]) +TEST_VIS_SMALL_PLANT = "setaria_small_plant_vis.png" +TEST_MASK_SMALL_PLANT = "setaria_small_plant_mask.png" +TEST_VIS_COMP_CONTOUR_SMALL_PLANT = "setaria_small_plant_composed_contours.npz" +TEST_SAMPLED_RGB_POINTS = "sampled_rgb_points.txt" +TEST_TARGET_IMG = "target_img.png" +TEST_TARGET_IMG_WITH_HEXAGON = "target_img_w_hexagon.png" +TEST_TARGET_IMG_TRIANGLE = "target_img copy.png" +TEST_SOURCE1_IMG = "source1_img.png" +TEST_SOURCE2_IMG = "source2_img.png" +TEST_TARGET_MASK = "mask_img.png" +TEST_TARGET_IMG_COLOR_CARD = "color_card_target.png" +TEST_SOURCE2_MASK = "mask2_img.png" +TEST_TARGET_MATRIX = "target_matrix.npz" +TEST_SOURCE1_MATRIX = "source1_matrix.npz" +TEST_SOURCE2_MATRIX = "source2_matrix.npz" +TEST_MATRIX_B1 = "matrix_b1.npz" +TEST_MATRIX_B2 = "matrix_b2.npz" +TEST_TRANSFORM1 = "transformation_matrix1.npz" +TEST_MATRIX_M1 = "matrix_m1.npz" +TEST_MATRIX_M2 = "matrix_m2.npz" +TEST_S1_CORRECTED = "source_corrected.png" +TEST_SKELETON_OBJECTS = "skeleton_objects.npz" +TEST_SKELETON_HIERARCHIES = "skeleton_hierarchies.npz" +TEST_THERMAL_ARRAY = "thermal_img.npz" +TEST_THERMAL_IMG_MASK = "thermal_img_mask.png" +TEST_INPUT_THERMAL_CSV = "FLIR2600.csv" +PIXEL_VALUES = "pixel_inspector_rgb_values.txt" +TEST_DISCS_MASK = 'discs_binary.png' + + +# ########################## +# Tests for the main package +# ########################## +@pytest.mark.parametrize("debug", ["print", "plot"]) +def test_plantcv_debug(debug, tmpdir): + from plantcv.plantcv._debug import _debug + # Create a test tmp directory + img_outdir = tmpdir.mkdir("sub") + pcv.params.debug = debug + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + _debug(visual=img, filename=os.path.join(img_outdir, TEST_INPUT_COLOR)) + assert True + + +@pytest.mark.parametrize("datatype,value", [[list, []], [int, 2], [float, 2.2], [bool, True], [str, "2"], [dict, {}], + [tuple, ()], [None, None]]) +def test_plantcv_outputs_add_observation(datatype, value): + # Create output instance + outputs = pcv.Outputs() + outputs.add_observation(sample='default', variable='test', trait='test variable', method='type', scale='none', + datatype=datatype, value=value, label=[]) + assert outputs.observations["default"]["test"]["value"] == value + + +def test_plantcv_outputs_add_observation_invalid_type(): + # Create output instance + outputs = pcv.Outputs() + with pytest.raises(RuntimeError): + outputs.add_observation(sample='default', variable='test', trait='test variable', method='type', scale='none', + datatype=list, value=np.array([2]), label=[]) + + +def test_plantcv_outputs_save_results_json_newfile(tmpdir): + # Create a test tmp directory + cache_dir = tmpdir.mkdir("sub") + outfile = os.path.join(cache_dir, "results.json") + # Create output instance + outputs = pcv.Outputs() + outputs.add_observation(sample='default', variable='test', trait='test variable', method='test', scale='none', + datatype=str, value="test", label="none") + outputs.save_results(filename=outfile, outformat="json") + with open(outfile, "r") as fp: + results = json.load(fp) + assert results["observations"]["default"]["test"]["value"] == "test" + + +def test_plantcv_outputs_save_results_json_existing_file(tmpdir): + # Create a test tmp directory + cache_dir = tmpdir.mkdir("sub") + outfile = os.path.join(cache_dir, "data_results.txt") + shutil.copyfile(os.path.join(TEST_DATA, "data_results.txt"), outfile) + # Create output instance + outputs = pcv.Outputs() + outputs.add_observation(sample='default', variable='test', trait='test variable', method='test', scale='none', + datatype=str, value="test", label="none") + outputs.save_results(filename=outfile, outformat="json") + with open(outfile, "r") as fp: + results = json.load(fp) + assert results["observations"]["default"]["test"]["value"] == "test" + + +def test_plantcv_outputs_save_results_csv(tmpdir): + # Create a test tmp directory + cache_dir = tmpdir.mkdir("sub") + outfile = os.path.join(cache_dir, "results.csv") + testfile = os.path.join(TEST_DATA, "data_results.csv") + # Create output instance + outputs = pcv.Outputs() + outputs.add_observation(sample='default', variable='string', trait='string variable', method='string', scale='none', + datatype=str, value="string", label="none") + outputs.add_observation(sample='default', variable='boolean', trait='boolean variable', method='boolean', + scale='none', datatype=bool, value=True, label="none") + outputs.add_observation(sample='default', variable='list', trait='list variable', method='list', + scale='none', datatype=list, value=[1, 2, 3], label=[1, 2, 3]) + outputs.add_observation(sample='default', variable='tuple', trait='tuple variable', method='tuple', + scale='none', datatype=tuple, value=(1, 2), label=(1, 2)) + outputs.add_observation(sample='default', variable='tuple_list', trait='list of tuples variable', + method='tuple_list', scale='none', datatype=list, value=[(1, 2), (3, 4)], label=[1, 2]) + outputs.save_results(filename=outfile, outformat="csv") + with open(outfile, "r") as fp: + results = fp.read() + with open(testfile, "r") as fp: + test_results = fp.read() + assert results == test_results + + +def test_plantcv_transform_warp_smaller(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR),-1) + bimg = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY),-1) + bimg_small = cv2.resize(bimg, (200,300)) #not sure why INTER_NEAREST doesn't preserve values + bimg_small[bimg_small>0]=255 + mrow, mcol = bimg_small.shape + vrow, vcol, vdepth = img.shape + pcv.params.debug = None + mask_warped = pcv.transform.warp(bimg_small, img[:,:,2], + pts = [(0,0),(mcol-1,0),(mcol-1,mrow-1),(0,mrow-1)], + refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1)]) + pcv.params.debug = 'plot' + mask_warped_plot = pcv.transform.warp(bimg_small, img[:,:,2], + pts = [(0,0),(mcol-1,0),(mcol-1,mrow-1),(0,mrow-1)], + refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1)]) + + assert np.count_nonzero(mask_warped)==93142 + assert np.count_nonzero(mask_warped_plot)==93142 + + +def test_plantcv_transform_warp_larger(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR),-1) + gimg = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) + gimg_large = cv2.resize(gimg, (5000,7000)) + mrow, mcol = gimg_large.shape + vrow, vcol, vdepth = img.shape + pcv.params.debug='print' + mask_warped_print = pcv.transform.warp(gimg_large, img, + pts = [(0,0),(mcol-1,0),(mcol-1,mrow-1),(0,mrow-1)], + refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1)]) + + assert np.sum(mask_warped_print)==83103814 + + +def test_plantcv_transform_warp_rgbimgerror(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR),-1) + gimg = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) + gimg_large = cv2.resize(gimg, (5000,7000)) + mrow, mcol = gimg_large.shape + vrow, vcol, vdepth = img.shape + + with pytest.raises(RuntimeError): + _ = pcv.transform.warp(img, img, + pts = [(0,0),(mcol-1,0),(mcol-1,mrow-1),(0,mrow-1)], + refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1)]) + + +def test_plantcv_transform_warp_4ptserror(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR),-1) + mrow, mcol, _ = img.shape + vrow, vcol, vdepth = img.shape + + with pytest.raises(RuntimeError): + _ = pcv.transform.warp(img[:,:,0], img, + pts = [(0,0),(mcol-1,0),(0,mrow-1)], + refpts = [(0,0),(vcol-1,0),(0,vrow-1)]) + + with pytest.raises(RuntimeError): + _ = pcv.transform.warp(img[:,:,1], img, + pts = [(0,0),(mcol-1,0),(0,mrow-1)], + refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1)]) + + with pytest.raises(RuntimeError): + _ = pcv.transform.warp(img[:,:,2], img, + pts = [(0,0),(mcol-1,0),(mcol-1,mrow-1),(0,mrow-1)], + refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1),(0,vrow-1)]) + + +def test_plantcv_acute(): + # Read in test data + mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR), encoding="latin1") + obj_contour = contours_npz['arr_0'] + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.acute(obj=obj_contour, win=5, thresh=15, mask=mask) + _ = pcv.acute(obj=obj_contour, win=0, thresh=15, mask=mask) + _ = pcv.acute(obj=np.array(([[213, 190]], [[83, 61]], [[149, 246]])), win=84, thresh=192, mask=mask) + _ = pcv.acute(obj=np.array(([[3, 29]], [[31, 102]], [[161, 63]])), win=148, thresh=56, mask=mask) + _ = pcv.acute(obj=np.array(([[103, 154]], [[27, 227]], [[152, 83]])), win=35, thresh=0, mask=mask) + # Test with debug = None + pcv.params.debug = None + _ = pcv.acute(obj=np.array(([[103, 154]], [[27, 227]], [[152, 83]])), win=35, thresh=0, mask=mask) + _ = pcv.acute(obj=obj_contour, win=0, thresh=15, mask=mask) + homology_pts = pcv.acute(obj=obj_contour, win=5, thresh=15, mask=mask) + assert all([i == j] for i, j in zip(np.shape(homology_pts), (29, 1, 2))) + + +def test_plantcv_acute_vertex(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_acute_vertex") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_VIS_SMALL)) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR), encoding="latin1") + obj_contour = contours_npz['arr_0'] + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.acute_vertex(obj=obj_contour, win=5, thresh=15, sep=5, img=img, label="prefix") + _ = pcv.acute_vertex(obj=[], win=5, thresh=15, sep=5, img=img) + _ = pcv.acute_vertex(obj=[], win=.01, thresh=.01, sep=1, img=img) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.acute_vertex(obj=obj_contour, win=5, thresh=15, sep=5, img=img) + # Test with debug = None + pcv.params.debug = None + acute = pcv.acute_vertex(obj=obj_contour, win=5, thresh=15, sep=5, img=img) + assert all([i == j] for i, j in zip(np.shape(acute), np.shape(TEST_ACUTE_RESULT))) + pcv.outputs.clear() + + +def test_plantcv_acute_vertex_bad_obj(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_VIS_SMALL)) + obj_contour = np.array([]) + pcv.params.debug = None + result = pcv.acute_vertex(obj=obj_contour, win=5, thresh=15, sep=5, img=img) + assert all([i == j] for i, j in zip(result, [0, ("NA", "NA")])) + pcv.outputs.clear() + + +def test_plantcv_analyze_bound_horizontal(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_bound_horizontal") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + img_above_bound_only = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL_PLANT)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + object_contours = contours_npz['arr_0'] + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=300, label="prefix") + pcv.outputs.clear() + _ = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=100) + _ = pcv.analyze_bound_horizontal(img=img_above_bound_only, obj=object_contours, mask=mask, line_position=1756) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=1756) + # Test with debug = None + pcv.params.debug = None + _ = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=1756) + assert len(pcv.outputs.observations["default"]) == 7 + + +def test_plantcv_analyze_bound_horizontal_grayscale_image(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + object_contours = contours_npz['arr_0'] + # Test with a grayscale reference image and debug="plot" + pcv.params.debug = "plot" + boundary_img1 = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=1756) + assert len(np.shape(boundary_img1)) == 3 + + +def test_plantcv_analyze_bound_horizontal_neg_y(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_bound_horizontal") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + object_contours = contours_npz['arr_0'] + # Test with debug=None, line position that will trigger -y + pcv.params.debug = "plot" + _ = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=-1000) + _ = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=0) + _ = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=2056) + assert pcv.outputs.observations['default']['height_above_reference']['value'] == 713 + + +def test_plantcv_analyze_bound_vertical(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_bound_vertical") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + object_contours = contours_npz['arr_0'] + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.analyze_bound_vertical(img=img, obj=object_contours, mask=mask, line_position=1000, label="prefix") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.analyze_bound_vertical(img=img, obj=object_contours, mask=mask, line_position=1000) + # Test with debug = None + pcv.params.debug = None + _ = pcv.analyze_bound_vertical(img=img, obj=object_contours, mask=mask, line_position=1000) + assert pcv.outputs.observations['default']['width_left_reference']['value'] == 94 + + +def test_plantcv_analyze_bound_vertical_grayscale_image(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_bound_vertical") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + object_contours = contours_npz['arr_0'] + # Test with a grayscale reference image and debug="plot" + pcv.params.debug = "plot" + _ = pcv.analyze_bound_vertical(img=img, obj=object_contours, mask=mask, line_position=1000) + assert pcv.outputs.observations['default']['width_left_reference']['value'] == 94 + pcv.outputs.clear() + + +def test_plantcv_analyze_bound_vertical_neg_x(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_bound_vertical") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + object_contours = contours_npz['arr_0'] + # Test with debug="plot", line position that will trigger -x + pcv.params.debug = "plot" + _ = pcv.analyze_bound_vertical(img=img, obj=object_contours, mask=mask, line_position=2454) + assert pcv.outputs.observations['default']['width_left_reference']['value'] == 441 + + +def test_plantcv_analyze_bound_vertical_small_x(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_bound_vertical") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + object_contours = contours_npz['arr_0'] + # Test with debug='plot', line position that will trigger -x, and two channel object + pcv.params.debug = "plot" + _ = pcv.analyze_bound_vertical(img=img, obj=object_contours, mask=mask, line_position=1) + assert pcv.outputs.observations['default']['width_right_reference']['value'] == 441 + + +def test_plantcv_analyze_color(): + # Clear previous outputs + pcv.outputs.clear() + # Test with debug = None + pcv.params.debug = None + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type="all") + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type=None, label="prefix") + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type=None) + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type='lab') + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type='hsv') + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type=None) + + # Test with debug = "print" + # pcv.params.debug = "print" + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type="all") + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type=None, label="prefix") + + # Test with debug = "plot" + # pcv.params.debug = "plot" + # _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type=None) + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type='lab') + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type='hsv') + # _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type=None) + + # Test with debug = None + # pcv.params.debug = None + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type='rgb') + assert pcv.outputs.observations['default']['hue_median']['value'] == 84.0 + +def test_plantcv_analyze_color_incorrect_image(): + img_binary = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + with pytest.raises(RuntimeError): + _ = pcv.analyze_color(rgb_img=img_binary, mask=mask, hist_plot_type=None) +# +# +def test_plantcv_analyze_color_bad_hist_type(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + pcv.params.debug = "plot" + with pytest.raises(RuntimeError): + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type='bgr') + + +def test_plantcv_analyze_color_incorrect_hist_plot_type(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + with pytest.raises(RuntimeError): + pcv.params.debug = "plot" + _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type="bgr") + + +def test_plantcv_analyze_nir(): + # Clear previous outputs + pcv.outputs.clear() + # Test with debug=None + pcv.params.debug = None + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), 0) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + + _ = pcv.analyze_nir_intensity(gray_img=img, mask=mask, bins=256, histplot=True) + result = len(pcv.outputs.observations['default']['nir_frequencies']['value']) + assert result == 256 + + +def test_plantcv_analyze_nir_16bit(): + # Clear previous outputs + pcv.outputs.clear() + # Test with debug=None + pcv.params.debug = None + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), 0) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + + _ = pcv.analyze_nir_intensity(gray_img=np.uint16(img), mask=mask, bins=256, histplot=True) + result = len(pcv.outputs.observations['default']['nir_frequencies']['value']) + assert result == 256 + + +def test_plantcv_analyze_object(): + # Test with debug = None + pcv.params.debug = None + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + obj_contour = contours_npz['arr_0'] + obj_images = pcv.analyze_object(img=img, obj=obj_contour, mask=mask) + pcv.outputs.clear() + assert len(obj_images) != 0 + + +def test_plantcv_analyze_object_grayscale_input(): + # Test with debug = None + pcv.params.debug = None + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), 0) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + obj_contour = contours_npz['arr_0'] + obj_images = pcv.analyze_object(img=img, obj=obj_contour, mask=mask) + assert len(obj_images) != 1 + + +def test_plantcv_analyze_object_zero_slope(): + # Test with debug = None + pcv.params.debug = None + # Create a test image + img = np.zeros((50, 50, 3), dtype=np.uint8) + img[10:11, 10:40, 0] = 255 + mask = img[:, :, 0] + obj_contour = np.array([[[10, 10]], [[11, 10]], [[12, 10]], [[13, 10]], [[14, 10]], [[15, 10]], [[16, 10]], + [[17, 10]], [[18, 10]], [[19, 10]], [[20, 10]], [[21, 10]], [[22, 10]], [[23, 10]], + [[24, 10]], [[25, 10]], [[26, 10]], [[27, 10]], [[28, 10]], [[29, 10]], [[30, 10]], + [[31, 10]], [[32, 10]], [[33, 10]], [[34, 10]], [[35, 10]], [[36, 10]], [[37, 10]], + [[38, 10]], [[39, 10]], [[38, 10]], [[37, 10]], [[36, 10]], [[35, 10]], [[34, 10]], + [[33, 10]], [[32, 10]], [[31, 10]], [[30, 10]], [[29, 10]], [[28, 10]], [[27, 10]], + [[26, 10]], [[25, 10]], [[24, 10]], [[23, 10]], [[22, 10]], [[21, 10]], [[20, 10]], + [[19, 10]], [[18, 10]], [[17, 10]], [[16, 10]], [[15, 10]], [[14, 10]], [[13, 10]], + [[12, 10]], [[11, 10]]], dtype=np.int32) + obj_images = pcv.analyze_object(img=img, obj=obj_contour, mask=mask) + assert len(obj_images) != 0 + + +def test_plantcv_analyze_object_longest_axis_2d(): + # Test with debug = None + pcv.params.debug = None + # Create a test image + img = np.zeros((50, 50, 3), dtype=np.uint8) + img[0:5, 45:49, 0] = 255 + img[0:5, 0:5, 0] = 255 + mask = img[:, :, 0] + obj_contour = np.array([[[45, 1]], [[45, 2]], [[45, 3]], [[45, 4]], [[46, 4]], [[47, 4]], [[48, 4]], + [[48, 3]], [[48, 2]], [[48, 1]], [[47, 1]], [[46, 1]], [[1, 1]], [[1, 2]], + [[1, 3]], [[1, 4]], [[2, 4]], [[3, 4]], [[4, 4]], [[4, 3]], [[4, 2]], + [[4, 1]], [[3, 1]], [[2, 1]]], dtype=np.int32) + obj_images = pcv.analyze_object(img=img, obj=obj_contour, mask=mask) + assert len(obj_images) != 0 + + +def test_plantcv_analyze_object_longest_axis_2e(): + # Test with debug = None + pcv.params.debug = None + # Create a test image + img = np.zeros((50, 50, 3), dtype=np.uint8) + img[10:15, 10:40, 0] = 255 + mask = img[:, :, 0] + obj_contour = np.array([[[10, 10]], [[10, 11]], [[10, 12]], [[10, 13]], [[10, 14]], [[11, 14]], [[12, 14]], + [[13, 14]], [[14, 14]], [[15, 14]], [[16, 14]], [[17, 14]], [[18, 14]], [[19, 14]], + [[20, 14]], [[21, 14]], [[22, 14]], [[23, 14]], [[24, 14]], [[25, 14]], [[26, 14]], + [[27, 14]], [[28, 14]], [[29, 14]], [[30, 14]], [[31, 14]], [[32, 14]], [[33, 14]], + [[34, 14]], [[35, 14]], [[36, 14]], [[37, 14]], [[38, 14]], [[39, 14]], [[39, 13]], + [[39, 12]], [[39, 11]], [[39, 10]], [[38, 10]], [[37, 10]], [[36, 10]], [[35, 10]], + [[34, 10]], [[33, 10]], [[32, 10]], [[31, 10]], [[30, 10]], [[29, 10]], [[28, 10]], + [[27, 10]], [[26, 10]], [[25, 10]], [[24, 10]], [[23, 10]], [[22, 10]], [[21, 10]], + [[20, 10]], [[19, 10]], [[18, 10]], [[17, 10]], [[16, 10]], [[15, 10]], [[14, 10]], + [[13, 10]], [[12, 10]], [[11, 10]]], dtype=np.int32) + obj_images = pcv.analyze_object(img=img, obj=obj_contour, mask=mask) + assert len(obj_images) != 0 + + +def test_plantcv_analyze_object_small_contour(): + # Test with debug = None + pcv.params.debug = None + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + obj_contour = [np.array([[[0, 0]], [[0, 50]], [[50, 50]], [[50, 0]]], dtype=np.int32)] + obj_images = pcv.analyze_object(img=img, obj=obj_contour, mask=mask) + assert obj_images is None + + +def test_plantcv_analyze_thermal_values(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_thermal_values") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + # img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), 0) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_THERMAL_IMG_MASK), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_THERMAL_ARRAY), encoding="latin1") + img = contours_npz['arr_0'] + + pcv.params.debug = None + thermal_hist = pcv.analyze_thermal_values(thermal_array=img, mask=mask, histplot=True) + assert thermal_hist is not None and pcv.outputs.observations['default']['median_temp']['value'] == 33.20922 + +def test_plantcv_apply_mask_white(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_apply_mask_white") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.apply_mask(img=img, mask=mask, mask_color="white") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.apply_mask(img=img, mask=mask, mask_color="white") + # Test with debug = None + pcv.params.debug = None + masked_img = pcv.apply_mask(img=img, mask=mask, mask_color="white") + assert all([i == j] for i, j in zip(np.shape(masked_img), TEST_COLOR_DIM)) + + +def test_plantcv_apply_mask_black(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_apply_mask_black") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.apply_mask(img=img, mask=mask, mask_color="black") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.apply_mask(img=img, mask=mask, mask_color="black") + # Test with debug = None + pcv.params.debug = None + masked_img = pcv.apply_mask(img=img, mask=mask, mask_color="black") + assert all([i == j] for i, j in zip(np.shape(masked_img), TEST_COLOR_DIM)) + + +def test_plantcv_apply_mask_hyperspectral(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_apply_mask_hyperspectral") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + hyper_array = pcv.hyperspectral.read_data(filename=spectral_filename) + + img = np.ones((2056, 2454)) + img_stacked = cv2.merge((img, img, img, img)) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.apply_mask(img=img_stacked, mask=img, mask_color="black") + # Test with debug = "plot" + pcv.params.debug = "plot" + masked_array = pcv.apply_mask(img=hyper_array.array_data, mask=img, mask_color="black") + assert np.mean(masked_array) == 13.97111260224949 + + +def test_plantcv_apply_mask_bad_input(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + with pytest.raises(RuntimeError): + pcv.params.debug = "plot" + _ = pcv.apply_mask(img=img, mask=mask, mask_color="wite") + + +def test_plantcv_auto_crop(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_auto_crop") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), -1) + contours = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_OBJECT), encoding="latin1") + roi_contours = [contours[arr_n] for arr_n in contours] + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.auto_crop(img=img1, obj=roi_contours[1], padding_x=(20, 10), padding_y=(20, 10), color='black') + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.auto_crop(img=img1, obj=roi_contours[1], color='image') + _ = pcv.auto_crop(img=img1, obj=roi_contours[1], padding_x=2000, padding_y=2000, color='image') + # Test with debug = None + pcv.params.debug = None + cropped = pcv.auto_crop(img=img1, obj=roi_contours[1], padding_x=20, padding_y=20, color='black') + x, y, z = np.shape(img1) + x1, y1, z1 = np.shape(cropped) + assert x > x1 + + +def test_plantcv_auto_crop_grayscale_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_auto_crop_grayscale_input") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), -1) + gray_img = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY) + contours = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_OBJECT), encoding="latin1") + roi_contours = [contours[arr_n] for arr_n in contours] + # Test with debug = "plot" + pcv.params.debug = "plot" + cropped = pcv.auto_crop(img=gray_img, obj=roi_contours[1], padding_x=20, padding_y=20, color='white') + x, y = np.shape(gray_img) + x1, y1 = np.shape(cropped) + assert x > x1 + + +def test_plantcv_auto_crop_bad_color_input(): + # Read in test data + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), -1) + gray_img = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY) + contours = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_OBJECT), encoding="latin1") + roi_contours = [contours[arr_n] for arr_n in contours] + with pytest.raises(RuntimeError): + _ = pcv.auto_crop(img=gray_img, obj=roi_contours[1], padding_x=20, padding_y=20, color='wite') + + +def test_plantcv_auto_crop_bad_padding_input(): + # Read in test data + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), -1) + gray_img = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY) + contours = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_OBJECT), encoding="latin1") + roi_contours = [contours[arr_n] for arr_n in contours] + with pytest.raises(RuntimeError): + _ = pcv.auto_crop(img=gray_img, obj=roi_contours[1], padding_x="one", padding_y=20, color='white') + + +def test_plantcv_canny_edge_detect(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_canny_edge_detect") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.canny_edge_detect(img=rgb_img, mask=mask, mask_color='white') + _ = pcv.canny_edge_detect(img=img, mask=mask, mask_color='black') + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.canny_edge_detect(img=img, thickness=2) + _ = pcv.canny_edge_detect(img=img) + # Test with debug = None + pcv.params.debug = None + edge_img = pcv.canny_edge_detect(img=img) + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(edge_img), TEST_BINARY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(edge_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_canny_edge_detect_bad_input(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_canny_edge_detect") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + with pytest.raises(RuntimeError): + _ = pcv.canny_edge_detect(img=img, mask=mask, mask_color="gray") + + +def test_plantcv_closing(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_closing") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), -1) + gray_img = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY) + bin_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug=None + pcv.params.debug = None + _ = pcv.closing(gray_img) + # Test with debug='plot' + pcv.params.debug = 'plot' + _ = pcv.closing(bin_img, np.ones((4, 4), np.uint8)) + # Test with debug='print' + pcv.params.debug = 'print' + filtered_img = pcv.closing(bin_img) + assert np.sum(filtered_img) == 16261860 + + +def test_plantcv_closing_bad_input(): + # Read in test data + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), -1) + with pytest.raises(RuntimeError): + _ = pcv.closing(rgb_img) + + +def test_plantcv_cluster_contours(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_cluster_contours") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), -1) + roi_objects = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_OBJECT), encoding="latin1") + hierarchy = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_HIERARCHY), encoding="latin1") + objs = [roi_objects[arr_n] for arr_n in roi_objects] + obj_hierarchy = hierarchy['arr_0'] + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, nrow=4, ncol=6) + _ = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, show_grid=True) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, nrow=4, ncol=6) + # Test with debug = None + pcv.params.debug = None + clusters_i, contours, hierarchy = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, + nrow=4, ncol=6) + lenori = len(objs) + lenclust = len(clusters_i) + assert lenori > lenclust + + +def test_plantcv_cluster_contours_grayscale_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_cluster_contours_grayscale_input") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), 0) + roi_objects = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_OBJECT), encoding="latin1") + hierachy = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_HIERARCHY), encoding="latin1") + objs = [roi_objects[arr_n] for arr_n in roi_objects] + obj_hierarchy = hierachy['arr_0'] + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, nrow=4, ncol=6) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, nrow=4, ncol=6) + # Test with debug = None + pcv.params.debug = None + clusters_i, contours, hierachy = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, + nrow=4, ncol=6) + lenori = len(objs) + lenclust = len(clusters_i) + assert lenori > lenclust + + +def test_plantcv_cluster_contours_splitimg(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_cluster_contours_splitimg") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), -1) + contours = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_CONTOUR), encoding="latin1") + clusters = np.load(os.path.join(TEST_DATA, TEST_INPUT_ClUSTER_CONTOUR), encoding="latin1") + hierachy = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_HIERARCHY), encoding="latin1") + cluster_names = os.path.join(TEST_DATA, TEST_INPUT_GENOTXT) + cluster_names_too_many = os.path.join(TEST_DATA, TEST_INPUT_GENOTXT_TOO_MANY) + roi_contours = [contours[arr_n] for arr_n in contours] + cluster_contours = [clusters[arr_n] for arr_n in clusters] + obj_hierarchy = hierachy['arr_0'] + # Test with debug = None + pcv.params.debug = None + _, _, _ = pcv.cluster_contour_splitimg(img=img1, grouped_contour_indexes=cluster_contours, + contours=roi_contours, + hierarchy=obj_hierarchy, outdir=cache_dir, file=None, filenames=None) + _, _, _ = pcv.cluster_contour_splitimg(img=img1, grouped_contour_indexes=[[0]], contours=[], + hierarchy=np.array([[[1, -1, -1, -1]]])) + _, _, _ = pcv.cluster_contour_splitimg(img=img1, grouped_contour_indexes=cluster_contours, + contours=roi_contours, + hierarchy=obj_hierarchy, outdir=cache_dir, file='multi', filenames=None) + _, _, _ = pcv.cluster_contour_splitimg(img=img1, grouped_contour_indexes=cluster_contours, + contours=roi_contours, + hierarchy=obj_hierarchy, outdir=None, file=None, filenames=cluster_names) + _, _, _ = pcv.cluster_contour_splitimg(img=img1, grouped_contour_indexes=cluster_contours, + contours=roi_contours, + hierarchy=obj_hierarchy, outdir=None, file=None, + filenames=cluster_names_too_many) + output_path, imgs, masks = pcv.cluster_contour_splitimg(img=img1, grouped_contour_indexes=cluster_contours, + contours=roi_contours, hierarchy=obj_hierarchy, outdir=None, + file=None, + filenames=None) + assert len(output_path) != 0 + + +def test_plantcv_cluster_contours_splitimg_grayscale(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_cluster_contours_splitimg_grayscale") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), 0) + contours = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_CONTOUR), encoding="latin1") + clusters = np.load(os.path.join(TEST_DATA, TEST_INPUT_ClUSTER_CONTOUR), encoding="latin1") + hierachy = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_HIERARCHY), encoding="latin1") + cluster_names = os.path.join(TEST_DATA, TEST_INPUT_GENOTXT) + cluster_names_too_many = os.path.join(TEST_DATA, TEST_INPUT_GENOTXT_TOO_MANY) + roi_contours = [contours[arr_n] for arr_n in contours] + cluster_contours = [clusters[arr_n] for arr_n in clusters] + obj_hierarchy = hierachy['arr_0'] + pcv.params.debug = None + output_path, imgs, masks = pcv.cluster_contour_splitimg(img=img1, grouped_contour_indexes=cluster_contours, + contours=roi_contours, hierarchy=obj_hierarchy, outdir=None, + file=None, + filenames=None) + assert len(output_path) != 0 + + +def test_plantcv_color_palette(): + # Return a color palette + colors = pcv.color_palette(num=10, saved=False) + assert np.shape(colors) == (10, 3) + + +def test_plantcv_color_palette_random(): + # Return a color palette in random order + pcv.params.color_sequence = "random" + colors = pcv.color_palette(num=10, saved=False) + assert np.shape(colors) == (10, 3) + + +def test_plantcv_color_palette_saved(): + # Return a color palette that was saved + pcv.params.saved_color_scale = [[0, 0, 0], [255, 255, 255]] + colors = pcv.color_palette(num=2, saved=True) + assert colors == [[0, 0, 0], [255, 255, 255]] + + +def test_plantcv_crop(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_crop") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img, _, _ = pcv.readimage(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK), 'gray') + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.crop(img=img, x=10, y=10, h=50, w=50) + # Test with debug = "plot" + pcv.params.debug = "plot" + cropped = pcv.crop(img=img, x=10, y=10, h=50, w=50) + assert np.shape(cropped) == (50, 50) + + +def test_plantcv_crop_hyperspectral(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_crop_hyperspectral") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = np.ones((2056, 2454)) + img_stacked = cv2.merge((img, img, img, img)) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.crop(img=img_stacked, x=10, y=10, h=50, w=50) + # Test with debug = "plot" + pcv.params.debug = "plot" + cropped = pcv.crop(img=img_stacked, x=10, y=10, h=50, w=50) + assert np.shape(cropped) == (50, 50, 4) + + +def test_plantcv_crop_position_mask(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_crop_position_mask") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + nir, path1, filename1 = pcv.readimage(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK), 'gray') + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK), -1) + mask_three_channel = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK), -1) + mask_resize = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK_RESIZE), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.crop_position_mask(nir, mask, x=40, y=3, v_pos="top", h_pos="right") + _ = pcv.crop_position_mask(nir, mask_resize, x=40, y=3, v_pos="top", h_pos="right") + _ = pcv.crop_position_mask(nir, mask_three_channel, x=40, y=3, v_pos="top", h_pos="right") + # Test with debug = "print" with bottom + _ = pcv.crop_position_mask(nir, mask, x=40, y=3, v_pos="bottom", h_pos="left") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.crop_position_mask(nir, mask, x=40, y=3, v_pos="top", h_pos="right") + # Test with debug = "plot" with bottom + _ = pcv.crop_position_mask(nir, mask, x=45, y=2, v_pos="bottom", h_pos="left") + # Test with debug = None + pcv.params.debug = None + newmask = pcv.crop_position_mask(nir, mask, x=40, y=3, v_pos="top", h_pos="right") + assert np.sum(newmask) == 707115 + + +def test_plantcv_crop_position_mask_color(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_crop_position_mask") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + nir, path1, filename1 = pcv.readimage(os.path.join(TEST_DATA, TEST_INPUT_COLOR), mode='native') + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK), -1) + mask_resize = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK_RESIZE)) + mask_non_binary = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK)) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.crop_position_mask(nir, mask, x=40, y=3, v_pos="top", h_pos="right") + # Test with debug = "print" with bottom + _ = pcv.crop_position_mask(nir, mask, x=40, y=3, v_pos="bottom", h_pos="left") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.crop_position_mask(nir, mask, x=40, y=3, v_pos="top", h_pos="right") + # Test with debug = "plot" with bottom + _ = pcv.crop_position_mask(nir, mask, x=45, y=2, v_pos="bottom", h_pos="left") + _ = pcv.crop_position_mask(nir, mask_non_binary, x=45, y=2, v_pos="bottom", h_pos="left") + _ = pcv.crop_position_mask(nir, mask_non_binary, x=45, y=2, v_pos="top", h_pos="left") + _ = pcv.crop_position_mask(nir, mask_non_binary, x=45, y=2, v_pos="bottom", h_pos="right") + _ = pcv.crop_position_mask(nir, mask_resize, x=45, y=2, v_pos="top", h_pos="left") + + # Test with debug = None + pcv.params.debug = None + newmask = pcv.crop_position_mask(nir, mask, x=40, y=3, v_pos="top", h_pos="right") + assert np.sum(newmask) == 707115 + + +def test_plantcv_crop_position_mask_bad_input_x(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_crop_position_mask") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK), -1) + # Read in test data + nir, path1, filename1 = pcv.readimage(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK)) + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.crop_position_mask(nir, mask, x=-1, y=-1, v_pos="top", h_pos="right") + + +def test_plantcv_crop_position_mask_bad_input_vpos(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_crop_position_mask") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK), -1) + # Read in test data + nir, path1, filename1 = pcv.readimage(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK)) + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.crop_position_mask(nir, mask, x=40, y=3, v_pos="below", h_pos="right") + + +def test_plantcv_crop_position_mask_bad_input_hpos(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_crop_position_mask") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK), -1) + # Read in test data + nir, path1, filename1 = pcv.readimage(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK)) + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.crop_position_mask(nir, mask, x=40, y=3, v_pos="top", h_pos="starboard") + + +def test_plantcv_dilate(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_dilate") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.dilate(gray_img=img, ksize=5, i=1) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.dilate(gray_img=img, ksize=5, i=1) + # Test with debug = None + pcv.params.debug = None + dilate_img = pcv.dilate(gray_img=img, ksize=5, i=1) + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(dilate_img), TEST_BINARY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(dilate_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_dilate_small_k(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = None + pcv.params.debug = None + with pytest.raises(ValueError): + _ = pcv.dilate(img, 1, 1) + + +def test_plantcv_erode(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_erode") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.erode(gray_img=img, ksize=5, i=1) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.erode(gray_img=img, ksize=5, i=1) + # Test with debug = None + pcv.params.debug = None + erode_img = pcv.erode(gray_img=img, ksize=5, i=1) + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(erode_img), TEST_BINARY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(erode_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_erode_small_k(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = None + pcv.params.debug = None + with pytest.raises(ValueError): + _ = pcv.erode(img, 1, 1) + + +def test_plantcv_distance_transform(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_distance_transform") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_CROPPED_MASK), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.distance_transform(bin_img=mask, distance_type=1, mask_size=3) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.distance_transform(bin_img=mask, distance_type=1, mask_size=3) + # Test with debug = None + pcv.params.debug = None + distance_transform_img = pcv.distance_transform(bin_img=mask, distance_type=1, mask_size=3) + # Assert that the output image has the dimensions of the input image + assert all([i == j] for i, j in zip(np.shape(distance_transform_img), np.shape(mask))) + + +def test_plantcv_fatal_error(): + # Verify that the fatal_error function raises a RuntimeError + with pytest.raises(RuntimeError): + pcv.fatal_error("Test error") + + +def test_plantcv_fill(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_fill") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.fill(bin_img=img, size=63632) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.fill(bin_img=img, size=63632) + # Test with debug = None + pcv.params.debug = None + fill_img = pcv.fill(bin_img=img, size=63632) + # Assert that the output image has the dimensions of the input image + # assert all([i == j] for i, j in zip(np.shape(fill_img), TEST_BINARY_DIM)) + assert np.sum(fill_img) == 0 + + +def test_plantcv_fill_bad_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_fill_bad_input") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + with pytest.raises(RuntimeError): + _ = pcv.fill(bin_img=img, size=1) + + +def test_plantcv_fill_holes(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_fill_holes") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.fill_holes(bin_img=img) + pcv.params.debug = "plot" + _ = pcv.fill_holes(bin_img=img) + # Test with debug = None + pcv.params.debug = None + fill_img = pcv.fill_holes(bin_img=img) + assert np.sum(fill_img) > np.sum(img) + + +def test_plantcv_fill_holes_bad_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_fill_holes_bad_input") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + with pytest.raises(RuntimeError): + _ = pcv.fill_holes(bin_img=img) + + +def test_plantcv_find_objects(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_find_objects") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.find_objects(img=img, mask=mask) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.find_objects(img=img, mask=mask) + # Test with debug = None + pcv.params.debug = None + contours, hierarchy = pcv.find_objects(img=img, mask=mask) + # Assert the correct number of contours are found + assert len(contours) == 2 + + +def test_plantcv_find_objects_grayscale_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_find_objects_grayscale_input") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), 0) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "plot" + pcv.params.debug = "plot" + contours, hierarchy = pcv.find_objects(img=img, mask=mask) + # Assert the correct number of contours are found + assert len(contours) == 2 + + +def test_plantcv_flip(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_flip") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + img_binary = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.flip(img=img, direction="horizontal") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.flip(img=img, direction="vertical") + _ = pcv.flip(img=img_binary, direction="vertical") + # Test with debug = None + pcv.params.debug = None + flipped_img = pcv.flip(img=img, direction="horizontal") + assert all([i == j] for i, j in zip(np.shape(flipped_img), TEST_COLOR_DIM)) + + +def test_plantcv_flip_bad_input(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.flip(img=img, direction="vert") + + +def test_plantcv_gaussian_blur(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_gaussian_blur") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + img_color = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.gaussian_blur(img=img, ksize=(51, 51), sigma_x=0, sigma_y=None) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.gaussian_blur(img=img, ksize=(51, 51), sigma_x=0, sigma_y=None) + _ = pcv.gaussian_blur(img=img_color, ksize=(51, 51), sigma_x=0, sigma_y=None) + # Test with debug = None + pcv.params.debug = None + gaussian_img = pcv.gaussian_blur(img=img, ksize=(51, 51), sigma_x=0, sigma_y=None) + imgavg = np.average(img) + gavg = np.average(gaussian_img) + assert gavg != imgavg + + +def test_plantcv_get_kernel_cross(): + kernel = pcv.get_kernel(size=(3, 3), shape="cross") + assert (kernel == np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])).all() + + +def test_plantcv_get_kernel_rectangle(): + kernel = pcv.get_kernel(size=(3, 3), shape="rectangle") + assert (kernel == np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])).all() + + +def test_plantcv_get_kernel_ellipse(): + kernel = pcv.get_kernel(size=(3, 3), shape="ellipse") + assert (kernel == np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])).all() + + +def test_plantcv_get_kernel_bad_input_size(): + with pytest.raises(ValueError): + _ = pcv.get_kernel(size=(1, 1), shape="ellipse") + + +def test_plantcv_get_kernel_bad_input_shape(): + with pytest.raises(RuntimeError): + _ = pcv.get_kernel(size=(3, 1), shape="square") + + +def test_plantcv_get_nir_sv(): + nirpath = pcv.get_nir(TEST_DATA, TEST_VIS) + nirpath1 = os.path.join(TEST_DATA, TEST_NIR) + assert nirpath == nirpath1 + + +def test_plantcv_get_nir_tv(): + nirpath = pcv.get_nir(TEST_DATA, TEST_VIS_TV) + nirpath1 = os.path.join(TEST_DATA, TEST_NIR_TV) + assert nirpath == nirpath1 + + +def test_plantcv_hist_equalization(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hist_equalization") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.hist_equalization(gray_img=img) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.hist_equalization(gray_img=img) + # Test with debug = None + pcv.params.debug = None + hist = pcv.hist_equalization(gray_img=img) + histavg = np.average(hist) + imgavg = np.average(img) + assert histavg != imgavg + + +def test_plantcv_hist_equalization_bad_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hist_equalization_bad_input") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), 1) + # Test with debug = None + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.hist_equalization(gray_img=img) + + +def test_plantcv_image_add(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_image_add") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + img2 = np.copy(img1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.image_add(gray_img1=img1, gray_img2=img2) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.image_add(gray_img1=img1, gray_img2=img2) + # Test with debug = None + pcv.params.debug = None + added_img = pcv.image_add(gray_img1=img1, gray_img2=img2) + assert all([i == j] for i, j in zip(np.shape(added_img), TEST_BINARY_DIM)) + + +def test_plantcv_image_subtract(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_image_sub") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # read in images + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + img2 = np.copy(img1) + # Test with debug = "print" + pcv.params.debug = 'print' + _ = pcv.image_subtract(img1, img2) + # Test with debug = "plot" + pcv.params.debug = 'plot' + _ = pcv.image_subtract(img1, img2) + # Test with debug = None + pcv.params.debug = None + new_img = pcv.image_subtract(img1, img2) + assert np.array_equal(new_img, np.zeros(np.shape(new_img), np.uint8)) + + +def test_plantcv_image_subtract_fail(): + # read in images + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + img2 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY)) + # test + with pytest.raises(RuntimeError): + _ = pcv.image_subtract(img1, img2) + + +def test_plantcv_invert(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_invert") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.invert(gray_img=img) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.invert(gray_img=img) + # Test with debug = None + pcv.params.debug = None + inverted_img = pcv.invert(gray_img=img) + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(inverted_img), TEST_BINARY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(inverted_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_landmark_reference_pt_dist(): + # Clear previous outputs + pcv.outputs.clear() + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_landmark_reference") + os.mkdir(cache_dir) + points_rescaled = [(0.0139, 0.2569), (0.2361, 0.2917), (0.3542, 0.3819), (0.3542, 0.4167), (0.375, 0.4236), + (0.7431, 0.3681), (0.8958, 0.3542), (0.9931, 0.3125), (0.1667, 0.5139), (0.4583, 0.8889), + (0.4931, 0.5903), (0.3889, 0.5694), (0.4792, 0.4306), (0.2083, 0.5417), (0.3194, 0.5278), + (0.3889, 0.375), (0.3681, 0.3472), (0.2361, 0.0139), (0.5417, 0.2292), (0.7708, 0.3472), + (0.6458, 0.3472), (0.6389, 0.5208), (0.6458, 0.625)] + centroid_rescaled = (0.4685, 0.4945) + bottomline_rescaled = (0.4685, 0.2569) + _ = pcv.landmark_reference_pt_dist(points_r=[], centroid_r=('a', 'b'), bline_r=(0, 0)) + _ = pcv.landmark_reference_pt_dist(points_r=[(10, 1000)], centroid_r=(10, 10), bline_r=(10, 10)) + _ = pcv.landmark_reference_pt_dist(points_r=[], centroid_r=(0, 0), bline_r=(0, 0)) + _ = pcv.landmark_reference_pt_dist(points_r=points_rescaled, centroid_r=centroid_rescaled, + bline_r=bottomline_rescaled, label="prefix") + assert len(pcv.outputs.observations['prefix'].keys()) == 8 + + +def test_plantcv_laplace_filter(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_laplace_filter") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.laplace_filter(gray_img=img, ksize=1, scale=1) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.laplace_filter(gray_img=img, ksize=1, scale=1) + # Test with debug = None + pcv.params.debug = None + lp_img = pcv.laplace_filter(gray_img=img, ksize=1, scale=1) + # Assert that the output image has the dimensions of the input image + assert all([i == j] for i, j in zip(np.shape(lp_img), TEST_GRAY_DIM)) + + +def test_plantcv_logical_and(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_logical_and") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + img2 = np.copy(img1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.logical_and(bin_img1=img1, bin_img2=img2) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.logical_and(bin_img1=img1, bin_img2=img2) + # Test with debug = None + pcv.params.debug = None + and_img = pcv.logical_and(bin_img1=img1, bin_img2=img2) + assert all([i == j] for i, j in zip(np.shape(and_img), TEST_BINARY_DIM)) + + +def test_plantcv_logical_or(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_logical_or") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + img2 = np.copy(img1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.logical_or(bin_img1=img1, bin_img2=img2) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.logical_or(bin_img1=img1, bin_img2=img2) + # Test with debug = None + pcv.params.debug = None + or_img = pcv.logical_or(bin_img1=img1, bin_img2=img2) + assert all([i == j] for i, j in zip(np.shape(or_img), TEST_BINARY_DIM)) + + +def test_plantcv_logical_xor(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_logical_xor") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + img2 = np.copy(img1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.logical_xor(bin_img1=img1, bin_img2=img2) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.logical_xor(bin_img1=img1, bin_img2=img2) + # Test with debug = None + pcv.params.debug = None + xor_img = pcv.logical_xor(bin_img1=img1, bin_img2=img2) + assert all([i == j] for i, j in zip(np.shape(xor_img), TEST_BINARY_DIM)) + + +def test_plantcv_median_blur(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_median_blur") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.median_blur(gray_img=img, ksize=5) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.median_blur(gray_img=img, ksize=5) + # Test with debug = None + pcv.params.debug = None + blur_img = pcv.median_blur(gray_img=img, ksize=5) + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(blur_img), TEST_BINARY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(blur_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_median_blur_bad_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_median_blur_bad_input") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + with pytest.raises(RuntimeError): + _ = pcv.median_blur(img, 5.) + + +def test_plantcv_naive_bayes_classifier(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_naive_bayes_classifier") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS)) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS)) + # Test with debug = None + pcv.params.debug = None + mask = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS)) + + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(mask), TEST_GRAY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(mask), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_naive_bayes_classifier_bad_input(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS_BAD)) + + +def test_plantcv_object_composition(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_object_composition") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + object_contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_CONTOURS), encoding="latin1") + object_contours = [object_contours_npz[arr_n] for arr_n in object_contours_npz] + object_hierarchy_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_HIERARCHY), encoding="latin1") + object_hierarchy = object_hierarchy_npz['arr_0'] + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.object_composition(img=img, contours=object_contours, hierarchy=object_hierarchy) + _ = pcv.object_composition(img=img, contours=[], hierarchy=object_hierarchy) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.object_composition(img=img, contours=object_contours, hierarchy=object_hierarchy) + # Test with debug = None + pcv.params.debug = None + contours, mask = pcv.object_composition(img=img, contours=object_contours, hierarchy=object_hierarchy) + # Assert that the objects have been combined + contour_shape = np.shape(contours) # type: tuple + assert contour_shape[1] == 1 + + +def test_plantcv_object_composition_grayscale_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_object_composition_grayscale_input") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), 0) + object_contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_CONTOURS), encoding="latin1") + object_contours = [object_contours_npz[arr_n] for arr_n in object_contours_npz] + object_hierarchy_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_HIERARCHY), encoding="latin1") + object_hierarchy = object_hierarchy_npz['arr_0'] + # Test with debug = "plot" + pcv.params.debug = "plot" + contours, mask = pcv.object_composition(img=img, contours=object_contours, hierarchy=object_hierarchy) + # Assert that the objects have been combined + contour_shape = np.shape(contours) # type: tuple + assert contour_shape[1] == 1 + + +def test_plantcv_within_frame(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_within_frame") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + mask_ib = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK), -1) + mask_oob = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK_OOB), -1) + in_bounds_ib = pcv.within_frame(mask=mask_ib, border_width=1, label="prefix") + in_bounds_oob = pcv.within_frame(mask=mask_oob, border_width=1) + assert (in_bounds_ib is True and in_bounds_oob is False) + + +def test_plantcv_within_frame_bad_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_within_frame") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + grayscale_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), 0) + with pytest.raises(RuntimeError): + _ = pcv.within_frame(grayscale_img) + + +def test_plantcv_opening(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_closing") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), -1) + gray_img = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY) + bin_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug=None + pcv.params.debug = None + _ = pcv.opening(gray_img) + # Test with debug='plot' + pcv.params.debug = 'plot' + _ = pcv.opening(bin_img, np.ones((4, 4), np.uint8)) + # Test with debug='print' + pcv.params.debug = 'print' + filtered_img = pcv.opening(bin_img) + assert np.sum(filtered_img) == 16184595 + + +def test_plantcv_opening_bad_input(): + # Read in test data + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), -1) + with pytest.raises(RuntimeError): + _ = pcv.opening(rgb_img) + + +def test_plantcv_output_mask(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_output_mask") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + img_color = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.output_mask(img=img, mask=mask, filename='test.png', outdir=None, mask_only=False) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.output_mask(img=img, mask=mask, filename='test.png', outdir=cache_dir, mask_only=False) + _ = pcv.output_mask(img=img_color, mask=mask, filename='test.png', outdir=None, mask_only=False) + # Remove tmp files in working direcctory + shutil.rmtree("ori-images") + shutil.rmtree("mask-images") + # Test with debug = None + pcv.params.debug = None + imgpath, maskpath, analysis_images = pcv.output_mask(img=img, mask=mask, filename='test.png', + outdir=cache_dir, mask_only=False) + assert all([os.path.exists(imgpath) is True, os.path.exists(maskpath) is True]) + + +def test_plantcv_output_mask_true(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_output_mask") + pcv.params.debug_outdir = cache_dir + os.mkdir(cache_dir) + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + img_color = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.output_mask(img=img, mask=mask, filename='test.png', outdir=cache_dir, mask_only=True) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.output_mask(img=img_color, mask=mask, filename='test.png', outdir=cache_dir, mask_only=True) + pcv.params.debug = None + imgpath, maskpath, analysis_images = pcv.output_mask(img=img, mask=mask, filename='test.png', outdir=cache_dir, + mask_only=False) + assert all([os.path.exists(imgpath) is True, os.path.exists(maskpath) is True]) + + +def test_plantcv_plot_image_matplotlib_input(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_pseudocolor") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + pimg = pcv.visualize.pseudocolor(gray_img=img, mask=mask, min_value=10, max_value=200) + with pytest.raises(RuntimeError): + pcv.plot_image(pimg) + + +def test_plantcv_plot_image_plotnine(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_plot_image_plotnine") + os.mkdir(cache_dir) + dataset = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [1, 2, 3, 4]}) + img = ggplot(data=dataset) + try: + pcv.plot_image(img=img) + except RuntimeError: + assert False + # Assert that the image was plotted without error + assert True + + +def test_plantcv_print_image(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_print_image") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img, path, img_name = pcv.readimage(filename=os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + filename = os.path.join(cache_dir, 'plantcv_print_image.png') + pcv.print_image(img=img, filename=filename) + # Assert that the file was created + assert os.path.exists(filename) is True + + +def test_plantcv_print_image_bad_type(): + with pytest.raises(RuntimeError): + pcv.print_image(img=[], filename="/dev/null") + + +def test_plantcv_print_image_plotnine(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_print_image_plotnine") + os.mkdir(cache_dir) + dataset = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [1, 2, 3, 4]}) + img = ggplot(data=dataset) + filename = os.path.join(cache_dir, 'plantcv_print_image.png') + pcv.print_image(img=img, filename=filename) + # Assert that the file was created + assert os.path.exists(filename) is True + + +def test_plantcv_print_results(tmpdir): + # Create a tmp directory + cache_dir = tmpdir.mkdir("sub") + outfile = os.path.join(cache_dir, "results.json") + pcv.print_results(filename=outfile) + assert os.path.exists(outfile) + + +def test_plantcv_readimage_native(): + # Test with debug = None + pcv.params.debug = None + _ = pcv.readimage(filename=os.path.join(TEST_DATA, TEST_INPUT_COLOR), mode='rgba') + _ = pcv.readimage(filename=os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + img, path, img_name = pcv.readimage(filename=os.path.join(TEST_DATA, TEST_INPUT_COLOR), mode='native') + # Assert that the image name returned equals the name of the input image + # Assert that the path of the image returned equals the path of the input image + # Assert that the dimensions of the returned image equals the expected dimensions + if img_name == TEST_INPUT_COLOR and path == TEST_DATA: + if all([i == j] for i, j in zip(np.shape(img), TEST_COLOR_DIM)): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_readimage_grayscale(): + # Test with debug = None + pcv.params.debug = None + _, _, _ = pcv.readimage(filename=os.path.join(TEST_DATA, TEST_INPUT_GRAY), mode="grey") + img, path, img_name = pcv.readimage(filename=os.path.join(TEST_DATA, TEST_INPUT_GRAY), mode="gray") + assert len(np.shape(img)) == 2 + + +def test_plantcv_readimage_rgb(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readimage(filename=os.path.join(TEST_DATA, TEST_INPUT_GRAY), mode="rgb") + assert len(np.shape(img)) == 3 + + +def test_plantcv_readimage_rgba_as_rgb(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readimage(filename=os.path.join(TEST_DATA, TEST_INPUT_RGBA), mode="native") + assert np.shape(img)[2] == 3 + + +def test_plantcv_readimage_csv(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readimage(filename=os.path.join(TEST_DATA, TEST_INPUT_THERMAL_CSV), mode="csv") + assert len(np.shape(img)) == 2 + + +def test_plantcv_readimage_envi(): + # Test with debug = None + pcv.params.debug = None + array_data = pcv.readimage(filename=os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA), mode="envi") + if sys.version_info[0] < 3: + assert len(array_data.array_type) == 8 + + +def test_plantcv_readimage_bad_file(): + with pytest.raises(RuntimeError): + _ = pcv.readimage(filename=TEST_INPUT_COLOR) + + +def test_plantcv_readbayer_default_bg(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_readbayer_default_bg") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Test with debug = "print" + pcv.params.debug = "print" + _, _, _ = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="BG", alg="default") + # Test with debug = "plot" + pcv.params.debug = "plot" + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="BG", alg="default") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_default_gb(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="GB", alg="default") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_default_rg(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="RG", alg="default") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_default_gr(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="GR", alg="default") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_edgeaware_bg(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="BG", alg="edgeaware") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_edgeaware_gb(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="GB", alg="edgeaware") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_edgeaware_rg(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="RG", alg="edgeaware") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_edgeaware_gr(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="GR", alg="edgeaware") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_variablenumbergradients_bg(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="BG", alg="variablenumbergradients") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_variablenumbergradients_gb(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="GB", alg="variablenumbergradients") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_variablenumbergradients_rg(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="RG", alg="variablenumbergradients") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_variablenumbergradients_gr(): + # Test with debug = None + pcv.params.debug = None + img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), + bayerpattern="GR", alg="variablenumbergradients") + assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) + + +def test_plantcv_readbayer_default_bad_input(): + # Test with debug = None + pcv.params.debug = None + with pytest.raises(RuntimeError): + _, _, _ = pcv.readbayer(filename=os.path.join(TEST_DATA, "no-image.png"), bayerpattern="GR", alg="default") + + +def test_plantcv_rectangle_mask(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_rectangle_mask") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + img_color = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.rectangle_mask(img=img, p1=(0, 0), p2=(2454, 2056), color="white") + _ = pcv.rectangle_mask(img=img, p1=(0, 0), p2=(2454, 2056), color="white") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.rectangle_mask(img=img_color, p1=(0, 0), p2=(2454, 2056), color="gray") + # Test with debug = None + pcv.params.debug = None + masked, hist, contour, heir = pcv.rectangle_mask(img=img, p1=(0, 0), p2=(2454, 2056), color="black") + maskedsum = np.sum(masked) + imgsum = np.sum(img) + assert maskedsum < imgsum + + +def test_plantcv_rectangle_mask_bad_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_rectangle_mask") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with debug = None + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.rectangle_mask(img=img, p1=(0, 0), p2=(2454, 2056), color="whit") + + +def test_plantcv_report_size_marker_detect(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_report_size_marker_detect") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MARKER), -1) + # ROI contour + roi_contour = [np.array([[[3550, 850]], [[3550, 1349]], [[4049, 1349]], [[4049, 850]]], dtype=np.int32)] + roi_hierarchy = np.array([[[-1, -1, -1, -1]]], dtype=np.int32) + + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.report_size_marker_area(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, marker='detect', + objcolor='light', thresh_channel='s', thresh=120, label="prefix") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.report_size_marker_area(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, marker='detect', + objcolor='light', thresh_channel='s', thresh=120) + # Test with debug = None + pcv.params.debug = None + images = pcv.report_size_marker_area(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, marker='detect', + objcolor='light', thresh_channel='s', thresh=120) + pcv.outputs.clear() + assert len(images) != 0 + + +def test_plantcv_report_size_marker_define(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MARKER), -1) + # ROI contour + roi_contour = [np.array([[[3550, 850]], [[3550, 1349]], [[4049, 1349]], [[4049, 850]]], dtype=np.int32)] + roi_hierarchy = np.array([[[-1, -1, -1, -1]]], dtype=np.int32) + + # Test with debug = None + pcv.params.debug = None + images = pcv.report_size_marker_area(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, marker='define', + objcolor='light', thresh_channel='s', thresh=120) + assert len(images) != 0 + + +def test_plantcv_report_size_marker_grayscale_input(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # ROI contour + roi_contour = [np.array([[[0, 0]], [[0, 49]], [[49, 49]], [[49, 0]]], dtype=np.int32)] + roi_hierarchy = np.array([[[-1, -1, -1, -1]]], dtype=np.int32) + + # Test with debug = None + pcv.params.debug = None + images = pcv.report_size_marker_area(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, marker='define', + objcolor='light', thresh_channel='s', thresh=120) + assert len(images) != 0 + + +def test_plantcv_report_size_marker_bad_marker_input(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MARKER), -1) + # ROI contour + roi_contour = [np.array([[[3550, 850]], [[3550, 1349]], [[4049, 1349]], [[4049, 850]]], dtype=np.int32)] + roi_hierarchy = np.array([[[-1, -1, -1, -1]]], dtype=np.int32) + with pytest.raises(RuntimeError): + _ = pcv.report_size_marker_area(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, marker='none', + objcolor='light', thresh_channel='s', thresh=120) + + +def test_plantcv_report_size_marker_bad_threshold_input(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MARKER), -1) + # ROI contour + roi_contour = [np.array([[[3550, 850]], [[3550, 1349]], [[4049, 1349]], [[4049, 850]]], dtype=np.int32)] + roi_hierarchy = np.array([[[-1, -1, -1, -1]]], dtype=np.int32) + with pytest.raises(RuntimeError): + _ = pcv.report_size_marker_area(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, marker='detect', + objcolor='light', thresh_channel=None, thresh=120) + + +def test_plantcv_rgb2gray_cmyk(): + # Test with debug = None + pcv.params.debug = None + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + c = pcv.rgb2gray_cmyk(rgb_img=img, channel="c") + # Assert that the output image has the dimensions of the input image but is only a single channel + assert all([i == j] for i, j in zip(np.shape(c), TEST_GRAY_DIM)) + + +def test_plantcv_rgb2gray_cmyk_bad_channel(): + # Test with debug = None + pcv.params.debug = None + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + # Channel S is not in CMYK + _ = pcv.rgb2gray_cmyk(rgb_img=img, channel="s") + + +def test_plantcv_rgb2gray_hsv(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_rgb2gray_hsv") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.rgb2gray_hsv(rgb_img=img, channel="s") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.rgb2gray_hsv(rgb_img=img, channel="s") + # Test with debug = None + pcv.params.debug = None + s = pcv.rgb2gray_hsv(rgb_img=img, channel="s") + # Assert that the output image has the dimensions of the input image but is only a single channel + assert all([i == j] for i, j in zip(np.shape(s), TEST_GRAY_DIM)) + + +def test_plantcv_rgb2gray_hsv_bad_input(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.rgb2gray_hsv(rgb_img=img, channel="l") + + +def test_plantcv_rgb2gray_lab(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_rgb2gray_lab") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.rgb2gray_lab(rgb_img=img, channel='b') + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.rgb2gray_lab(rgb_img=img, channel='b') + # Test with debug = None + pcv.params.debug = None + b = pcv.rgb2gray_lab(rgb_img=img, channel='b') + # Assert that the output image has the dimensions of the input image but is only a single channel + assert all([i == j] for i, j in zip(np.shape(b), TEST_GRAY_DIM)) + + +def test_plantcv_rgb2gray_lab_bad_input(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.rgb2gray_lab(rgb_img=img, channel="v") + + +def test_plantcv_rgb2gray(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_rgb2gray") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.rgb2gray(rgb_img=img) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.rgb2gray(rgb_img=img) + # Test with debug = None + pcv.params.debug = None + gray = pcv.rgb2gray(rgb_img=img) + # Assert that the output image has the dimensions of the input image but is only a single channel + assert all([i == j] for i, j in zip(np.shape(gray), TEST_GRAY_DIM)) + + +def test_plantcv_roi2mask(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_acute_vertex") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_VIS_SMALL)) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR), encoding="latin1") + obj_contour = contours_npz['arr_0'] + pcv.params.debug = "plot" + _ = pcv.roi.roi2mask(img=img, contour=obj_contour) + pcv.params.debug = "print" + mask = pcv.roi.roi2mask(img=img, contour=obj_contour) + assert np.shape(mask)[0:2] == np.shape(img)[0:2] and np.sum(mask) == 255 + + +def test_plantcv_roi_objects(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_roi_objects") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + roi_contour_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_ROI_CONTOUR), encoding="latin1") + roi_contour = [roi_contour_npz[arr_n] for arr_n in roi_contour_npz] + roi_hierarchy_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_ROI_HIERARCHY), encoding="latin1") + roi_hierarchy = roi_hierarchy_npz['arr_0'] + object_contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_CONTOURS), encoding="latin1") + object_contours = [object_contours_npz[arr_n] for arr_n in object_contours_npz] + object_hierarchy_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_HIERARCHY), encoding="latin1") + object_hierarchy = object_hierarchy_npz['arr_0'] + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.roi_objects(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, + object_contour=object_contours, obj_hierarchy=object_hierarchy, roi_type="largest") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.roi_objects(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, + object_contour=object_contours, obj_hierarchy=object_hierarchy, roi_type="partial") + # Test with debug = None and roi_type = cutto + pcv.params.debug = None + _ = pcv.roi_objects(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, + object_contour=object_contours, obj_hierarchy=object_hierarchy, roi_type="cutto") + # Test with debug = None + kept_contours, kept_hierarchy, mask, area = pcv.roi_objects(img=img, roi_contour=roi_contour, + roi_hierarchy=roi_hierarchy, + object_contour=object_contours, + obj_hierarchy=object_hierarchy, roi_type="partial") + # Assert that the contours were filtered as expected + assert len(kept_contours) == 1891 + + +def test_plantcv_roi_objects_bad_input(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + roi_contour_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_ROI_CONTOUR), encoding="latin1") + roi_contour = [roi_contour_npz[arr_n] for arr_n in roi_contour_npz] + roi_hierarchy_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_ROI_HIERARCHY), encoding="latin1") + roi_hierarchy = roi_hierarchy_npz['arr_0'] + object_contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_CONTOURS), encoding="latin1") + object_contours = [object_contours_npz[arr_n] for arr_n in object_contours_npz] + object_hierarchy_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_HIERARCHY), encoding="latin1") + object_hierarchy = object_hierarchy_npz['arr_0'] + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.roi_objects(img=img, roi_type="cut", roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, + object_contour=object_contours, obj_hierarchy=object_hierarchy) + + +def test_plantcv_roi_objects_grayscale_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_roi_objects_grayscale_input") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), 0) + roi_contour_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_ROI_CONTOUR), encoding="latin1") + roi_contour = [roi_contour_npz[arr_n] for arr_n in roi_contour_npz] + roi_hierarchy_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_ROI_HIERARCHY), encoding="latin1") + roi_hierarchy = roi_hierarchy_npz['arr_0'] + object_contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_CONTOURS), encoding="latin1") + object_contours = [object_contours_npz[arr_n] for arr_n in object_contours_npz] + object_hierarchy_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_HIERARCHY), encoding="latin1") + object_hierarchy = object_hierarchy_npz['arr_0'] + # Test with debug = "plot" + pcv.params.debug = "plot" + kept_contours, kept_hierarchy, mask, area = pcv.roi_objects(img=img, roi_type="partial", roi_contour=roi_contour, + roi_hierarchy=roi_hierarchy, + object_contour=object_contours, + obj_hierarchy=object_hierarchy) + # Assert that the contours were filtered as expected + assert len(kept_contours) == 1891 + +def test_plantcv_rotate(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + rotated = pcv.rotate(img=img, rotation_deg=45, crop=True) + imgavg = np.average(img) + rotateavg = np.average(rotated) + assert rotateavg != imgavg + +def test_plantcv_transform_rotate(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_rotate_img") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.transform.rotate(img=img, rotation_deg=45, crop=True) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.transform.rotate(img=img, rotation_deg=45, crop=True) + # Test with debug = None + pcv.params.debug = None + rotated = pcv.transform.rotate(img=img, rotation_deg=45, crop=True) + imgavg = np.average(img) + rotateavg = np.average(rotated) + assert rotateavg != imgavg + + +def test_plantcv_transform_rotate_gray(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.transform.rotate(img=img, rotation_deg=45, crop=False) + # Test with debug = None + pcv.params.debug = None + rotated = pcv.transform.rotate(img=img, rotation_deg=45, crop=False) + imgavg = np.average(img) + rotateavg = np.average(rotated) + assert rotateavg != imgavg + + +def test_plantcv_scale_features(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_scale_features") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR), encoding="latin1") + obj_contour = contours_npz['arr_0'] + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.scale_features(obj=obj_contour, mask=mask, points=TEST_ACUTE_RESULT, line_position=50) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.scale_features(obj=obj_contour, mask=mask, points=TEST_ACUTE_RESULT, line_position='NA') + # Test with debug = None + pcv.params.debug = None + points_rescaled, centroid_rescaled, bottomline_rescaled = pcv.scale_features(obj=obj_contour, mask=mask, + points=TEST_ACUTE_RESULT, + line_position=50) + assert len(points_rescaled) == 23 + + +def test_plantcv_scale_features_bad_input(): + mask = np.array([]) + obj_contour = np.array([]) + pcv.params.debug = None + result = pcv.scale_features(obj=obj_contour, mask=mask, points=TEST_ACUTE_RESULT, line_position=50) + assert all([i == j] for i, j in zip(result, [("NA", "NA"), ("NA", "NA"), ("NA", "NA")])) + + +def test_plantcv_scharr_filter(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_scharr_filter") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + pcv.params.debug = "print" + # Test with debug = "print" + _ = pcv.scharr_filter(img=img, dx=1, dy=0, scale=1) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.scharr_filter(img=img, dx=1, dy=0, scale=1) + # Test with debug = None + pcv.params.debug = None + scharr_img = pcv.scharr_filter(img=img, dx=1, dy=0, scale=1) + # Assert that the output image has the dimensions of the input image + assert all([i == j] for i, j in zip(np.shape(scharr_img), TEST_GRAY_DIM)) + + +def test_plantcv_shift_img(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_shift_img") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.shift_img(img=img, number=300, side="top") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.shift_img(img=img, number=300, side="top") + # Test with debug = "plot" + _ = pcv.shift_img(img=img, number=300, side="bottom") + # Test with debug = "plot" + _ = pcv.shift_img(img=img, number=300, side="right") + # Test with debug = "plot" + _ = pcv.shift_img(img=mask, number=300, side="left") + # Test with debug = None + pcv.params.debug = None + rotated = pcv.shift_img(img=img, number=300, side="top") + imgavg = np.average(img) + shiftavg = np.average(rotated) + assert shiftavg != imgavg + + +def test_plantcv_shift_img_bad_input(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + pcv.params.debug = None + _ = pcv.shift_img(img=img, number=-300, side="top") + + +def test_plantcv_shift_img_bad_side_input(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + pcv.params.debug = None + _ = pcv.shift_img(img=img, number=300, side="starboard") + + +def test_plantcv_sobel_filter(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_sobel_filter") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.sobel_filter(gray_img=img, dx=1, dy=0, ksize=1) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.sobel_filter(gray_img=img, dx=1, dy=0, ksize=1) + # Test with debug = None + pcv.params.debug = None + sobel_img = pcv.sobel_filter(gray_img=img, dx=1, dy=0, ksize=1) + # Assert that the output image has the dimensions of the input image + assert all([i == j] for i, j in zip(np.shape(sobel_img), TEST_GRAY_DIM)) + + +def test_plantcv_stdev_filter(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_sobel_filter") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY_SMALL), -1) + pcv.params.debug = "plot" + _ = pcv.stdev_filter(img=img, ksize=11) + pcv.params.debug = "print" + filter_img = pcv.stdev_filter(img=img, ksize=11) + assert (np.shape(filter_img) == np.shape(img)) + + +def test_plantcv_watershed_segmentation(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_watershed_segmentation") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_CROPPED)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_CROPPED_MASK), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.watershed_segmentation(rgb_img=img, mask=mask, distance=10, label="prefix") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.watershed_segmentation(rgb_img=img, mask=mask, distance=10) + # Test with debug = None + pcv.params.debug = None + _ = pcv.watershed_segmentation(rgb_img=img, mask=mask, distance=10) + assert pcv.outputs.observations['default']['estimated_object_count']['value'] > 9 + + +def test_plantcv_white_balance_gray_16bit(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_white_balance_gray_16bit") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.white_balance(img=img, mode='hist', roi=(5, 5, 80, 80)) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.white_balance(img=img, mode='max', roi=(5, 5, 80, 80)) + # Test without an ROI + pcv.params.debug = None + _ = pcv.white_balance(img=img, mode='hist', roi=None) + # Test with debug = None + white_balanced = pcv.white_balance(img=img, roi=(5, 5, 80, 80)) + imgavg = np.average(img) + balancedavg = np.average(white_balanced) + assert balancedavg != imgavg + + +def test_plantcv_white_balance_gray_8bit(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_white_balance_gray_8bit") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK)) + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.white_balance(img=img, mode='hist', roi=(5, 5, 80, 80)) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.white_balance(img=img, mode='max', roi=(5, 5, 80, 80)) + # Test without an ROI + pcv.params.debug = None + _ = pcv.white_balance(img=img, mode='hist', roi=None) + # Test with debug = None + white_balanced = pcv.white_balance(img=img, roi=(5, 5, 80, 80)) + imgavg = np.average(img) + balancedavg = np.average(white_balanced) + assert balancedavg != imgavg + + +def test_plantcv_white_balance_rgb(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_white_balance_rgb") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MARKER)) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.white_balance(img=img, mode='hist', roi=(5, 5, 80, 80)) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.white_balance(img=img, mode='max', roi=(5, 5, 80, 80)) + # Test without an ROI + pcv.params.debug = None + _ = pcv.white_balance(img=img, mode='hist', roi=None) + # Test with debug = None + white_balanced = pcv.white_balance(img=img, roi=(5, 5, 80, 80)) + imgavg = np.average(img) + balancedavg = np.average(white_balanced) + assert balancedavg != imgavg + + +def test_plantcv_white_balance_bad_input(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK), -1) + # Test with debug = None + with pytest.raises(RuntimeError): + pcv.params.debug = "plot" + _ = pcv.white_balance(img=img, mode='hist', roi=(5, 5, 5, 5, 5)) + + +def test_plantcv_white_balance_bad_mode_input(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MARKER)) + # Test with debug = None + with pytest.raises(RuntimeError): + pcv.params.debug = "plot" + _ = pcv.white_balance(img=img, mode='histogram', roi=(5, 5, 80, 80)) + + +def test_plantcv_white_balance_bad_input_int(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK), -1) + # Test with debug = None + with pytest.raises(RuntimeError): + pcv.params.debug = "plot" + _ = pcv.white_balance(img=img, mode='hist', roi=(5., 5, 5, 5)) + + +def test_plantcv_x_axis_pseudolandmarks(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_x_axis_pseudolandmarks_debug") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_VIS_SMALL)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR), encoding="latin1") + obj_contour = contours_npz['arr_0'] + pcv.params.debug = "print" + _ = pcv.x_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.x_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img, label="prefix") + _ = pcv.x_axis_pseudolandmarks(obj=np.array([[0, 0], [0, 0]]), mask=np.array([[0, 0], [0, 0]]), img=img) + _ = pcv.x_axis_pseudolandmarks(obj=np.array(([[89, 222]], [[252, 39]], [[89, 207]])), + mask=np.array(([[42, 161]], [[2, 47]], [[211, 222]])), img=img) + + _ = pcv.x_axis_pseudolandmarks(obj=(), mask=mask, img=img) + # Test with debug = None + pcv.params.debug = None + top, bottom, center_v = pcv.x_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) + pcv.outputs.clear() + assert all([all([i == j] for i, j in zip(np.shape(top), (20, 1, 2))), + all([i == j] for i, j in zip(np.shape(bottom), (20, 1, 2))), + all([i == j] for i, j in zip(np.shape(center_v), (20, 1, 2)))]) + + +def test_plantcv_x_axis_pseudolandmarks_small_obj(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_VIS_SMALL_PLANT)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL_PLANT), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR_SMALL_PLANT), encoding="latin1") + obj_contour = contours_npz['arr_0'] + # Test with debug = "print" + pcv.params.debug = "print" + _, _, _ = pcv.x_axis_pseudolandmarks(obj=[], mask=mask, img=img) + _, _, _ = pcv.x_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) + # Test with debug = "plot" + pcv.params.debug = "plot" + _, _, _ = pcv.x_axis_pseudolandmarks(obj=[], mask=mask, img=img) + top, bottom, center_v = pcv.x_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) + assert all([all([i == j] for i, j in zip(np.shape(top), (20, 1, 2))), + all([i == j] for i, j in zip(np.shape(bottom), (20, 1, 2))), + all([i == j] for i, j in zip(np.shape(center_v), (20, 1, 2)))]) + + +def test_plantcv_x_axis_pseudolandmarks_bad_input(): + img = np.array([]) + mask = np.array([]) + obj_contour = np.array([]) + pcv.params.debug = None + result = pcv.x_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) + assert all([i == j] for i, j in zip(result, [("NA", "NA"), ("NA", "NA"), ("NA", "NA")])) + + +def test_plantcv_x_axis_pseudolandmarks_bad_obj_input(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_VIS_SMALL_PLANT)) + with pytest.raises(RuntimeError): + _ = pcv.x_axis_pseudolandmarks(obj=np.array([[-2, -2], [-2, -2]]), mask=np.array([[-2, -2], [-2, -2]]), img=img) + + +def test_plantcv_y_axis_pseudolandmarks(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_y_axis_pseudolandmarks_debug") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_VIS_SMALL)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR), encoding="latin1") + obj_contour = contours_npz['arr_0'] + pcv.params.debug = "print" + _ = pcv.y_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img, label="prefix") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.y_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) + pcv.outputs.clear() + _ = pcv.y_axis_pseudolandmarks(obj=[], mask=mask, img=img) + _ = pcv.y_axis_pseudolandmarks(obj=(), mask=mask, img=img) + _ = pcv.y_axis_pseudolandmarks(obj=np.array(([[89, 222]], [[252, 39]], [[89, 207]])), + mask=np.array(([[42, 161]], [[2, 47]], [[211, 222]])), img=img) + _ = pcv.y_axis_pseudolandmarks(obj=np.array(([[21, 11]], [[159, 155]], [[237, 11]])), + mask=np.array(([[38, 54]], [[144, 169]], [[81, 137]])), img=img) + # Test with debug = None + pcv.params.debug = None + left, right, center_h = pcv.y_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) + pcv.outputs.clear() + assert all([all([i == j] for i, j in zip(np.shape(left), (20, 1, 2))), + all([i == j] for i, j in zip(np.shape(right), (20, 1, 2))), + all([i == j] for i, j in zip(np.shape(center_h), (20, 1, 2)))]) + + +def test_plantcv_y_axis_pseudolandmarks_small_obj(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_y_axis_pseudolandmarks_debug") + os.mkdir(cache_dir) + img = cv2.imread(os.path.join(TEST_DATA, TEST_VIS_SMALL_PLANT)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL_PLANT), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR_SMALL_PLANT), encoding="latin1") + obj_contour = contours_npz['arr_0'] + # Test with debug = "print" + pcv.params.debug = "print" + _, _, _ = pcv.y_axis_pseudolandmarks(obj=[], mask=mask, img=img) + _, _, _ = pcv.y_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) + # Test with debug = "plot" + pcv.params.debug = "plot" + pcv.outputs.clear() + left, right, center_h = pcv.y_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) + pcv.outputs.clear() + assert all([all([i == j] for i, j in zip(np.shape(left), (20, 1, 2))), + all([i == j] for i, j in zip(np.shape(right), (20, 1, 2))), + all([i == j] for i, j in zip(np.shape(center_h), (20, 1, 2)))]) + + +def test_plantcv_y_axis_pseudolandmarks_bad_input(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_y_axis_pseudolandmarks_debug") + os.mkdir(cache_dir) + img = np.array([]) + mask = np.array([]) + obj_contour = np.array([]) + pcv.params.debug = None + result = pcv.y_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) + pcv.outputs.clear() + assert all([i == j] for i, j in zip(result, [("NA", "NA"), ("NA", "NA"), ("NA", "NA")])) + + +def test_plantcv_y_axis_pseudolandmarks_bad_obj_input(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_VIS_SMALL_PLANT)) + with pytest.raises(RuntimeError): + _ = pcv.y_axis_pseudolandmarks(obj=np.array([[-2, -2], [-2, -2]]), mask=np.array([[-2, -2], [-2, -2]]), img=img) + + +def test_plantcv_background_subtraction(): + # List to hold result of all tests. + truths = [] + fg_img = cv2.imread(os.path.join(TEST_DATA, TEST_FOREGROUND)) + bg_img = cv2.imread(os.path.join(TEST_DATA, TEST_BACKGROUND)) + big_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Testing if background subtraction is actually still working. + # This should return an array whose sum is greater than one + pcv.params.debug = None + fgmask = pcv.background_subtraction(background_image=bg_img, foreground_image=fg_img) + truths.append(np.sum(fgmask) > 0) + fgmask = pcv.background_subtraction(background_image=big_img, foreground_image=bg_img) + truths.append(np.sum(fgmask) > 0) + # The same foreground subtracted from itself should be 0 + fgmask = pcv.background_subtraction(background_image=fg_img, foreground_image=fg_img) + truths.append(np.sum(fgmask) == 0) + # The same background subtracted from itself should be 0 + fgmask = pcv.background_subtraction(background_image=bg_img, foreground_image=bg_img) + truths.append(np.sum(fgmask) == 0) + # All of these should be true for the function to pass testing. + assert (all(truths)) + + +def test_plantcv_background_subtraction_debug(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_background_subtraction_debug") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # List to hold result of all tests. + truths = [] + fg_img = cv2.imread(os.path.join(TEST_DATA, TEST_FOREGROUND)) + bg_img = cv2.imread(os.path.join(TEST_DATA, TEST_BACKGROUND)) + # Test with debug = "print" + pcv.params.debug = "print" + fgmask = pcv.background_subtraction(background_image=bg_img, foreground_image=fg_img) + truths.append(np.sum(fgmask) > 0) + # Test with debug = "plot" + pcv.params.debug = "plot" + fgmask = pcv.background_subtraction(background_image=bg_img, foreground_image=fg_img) + truths.append(np.sum(fgmask) > 0) + # All of these should be true for the function to pass testing. + assert (all(truths)) + + +def test_plantcv_background_subtraction_bad_img_type(): + fg_color = cv2.imread(os.path.join(TEST_DATA, TEST_FOREGROUND)) + bg_gray = cv2.imread(os.path.join(TEST_DATA, TEST_BACKGROUND), 0) + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.background_subtraction(background_image=bg_gray, foreground_image=fg_color) + + +def test_plantcv_background_subtraction_different_sizes(): + fg_img = cv2.imread(os.path.join(TEST_DATA, TEST_FOREGROUND)) + bg_img = cv2.imread(os.path.join(TEST_DATA, TEST_BACKGROUND)) + bg_shp = np.shape(bg_img) # type: tuple + bg_img_resized = cv2.resize(bg_img, (int(bg_shp[0] / 2), int(bg_shp[1] / 2)), interpolation=cv2.INTER_AREA) + pcv.params.debug = None + fgmask = pcv.background_subtraction(background_image=bg_img_resized, foreground_image=fg_img) + assert np.sum(fgmask) > 0 + + +def test_plantcv_spatial_clustering_dbscan(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_spatial_clustering_dbscan") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI_MASK), -1) + pcv.params.debug = "print" + _ = pcv.spatial_clustering(img, algorithm="DBSCAN", min_cluster_size=10, max_distance=None) + pcv.params.debug = "plot" + spmask = pcv.spatial_clustering(img, algorithm="DBSCAN", min_cluster_size=10, max_distance=None) + assert len(spmask[1]) == 2 + + +def test_plantcv_spatial_clustering_optics(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_spatial_clustering_optics") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI_MASK), -1) + pcv.params.debug = None + spmask = pcv.spatial_clustering(img, algorithm="OPTICS", min_cluster_size=100, max_distance=5000) + assert len(spmask[1]) == 2 + + +def test_plantcv_spatial_clustering_badinput(): + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI_MASK), -1) + pcv.params.debug = None + with pytest.raises(NameError): + _ = pcv.spatial_clustering(img, algorithm="Hydra", min_cluster_size=5, max_distance=100) + +def test_detect_discs(): + # Read in test data + mask = cv2.imread(os.path.join(TEST_DATA, TEST_DISCS_MASK), -1) + + # Test with debug = None + pcv.params.debug = None + _,coor = pcv.detect_discs(bin_img=mask, ecc_thresh=0.3) + + assert len(coor) == 3 + + +# ############################## +# Tests for the learn subpackage +# ############################## +def test_plantcv_learn_naive_bayes(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_learn_naive_bayes") + os.mkdir(cache_dir) + # Make image and mask directories in the cache directory + imgdir = os.path.join(cache_dir, "images") + maskdir = os.path.join(cache_dir, "masks") + if not os.path.exists(imgdir): + os.mkdir(imgdir) + if not os.path.exists(maskdir): + os.mkdir(maskdir) + # Copy and image and mask to the image/mask directories + shutil.copyfile(os.path.join(TEST_DATA, TEST_VIS_SMALL), os.path.join(imgdir, "image.png")) + shutil.copyfile(os.path.join(TEST_DATA, TEST_MASK_SMALL), os.path.join(maskdir, "image.png")) + # Run the naive Bayes training module + outfile = os.path.join(cache_dir, "naive_bayes_pdfs.txt") + plantcv.learn.naive_bayes(imgdir=imgdir, maskdir=maskdir, outfile=outfile, mkplots=True) + assert os.path.exists(outfile) + + +def test_plantcv_learn_naive_bayes_multiclass(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_learn_naive_bayes_multiclass") + os.mkdir(cache_dir) + # Run the naive Bayes multiclass training module + outfile = os.path.join(cache_dir, "naive_bayes_multiclass_pdfs.txt") + plantcv.learn.naive_bayes_multiclass(samples_file=os.path.join(TEST_DATA, TEST_SAMPLED_RGB_POINTS), outfile=outfile, + mkplots=True) + assert os.path.exists(outfile) + + +# #################################### +# Tests for the morphology subpackage +# #################################### +def test_plantcv_morphology_segment_curvature(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_curvature") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON_PRUNED), -1) + pcv.params.debug = "print" + segmented_img, seg_objects = pcv.morphology.segment_skeleton(skel_img=skeleton) + pcv.outputs.clear() + _ = pcv.morphology.segment_curvature(segmented_img, seg_objects, label="prefix") + pcv.params.debug = "plot" + pcv.outputs.clear() + _ = pcv.morphology.segment_curvature(segmented_img, seg_objects) + assert len(pcv.outputs.observations['default']['segment_curvature']['value']) == 22 + + +def test_plantcv_morphology_check_cycles(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_branches") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + pcv.params.debug = "print" + _ = pcv.morphology.check_cycles(mask, label="prefix") + pcv.params.debug = "plot" + _ = pcv.morphology.check_cycles(mask) + pcv.params.debug = None + _ = pcv.morphology.check_cycles(mask) + assert pcv.outputs.observations['default']['num_cycles']['value'] == 1 + + +def test_plantcv_morphology_find_branch_pts(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_branches") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + pcv.params.debug = "print" + _ = pcv.morphology.find_branch_pts(skel_img=skeleton, mask=mask, label="prefix") + pcv.params.debug = "plot" + _ = pcv.morphology.find_branch_pts(skel_img=skeleton) + pcv.params.debug = None + branches = pcv.morphology.find_branch_pts(skel_img=skeleton) + assert np.sum(branches) == 9435 + + +def test_plantcv_morphology_find_tips(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_tips") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + pcv.params.debug = "print" + _ = pcv.morphology.find_tips(skel_img=skeleton, mask=mask, label="prefix") + pcv.params.debug = "plot" + _ = pcv.morphology.find_tips(skel_img=skeleton) + pcv.params.debug = None + tips = pcv.morphology.find_tips(skel_img=skeleton) + assert np.sum(tips) == 9435 + + +def test_plantcv_morphology_prune(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_pruned") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + pcv.params.debug = "print" + _ = pcv.morphology.prune(skel_img=skeleton, size=1) + pcv.params.debug = "plot" + _ = pcv.morphology.prune(skel_img=skeleton, size=1, mask=skeleton) + pcv.params.debug = None + pruned_img, _, _ = pcv.morphology.prune(skel_img=skeleton, size=3) + assert np.sum(pruned_img) < np.sum(skeleton) + + +def test_plantcv_morphology_prune_size0(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_pruned") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + pruned_img, _, _ = pcv.morphology.prune(skel_img=skeleton, size=0) + assert np.sum(pruned_img) == np.sum(skeleton) + + +def test_plantcv_morphology_iterative_prune(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_pruned") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + pruned_img = pcv.morphology._iterative_prune(skel_img=skeleton, size=3) + assert np.sum(pruned_img) < np.sum(skeleton) + + +def test_plantcv_morphology_segment_skeleton(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_skeleton") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + pcv.params.debug = "print" + _ = pcv.morphology.segment_skeleton(skel_img=skeleton, mask=mask) + pcv.params.debug = "plot" + segmented_img, segment_objects = pcv.morphology.segment_skeleton(skel_img=skeleton) + assert len(segment_objects) == 73 + + +def test_plantcv_morphology_fill_segments(): + # Clear previous outputs + pcv.outputs.clear() + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + obj_dic = np.load(os.path.join(TEST_DATA, TEST_SKELETON_OBJECTS)) + obj = [] + for key, val in obj_dic.items(): + obj.append(val) + pcv.params.debug = None + _ = pcv.morphology.fill_segments(mask, obj) + tests = [pcv.outputs.observations['default']['segment_area']['value'][42] == 5529, + pcv.outputs.observations['default']['segment_area']['value'][20] == 5057, + pcv.outputs.observations['default']['segment_area']['value'][49] == 3323] + assert all(tests) + + +def test_plantcv_morphology_fill_segments_with_stem(): + # Clear previous outputs + pcv.outputs.clear() + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + obj_dic = np.load(os.path.join(TEST_DATA, TEST_SKELETON_OBJECTS)) + obj = [] + for key, val in obj_dic.items(): + obj.append(val) + + stem_obj = obj[0:4] + pcv.params.debug = None + _ = pcv.morphology.fill_segments(mask, obj, stem_obj) + num_objects = len(pcv.outputs.observations['default']['leaf_area']['value']) + assert num_objects == 70 + + +def test_plantcv_morphology_segment_angle(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_angles") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON_PRUNED), -1) + pcv.params.debug = "print" + segmented_img, segment_objects = pcv.morphology.segment_skeleton(skel_img=skeleton) + _ = pcv.morphology.segment_angle(segmented_img=segmented_img, objects=segment_objects, label="prefix") + pcv.params.debug = "plot" + _ = pcv.morphology.segment_angle(segmented_img, segment_objects) + assert len(pcv.outputs.observations['default']['segment_angle']['value']) == 22 + + +def test_plantcv_morphology_segment_angle_overflow(): + # Clear previous outputs + pcv.outputs.clear() + # Don't prune, would usually give overflow error without extra if statement in segment_angle + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_angles") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + segmented_img, segment_objects = pcv.morphology.segment_skeleton(skel_img=skeleton) + _ = pcv.morphology.segment_angle(segmented_img, segment_objects) + assert len(pcv.outputs.observations['default']['segment_angle']['value']) == 73 + + +def test_plantcv_morphology_segment_euclidean_length(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_eu_length") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON_PRUNED), -1) + pcv.params.debug = "print" + segmented_img, segment_objects = pcv.morphology.segment_skeleton(skel_img=skeleton) + _ = pcv.morphology.segment_euclidean_length(segmented_img, segment_objects, label="prefix") + pcv.params.debug = "plot" + _ = pcv.morphology.segment_euclidean_length(segmented_img, segment_objects) + assert len(pcv.outputs.observations['default']['segment_eu_length']['value']) == 22 + + +def test_plantcv_morphology_segment_euclidean_length_bad_input(): + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + skel = pcv.morphology.skeletonize(mask=mask) + pcv.params.debug = None + segmented_img, segment_objects = pcv.morphology.segment_skeleton(skel_img=skel) + with pytest.raises(RuntimeError): + _ = pcv.morphology.segment_euclidean_length(segmented_img, segment_objects) + + +def test_plantcv_morphology_segment_path_length(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_path_length") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON_PRUNED), -1) + pcv.params.debug = "print" + segmented_img, segment_objects = pcv.morphology.segment_skeleton(skel_img=skeleton) + _ = pcv.morphology.segment_path_length(segmented_img, segment_objects, label="prefix") + pcv.params.debug = "plot" + _ = pcv.morphology.segment_path_length(segmented_img, segment_objects) + assert len(pcv.outputs.observations['default']['segment_path_length']['value']) == 22 + + +def test_plantcv_morphology_skeletonize(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_skeletonize") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + input_skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + pcv.params.debug = "print" + _ = pcv.morphology.skeletonize(mask=mask) + pcv.params.debug = "plot" + _ = pcv.morphology.skeletonize(mask=mask) + pcv.params.debug = None + skeleton = pcv.morphology.skeletonize(mask=mask) + arr = np.array(skeleton == input_skeleton) + assert arr.all() + + +def test_plantcv_morphology_segment_sort(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_sort") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + segmented_img, seg_objects = pcv.morphology.segment_skeleton(skel_img=skeleton) + pcv.params.debug = "print" + _ = pcv.morphology.segment_sort(skeleton, seg_objects, mask=skeleton) + pcv.params.debug = "plot" + leaf_obj, stem_obj = pcv.morphology.segment_sort(skeleton, seg_objects) + assert len(leaf_obj) == 36 + + +def test_plantcv_morphology_segment_tangent_angle(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_tangent_angle") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skel = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON_PRUNED), -1) + objects = np.load(os.path.join(TEST_DATA, TEST_SKELETON_OBJECTS), encoding="latin1") + objs = [objects[arr_n] for arr_n in objects] + pcv.params.debug = "print" + _ = pcv.morphology.segment_tangent_angle(skel, objs, 2, label="prefix") + pcv.params.debug = "plot" + _ = pcv.morphology.segment_tangent_angle(skel, objs, 2) + assert len(pcv.outputs.observations['default']['segment_tangent_angle']['value']) == 73 + + +def test_plantcv_morphology_segment_id(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_tangent_angle") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skel = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON_PRUNED), -1) + objects = np.load(os.path.join(TEST_DATA, TEST_SKELETON_OBJECTS), encoding="latin1") + objs = [objects[arr_n] for arr_n in objects] + pcv.params.debug = "print" + _ = pcv.morphology.segment_id(skel, objs) + pcv.params.debug = "plot" + _, labeled_img = pcv.morphology.segment_id(skel, objs, mask=skel) + assert np.sum(labeled_img) > np.sum(skel) + + +def test_plantcv_morphology_segment_insertion_angle(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_insertion_angle") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + pruned, _, _ = pcv.morphology.prune(skel_img=skeleton, size=6) + segmented_img, seg_objects = pcv.morphology.segment_skeleton(skel_img=pruned) + leaf_obj, stem_obj = pcv.morphology.segment_sort(pruned, seg_objects) + pcv.params.debug = "plot" + _ = pcv.morphology.segment_insertion_angle(pruned, segmented_img, leaf_obj, stem_obj, 3, label="prefix") + pcv.params.debug = "print" + _ = pcv.morphology.segment_insertion_angle(pruned, segmented_img, leaf_obj, stem_obj, 10) + assert pcv.outputs.observations['default']['segment_insertion_angle']['value'][:6] == ['NA', 'NA', 'NA', + 24.956918822001636, + 50.7313343343401, + 56.427712102130734] + + +def test_plantcv_morphology_segment_insertion_angle_bad_stem(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_insertion_angle") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + pruned, _, _ = pcv.morphology.prune(skel_img=skeleton, size=5) + segmented_img, seg_objects = pcv.morphology.segment_skeleton(skel_img=pruned) + leaf_obj, stem_obj = pcv.morphology.segment_sort(pruned, seg_objects) + stem_obj = [leaf_obj[0], leaf_obj[10]] + with pytest.raises(RuntimeError): + _ = pcv.morphology.segment_insertion_angle(pruned, segmented_img, leaf_obj, stem_obj, 10) + + +def test_plantcv_morphology_segment_combine(): + skel = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON_PRUNED), -1) + segmented_img, seg_objects = pcv.morphology.segment_skeleton(skel_img=skel) + pcv.params.debug = "plot" + # Test with list of IDs input + _, new_objects = pcv.morphology.segment_combine([0, 1], seg_objects, skel) + assert len(new_objects) + 1 == len(seg_objects) + + +def test_plantcv_morphology_segment_combine_lists(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_insertion_angle") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skel = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON_PRUNED), -1) + segmented_img, seg_objects = pcv.morphology.segment_skeleton(skel_img=skel) + pcv.params.debug = "print" + # Test with list of lists input + _, new_objects = pcv.morphology.segment_combine([[0, 1, 2], [3, 4]], seg_objects, skel) + assert len(new_objects) + 3 == len(seg_objects) + + +def test_plantcv_morphology_segment_combine_bad_input(): + skel = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON_PRUNED), -1) + segmented_img, seg_objects = pcv.morphology.segment_skeleton(skel_img=skel) + pcv.params.debug = "plot" + with pytest.raises(RuntimeError): + _, new_objects = pcv.morphology.segment_combine([0.5, 1.5], seg_objects, skel) + + +def test_plantcv_morphology_analyze_stem(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_analyze_stem") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + pruned, segmented_img, _ = pcv.morphology.prune(skel_img=skeleton, size=6) + segmented_img, seg_objects = pcv.morphology.segment_skeleton(skel_img=pruned) + leaf_obj, stem_obj = pcv.morphology.segment_sort(pruned, seg_objects) + pcv.params.debug = "plot" + _ = pcv.morphology.analyze_stem(rgb_img=segmented_img, stem_objects=stem_obj, label="prefix") + pcv.params.debug = "print" + _ = pcv.morphology.analyze_stem(rgb_img=segmented_img, stem_objects=stem_obj) + assert pcv.outputs.observations['default']['stem_angle']['value'] == -12.531776428222656 + + +def test_plantcv_morphology_analyze_stem_bad_angle(): + # Clear previous outputs + pcv.outputs.clear() + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_morphology_segment_insertion_angle") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + skeleton = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_SKELETON), -1) + pruned, _, _ = pcv.morphology.prune(skel_img=skeleton, size=5) + segmented_img, seg_objects = pcv.morphology.segment_skeleton(skel_img=pruned) + _, _ = pcv.morphology.segment_sort(pruned, seg_objects) + # print([stem_obj[3]]) + # stem_obj = [stem_obj[3]] + stem_obj = [[[[1116, 1728]], [[1116, 1]]]] + _ = pcv.morphology.analyze_stem(rgb_img=segmented_img, stem_objects=stem_obj) + assert pcv.outputs.observations['default']['stem_angle']['value'] == 22877334.0 + + +# ######################################## +# Tests for the hyperspectral subpackage +# ######################################## +def test_plantcv_hyperspectral_read_data_default(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_read_data_default") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = "plot" + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + _ = pcv.hyperspectral.read_data(filename=spectral_filename) + pcv.params.debug = "print" + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + assert np.shape(array_data.array_data) == (1, 1600, 978) + + +def test_plantcv_hyperspectral_read_data_no_default_bands(): + pcv.params.debug = "plot" + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA_NO_DEFAULT) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + assert np.shape(array_data.array_data) == (1, 1600, 978) + + +def test_plantcv_hyperspectral_read_data_approx_pseudorgb(): + pcv.params.debug = "plot" + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA_APPROX_PSEUDO) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + assert np.shape(array_data.array_data) == (1, 1600, 978) + + +def test_plantcv_spectral_index_ndvi(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_ndvi") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.ndvi(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_ndvi_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.ndvi(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.ndvi(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_gdvi(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_gdvi") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.gdvi(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_gdvi_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.gdvi(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.gdvi(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_savi(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_savi") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.savi(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_savi_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.savi(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.savi(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_pri(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_pri") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.pri(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_pri_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.pri(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.pri(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_ari(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_ari") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.ari(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_ari_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.ari(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.ari(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_ci_rededge(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_ci_rededge") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.ci_rededge(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_ci_rededge_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.ci_rededge(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.ci_rededge(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_cri550(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_cri550") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.cri550(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_cri550_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.cri550(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.cri550(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_cri700(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_cri700") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.cri700(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_cri700_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.cri700(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.cri700(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_egi(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_egi") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + index_array = pcv.spectral_index.egi(rgb_img=rgb_img) + assert np.shape(index_array.array_data) == (2056, 2454) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_evi(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_evi") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.evi(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_evi_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.evi(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.evi(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_mari(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_mari") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.mari(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_mari_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.mari(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.mari(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_mcari(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_mcari") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.mcari(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_mcari_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.mcari(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.mcari(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_mtci(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_mtci") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.mtci(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_mtci_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.mtci(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.mtci(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_ndre(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_ndre") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.ndre(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_ndre_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.ndre(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.ndre(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_psnd_chla(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_psnd_chla") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.psnd_chla(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_psnd_chla_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.psnd_chla(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.psnd_chla(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_psnd_chlb(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_psnd_chlb") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.psnd_chlb(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_psnd_chlb_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.psnd_chlb(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.psnd_chlb(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_psnd_car(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_psnd_car") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.psnd_car(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_psnd_car_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.psnd_car(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.psnd_car(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_psri(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_psri") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.psri(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_psri_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.psri(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.psri(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_pssr_chla(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_pssr_chla") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.pssr_chla(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_pssr_chla_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.pssr_chla(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.pssr_chla(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_pssr_chlb(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_pssr_chlb") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.pssr_chlb(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_pssr_chlb_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.pssr_chlb(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.pssr_chlb(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_pssr_car(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_pssr_car") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.pssr_car(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_pssr_car_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.pssr_car(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.pssr_car(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_rgri(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_rgri") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.rgri(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_rgri_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.rgri(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.rgri(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_rvsi(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_rvsi") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.rvsi(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_rvsi_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.rvsi(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.rvsi(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_sipi(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_sipi") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.sipi(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_sipi_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.sipi(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.sipi(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_sr(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_sr") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.sr(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_sr_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.sr(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.sr(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_vari(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_vari") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.vari(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_vari_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.vari(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.vari(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_vi_green(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_vi_green") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.vi_green(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_vi_green_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.vi_green(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.vi_green(hsi=index_array, distance=20) + + +def test_plantcv_spectral_index_wi(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_wi") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.wi(hsi=array_data, distance=20) + assert np.shape(index_array.array_data) == (1, 1600) and np.nanmax(index_array.pseudo_rgb) == 255 + + +def test_plantcv_spectral_index_wi_bad_input(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + pcv.params.debug = None + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.wi(hsi=array_data, distance=20) + with pytest.raises(RuntimeError): + _ = pcv.spectral_index.wi(hsi=index_array, distance=20) + + +def test_plantcv_hyperspectral_analyze_spectral(): + # Clear previous outputs + pcv.outputs.clear() + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_analyze_spectral") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + mask = cv2.imread(os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_MASK), -1) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + # pcv.params.debug = "plot" + # _ = pcv.hyperspectral.analyze_spectral(array=array_data, mask=mask, histplot=True) + # pcv.params.debug = "print" + # _ = pcv.hyperspectral.analyze_spectral(array=array_data, mask=mask, histplot=True, label="prefix") + pcv.params.debug = None + _ = pcv.hyperspectral.analyze_spectral(array=array_data, mask=mask, histplot=True, label="prefix") + assert len(pcv.outputs.observations['prefix']['spectral_frequencies']['value']) == 978 + + +def test_plantcv_hyperspectral_analyze_index(): + # Clear previous outputs + pcv.outputs.clear() + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_analyze_index") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.savi(hsi=array_data, distance=801) + mask_img = np.ones(np.shape(index_array.array_data), dtype=np.uint8) * 255 + # pcv.params.debug = "print" + # pcv.hyperspectral.analyze_index(index_array=index_array, mask=mask_img, histplot=True) + # pcv.params.debug = "plot" + # pcv.hyperspectral.analyze_index(index_array=index_array, mask=mask_img, histplot=True) + + pcv.params.debug = None + pcv.hyperspectral.analyze_index(index_array=index_array, mask=mask_img, histplot=True) + + assert pcv.outputs.observations['default']['mean_index_savi']['value'] > 0 + + +def test_plantcv_hyperspectral_analyze_index_set_range(): + # Clear previous outputs + pcv.outputs.clear() + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_analyze_index_set_range") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.savi(hsi=array_data, distance=801) + mask_img = np.ones(np.shape(index_array.array_data), dtype=np.uint8) * 255 + pcv.params.debug = None + pcv.hyperspectral.analyze_index(index_array=index_array, mask=mask_img, histplot=True, min_bin=0, max_bin=1) + assert pcv.outputs.observations['default']['mean_index_savi']['value'] > 0 + + +def test_plantcv_hyperspectral_analyze_index_auto_range(): + # Clear previous outputs + pcv.outputs.clear() + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_analyze_index_auto_range") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.savi(hsi=array_data, distance=801) + mask_img = np.ones(np.shape(index_array.array_data), dtype=np.uint8) * 255 + pcv.params.debug = None + pcv.hyperspectral.analyze_index(index_array=index_array, mask=mask_img, min_bin="auto", max_bin="auto") + assert pcv.outputs.observations['default']['mean_index_savi']['value'] > 0 + + +def test_plantcv_hyperspectral_analyze_index_outside_range_warning(): + import io + from contextlib import redirect_stdout + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_analyze_index_auto_range") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.savi(hsi=array_data, distance=801) + mask_img = np.ones(np.shape(index_array.array_data), dtype=np.uint8) * 255 + f = io.StringIO() + with redirect_stdout(f): + pcv.params.debug = None + pcv.hyperspectral.analyze_index(index_array=index_array, mask=mask_img, min_bin=.5, max_bin=.55, label="i") + out = f.getvalue() + # assert os.listdir(cache_dir) is 0 + assert out[0:10] == 'WARNING!!!' + + +def test_plantcv_hyperspectral_analyze_index_bad_input_mask(): + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.savi(hsi=array_data, distance=801) + mask_img = cv2.imread(os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_MASK)) + with pytest.raises(RuntimeError): + pcv.hyperspectral.analyze_index(index_array=index_array, mask=mask_img) + + +def test_plantcv_hyperspectral_analyze_index_bad_input_index(): + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + index_array = pcv.spectral_index.savi(hsi=array_data, distance=801) + mask_img = cv2.imread(os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_MASK), -1) + index_array.array_data = cv2.imread(os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_MASK)) + with pytest.raises(RuntimeError): + pcv.hyperspectral.analyze_index(index_array=index_array, mask=mask_img) + + +def test_plantcv_hyperspectral_analyze_index_bad_input_datatype(): + pcv.params.debug = None + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array_data = pcv.hyperspectral.read_data(filename=spectral_filename) + mask_img = cv2.imread(os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_MASK), -1) + with pytest.raises(RuntimeError): + pcv.hyperspectral.analyze_index(index_array=array_data, mask=mask_img) + + +def test_plantcv_hyperspectral_calibrate(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_calibrate") + os.mkdir(cache_dir) + raw = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + white = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_WHITE) + dark = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DARK) + raw = pcv.hyperspectral.read_data(filename=raw) + white = pcv.hyperspectral.read_data(filename=white) + dark = pcv.hyperspectral.read_data(filename=dark) + pcv.params.debug = "plot" + _ = pcv.hyperspectral.calibrate(raw_data=raw, white_reference=white, dark_reference=dark) + pcv.params.debug = "print" + calibrated = pcv.hyperspectral.calibrate(raw_data=raw, white_reference=white, dark_reference=dark) + assert np.shape(calibrated.array_data) == (1, 1600, 978) + + +def test_plantcv_hyperspectral_extract_wavelength(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_extract_wavelength") + os.mkdir(cache_dir) + spectral = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + spectral = pcv.hyperspectral.read_data(filename=spectral) + pcv.params.debug = "plot" + _ = pcv.hyperspectral.extract_wavelength(spectral_data=spectral, wavelength=500) + pcv.params.debug = "print" + new = pcv.hyperspectral.extract_wavelength(spectral_data=spectral, wavelength=500) + assert np.shape(new.array_data) == (1, 1600) + + +def test_plantcv_hyperspectral_avg_reflectance(): + spectral = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + mask_img = cv2.imread(os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_MASK), -1) + spectral = pcv.hyperspectral.read_data(filename=spectral) + avg_reflect = pcv.hyperspectral._avg_reflectance(spectral, mask=mask_img) + assert len(avg_reflect) == 978 + + +def test_plantcv_hyperspectral_inverse_covariance(): + spectral = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + spectral = pcv.hyperspectral.read_data(filename=spectral) + inv_cov = pcv.hyperspectral._inverse_covariance(spectral) + assert np.shape(inv_cov) == (978, 978) + + +# ######################################## +# Tests for the photosynthesis subpackage +# ######################################## +def test_plantcv_photosynthesis_read_dat(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_photosynthesis_read_dat") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + pcv.params.debug = "plot" + fluor_filename = os.path.join(FLUOR_TEST_DATA, FLUOR_IMG) + _, _, _ = pcv.photosynthesis.read_cropreporter(filename=fluor_filename) + pcv.params.debug = "print" + fdark, fmin, fmax = pcv.photosynthesis.read_cropreporter(filename=fluor_filename) + assert np.sum(fmin) < np.sum(fmax) + + +def test_plantcv_photosynthesis_analyze_fvfm(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_fvfm") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # filename = os.path.join(cache_dir, 'plantcv_fvfm_hist.png') + # Read in test data + fdark = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FDARK), -1) + fmin = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMIN), -1) + fmax = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMAX), -1) + fmask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMASK), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.photosynthesis.analyze_fvfm(fdark=fdark, fmin=fmin, fmax=fmax, mask=fmask, bins=1000, label="prefix") + # Test with debug = "plot" + pcv.params.debug = "plot" + fvfm_images = pcv.photosynthesis.analyze_fvfm(fdark=fdark, fmin=fmin, fmax=fmax, mask=fmask, bins=1000) + assert len(fvfm_images) != 0 + + +def test_plantcv_photosynthesis_analyze_fvfm_print_analysis_results(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_fvfm") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + fdark = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FDARK), -1) + fmin = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMIN), -1) + fmax = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMAX), -1) + fmask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMASK), -1) + _ = pcv.photosynthesis.analyze_fvfm(fdark=fdark, fmin=fmin, fmax=fmax, mask=fmask, bins=1000) + result_file = os.path.join(cache_dir, "results.txt") + pcv.print_results(result_file) + pcv.outputs.clear() + assert os.path.exists(result_file) + + +def test_plantcv_photosynthesis_analyze_fvfm_bad_fdark(): + # Clear previous outputs + pcv.outputs.clear() + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_fvfm") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + fdark = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FDARK), -1) + fmin = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMIN), -1) + fmax = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMAX), -1) + fmask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMASK), -1) + _ = pcv.photosynthesis.analyze_fvfm(fdark=fdark + 3000, fmin=fmin, fmax=fmax, mask=fmask, bins=1000) + check = pcv.outputs.observations['default']['fdark_passed_qc']['value'] is False + assert check + + +def test_plantcv_photosynthesis_analyze_fvfm_bad_input(): + fdark = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + fmin = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMIN), -1) + fmax = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMAX), -1) + fmask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMASK), -1) + with pytest.raises(RuntimeError): + _ = pcv.photosynthesis.analyze_fvfm(fdark=fdark, fmin=fmin, fmax=fmax, mask=fmask, bins=1000) + + +# ############################## +# Tests for the roi subpackage +# ############################## +def test_plantcv_roi_from_binary_image(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_roi_from_binary_image") + os.mkdir(cache_dir) + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Create a binary image + bin_img = np.zeros(np.shape(rgb_img)[0:2], dtype=np.uint8) + cv2.rectangle(bin_img, (100, 100), (1000, 1000), 255, -1) + # Test with debug = "print" + pcv.params.debug = "print" + pcv.params.debug_outdir = cache_dir + _, _ = pcv.roi.from_binary_image(bin_img=bin_img, img=rgb_img) + # Test with debug = "plot" + pcv.params.debug = "plot" + _, _ = pcv.roi.from_binary_image(bin_img=bin_img, img=rgb_img) + # Test with debug = None + pcv.params.debug = None + roi_contour, roi_hierarchy = pcv.roi.from_binary_image(bin_img=bin_img, img=rgb_img) + # Assert the contours and hierarchy lists contain only the ROI + assert np.shape(roi_contour) == (1, 3600, 1, 2) + + +def test_plantcv_roi_from_binary_image_grayscale_input(): + # Read in a test grayscale image + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Create a binary image + bin_img = np.zeros(np.shape(gray_img)[0:2], dtype=np.uint8) + cv2.rectangle(bin_img, (100, 100), (1000, 1000), 255, -1) + # Test with debug = "plot" + pcv.params.debug = "plot" + roi_contour, roi_hierarchy = pcv.roi.from_binary_image(bin_img=bin_img, img=gray_img) + # Assert the contours and hierarchy lists contain only the ROI + assert np.shape(roi_contour) == (1, 3600, 1, 2) + + +def test_plantcv_roi_from_binary_image_bad_binary_input(): + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Binary input is required but an RGB input is provided + with pytest.raises(RuntimeError): + _, _ = pcv.roi.from_binary_image(bin_img=rgb_img, img=rgb_img) + + +def test_plantcv_roi_rectangle(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_roi_rectangle") + os.mkdir(cache_dir) + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + pcv.params.debug_outdir = cache_dir + _, _ = pcv.roi.rectangle(x=100, y=100, h=500, w=500, img=rgb_img) + # Test with debug = "plot" + pcv.params.debug = "plot" + _, _ = pcv.roi.rectangle(x=100, y=100, h=500, w=500, img=rgb_img) + # Test with debug = None + pcv.params.debug = None + roi_contour, roi_hierarchy = pcv.roi.rectangle(x=100, y=100, h=500, w=500, img=rgb_img) + # Assert the contours and hierarchy lists contain only the ROI + assert np.shape(roi_contour) == (1, 4, 1, 2) + + +def test_plantcv_roi_rectangle_grayscale_input(): + # Read in a test grayscale image + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with debug = "plot" + pcv.params.debug = "plot" + roi_contour, roi_hierarchy = pcv.roi.rectangle(x=100, y=100, h=500, w=500, img=gray_img) + # Assert the contours and hierarchy lists contain only the ROI + assert np.shape(roi_contour) == (1, 4, 1, 2) + + +def test_plantcv_roi_rectangle_out_of_frame(): + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # The resulting rectangle needs to be within the dimensions of the image + with pytest.raises(RuntimeError): + _, _ = pcv.roi.rectangle(x=100, y=100, h=500, w=3000, img=rgb_img) + + +def test_plantcv_roi_circle(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_roi_circle") + os.mkdir(cache_dir) + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + pcv.params.debug_outdir = cache_dir + _, _ = pcv.roi.circle(x=100, y=100, r=50, img=rgb_img) + # Test with debug = "plot" + pcv.params.debug = "plot" + _, _ = pcv.roi.circle(x=100, y=100, r=50, img=rgb_img) + # Test with debug = None + pcv.params.debug = None + roi_contour, roi_hierarchy = pcv.roi.circle(x=200, y=225, r=75, img=rgb_img) + # Assert the contours and hierarchy lists contain only the ROI + assert np.shape(roi_contour) == (1, 424, 1, 2) + + +def test_plantcv_roi_circle_grayscale_input(): + # Read in a test grayscale image + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with debug = "plot" + pcv.params.debug = "plot" + roi_contour, roi_hierarchy = pcv.roi.circle(x=200, y=225, r=75, img=gray_img) + # Assert the contours and hierarchy lists contain only the ROI + assert np.shape(roi_contour) == (1, 424, 1, 2) + + +def test_plantcv_roi_circle_out_of_frame(): + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # The resulting rectangle needs to be within the dimensions of the image + with pytest.raises(RuntimeError): + _, _ = pcv.roi.circle(x=50, y=225, r=75, img=rgb_img) + + +def test_plantcv_roi_ellipse(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_roi_ellipse") + os.mkdir(cache_dir) + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + pcv.params.debug_outdir = cache_dir + _, _ = pcv.roi.ellipse(x=200, y=200, r1=75, r2=50, angle=0, img=rgb_img) + # Test with debug = "plot" + pcv.params.debug = "plot" + _, _ = pcv.roi.ellipse(x=200, y=200, r1=75, r2=50, angle=0, img=rgb_img) + # Test with debug = None + pcv.params.debug = None + roi_contour, roi_hierarchy = pcv.roi.ellipse(x=200, y=200, r1=75, r2=50, angle=0, img=rgb_img) + # Assert the contours and hierarchy lists contain only the ROI + assert np.shape(roi_contour) == (1, 360, 1, 2) + + +def test_plantcv_roi_ellipse_grayscale_input(): + # Read in a test grayscale image + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with debug = "plot" + pcv.params.debug = "plot" + roi_contour, roi_hierarchy = pcv.roi.ellipse(x=200, y=200, r1=75, r2=50, angle=0, img=gray_img) + # Assert the contours and hierarchy lists contain only the ROI + assert np.shape(roi_contour) == (1, 360, 1, 2) + + +def test_plantcv_roi_ellipse_out_of_frame(): + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # The resulting rectangle needs to be within the dimensions of the image + with pytest.raises(RuntimeError): + _, _ = pcv.roi.ellipse(x=50, y=225, r1=75, r2=50, angle=0, img=rgb_img) + + +def test_plantcv_roi_multi(): + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.roi.multi(rgb_img, coord=[(25, 120), (100, 100)], radius=20) + # Test with debug = None + pcv.params.debug = None + rois1, roi_hierarchy1 = pcv.roi.multi(rgb_img, coord=(25, 120), radius=20, spacing=(10, 10), nrows=3, ncols=6) + # Assert the contours has 18 ROIs + assert len(rois1) == 18 + + +def test_plantcv_roi_multi_bad_input(): + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # The user must input a list of custom coordinates OR inputs to make a grid. Not both + with pytest.raises(RuntimeError): + _, _ = pcv.roi.multi(rgb_img, coord=[(25, 120), (100, 100)], radius=20, spacing=(10, 10), nrows=3, ncols=6) + + +def test_plantcv_roi_multi_bad_input_oob(): + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # nputs to make a grid make ROIs that go off the screen + with pytest.raises(RuntimeError): + _, _ = pcv.roi.multi(rgb_img, coord=(25000, 12000), radius=2, spacing=(1, 1), nrows=3, ncols=6) + + +def test_plantcv_roi_multi_bad_input_oob_list(): + # Read in test RGB image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # All vertices in the list of centers must draw roi's that are inside the image + with pytest.raises(RuntimeError): + _, _ = pcv.roi.multi(rgb_img, coord=[(25000, 25000), (25000, 12000), (12000, 12000)], radius=20) + + +def test_plantcv_roi_custom(): + # Read in test RGB image + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + pcv.params.debug = "plot" + cnt, hier = pcv.roi.custom(img=img, vertices=[[226, 1], [313, 184], [240, 202], [220, 229], [161, 171]]) + assert np.shape(cnt) == (1, 5, 2) + + +def test_plantcv_roi_custom_bad_input(): + # Read in test RGB image + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # ROI goes out of bounds + with pytest.raises(RuntimeError): + _ = pcv.roi.custom(img=img, vertices=[[226, -1], [3130, 1848], [2404, 2029], [2205, 2298], [1617, 1761]]) + + +# ############################## +# Tests for the transform subpackage +# ############################## +def test_plantcv_transform_get_color_matrix(): + # load in target_matrix + matrix_file = np.load(os.path.join(TEST_DATA, TEST_TARGET_MATRIX), encoding="latin1") + matrix_compare = matrix_file['arr_0'] + # Read in rgb_img and gray-scale mask + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_MASK), -1) + # The result should be a len(np.unique(mask))-1 x 4 matrix + headers, matrix = pcv.transform.get_color_matrix(rgb_img, mask) + assert np.array_equal(matrix, matrix_compare) + + +def test_plantcv_transform_get_color_matrix_img(): + # Read in two gray-scale images + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_MASK), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_MASK), -1) + # The input for rgb_img needs to be an RGB image + with pytest.raises(RuntimeError): + _, _ = pcv.transform.get_color_matrix(rgb_img, mask) + + +def test_plantcv_transform_get_color_matrix_mask(): + # Read in two gray-scale images + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_MASK)) + # The input for rgb_img needs to be an RGB image + with pytest.raises(RuntimeError): + _, _ = pcv.transform.get_color_matrix(rgb_img, mask) + + +def test_plantcv_transform_get_matrix_m(): + # load in comparison matrices + matrix_m_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_M1), encoding="latin1") + matrix_compare_m = matrix_m_file['arr_0'] + matrix_b_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_B1), encoding="latin1") + matrix_compare_b = matrix_b_file['arr_0'] + # read in matrices + t_matrix_file = np.load(os.path.join(TEST_DATA, TEST_TARGET_MATRIX), encoding="latin1") + t_matrix = t_matrix_file['arr_0'] + s_matrix_file = np.load(os.path.join(TEST_DATA, TEST_SOURCE1_MATRIX), encoding="latin1") + s_matrix = s_matrix_file['arr_0'] + # apply matrices to function + matrix_a, matrix_m, matrix_b = pcv.transform.get_matrix_m(t_matrix, s_matrix) + matrix_compare_m = np.rint(matrix_compare_m) + matrix_compare_b = np.rint(matrix_compare_b) + matrix_m = np.rint(matrix_m) + matrix_b = np.rint(matrix_b) + assert np.array_equal(matrix_m, matrix_compare_m) and np.array_equal(matrix_b, matrix_compare_b) + + +def test_plantcv_transform_get_matrix_m_unequal_data(): + # load in comparison matrices + matrix_m_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_M2), encoding="latin1") + matrix_compare_m = matrix_m_file['arr_0'] + matrix_b_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_B2), encoding="latin1") + matrix_compare_b = matrix_b_file['arr_0'] + # read in matrices + t_matrix_file = np.load(os.path.join(TEST_DATA, TEST_TARGET_MATRIX), encoding="latin1") + t_matrix = t_matrix_file['arr_0'] + s_matrix_file = np.load(os.path.join(TEST_DATA, TEST_SOURCE2_MATRIX), encoding="latin1") + s_matrix = s_matrix_file['arr_0'] + # apply matrices to function + matrix_a, matrix_m, matrix_b = pcv.transform.get_matrix_m(t_matrix, s_matrix) + matrix_compare_m = np.rint(matrix_compare_m) + matrix_compare_b = np.rint(matrix_compare_b) + matrix_m = np.rint(matrix_m) + matrix_b = np.rint(matrix_b) + assert np.array_equal(matrix_m, matrix_compare_m) and np.array_equal(matrix_b, matrix_compare_b) + + +def test_plantcv_transform_calc_transformation_matrix(): + # load in comparison matrices + matrix_file = np.load(os.path.join(TEST_DATA, TEST_TRANSFORM1), encoding="latin1") + matrix_compare = matrix_file['arr_0'] + # read in matrices + matrix_m_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_M1), encoding="latin1") + matrix_m = matrix_m_file['arr_0'] + matrix_b_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_B1), encoding="latin1") + matrix_b = matrix_b_file['arr_0'] + # apply to function + _, matrix_t = pcv.transform.calc_transformation_matrix(matrix_m, matrix_b) + matrix_t = np.rint(matrix_t) + matrix_compare = np.rint(matrix_compare) + assert np.array_equal(matrix_t, matrix_compare) + + +def test_plantcv_transform_calc_transformation_matrix_b_incorrect(): + # read in matrices + matrix_m_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_M1), encoding="latin1") + matrix_m = matrix_m_file['arr_0'] + matrix_b_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_B1), encoding="latin1") + matrix_b = matrix_b_file['arr_0'] + matrix_b = np.asmatrix(matrix_b, float) + with pytest.raises(RuntimeError): + _, _ = pcv.transform.calc_transformation_matrix(matrix_m, matrix_b.T) + + +def test_plantcv_transform_calc_transformation_matrix_not_mult(): + # read in matrices + matrix_m_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_M1), encoding="latin1") + matrix_m = matrix_m_file['arr_0'] + matrix_b_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_B1), encoding="latin1") + matrix_b = matrix_b_file['arr_0'] + with pytest.raises(RuntimeError): + _, _ = pcv.transform.calc_transformation_matrix(matrix_m, matrix_b[:3]) + + +def test_plantcv_transform_calc_transformation_matrix_not_mat(): + # read in matrices + matrix_m_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_M1), encoding="latin1") + matrix_m = matrix_m_file['arr_0'] + matrix_b_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_B1), encoding="latin1") + matrix_b = matrix_b_file['arr_0'] + with pytest.raises(RuntimeError): + _, _ = pcv.transform.calc_transformation_matrix(matrix_m[:, 1], matrix_b[:, 1]) + + +def test_plantcv_transform_apply_transformation(): + # load corrected image to compare + corrected_compare = cv2.imread(os.path.join(TEST_DATA, TEST_S1_CORRECTED)) + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform") + os.mkdir(cache_dir) + # Make image and mask directories in the cache directory + imgdir = os.path.join(cache_dir, "images") + # read in matrices + matrix_t_file = np.load(os.path.join(TEST_DATA, TEST_TRANSFORM1), encoding="latin1") + matrix_t = matrix_t_file['arr_0'] + # read in images + target_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + source_img = cv2.imread(os.path.join(TEST_DATA, TEST_SOURCE1_IMG)) + # Test with debug = "print" + pcv.params.debug = "print" + pcv.params.debug_outdir = imgdir + _ = pcv.transform.apply_transformation_matrix(source_img, target_img, matrix_t) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.transform.apply_transformation_matrix(source_img, target_img, matrix_t) + # Test with debug = None + pcv.params.debug = None + corrected_img = pcv.transform.apply_transformation_matrix(source_img, target_img, matrix_t) + # assert source and corrected have same shape + assert np.array_equal(corrected_img, corrected_compare) + + +def test_plantcv_transform_apply_transformation_incorrect_t(): + # read in matrices + matrix_t_file = np.load(os.path.join(TEST_DATA, TEST_MATRIX_B1), encoding="latin1") + matrix_t = matrix_t_file['arr_0'] + # read in images + target_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + source_img = cv2.imread(os.path.join(TEST_DATA, TEST_SOURCE1_IMG)) + with pytest.raises(RuntimeError): + _ = pcv.transform.apply_transformation_matrix(source_img, target_img, matrix_t) + + +def test_plantcv_transform_apply_transformation_incorrect_img(): + # read in matrices + matrix_t_file = np.load(os.path.join(TEST_DATA, TEST_TRANSFORM1), encoding="latin1") + matrix_t = matrix_t_file['arr_0'] + # read in images + target_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + source_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_MASK), -1) + with pytest.raises(RuntimeError): + _ = pcv.transform.apply_transformation_matrix(source_img, target_img, matrix_t) + + +def test_plantcv_transform_save_matrix(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform") + os.mkdir(cache_dir) + # read in matrix + matrix_t_file = np.load(os.path.join(TEST_DATA, TEST_TRANSFORM1), encoding="latin1") + matrix_t = matrix_t_file['arr_0'] + # .npz filename + filename = os.path.join(cache_dir, 'test.npz') + pcv.transform.save_matrix(matrix_t, filename) + assert os.path.exists(filename) is True + + +def test_plantcv_transform_save_matrix_incorrect_filename(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform") + os.mkdir(cache_dir) + # read in matrix + matrix_t_file = np.load(os.path.join(TEST_DATA, TEST_TRANSFORM1), encoding="latin1") + matrix_t = matrix_t_file['arr_0'] + # .npz filename + filename = "test" + with pytest.raises(RuntimeError): + pcv.transform.save_matrix(matrix_t, filename) + + +def test_plantcv_transform_load_matrix(): + # read in matrix_t + matrix_t_file = np.load(os.path.join(TEST_DATA, TEST_TRANSFORM1), encoding="latin1") + matrix_t = matrix_t_file['arr_0'] + # test load function with matrix_t + matrix_t_loaded = pcv.transform.load_matrix(os.path.join(TEST_DATA, TEST_TRANSFORM1)) + assert np.array_equal(matrix_t, matrix_t_loaded) + + +def test_plantcv_transform_correct_color(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform") + os.mkdir(cache_dir) + # load corrected image to compare + corrected_compare = cv2.imread(os.path.join(TEST_DATA, TEST_S1_CORRECTED)) + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_correct_color") + os.mkdir(cache_dir) + # Make image and mask directories in the cache directory + imgdir = os.path.join(cache_dir, "images") + matdir = os.path.join(cache_dir, "saved_matrices") + # Read in target, source, and gray-scale mask + target_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + source_img = cv2.imread(os.path.join(TEST_DATA, TEST_SOURCE1_IMG)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_MASK), -1) + output_path = os.path.join(matdir) + # Test with debug = "print" + pcv.params.debug = "print" + pcv.params.debug_outdir = imgdir + _, _, _, _ = pcv.transform.correct_color(target_img, mask, source_img, mask, cache_dir) + # Test with debug = "plot" + pcv.params.debug = "plot" + _, _, _, _ = pcv.transform.correct_color(target_img, mask, source_img, mask, output_path) + # Test with debug = None + pcv.params.debug = None + _, _, matrix_t, corrected_img = pcv.transform.correct_color(target_img, mask, source_img, mask, output_path) + # assert source and corrected have same shape + assert all([np.array_equal(corrected_img, corrected_compare), + os.path.exists(os.path.join(output_path, "target_matrix.npz")) is True, + os.path.exists(os.path.join(output_path, "source_matrix.npz")) is True, + os.path.exists(os.path.join(output_path, "transformation_matrix.npz")) is True]) + + +def test_plantcv_transform_correct_color_output_dne(): + # load corrected image to compare + corrected_compare = cv2.imread(os.path.join(TEST_DATA, TEST_S1_CORRECTED)) + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_correct_color_output_dne") + os.mkdir(cache_dir) + # Make image and mask directories in the cache directory + imgdir = os.path.join(cache_dir, "images") + # Read in target, source, and gray-scale mask + target_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + source_img = cv2.imread(os.path.join(TEST_DATA, TEST_SOURCE1_IMG)) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_MASK), -1) + output_path = os.path.join(cache_dir, "saved_matrices_1") # output_directory that does not currently exist + # Test with debug = "print" + pcv.params.debug = "print" + pcv.params.debug_outdir = imgdir + _, _, _, _ = pcv.transform.correct_color(target_img, mask, source_img, mask, output_path) + # Test with debug = "plot" + pcv.params.debug = "plot" + _, _, _, _ = pcv.transform.correct_color(target_img, mask, source_img, mask, output_path) + # Test with debug = None + pcv.params.debug = None + _, _, matrix_t, corrected_img = pcv.transform.correct_color(target_img, mask, source_img, mask, output_path) + # assert source and corrected have same shape + assert all([np.array_equal(corrected_img, corrected_compare), + os.path.exists(os.path.join(output_path, "target_matrix.npz")) is True, + os.path.exists(os.path.join(output_path, "source_matrix.npz")) is True, + os.path.exists(os.path.join(output_path, "transformation_matrix.npz")) is True]) + + +def test_plantcv_transform_create_color_card_mask(): + # Load target image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_create_color_card_mask") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.transform.create_color_card_mask(rgb_img=rgb_img, radius=6, start_coord=(166, 166), + spacing=(21, 21), nrows=6, ncols=4, exclude=[20, 0]) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.transform.create_color_card_mask(rgb_img=rgb_img, radius=6, start_coord=(166, 166), + spacing=(21, 21), nrows=6, ncols=4, exclude=[20, 0]) + # Test with debug = None + pcv.params.debug = None + mask = pcv.transform.create_color_card_mask(rgb_img=rgb_img, radius=6, start_coord=(166, 166), + spacing=(21, 21), nrows=6, ncols=4, exclude=[20, 0]) + assert all([i == j] for i, j in zip(np.unique(mask), np.array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, + 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, + 220], dtype=np.uint8))) + + +def test_plantcv_transform_quick_color_check(): + # Load target image + t_matrix = np.load(os.path.join(TEST_DATA, TEST_TARGET_MATRIX), encoding="latin1") + target_matrix = t_matrix['arr_0'] + s_matrix = np.load(os.path.join(TEST_DATA, TEST_SOURCE1_MATRIX), encoding="latin1") + source_matrix = s_matrix['arr_0'] + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_quick_color_check") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Test with debug = "print" + pcv.params.debug = "print" + pcv.transform.quick_color_check(target_matrix, source_matrix, num_chips=22) + # Test with debug = "plot" + pcv.params.debug = "plot" + pcv.transform.quick_color_check(target_matrix, source_matrix, num_chips=22) + # Test with debug = None + pcv.params.debug = None + pcv.transform.quick_color_check(target_matrix, source_matrix, num_chips=22) + assert os.path.exists(os.path.join(cache_dir, "color_quick_check.png")) + + +def test_plantcv_transform_find_color_card(): + # Load rgb image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_find_color_card") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + df, start, space = pcv.transform.find_color_card(rgb_img=rgb_img, threshold_type='adaptgauss', blurry=False, + threshvalue=90) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.transform.create_color_card_mask(rgb_img=rgb_img, radius=6, start_coord=start, + spacing=space, nrows=6, ncols=4, exclude=[20, 0]) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.transform.create_color_card_mask(rgb_img=rgb_img, radius=6, start_coord=start, + spacing=space, nrows=6, ncols=4, exclude=[20, 0]) + # Test with debug = None + pcv.params.debug = None + mask = pcv.transform.create_color_card_mask(rgb_img=rgb_img, radius=6, start_coord=start, + spacing=space, nrows=6, ncols=4, exclude=[20, 0]) + assert all([i == j] for i, j in zip(np.unique(mask), np.array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, + 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, + 220], dtype=np.uint8))) + + +def test_plantcv_transform_find_color_card_optional_parameters(): + # Clear previous outputs + pcv.outputs.clear() + # Load rgb image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG_COLOR_CARD)) + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_find_color_card") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Test with threshold ='normal' + df1, start1, space1 = pcv.transform.find_color_card(rgb_img=rgb_img, threshold_type='normal', blurry=True, + background='light', threshvalue=90, label="prefix") + assert pcv.outputs.observations["prefix"]["color_chip_size"]["value"] > 15000 + + +def test_plantcv_transform_find_color_card_otsu(): + # Clear previous outputs + pcv.outputs.clear() + # Load rgb image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG_COLOR_CARD)) + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_find_color_card_otsu") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Test with threshold ='normal' + df1, start1, space1 = pcv.transform.find_color_card(rgb_img=rgb_img, threshold_type='otsu', blurry=True, + background='light', threshvalue=90, label="prefix") + assert pcv.outputs.observations["prefix"]["color_chip_size"]["value"] > 15000 + + +def test_plantcv_transform_find_color_card_optional_size_parameters(): + # Clear previous outputs + pcv.outputs.clear() + # Load rgb image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG_COLOR_CARD)) + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_find_color_card") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + _, _, _ = pcv.transform.find_color_card(rgb_img=rgb_img, record_chip_size="mean") + assert pcv.outputs.observations["default"]["color_chip_size"]["value"] > 15000 + + +def test_plantcv_transform_find_color_card_optional_size_parameters_none(): + # Clear previous outputs + pcv.outputs.clear() + # Load rgb image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG_COLOR_CARD)) + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_find_color_card") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + _, _, _ = pcv.transform.find_color_card(rgb_img=rgb_img, record_chip_size=None) + assert pcv.outputs.observations.get("default") is None + + +def test_plantcv_transform_find_color_card_bad_record_chip_size(): + # Clear previous outputs + pcv.outputs.clear() + # Load rgb image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + pcv.params.debug = None + _, _, _ = pcv.transform.find_color_card(rgb_img=rgb_img, record_chip_size='averageeeed') + assert pcv.outputs.observations["default"]["color_chip_size"]["value"] is None + + +def test_plantcv_transform_find_color_card_bad_thresh_input(): + # Load rgb image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + with pytest.raises(RuntimeError): + pcv.params.debug = None + _, _, _ = pcv.transform.find_color_card(rgb_img=rgb_img, threshold_type='gaussian') + + +def test_plantcv_transform_find_color_card_bad_background_input(): + # Load rgb image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + with pytest.raises(RuntimeError): + pcv.params.debug = None + _, _, _ = pcv.transform.find_color_card(rgb_img=rgb_img, background='lite') + + +def test_plantcv_transform_find_color_card_bad_colorcard(): + # Load rgb image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG_WITH_HEXAGON)) + with pytest.raises(RuntimeError): + pcv.params.debug = None + _, _, _ = pcv.transform.find_color_card(rgb_img=rgb_img) + + +def test_plantcv_transform_rescale(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_rescale") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.transform.rescale(gray_img=gray_img, min_value=0, max_value=100) + pcv.params.debug = "plot" + rescaled_img = pcv.transform.rescale(gray_img=gray_img, min_value=0, max_value=100) + assert max(np.unique(rescaled_img)) == 100 + + +def test_plantcv_transform_rescale_bad_input(): + # Load rgb image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + with pytest.raises(RuntimeError): + _ = pcv.transform.rescale(gray_img=rgb_img) + + +def test_plantcv_transform_resize(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_trancform_resize") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY_SMALL), -1) + size = (100, 100) + # Test with debug "print" + pcv.params.debug = "print" + _ = pcv.transform.resize(img=gray_img, size=size, interpolation="auto") + # Test with debug "plot" + pcv.params.debug = "plot" + resized_img = pcv.transform.resize(img=gray_img, size=size, interpolation="auto") + assert resized_img.shape == size + + +def test_plantcv_transform_resize_unsupported_method(): + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY_SMALL), -1) + with pytest.raises(RuntimeError): + _ = pcv.transform.resize(img=gray_img, size=(100, 100), interpolation="mymethod") + + +def test_plantcv_transform_resize_crop(): + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY_SMALL), -1) + size = (20, 20) + resized_im = pcv.transform.resize(img=gray_img, size=size, interpolation=None) + assert resized_im.shape == size + + +def test_plantcv_transform_resize_pad(): + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY_SMALL), -1) + size = (100, 100) + resized_im = pcv.transform.resize(img=gray_img, size=size, interpolation=None) + assert resized_im.shape == size + + +def test_plantcv_transform_resize_pad_crop_color(): + color_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY_SMALL)) + size = (100, 100) + resized_im = pcv.transform.resize(img=color_img, size=size, interpolation=None) + assert resized_im.shape == (size[1], size[0], 3) + + +def test_plantcv_transform_resize_factor(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_trancform_resize_factor") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY_SMALL), -1) + # Resizing factors + factor_x = 0.5 + factor_y = 0.2 + # Test with debug "print" + pcv.params.debug = "print" + _ = pcv.transform.resize_factor(img=gray_img, factors=(factor_x, factor_y), interpolation="auto") + # Test with debug "plot" + pcv.params.debug = "plot" + resized_img = pcv.transform.resize_factor(img=gray_img, factors=(factor_x, factor_y), interpolation="auto") + output_size = resized_img.shape + expected_size = (int(gray_img.shape[0] * factor_y), int(gray_img.shape[1] * factor_x)) + assert output_size == expected_size + + +def test_plantcv_transform_resize_factor_bad_input(): + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY_SMALL), -1) + with pytest.raises(RuntimeError): + _ = pcv.transform.resize_factor(img=gray_img, factors=(0, 2), interpolation="auto") + + +def test_plantcv_transform_nonuniform_illumination_rgb(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_nonuniform_illumination") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Load rgb image + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_TARGET_IMG)) + pcv.params.debug = "plot" + _ = pcv.transform.nonuniform_illumination(img=rgb_img, ksize=11) + pcv.params.debug = "print" + corrected = pcv.transform.nonuniform_illumination(img=rgb_img, ksize=11) + assert np.mean(corrected) < np.mean(rgb_img) + + +def test_plantcv_transform_nonuniform_illumination_gray(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_transform_nonuniform_illumination") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Load rgb image + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + pcv.params.debug = "plot" + _ = pcv.transform.nonuniform_illumination(img=gray_img, ksize=11) + pcv.params.debug = "print" + corrected = pcv.transform.nonuniform_illumination(img=gray_img, ksize=11) + assert np.shape(corrected) == np.shape(gray_img) + + +# ############################## +# Tests for the threshold subpackage +# ############################## +def test_plantcv_threshold_binary(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_binary") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with object type = dark + pcv.params.debug = None + _ = pcv.threshold.binary(gray_img=gray_img, threshold=25, max_value=255, object_type="dark") + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.threshold.binary(gray_img=gray_img, threshold=25, max_value=255, object_type="light") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.threshold.binary(gray_img=gray_img, threshold=25, max_value=255, object_type="light") + # Test with debug = None + pcv.params.debug = None + binary_img = pcv.threshold.binary(gray_img=gray_img, threshold=25, max_value=255, object_type="light") + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(binary_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_threshold_binary_incorrect_object_type(): + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + with pytest.raises(RuntimeError): + pcv.params.debug = None + _ = pcv.threshold.binary(gray_img=gray_img, threshold=25, max_value=255, object_type="lite") + + +def test_plantcv_threshold_gaussian(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_gaussian") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with object type = dark + pcv.params.debug = None + _ = pcv.threshold.gaussian(gray_img=gray_img, max_value=255, object_type="dark") + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.threshold.gaussian(gray_img=gray_img, max_value=255, object_type="light") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.threshold.gaussian(gray_img=gray_img, max_value=255, object_type="light") + # Test with debug = None + pcv.params.debug = None + binary_img = pcv.threshold.gaussian(gray_img=gray_img, max_value=255, object_type="light") + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(binary_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_threshold_gaussian_incorrect_object_type(): + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + with pytest.raises(RuntimeError): + pcv.params.debug = None + _ = pcv.threshold.gaussian(gray_img=gray_img, max_value=255, object_type="lite") + + +def test_plantcv_threshold_mean(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_mean") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with object type = dark + pcv.params.debug = None + _ = pcv.threshold.mean(gray_img=gray_img, max_value=255, object_type="dark") + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.threshold.mean(gray_img=gray_img, max_value=255, object_type="light") + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.threshold.mean(gray_img=gray_img, max_value=255, object_type="light") + # Test with debug = None + pcv.params.debug = None + binary_img = pcv.threshold.mean(gray_img=gray_img, max_value=255, object_type="light") + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(binary_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_threshold_mean_incorrect_object_type(): + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + with pytest.raises(RuntimeError): + pcv.params.debug = None + _ = pcv.threshold.mean(gray_img=gray_img, max_value=255, object_type="lite") + + +def test_plantcv_threshold_otsu(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_otsu") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GREENMAG), -1) + # Test with object set to light + pcv.params.debug = None + _ = pcv.threshold.otsu(gray_img=gray_img, max_value=255, object_type="light") + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.threshold.otsu(gray_img=gray_img, max_value=255, object_type='dark') + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.threshold.otsu(gray_img=gray_img, max_value=255, object_type='dark') + # Test with debug = None + pcv.params.debug = None + binary_img = pcv.threshold.otsu(gray_img=gray_img, max_value=255, object_type='dark') + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(binary_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_threshold_otsu_incorrect_object_type(): + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + with pytest.raises(RuntimeError): + pcv.params.debug = None + _ = pcv.threshold.otsu(gray_img=gray_img, max_value=255, object_type="lite") + + +def test_plantcv_threshold_custom_range(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with debug = "print" + pcv.params.debug = 'print' + # Test channel='gray' + _, _ = pcv.threshold.custom_range(img, lower_thresh=[0], upper_thresh=[255], channel='gray') + _, _ = pcv.threshold.custom_range(gray_img, lower_thresh=[0], upper_thresh=[255], channel='gray') + # Test channel='HSV' + _, _ = pcv.threshold.custom_range(img, lower_thresh=[0, 0, 0], upper_thresh=[255, 255, 255], channel='HSV') + # Test channel='LAB' + _, _ = pcv.threshold.custom_range(img, lower_thresh=[0, 0, 0], upper_thresh=[255, 255, 255], channel='LAB') + pcv.params.debug = 'plot' + # Test channel='RGB' + mask, binary_img = pcv.threshold.custom_range(img, lower_thresh=[0, 0, 0], upper_thresh=[255, 255, 255], + channel='RGB') + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(binary_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_threshold_custom_range_bad_input_hsv(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + _, _ = pcv.threshold.custom_range(img, lower_thresh=[0, 0], upper_thresh=[2, 2, 2, 2], channel='HSV') + + +def test_plantcv_threshold_custom_range_bad_input_rgb(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + _, _ = pcv.threshold.custom_range(img, lower_thresh=[0, 0], upper_thresh=[2, 2, 2, 2], channel='RGB') + + +def test_plantcv_threshold_custom_range_bad_input_lab(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + _, _ = pcv.threshold.custom_range(img, lower_thresh=[0, 0], upper_thresh=[2, 2, 2], channel='LAB') + + +def test_plantcv_threshold_custom_range_bad_input_gray(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + _, _ = pcv.threshold.custom_range(img, lower_thresh=[0, 0], upper_thresh=[2], channel='gray') + + +def test_plantcv_threshold_custom_range_bad_input_channel(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + _, _ = pcv.threshold.custom_range(img, lower_thresh=[0], upper_thresh=[2], channel='CMYK') + + +def test_plantcv_threshold_saturation(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_saturation") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.threshold.saturation(rgb_img=rgb_img, threshold=254, channel="all") + # Test with debug = "plot" + pcv.params.debug = "plot" + thresh = pcv.threshold.saturation(rgb_img=rgb_img, threshold=254, channel="any") + assert np.sum(thresh) == 920050455 and len(np.unique(thresh)) == 2 + + +def test_plantcv_threshold_saturation_bad_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_saturation_bad_input") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + _ = pcv.threshold.saturation(rgb_img=rgb_img, threshold=254, channel="red") + + +def test_plantcv_threshold_triangle(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_triangle") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.threshold.triangle(gray_img=gray_img, max_value=255, object_type="dark", xstep=10) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.threshold.triangle(gray_img=gray_img, max_value=255, object_type="light", xstep=10) + # Test with debug = None + pcv.params.debug = None + binary_img = pcv.threshold.triangle(gray_img=gray_img, max_value=255, object_type="light", xstep=10) + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(binary_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_threshold_triangle_incorrect_object_type(): + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + with pytest.raises(RuntimeError): + pcv.params.debug = None + _ = pcv.threshold.triangle(gray_img=gray_img, max_value=255, object_type="lite", xstep=10) + + +def test_plantcv_threshold_texture(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_texture") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY_SMALL), -1) + binary_img = pcv.threshold.texture(gray_img, ksize=6, threshold=7, offset=3, texture_method='dissimilarity', + borders='nearest', max_value=255) + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(binary_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +# ################################### +# Tests for the visualize subpackage +# ################################### +def test_plantcv_visualize_auto_threshold_methods_bad_input(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_auto_threshold_methods") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + _ = pcv.visualize.auto_threshold_methods(gray_img=img) + + +def test_plantcv_visualize_auto_threshold_methods(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_auto_threshold_methods") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + pcv.params.debug = "print" + _ = pcv.visualize.auto_threshold_methods(gray_img=img) + pcv.params.debug = "plot" + labeled_imgs = pcv.visualize.auto_threshold_methods(gray_img=img) + assert len(labeled_imgs) == 5 and np.shape(labeled_imgs[0])[0] == np.shape(img)[0] + + +def test_plantcv_visualize_pseudocolor(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_pseudocolor") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + obj_contour = contours_npz['arr_0'] + filename = os.path.join(cache_dir, 'plantcv_pseudo_image.png') + # Test with debug = "print" + pcv.params.debug = "print" + _ = pcv.visualize.pseudocolor(gray_img=img, mask=None) + _ = pcv.visualize.pseudocolor(gray_img=img, mask=None) + + pimg = pcv.visualize.pseudocolor(gray_img=img, mask=mask, min_value=10, max_value=200) + pcv.print_image(pimg, filename) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.visualize.pseudocolor(gray_img=img, mask=mask, background="image") + _ = pcv.visualize.pseudocolor(gray_img=img, mask=mask, background="image", title="customized title") + _ = pcv.visualize.pseudocolor(gray_img=img, mask=None) + _ = pcv.visualize.pseudocolor(gray_img=img, mask=mask, background="black", obj=obj_contour, axes=False, + colorbar=False) + _ = pcv.visualize.pseudocolor(gray_img=img, mask=mask, background="image", obj=obj_contour, obj_padding=15) + _ = pcv.visualize.pseudocolor(gray_img=img, mask=None, axes=False, colorbar=False) + # Test with debug = None + pcv.params.debug = None + _ = pcv.visualize.pseudocolor(gray_img=img, mask=None) + pseudo_img = pcv.visualize.pseudocolor(gray_img=img, mask=mask, background="white") + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(pseudo_img), TEST_BINARY_DIM)): + assert 1 + else: + assert 0 + + +def test_plantcv_visualize_pseudocolor_bad_input(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_pseudocolor") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + _ = pcv.visualize.pseudocolor(gray_img=img) + + +def test_plantcv_visualize_pseudocolor_bad_background(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_pseudocolor_bad_background") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + with pytest.raises(RuntimeError): + _ = pcv.visualize.pseudocolor(gray_img=img, mask=mask, background="pink") + + +def test_plantcv_visualize_pseudocolor_bad_padding(): + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_pseudocolor_bad_background") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + obj_contour = contours_npz['arr_0'] + with pytest.raises(RuntimeError): + _ = pcv.visualize.pseudocolor(gray_img=img, mask=mask, obj=obj_contour, obj_padding="pink") + + +def test_plantcv_visualize_colorize_masks(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_naive_bayes_classifier") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + mask = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS)) + _ = pcv.visualize.colorize_masks(masks=[mask['plant'], mask['background']], + colors=[(0, 0, 0), (1, 1, 1)]) + # Test with debug = "plot" + pcv.params.debug = "plot" + _ = pcv.visualize.colorize_masks(masks=[mask['plant'], mask['background']], + colors=[(0, 0, 0), (1, 1, 1)]) + # Test with debug = None + pcv.params.debug = None + colored_img = pcv.visualize.colorize_masks(masks=[mask['plant'], mask['background']], + colors=['red', 'blue']) + # Assert that the output image has the dimensions of the input image + assert not np.average(colored_img) == 0 + + +def test_plantcv_visualize_colorize_masks_bad_input_empty(): + with pytest.raises(RuntimeError): + _ = pcv.visualize.colorize_masks(masks=[], colors=[]) + + +def test_plantcv_visualize_colorize_masks_bad_input_mismatch_number(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + mask = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS)) + with pytest.raises(RuntimeError): + _ = pcv.visualize.colorize_masks(masks=[mask['plant'], mask['background']], colors=['red', 'green', 'blue']) + + +def test_plantcv_visualize_colorize_masks_bad_color_input(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = "print" + pcv.params.debug = "print" + mask = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS)) + with pytest.raises(RuntimeError): + _ = pcv.visualize.colorize_masks(masks=[mask['plant'], mask['background']], colors=['red', 1.123]) + + +@pytest.mark.parametrize("bins,lb,ub,title", [[200, 0, 255, "Include Title"], [100, None, None, None]]) +def test_plantcv_visualize_histogram(bins, lb, ub, title): + # Test with debug = None + pcv.params.debug = None + # Read test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + fig_hist, hist_df = pcv.visualize.histogram(img=img, mask=mask, bins=bins, lower_bound=lb, upper_bound=ub, + title=title, hist_data=True) + assert all([isinstance(fig_hist, ggplot), isinstance(hist_df, pd.core.frame.DataFrame)]) + + +def test_plantcv_visualize_histogram_no_mask(): + # Test with debug = None + pcv.params.debug = None + # Read test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + fig_hist = pcv.visualize.histogram(img=img, mask=None) + assert isinstance(fig_hist, ggplot) + + +def test_plantcv_visualize_histogram_rgb_img(): + # Test with debug = None + pcv.params.debug = None + # Test RGB input image + img_rgb = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + fig_hist = pcv.visualize.histogram(img=img_rgb) + assert isinstance(fig_hist, ggplot) + + +def test_plantcv_visualize_histogram_multispectral_img(): + # Test with debug = None + pcv.params.debug = None + # Test multi-spectral image + img_rgb = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + img_multi = np.concatenate((img_rgb, img_rgb), axis=2) + fig_hist = pcv.visualize.histogram(img=img_multi) + assert isinstance(fig_hist, ggplot) + + +def test_plantcv_visualize_histogram_no_img(): + with pytest.raises(RuntimeError): + _ = pcv.visualize.histogram(img=None) + + +def test_plantcv_visualize_histogram_array(): + # Read test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + with pytest.raises(RuntimeError): + _ = pcv.visualize.histogram(img=img[0, :]) + + +def test_plantcv_visualize_clustered_contours(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_plot_hist") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_VISUALIZE_BACKGROUND), -1) + roi_objects = np.load(os.path.join(TEST_DATA, TEST_INPUT_VISUALIZE_CONTOUR), encoding="latin1") + hierarchy = np.load(os.path.join(TEST_DATA, TEST_INPUT_VISUALIZE_HIERARCHY), encoding="latin1") + cluster_i = np.load(os.path.join(TEST_DATA, TEST_INPUT_VISUALIZE_CLUSTERS), encoding="latin1") + objs = [roi_objects[arr_n] for arr_n in roi_objects] + obj_hierarchy = hierarchy['arr_0'] + cluster = [cluster_i[arr_n] for arr_n in cluster_i] + # Test in plot mode + pcv.params.debug = "plot" + # Reset the saved color scale (can be saved between tests) + pcv.params.saved_color_scale = None + _ = pcv.visualize.clustered_contours(img=img1, grouped_contour_indices=cluster, roi_objects=objs, + roi_obj_hierarchy=obj_hierarchy, bounding=False) + # Test in print mode + pcv.params.debug = "print" + # Reset the saved color scale (can be saved between tests) + pcv.params.saved_color_scale = None + cluster_img = pcv.visualize.clustered_contours(img=img, grouped_contour_indices=cluster, roi_objects=objs, + roi_obj_hierarchy=obj_hierarchy, nrow=2, ncol=2, bounding=True) + assert np.sum(cluster_img) > np.sum(img) + + +def test_plantcv_visualize_colorspaces(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_plot_hist") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + pcv.params.debug = "plot" + vis_img_small = pcv.visualize.colorspaces(rgb_img=img, original_img=False) + pcv.params.debug = "print" + vis_img = pcv.visualize.colorspaces(rgb_img=img) + assert np.shape(vis_img)[1] > (np.shape(img)[1]) and np.shape(vis_img_small)[1] > (np.shape(img)[1]) + + +def test_plantcv_visualize_colorspaces_bad_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_plot_hist") + os.mkdir(cache_dir) + pcv.params.debug_outdir = cache_dir + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) + with pytest.raises(RuntimeError): + _ = pcv.visualize.colorspaces(rgb_img=img) + + +def test_plantcv_visualize_overlay_two_imgs(): + pcv.params.debug = None + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_visualize_overlay_two_imgs") + os.mkdir(cache_dir) + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + img2 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY)) + + pcv.params.debug = None + out_img = pcv.visualize.overlay_two_imgs(img1=img1, img2=img2) + sample_pt1 = img1[1445, 1154] + sample_pt2 = img2[1445, 1154] + sample_pt3 = out_img[1445, 1154] + pred_rgb = (sample_pt1 * 0.5) + (sample_pt2 * 0.5) + pred_rgb = pred_rgb.astype(np.uint8) + assert np.array_equal(sample_pt3, pred_rgb) + + +def test_plantcv_visualize_overlay_two_imgs_grayscale(): + pcv.params.debug = None + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_visualize_overlay_two_imgs_grayscale") + os.mkdir(cache_dir) + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + img2 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + out_img = pcv.visualize.overlay_two_imgs(img1=img1, img2=img2) + sample_pt1 = np.array([255, 255, 255], dtype=np.uint8) + sample_pt2 = np.array([255, 255, 255], dtype=np.uint8) + sample_pt3 = out_img[1445, 1154] + pred_rgb = (sample_pt1 * 0.5) + (sample_pt2 * 0.5) + pred_rgb = pred_rgb.astype(np.uint8) + assert np.array_equal(sample_pt3, pred_rgb) + + +def test_plantcv_visualize_overlay_two_imgs_bad_alpha(): + pcv.params.debug = None + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_visualize_overlay_two_imgs_bad_alpha") + os.mkdir(cache_dir) + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + img2 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY)) + alpha = -1 + with pytest.raises(RuntimeError): + _ = pcv.visualize.overlay_two_imgs(img1=img1, img2=img2, alpha=alpha) + + +def test_plantcv_visualize_overlay_two_imgs_size_mismatch(): + pcv.params.debug = None + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_visualize_overlay_two_imgs_size_mismatch") + os.mkdir(cache_dir) + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + img2 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_CROPPED)) + with pytest.raises(RuntimeError): + _ = pcv.visualize.overlay_two_imgs(img1=img1, img2=img2) + + +# ############################## +# Tests for the utils subpackage +# ############################## +def test_plantcv_utils_json2csv(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_utils_json2csv") + os.mkdir(cache_dir) + plantcv.utils.json2csv(json_file=os.path.join(TEST_DATA, "merged_output.json"), + csv_file=os.path.join(cache_dir, "exports")) + assert all([os.path.exists(os.path.join(cache_dir, "exports-single-value-traits.csv")), + os.path.exists(os.path.join(cache_dir, "exports-multi-value-traits.csv"))]) + + +def test_plantcv_utils_json2csv_no_json(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_utils_json2csv_no_json") + os.mkdir(cache_dir) + with pytest.raises(IOError): + plantcv.utils.json2csv(json_file=os.path.join(TEST_DATA, "not_a_file.json"), + csv_file=os.path.join(cache_dir, "exports")) + + +def test_plantcv_utils_json2csv_bad_json(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_utils_json2csv_bad_json") + os.mkdir(cache_dir) + with pytest.raises(ValueError): + plantcv.utils.json2csv(json_file=os.path.join(TEST_DATA, "incorrect_json_data.txt"), + csv_file=os.path.join(cache_dir, "exports")) + + +def test_plantcv_utils_sample_images_snapshot(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_utils_sample_images") + os.mkdir(cache_dir) + snapshot_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + img_outdir = os.path.join(cache_dir, "snapshot") + plantcv.utils.sample_images(source_path=snapshot_dir, dest_path=img_outdir, num=3) + assert os.path.exists(os.path.join(cache_dir, "snapshot")) + + +def test_plantcv_utils_sample_images_flatdir(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_utils_sample_images") + os.mkdir(cache_dir) + flat_dir = os.path.join(TEST_DATA) + img_outdir = os.path.join(cache_dir, "images") + plantcv.utils.sample_images(source_path=flat_dir, dest_path=img_outdir, num=30) + random_images = os.listdir(img_outdir) + assert all([len(random_images) == 30, len(np.unique(random_images)) == 30]) + + +def test_plantcv_utils_sample_images_bad_source(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_utils_sample_images") + os.mkdir(cache_dir) + fake_dir = os.path.join(TEST_DATA, "snapshot") + img_outdir = os.path.join(cache_dir, "images") + with pytest.raises(IOError): + plantcv.utils.sample_images(source_path=fake_dir, dest_path=img_outdir, num=3) + + +def test_plantcv_utils_sample_images_bad_flat_num(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_utils_sample_images") + os.mkdir(cache_dir) + flat_dir = os.path.join(TEST_DATA) + img_outdir = os.path.join(cache_dir, "images") + with pytest.raises(RuntimeError): + plantcv.utils.sample_images(source_path=flat_dir, dest_path=img_outdir, num=300) + + +def test_plantcv_utils_sample_images_bad_phenofront_num(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_utils_sample_images") + os.mkdir(cache_dir) + snapshot_dir = os.path.join(PARALLEL_TEST_DATA, TEST_SNAPSHOT_DIR) + img_outdir = os.path.join(cache_dir, "images") + with pytest.raises(RuntimeError): + plantcv.utils.sample_images(source_path=snapshot_dir, dest_path=img_outdir, num=300) + + +def test_plantcv_utils_tabulate_bayes_classes(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_utils_tabulate_bayes_classes") + os.mkdir(cache_dir) + outfile = os.path.join(cache_dir, "rgb_table.txt") + plantcv.utils.tabulate_bayes_classes(input_file=os.path.join(TEST_DATA, PIXEL_VALUES), output_file=outfile) + table = pd.read_csv(outfile, sep="\t") + assert table.shape == (228, 2) + + +def test_plantcv_utils_tabulate_bayes_classes_missing_input(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_utils_tabulate_bayes_classes_missing_input") + os.mkdir(cache_dir) + outfile = os.path.join(cache_dir, "rgb_table.txt") + with pytest.raises(IOError): + plantcv.utils.tabulate_bayes_classes(input_file=os.path.join(PIXEL_VALUES), output_file=outfile) + + +# ############################## +# Clean up test files +# ############################## +def teardown_function(): + shutil.rmtree(TEST_TMPDIR) From e8c73eefaa6a7456a5a6deff077e5fb1e640fab6 Mon Sep 17 00:00:00 2001 From: JorgeGtz Date: Wed, 14 Apr 2021 16:18:15 -0500 Subject: [PATCH 012/178] add get centroids function and update init --- plantcv/plantcv/__init__.py | 6 ++++-- plantcv/plantcv/get_centroids.py | 30 ++++++++++++++++++++++++++++++ 2 files changed, 34 insertions(+), 2 deletions(-) create mode 100644 plantcv/plantcv/get_centroids.py diff --git a/plantcv/plantcv/__init__.py b/plantcv/plantcv/__init__.py index dce46cbe2..16b70454f 100644 --- a/plantcv/plantcv/__init__.py +++ b/plantcv/plantcv/__init__.py @@ -75,6 +75,7 @@ from plantcv.plantcv.floodfill import floodfill from plantcv.plantcv import analyze from plantcv.plantcv.detect_discs import detect_discs +from plantcv.plantcv.get_centroids import get_centroids # add new functions to end of lists # Auto versioning @@ -151,6 +152,7 @@ "segment_image_series", "create_labels", "analyze", - "floodfill" - "detect_discs" + "floodfill", + "detect_discs", + "get_centroids" ] diff --git a/plantcv/plantcv/get_centroids.py b/plantcv/plantcv/get_centroids.py new file mode 100644 index 000000000..2be9b2d0d --- /dev/null +++ b/plantcv/plantcv/get_centroids.py @@ -0,0 +1,30 @@ +import cv2 + +def get_centroids(bin_img): + """ Get the coordinates (row,column) of the centroid of each connected + region in a binary image. + + Inputs: + bin_img = Binary image containing the connected regions to consider + + + Returns: + coor = List of coordinates (row,column) of the centroids of the regions + + :param bin_img: numpy.ndarray + :return coor: list + """ + + # find contours in the binary image + _, contours, _ = cv2.findContours(bin_img, cv2.RETR_TREE, + cv2.CHAIN_APPROX_SIMPLE) + coor = [] + for c in contours: + # calculate moments for each contour + M = cv2.moments(c) + # calculate row,col coordinates of centroid + col = int(M["m10"] / M["m00"]) + row = int(M["m01"] / M["m00"]) + coor.append([row,col]) + + return coor From e81c4ec545975cf1d55183226ce5d2589adb7955 Mon Sep 17 00:00:00 2001 From: JorgeGtz Date: Wed, 14 Apr 2021 16:22:04 -0500 Subject: [PATCH 013/178] add testing for get_centroids --- tests/tests.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/tests/tests.py b/tests/tests.py index eaae0b720..3ae18af50 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -3711,6 +3711,16 @@ def test_detect_discs(): assert len(coor) == 3 +def test_get_centroids(): + # Read in test data + mask = cv2.imread(os.path.join(TEST_DATA, TEST_DISCS_MASK), -1) + + # Test with debug = None + pcv.params.debug = None + coor = pcv.get_centroids(bin_img=mask) + + assert len(coor) == 5 + # ############################## # Tests for the learn subpackage From a16ad1709f20917f09d99e2bd5d98e6d35c4ac8a Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 14:21:39 -0500 Subject: [PATCH 014/178] Create click_count.py --- plantcv/plantcv/visualize/click_count.py | 122 +++++++++++++++++++++++ 1 file changed, 122 insertions(+) create mode 100644 plantcv/plantcv/visualize/click_count.py diff --git a/plantcv/plantcv/visualize/click_count.py b/plantcv/plantcv/visualize/click_count.py new file mode 100644 index 000000000..cad392226 --- /dev/null +++ b/plantcv/plantcv/visualize/click_count.py @@ -0,0 +1,122 @@ +# Interactively click and count objects in a given image + +import os +import cv2 +import numpy as np +from matplotlib import pyplot as plt +from plantcv.plantcv import params +from plantcv.plantcv import fatal_error +from scipy.spatial import distance + + +def _find_closest(pt, pts): + """ Given coordinates of a point and a list of coordinates of a bunch of points, find the point that has the smallest Euclidean to the given point + + :param pt: (tuple) coordinates of a point + :param pts: (a list of tuples) coordinates of a list of points + :return: index of the closest point and the coordinates of that point + """ + if pt in pts: + return pts.index(pt), pt + dists = distance.cdist([pt], pts, 'euclidean') + idx = np.argmin(dists) + return idx, pts[idx] + + +class ClickCount(object): + def __init__(self, img, figsize=(12, 6)): + print("If you have coordinates to import, the label represent for total count should be 'total'!") + self.img = img + self.points = {} + self.colors = {} + self.count = {} # a dictionary that saves the counts of different classes (labels) + self.figsize = figsize + self.events = [] + self.label = None # current label + self.color = None # current color + self.view_all = None # a flag indicating whether or not view all labels + self.fig = None + self.ax = None + self.p_not_current = None + + def import_coords(self, coords, label="total"): + """ Import center coordinates of already detected objects + Inputs: + coords = list of center coordinates of already detected objects. + label = class label for imported coordinates, by default label="total". + + Returns: + :param coords: list + :param label: string + :return: + """ + if label not in self.points: + self.points[label] = [] + for (y, x) in coords: + self.points[label].append((x, y)) + self.count[label] = len(self.points[label]) + + else: + print(f"Warning: {label} already included and counted, nothing is imported!") + + def view(self, label="total", color="c", view_all=False): + """ + View the label for a specific class label + Inputs: + label = (optional) class label, by default label="total" + color = desired color, by default color="c" + view_all = indicator of whether view all classes, by default view_all=False + + :param label: string + :param color: string + :param view_all: boolean + :return: + """ + if label not in self.points and color in self.colors.values(): + print("Warning: The color assigned to the new class label is already used, " + "if proceeding, items from different classes will not be distinguishable in plots!") + + self.label = label + self.color = color + self.view_all = view_all + + if label not in self.points: + self.points[label] = [] + self.count[label] = 0 + self.colors[label] = color + + print("Warning: this tool is under development and is expected to have updates frequently, " + "please check the documentation page to make sure you are using the correct version!") + self.fig, self.ax = plt.subplots(1, 1, figsize=self.figsize) + + self.events = [] + self.fig.canvas.mpl_connect('button_press_event', self.onclick) + + self.ax.imshow(cv2.cvtColor(self.img, cv2.COLOR_BGR2RGB)) + self.ax.set_title("Please left click on missing pollens\n Right click on those you want to remove") + self.p_not_current = 0 + # if view_all is True, show all already marked markers + if view_all: + for k in self.points.keys(): + for (x, y) in self.points[k]: + self.ax.plot(x, y, marker='x', c=self.colors[k]) + if self.label not in self.points or len(self.points[self.label]) == 0: + self.p_not_current += 1 + else: + for (x, y) in self.points[label]: + self.ax.plot(x, y, marker='x', c=color) + + def onclick(self, event): + self.events.append(event) + if event.button == 1: + self.ax.plot(event.xdata, event.ydata, marker='x', c=self.color) + self.points[self.label].append((event.xdata, event.ydata)) + self.count[self.label] += 1 + else: + idx_remove, _ = _find_closest((event.xdata, event.ydata), self.points[self.label]) + self.points[self.label].pop(idx_remove) + idx_remove = idx_remove + self.p_not_current + ax0plots = self.ax.lines + self.ax.lines.remove(ax0plots[idx_remove]) + self.count[self.label] -= 1 + self.fig.canvas.draw() \ No newline at end of file From a28bcbe386b79335086eb9cd9e4f77d70a294a84 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 15:06:41 -0500 Subject: [PATCH 015/178] Update __init__.py --- plantcv/plantcv/visualize/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/visualize/__init__.py b/plantcv/plantcv/visualize/__init__.py index b293f0610..95ca78c4a 100644 --- a/plantcv/plantcv/visualize/__init__.py +++ b/plantcv/plantcv/visualize/__init__.py @@ -11,8 +11,8 @@ from plantcv.plantcv.visualize.hyper_histogram import hyper_histogram from plantcv.plantcv.visualize.pixel_scatter_vis import pixel_scatter_plot from plantcv.plantcv.visualize.chlorophyll_fluorescence import chlorophyll_fluorescence +from plantcv.plantcv.visualize.click_count import ClickCount __all__ = ["pseudocolor", "colorize_masks", "histogram", "colorspaces", "auto_threshold_methods", "overlay_two_imgs", "colorize_label_img", "obj_size_ecdf", "obj_sizes", "hyper_histogram", - "pixel_scatter_plot", "time_lapse_video", "chlorophyll_fluorescence"] - + "pixel_scatter_plot", "time_lapse_video", "chlorophyll_fluorescence", "ClickCount"] From f5e4c151c77f8c7915a4c148563a0f261b058701 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 15:06:43 -0500 Subject: [PATCH 016/178] Update tests.py --- tests/tests.py | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/tests/tests.py b/tests/tests.py index 3ae18af50..25596b7f1 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6341,6 +6341,41 @@ def test_plantcv_visualize_overlay_two_imgs_size_mismatch(): _ = pcv.visualize.overlay_two_imgs(img1=img1, img2=img2) +def test_plantcv_visualize_click_count(): + pcv.params.debug = None + # generate fake testing image + temp = pcv.transform.rescale(np.random.rand(5,5)) + img = np.stack((temp,)*3, axis=-1) + counter = pcv.visualize.ClickCount(img, figsize=(12, 6)) + assert len(counter.count) == 0 + + # import coordinates + counter.import_coords([(0,0)], label="total") + counter.import_coords([(0,1)], label="c1") + counter.import_coords([(0,2)], label="total") + assert len(counter.count) == 2 + + # view different classes + counter.view() + counter.view(label="c1", color="r", view_all=True) + counter.view(label="c2", color="r", view_all=True) + counter.view(label="c2", color="k", view_all=True) + assert len(counter.points) == 3 + + # create mock events + # left click + e1 = matplotlib.backend_bases.MouseEvent(name="button_press_event", canvas=counter.fig.canvas, x=0, y=0, button=1) + e1.xdata = 0 + e1.ydata = 0 + + # right click + e1_ = matplotlib.backend_bases.MouseEvent(name="button_press_event", canvas=counter.fig.canvas, x=0, y=0, button=3) + e1_.xdata = 0 + e1_.ydata = 0 + counter.onclick(e1) + counter.onclick(e1_) + assert len(counter.events) == 2 + # ############################## # Tests for the utils subpackage # ############################## From 6354400242c490a933a7dd895d090e35302306fa Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 16:07:41 -0500 Subject: [PATCH 017/178] Create visualize_click_count.md --- docs/visualize_click_count.md | 65 +++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 docs/visualize_click_count.md diff --git a/docs/visualize_click_count.md b/docs/visualize_click_count.md new file mode 100644 index 000000000..410501356 --- /dev/null +++ b/docs/visualize_click_count.md @@ -0,0 +1,65 @@ +## Click and count objects (developing) + +`ClickCount` is a class that allows user interactive to count objects in images. + +*class* **plantcv.visualize.ClickCount**(*img*, *figsize*=(12, 6)) +- To initialize a `ClickCount` class object, the only required input variable is `img`. +- Another optional input is the desired figure size `figsize`, by default `figsize=(12,6)`. + +### Attributes +**img**: input image. + +**counts**: a dictionary of count for every class. + +### Methods +**import_coords(*coords*, *label*="total")** +- Import available coordinated for a specific class (if applicable). +- **Parameters:** + - coords - a list of available coordintes. + - label - class label for imported coordinates. By default `label="total`. + +**view(*label*="total", *color*="c", *view_all*=False)** +- View marked image, and update markers if needed. +- **Parameters**: + - label - class label to show on the marked image. By default `label="total`. + - color - desired color to show the class. By default `color="c"`. + - view_all - a flag indicating whether to show markers for all classes or not. + +- **Example use:** + - Below +- **Note**: used in Jupyter notebook. + +**Input image** + +![ori_im](img/documentation_images/visualize_click_count/count_img.jpg) + +**Mask of automated detected objects** +Note: for how to get this mask, refer to function `pcv.detect_descs`. +![count_im](img/documentation_images/visualize_click_count/count_mask.png) + + +```python +# include the line of code below to allow interactive activities +%matplotlib notebook + +from plantcv import plantcv as pcv +# initialization +counter = pcv.visualize.ClickCount(img) +# import coordinates (if available) +counter.import_coords(coords, label="total") +# view "total" class +counter.view(label="total", color="c", view_all=True) +# view "c1" class +counter.view(label="c1", color="r", view_all=True) +``` + +**View markers for `total` class** + +![total_mask](img/documentation_images/visualize_click_count/with_totalmask.png) + +(When interactivity is enabled, you can left click to add markers for the class, or right click to remove markers) + +**View markers for `c1` class** + +![c1_mask](img/documentation_images/visualize_click_count/with_clickc1.png) +**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/visualize/click_count.py) From a1ea6791a006f5d53bebb9c19bbc03cec90d207e Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Fri, 23 Apr 2021 16:07:50 -0500 Subject: [PATCH 018/178] add documentation images --- .../visualize_click_count/count_img.jpg | Bin 0 -> 129709 bytes .../visualize_click_count/count_mask.png | Bin 0 -> 15772 bytes .../visualize_click_count/with_clickc1.png | Bin 0 -> 198324 bytes .../visualize_click_count/with_totalmask.png | Bin 0 -> 192838 bytes 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 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zJx>bFk*~9r<4=+o7fb`9OjQz((-kENKgC}Pzf*vY?k-Km@09H3kpVB)p5?VoGZ|1iECeWrGH(D9Zl z?I+EP3#tK`s{)?Kd!7UH9GK_88<7LA_6gsJ)X&pA&w+Uk%yVF#1FxF{^9{)BX5D&r?6ofq4$hbKrGzV7>u)-K?80IM0E34$O1ljm?4i2IP$` z|9R@?IWW(Gc@Dg84$L{(re=9JbJ!TIc`(002ovPDHLkV1fc6`Yr$f literal 0 HcmV?d00001 From eb126f829bf24d1e297443bb1e704e21893658c3 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Mon, 26 Apr 2021 09:34:14 -0500 Subject: [PATCH 019/178] Update tests.py --- tests/tests.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/tests/tests.py b/tests/tests.py index 25596b7f1..d42549cae 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6372,9 +6372,22 @@ def test_plantcv_visualize_click_count(): e1_ = matplotlib.backend_bases.MouseEvent(name="button_press_event", canvas=counter.fig.canvas, x=0, y=0, button=3) e1_.xdata = 0 e1_.ydata = 0 + + # left click + e2 = matplotlib.backend_bases.MouseEvent(name="button_press_event", canvas=counter.fig.canvas, x=0, y=0, button=1) + e2.xdata = 1 + e2.ydata = 1 + + # right click + e2_ = matplotlib.backend_bases.MouseEvent(name="button_press_event", canvas=counter.fig.canvas, x=0, y=0, button=3) + e2_.xdata = 1.2 + e2_.ydata = 1.2 + counter.onclick(e1) counter.onclick(e1_) - assert len(counter.events) == 2 + counter.onclick(e2) + counter.onclick(e2_) + assert len(counter.events) == 4 # ############################## # Tests for the utils subpackage From b9a1c1d7afdfa2c786dbbb39ddb91293f865eeba Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 27 Apr 2021 14:41:36 -0500 Subject: [PATCH 020/178] Update click_count.py --- plantcv/plantcv/visualize/click_count.py | 36 +++++++++++++++++++++++- 1 file changed, 35 insertions(+), 1 deletion(-) diff --git a/plantcv/plantcv/visualize/click_count.py b/plantcv/plantcv/visualize/click_count.py index cad392226..93e94d8ab 100644 --- a/plantcv/plantcv/visualize/click_count.py +++ b/plantcv/plantcv/visualize/click_count.py @@ -7,7 +7,7 @@ from plantcv.plantcv import params from plantcv.plantcv import fatal_error from scipy.spatial import distance - +import json def _find_closest(pt, pts): """ Given coordinates of a point and a list of coordinates of a bunch of points, find the point that has the smallest Euclidean to the given point @@ -59,6 +59,40 @@ def import_coords(self, coords, label="total"): else: print(f"Warning: {label} already included and counted, nothing is imported!") + def save_coords(self, coord_file): + """Save collected coordinates to a file. + Input variables: + coord_file = Name of the file to save collected coordinate + :param coord_file: str + """ + # Open the file for writing + with open(coord_file, "w") as fp: + # Save the data in JSON format with indentation + json.dump(obj=self.points, fp=fp, indent=4) + + # def save_counter(self, counter_file): + # """Save a counter object to a file + # Input variables: + # counter_file = Filename to write counter to + # :param counter_file: str + # """ + # # Open the file for writing + # with open(counter_file, "w") as fp: + # # Save the data in JSON format with indentation + # json.dump(obj=vars(self), fp=fp, indent=4) + # + # def import_counter(self, counter_file): + # """Import a counter object from a file + # Input variables: + # counter_file = Counter file to import + # :param counter_file: str + # """ + # # Open the file for reading + # with open(counter_file, "r") as fp: + # counter = json.load(fp) + # for key, value in counter.items(): + # setattr(self, key, value) + def view(self, label="total", color="c", view_all=False): """ View the label for a specific class label From 993bf27141aa9b50441633226931b1240ff27369 Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 27 Apr 2021 14:41:41 -0500 Subject: [PATCH 021/178] Update tests.py --- tests/tests.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/tests/tests.py b/tests/tests.py index d42549cae..9d23fdaee 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -6343,6 +6343,8 @@ def test_plantcv_visualize_overlay_two_imgs_size_mismatch(): def test_plantcv_visualize_click_count(): pcv.params.debug = None + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_visualize_visualize_click_count") + os.mkdir(cache_dir) # generate fake testing image temp = pcv.transform.rescale(np.random.rand(5,5)) img = np.stack((temp,)*3, axis=-1) @@ -6355,6 +6357,11 @@ def test_plantcv_visualize_click_count(): counter.import_coords([(0,2)], label="total") assert len(counter.count) == 2 + # save coordinates + coord_file = os.path.join(cache_dir, 'coord.json') + counter.save_coords(coord_file) + assert os.path.exists(coord_file) + # view different classes counter.view() counter.view(label="c1", color="r", view_all=True) From 91add2e05ece2e3b6053a4fb930eb4880f2a265c Mon Sep 17 00:00:00 2001 From: Hudanyun Sheng Date: Tue, 27 Apr 2021 14:41:43 -0500 Subject: [PATCH 022/178] Update visualize_click_count.md --- docs/visualize_click_count.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/visualize_click_count.md b/docs/visualize_click_count.md index 410501356..75a1b7cff 100644 --- a/docs/visualize_click_count.md +++ b/docs/visualize_click_count.md @@ -24,6 +24,11 @@ - label - class label to show on the marked image. By default `label="total`. - color - desired color to show the class. By default `color="c"`. - view_all - a flag indicating whether to show markers for all classes or not. + +**save_coords(*coord_file*)** +- Save the collected coordinates to a json file. +- **Parameter** + - coord_file - (json) file name to save the coordinates of collected points. - **Example use:** - Below From 926bdb7e01bb7f8d6791adb438046392929eee6a Mon Sep 17 00:00:00 2001 From: JorgeGtz Date: Wed, 28 Apr 2021 15:14:43 -0500 Subject: [PATCH 023/178] add documentation for detect discs --- docs/detect_discs.md | 51 ++++++++++++++++++ .../detect_discs/discs_mask_scaled.png | Bin 0 -> 2726 bytes .../detect_discs/discs_pre_scaled.png | Bin 0 -> 6692 bytes 3 files changed, 51 insertions(+) create mode 100644 docs/detect_discs.md create mode 100644 docs/img/documentation_images/detect_discs/discs_mask_scaled.png create mode 100644 docs/img/documentation_images/detect_discs/discs_pre_scaled.png diff --git a/docs/detect_discs.md b/docs/detect_discs.md new file mode 100644 index 000000000..6a9085039 --- /dev/null +++ b/docs/detect_discs.md @@ -0,0 +1,51 @@ +## Detect discs (developing) + +Detects disc-shaped regions in a binary image based on eccentricity. +A value of eccentricity between 0 and 1 corresponds to an ellipse. +The closer the value to 0 the closer the shape is to a circle. + +**plantcv.detect_discs**(*bin_img, ecc_thresh=0*) + +**returns** mask, coordinates of centroids + +- **Parameters:** + - bin_img - Binary image containing the connected regions to consider + - ecc_thresh - Eccentricity threshold below which a region is detected +- **Context:** + - Used to isolate disc-shaped objects of interest in a binary image. The output mask can be used for further analysis and the coordinates can be used for object counting along with the class `plantcv.visualize.ClickCount`. +- **Example use:** + - Below + +**Original image** + +![ori_im](img/documentation_images/visualize_click_count/count_img.jpg) + +**Mask generated using binary threshold in the blue channel** +Note: For more on binary thresholding see the the [Use VIS Tutorial](vis_tutorial.md). +![bin_im](img/documentation_images/detect_discs/discs_pre_scaled.png) + +```python + +from plantcv import plantcv as pcv + +# Set global debug behavior to None (default), "print" (to file), +# or "plot" +pcv.params.debug = "plot" + +# Apply detect discs to the binary image with an +# eccentricity threshold of 0.9 +discs_mask, discs_coor = pcv.detect_discs(bin_img=binary_img, ecc_thresh=0.9) + +# Apply detect discs to the binary image with an +# eccentricity threshold of 0.5 +discs_mask, discs_coor = pcv.detect_discs(bin_img=binary_img, ecc_thresh=0.5) + +``` + +**Mask of detected objects with eccentricity threshold of 0.9** +![count_im](img/documentation_images/visualize_click_count/count_mask.png) + +**Mask of detected objects with eccentricity threshold of 0.5** +![count_im](img/documentation_images/detect_discs/discs_mask_scaled.png) + +**Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/detect_discs.py) diff --git a/docs/img/documentation_images/detect_discs/discs_mask_scaled.png b/docs/img/documentation_images/detect_discs/discs_mask_scaled.png new file mode 100644 index 0000000000000000000000000000000000000000..357fc236abc29c6c60fea4e9bd0bd619c6925514 GIT binary patch literal 2726 zcmV;X3R(4uP)phZf|fcERI1iGEteT@e`srU3S;Z2omQtPGa_23tyUN< zl~S~ER4NvYs92+d5s8Ed=Kk#;kn}`FPR?cbJ=y0o!!YNPJ?}ohJ^SwN*?recB(O$U zPle!)>`Hd+IYT)(<5GZ)%E1}01N>PzIODqj`%@K!E7G^Yp*YqfWM${~QmnC=cs;k{W`A70&{$V5a$L_|bHL_|cq)9YkqaBLZc zUVmc$ebf(4vXuIG_DQMFH$Cg|%v}ilG^xsQb0f8rJo>D&fTiBf-pS3dGMVae0>D9! 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z55q|vB{>ONKkY|EL_@bYk3ePtIK&uD=K%oEtN*W8t)d&zd3~HlAPt|uhDdDuVz3Ut zxqxk7m)=?MtxbXW0wepqBsqz=#Upn!8xwu^5>M?Z>k4r7F;14w<;|xIx4yWb|J?n% z?gZ)ek(~hAGC@h}xP@mg*Ce7&`+s=pFo3ccf(xuQ2-n{C{r0bGNOI7c4rHxZ&Ky}N zH@S8C*_z*~Y8?7hy0eJu#H9W-h&i(6q7Y-sE_Yjz=VttM43qriKAO3;*FVo0j{kkE zIKN|uwySdq1ea@BIB2yKWpK++ zcDl#_T`W9KaNSP~>QonO6o_;;Qx018TGX*eqLAleVYB*{sT6PQ z6K9S~PLl;vDKbPl*Xl%DBGzGxI9}K4>++CQ`S)EAC*OuXGgbI6iPZ+}yG)z^i;IfT^-$h)@a4xNRumEOLs3%`4SuTC;reE*M1;kpm_v)EoT zGC(9ERF^BQIr=HkpQpys5&#%WMOXd(fkdwXzyTs63dDEQTvrRafGasgoRLI60bBqI0B7saB* z(?|jx8pUHGmp0+Sz7)oUXR*eeh?)Wb?z=*VTfRg@L*qmvD0-`CRX6RpQv4_`sudqM zAx{9fzk6p`pD3k*h`Juoibha<5eIXZFS4qM-$!a5O6jl_z?uf?kfLE!DW(_T&ib6L zC#}TVn;(iV5ei@?F=U7vyV^j60vsolY^PyLe)iDVgoTaDQf)zM%L^h&T2WC zk-}u^m;K3@@0@Y;mIzWyP-`!%-X9DAo=zzcL3~F>0C%lo?>qp8{;o2(#n%9=IvLaf uKtXkxaeUz&Aj7(?`wqvC#L9fL4*w4pTc^#-4=u?60000 Date: Wed, 28 Apr 2021 15:38:50 -0500 Subject: [PATCH 024/178] add value of ecc_thresh x 10 to filename for debug print --- plantcv/plantcv/detect_discs.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/detect_discs.py b/plantcv/plantcv/detect_discs.py index f5aac1383..4f8ec9887 100644 --- a/plantcv/plantcv/detect_discs.py +++ b/plantcv/plantcv/detect_discs.py @@ -6,7 +6,7 @@ def detect_discs(bin_img, ecc_thresh=0): """ Detect disc-shaped regions in a binary image based on eccentricity. - A value of eccentricity between 0 and 1 correspond to an ellipse. + A value of eccentricity between 0 and 1 corresponds to an ellipse. The closer the value to 0 the closer the shape is to a circle. Inputs: @@ -41,6 +41,7 @@ def detect_discs(bin_img, ecc_thresh=0): discs_mask = discs_mask + (labeled_img == i+1) _debug(visual=255*discs_mask, filename=os.path.join(params.debug_outdir, - str(params.device) + "_discs_mask.png")) + str(params.device) + "_discs_mask" + + str(int(ecc_thresh*10)) + ".png")) return discs_mask, discs_coor From d764d8a2f63453bd01fe9ac303cfa6804d0370ec Mon Sep 17 00:00:00 2001 From: JorgeGtz Date: Wed, 28 Apr 2021 16:03:22 -0500 Subject: [PATCH 025/178] add documentation for get centroids --- docs/get_centroids.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 docs/get_centroids.md diff --git a/docs/get_centroids.md b/docs/get_centroids.md new file mode 100644 index 000000000..ca527ba57 --- /dev/null +++ b/docs/get_centroids.md @@ -0,0 +1,35 @@ +## Get centroids (developing) + +Get the coordinates (row,column) of the centroid of each connected region +in a binary image. + +**plantcv.get_centroids**(*bin_img*) + +**returns** coordinates of centroids + +- **Parameters:** + - bin_img - Binary image containing the connected regions to consider +- **Context:** + - Given an arbitrary mask of the objects of interest, `get_centroids` + returns a list of coordinates that can the be imported by + `plantcv.visualize.ClickCount`. + +- **Example use:** + - Below + +**Binary image** + +![count_im](img/documentation_images/detect_discs/discs_mask_scaled.png) + +```python + +from plantcv import plantcv as pcv + + +# Apply get centroids to the binary image +coordinates = pcv.get_centroids(bin_img=binary_img) +print(coordinates) +#[[1902, 600], [1839, 1363], [1837, 383], [1669, 1977], [1631, 1889], [1590, 1372], [1550, 1525], [1538, 1633], [1522, 1131], [1494, 2396], [1482, 1917], [1446, 1808], [1425, 726], [1418, 2392], [1389, 198], [1358, 1712], [1288, 522], [1289, 406], [1279, 368], [1262, 1376], [1244, 1795], [1224, 1327], [1201, 624], [1181, 725], [1062, 85], [999, 840], [885, 399], [740, 324], [728, 224], [697, 860], [660, 650], [638, 2390], [622, 1565], [577, 497], [572, 2179], [550, 2230], [547, 1826], [537, 892], [538, 481], [524, 2144], [521, 2336], [497, 201], [385, 1141], [342, 683], [342, 102], [332, 1700], [295, 646], [271, 60], [269, 1626], [210, 1694], [189, 878], [178, 1570], [171, 2307], [61, 286], [28, 2342]] + + +``` From d05a98f10ef28aa062ee78735129c0d8dcfe2eae Mon Sep 17 00:00:00 2001 From: JorgeGtz Date: Wed, 28 Apr 2021 16:05:23 -0500 Subject: [PATCH 026/178] switch mask type to uint8 (input in get centroids, output in detect discs) --- plantcv/plantcv/detect_discs.py | 2 +- plantcv/plantcv/get_centroids.py | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/detect_discs.py b/plantcv/plantcv/detect_discs.py index 4f8ec9887..da10cba8e 100644 --- a/plantcv/plantcv/detect_discs.py +++ b/plantcv/plantcv/detect_discs.py @@ -32,7 +32,7 @@ def detect_discs(bin_img, ecc_thresh=0): # Check the eccentricity of each region. # A value closer to 0 keeps only the most circular objects - discs_mask = np.zeros(labeled_img.shape, dtype=np.int32) + discs_mask = np.zeros(labeled_img.shape, dtype=np.uint8) # Store the list of coordinates (row,col) for the objects that pass the discs_coor = [] for i,obj in enumerate(obj_measures): diff --git a/plantcv/plantcv/get_centroids.py b/plantcv/plantcv/get_centroids.py index 2be9b2d0d..5c3b2a9f6 100644 --- a/plantcv/plantcv/get_centroids.py +++ b/plantcv/plantcv/get_centroids.py @@ -1,4 +1,5 @@ import cv2 +import numpy as np def get_centroids(bin_img): """ Get the coordinates (row,column) of the centroid of each connected @@ -16,7 +17,8 @@ def get_centroids(bin_img): """ # find contours in the binary image - _, contours, _ = cv2.findContours(bin_img, cv2.RETR_TREE, + _, contours, _ = cv2.findContours(bin_img.astype(np.uint8), + cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) coor = [] for c in contours: From 0f0cc8fc3ed167b922b1b742e08ed9da090696d8 Mon Sep 17 00:00:00 2001 From: JorgeGtz Date: Wed, 28 Apr 2021 16:07:25 -0500 Subject: [PATCH 027/178] output binary mask as 0-255 instead of 0-1 --- plantcv/plantcv/detect_discs.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/plantcv/plantcv/detect_discs.py b/plantcv/plantcv/detect_discs.py index da10cba8e..d12eee82c 100644 --- a/plantcv/plantcv/detect_discs.py +++ b/plantcv/plantcv/detect_discs.py @@ -44,4 +44,4 @@ def detect_discs(bin_img, ecc_thresh=0): str(params.device) + "_discs_mask" + str(int(ecc_thresh*10)) + ".png")) - return discs_mask, discs_coor + return 255*discs_mask, discs_coor From 6c0cc09f3376728aeaf0c2ebd5e1147a3d515585 Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Fri, 17 Sep 2021 10:13:13 -0500 Subject: [PATCH 028/178] Update visualize_click_count.md minor doc updates --- docs/visualize_click_count.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/visualize_click_count.md b/docs/visualize_click_count.md index 75a1b7cff..4a31fb051 100644 --- a/docs/visualize_click_count.md +++ b/docs/visualize_click_count.md @@ -1,4 +1,4 @@ -## Click and count objects (developing) +## Click and count objects `ClickCount` is a class that allows user interactive to count objects in images. @@ -39,7 +39,7 @@ ![ori_im](img/documentation_images/visualize_click_count/count_img.jpg) **Mask of automated detected objects** -Note: for how to get this mask, refer to function `pcv.detect_descs`. +Note: for how to get this mask, refer to function `pcv.detect_discs`. ![count_im](img/documentation_images/visualize_click_count/count_mask.png) From 7d2a653f4ddd612d65bf398dc5cd6669e7b80272 Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Fri, 17 Sep 2021 10:14:27 -0500 Subject: [PATCH 029/178] remove developing tag --- docs/detect_discs.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/detect_discs.md b/docs/detect_discs.md index 6a9085039..622565c3e 100644 --- a/docs/detect_discs.md +++ b/docs/detect_discs.md @@ -1,4 +1,4 @@ -## Detect discs (developing) +## Detect discs Detects disc-shaped regions in a binary image based on eccentricity. A value of eccentricity between 0 and 1 corresponds to an ellipse. From 7c77dea02b481f513f9a5c80c191b7059360fe33 Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Fri, 17 Sep 2021 10:30:28 -0500 Subject: [PATCH 030/178] Convert coord values to int --- plantcv/plantcv/detect_discs.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/plantcv/plantcv/detect_discs.py b/plantcv/plantcv/detect_discs.py index d12eee82c..a0a225ddf 100644 --- a/plantcv/plantcv/detect_discs.py +++ b/plantcv/plantcv/detect_discs.py @@ -37,7 +37,8 @@ def detect_discs(bin_img, ecc_thresh=0): discs_coor = [] for i,obj in enumerate(obj_measures): if obj.eccentricity < ecc_thresh: - discs_coor.append(obj.centroid) + # Convert coord values to int + discs_coor.append(tuple(map(int, obj.centroid))) discs_mask = discs_mask + (labeled_img == i+1) _debug(visual=255*discs_mask, filename=os.path.join(params.debug_outdir, From 4782b597dbf9d76ed26e67092b765dfd65872c1e Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Fri, 17 Sep 2021 11:06:07 -0500 Subject: [PATCH 031/178] Update detect_discs.py --- plantcv/plantcv/detect_discs.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/plantcv/plantcv/detect_discs.py b/plantcv/plantcv/detect_discs.py index a0a225ddf..280378181 100644 --- a/plantcv/plantcv/detect_discs.py +++ b/plantcv/plantcv/detect_discs.py @@ -37,8 +37,10 @@ def detect_discs(bin_img, ecc_thresh=0): discs_coor = [] for i,obj in enumerate(obj_measures): if obj.eccentricity < ecc_thresh: - # Convert coord values to int - discs_coor.append(tuple(map(int, obj.centroid))) + # Convert coord values to int + #coords = tuple(map(int, obj.centroid)) + #discs_coor.append(coords) + discs_coor.append(obj.centroid) discs_mask = discs_mask + (labeled_img == i+1) _debug(visual=255*discs_mask, filename=os.path.join(params.debug_outdir, From c29f85f881e969130279e0eb44ead7a46b97c389 Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Fri, 17 Sep 2021 13:30:41 -0500 Subject: [PATCH 032/178] remove accidental indent --- tests/tests.py | 1715 +++++++++++++++++++++++++----------------------- 1 file changed, 885 insertions(+), 830 deletions(-) diff --git a/tests/tests.py b/tests/tests.py index 9d23fdaee..01ddeb5bc 100755 --- a/tests/tests.py +++ b/tests/tests.py @@ -13,11 +13,14 @@ import plantcv.learn import plantcv.parallel import plantcv.utils +import xarray as xr # Import matplotlib and use a null Template to block plotting to screen # This will let us test debug = "plot" import matplotlib +import matplotlib.pyplot as plt import dask from dask.distributed import Client +from skimage import img_as_ubyte PARALLEL_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "parallel_data") TEST_TMPDIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", ".cache") @@ -383,6 +386,90 @@ def test_plantcv_parallel_metadata_parser_images(): meta = plantcv.parallel.metadata_parser(config=config) assert meta == expected + +def test_plantcv_parallel_metadata_parser_multivalue_filter(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_IMG_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_images", "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": ["VIS", "NIR"]} + config.imgformat = "jpg" + + meta = plantcv.parallel.metadata_parser(config=config) + expected = { + 'VIS_SV_0_z1_h1_g0_e82_117770.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, TEST_IMG_DIR, 'VIS_SV_0_z1_h1_g0_e82_117770.jpg'), + 'camera': 'SV', + 'imgtype': 'VIS', + 'zoom': 'z1', + 'exposure': 'e82', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': None, + 'id': '117770', + 'plantbarcode': 'none', + 'treatment': 'none', + 'cartag': 'none', + 'measurementlabel': 'none', + 'other': 'none' + }, + 'NIR_SV_0_z1_h1_g0_e65_117779.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, TEST_IMG_DIR, 'NIR_SV_0_z1_h1_g0_e65_117779.jpg'), + 'camera': 'SV', + 'imgtype': 'NIR', + 'zoom': 'z1', + 'exposure': 'e65', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': None, + 'id': '117779', + 'plantbarcode': 'none', + 'treatment': 'none', + 'cartag': 'none', + 'measurementlabel': 'none', + 'other': 'none' + } + } + assert meta == expected + + +def test_plantcv_parallel_metadata_parser_multivalue_filter_nomatch(): + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_IMG_DIR) + config.json = os.path.join(TEST_TMPDIR, "test_plantcv_parallel_metadata_parser_images", "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "id"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": ["VIS", "PSII"]} + config.imgformat = "jpg" + + meta = plantcv.parallel.metadata_parser(config=config) + expected = { + 'VIS_SV_0_z1_h1_g0_e82_117770.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, TEST_IMG_DIR, 'VIS_SV_0_z1_h1_g0_e82_117770.jpg'), + 'camera': 'SV', + 'imgtype': 'VIS', + 'zoom': 'z1', + 'exposure': 'e82', + 'gain': 'g0', + 'frame': '0', + 'lifter': 'h1', + 'timestamp': None, + 'id': '117770', + 'plantbarcode': 'none', + 'treatment': 'none', + 'cartag': 'none', + 'measurementlabel': 'none', + 'other': 'none' + } + } + assert meta == expected + + def test_plantcv_parallel_metadata_parser_regex(): # Create config instance config = plantcv.parallel.WorkflowConfig() @@ -455,6 +542,47 @@ def test_plantcv_parallel_metadata_parser_no_default_dates(): assert meta == METADATA_VIS_ONLY +def test_plantcv_parallel_workflowconfig_subdaily_timestampformat(): + ''' + timestampformats with only hours and smaller units of time were failing if the script was run earlier in the day than the images were taken. this was fixed by setting end_date to 23-59-59 if we don't detect the year-month-day + ''' + # Create config instance + config = plantcv.parallel.WorkflowConfig() + config.input_dir = os.path.join(PARALLEL_TEST_DATA, TEST_IMG_DIR2) + config.json = os.path.join( + TEST_IMG_DIR2, "test_plantcv_parallel_metadata_parser_subdaily_timestampformat", "output.json") + config.filename_metadata = ["imgtype", "camera", "frame", "zoom", "lifter", "gain", "exposure", "timestamp"] + config.workflow = TEST_PIPELINE + config.metadata_filters = {"imgtype": "NIR", "camera": "SV"} + config.start_date = None + config.end_date = None + config.timestampformat = "%H_%M_%S" + config.imgformat = "jpg" + + config.delimiter = r"(NIR)_(SV)_(\d)_(z1)_(h1)_(g0)_(e65)_(\d{2}_\d{2}_\d{2})" + + meta = plantcv.parallel.metadata_parser(config=config) + assert meta == { + 'NIR_SV_0_z1_h1_g0_e65_23_59_59.jpg': { + 'path': os.path.join(PARALLEL_TEST_DATA, 'images_w_date', 'NIR_SV_0_z1_h1_g0_e65_23_59_59.jpg'), + 'imgtype': 'NIR', + 'camera': 'SV', + 'frame': '0', + 'zoom': 'z1', + 'lifter': 'h1', + 'gain': 'g0', + 'exposure': 'e65', + 'timestamp': '23_59_59', + 'measurementlabel': 'none', + 'cartag': 'none', + 'id': 'none', + 'treatment': 'none', + 'plantbarcode': 'none', + 'other': 'none' + } + } + + def test_plantcv_parallel_check_date_range_wrongdateformat(): start_date = 10 end_date = 10 @@ -852,13 +980,18 @@ def test_plantcv_parallel_process_results_invalid_json(): HYPERSPECTRAL_HDR_NO_DEFAULT = "darkReference2.hdr" HYPERSPECTRAL_DATA_APPROX_PSEUDO = "darkReference3" HYPERSPECTRAL_HDR_APPROX_PSEUDO = "darkReference3.hdr" +HYPERSPECTRAL_DATA_BAD_INTERLEAVE = "darkReference4" +HYPERSPECTRAL_HDR_BAD_INTERLEAVE = "darkReference4.hdr" HYPERSPECTRAL_HDR_SMALL_RANGE = {'description': '{[HEADWALL Hyperspec III]}', 'samples': '800', 'lines': '1', 'bands': '978', 'header offset': '0', 'file type': 'ENVI Standard', 'interleave': 'bil', 'sensor type': 'Unknown', 'byte order': '0', 'default bands': '159,253,520', 'wavelength units': 'nm', 'wavelength': ['379.027', '379.663', '380.3', '380.936', '381.573', '382.209']} -FLUOR_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "photosynthesis_data") -FLUOR_IMG = "PSII_PSD_supopt_temp_btx623_22_rep1.DAT" +PHOTOSYNTHESIS_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "photosynthesis_data") +PHOTOSYNTHESIS_IMG_INF = "PSII_HDR_supopt_temp_btx623_22_rep1.INF" +PHOTOSYNTHESIS_NPQ_IMG_INF = "PSII_HDR_020321_WT_TOP_1.INF" +PHOTOSYNTHESIS_FMASK_SV = "PSII_HDR_supopt_temp_btx623_22_rep1_mask.png" +PHOTOSYNTHESIS_FMASK_TV = "PSII_HDR_020321_WT_TOP_1_mask.png" TEST_COLOR_DIM = (2056, 2454, 3) TEST_GRAY_DIM = (2056, 2454) TEST_BINARY_DIM = TEST_GRAY_DIM @@ -943,8 +1076,53 @@ def test_plantcv_parallel_process_results_invalid_json(): TEST_THERMAL_ARRAY = "thermal_img.npz" TEST_THERMAL_IMG_MASK = "thermal_img_mask.png" TEST_INPUT_THERMAL_CSV = "FLIR2600.csv" +# TEST_BAD_MASK = "bad_mask_test.pkl" +# TEST_IM_BAD_NONE = "bad_mask_none.pkl" +# TEST_IM_BAD_BOTH = "bad_mask_both.pkl" +# TEST_IM_BAD_NAN = "bad_mask_nan.pkl" +# TEST_IM_BAD_INF = "bad_mask_inf.pkl" PIXEL_VALUES = "pixel_inspector_rgb_values.txt" TEST_DISCS_MASK = 'discs_binary.png' +TEST_INPUT_LEAF_MASK = "leaves_mask.png" + +# leaving photosynthesis data here so it can be used to test plot_image and print_image + +def ps_mask(): + """Create simple mask for PSII""" + mask = np.zeros((10, 10), dtype=np.uint8) + mask[5, 5] = 255 + return(mask) + + +def psii_cropreporter(var): + """Create simple data for PSII""" + # sample images + f0 = ps_mask() + f0[5, 5] = 1 + f1 = ps_mask() + f1[5, 5] = 2 + f2 = ps_mask() + f2[5, 5] = 10 + f3 = ps_mask() + f3[5, 5] = 8 + + # set specific labels for xarray for dark and light adapted + if var == 'darkadapted': + frame_labels = ['Fdark', 'F0', 'Fm', '3'] + measurements = ['t0'] + elif var == 'lightadapted': + frame_labels = ['Fdark', 'Fp', '2', 'Fmp'] + measurements = ['t1'] + + # Create DataArray + da = xr.DataArray(data=np.dstack([f0, f1, f2, f3])[..., None], + dims=('x', 'y', 'frame_label', 'measurement'), + coords={'frame_label': frame_labels, + 'frame_num': ('frame_label', [0, 1, 2, 3]), + 'measurement': measurements}, + name=var + ) + return(da) # ########################## @@ -1033,74 +1211,6 @@ def test_plantcv_outputs_save_results_csv(tmpdir): assert results == test_results -def test_plantcv_transform_warp_smaller(): - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR),-1) - bimg = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY),-1) - bimg_small = cv2.resize(bimg, (200,300)) #not sure why INTER_NEAREST doesn't preserve values - bimg_small[bimg_small>0]=255 - mrow, mcol = bimg_small.shape - vrow, vcol, vdepth = img.shape - pcv.params.debug = None - mask_warped = pcv.transform.warp(bimg_small, img[:,:,2], - pts = [(0,0),(mcol-1,0),(mcol-1,mrow-1),(0,mrow-1)], - refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1)]) - pcv.params.debug = 'plot' - mask_warped_plot = pcv.transform.warp(bimg_small, img[:,:,2], - pts = [(0,0),(mcol-1,0),(mcol-1,mrow-1),(0,mrow-1)], - refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1)]) - - assert np.count_nonzero(mask_warped)==93142 - assert np.count_nonzero(mask_warped_plot)==93142 - - -def test_plantcv_transform_warp_larger(): - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR),-1) - gimg = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) - gimg_large = cv2.resize(gimg, (5000,7000)) - mrow, mcol = gimg_large.shape - vrow, vcol, vdepth = img.shape - pcv.params.debug='print' - mask_warped_print = pcv.transform.warp(gimg_large, img, - pts = [(0,0),(mcol-1,0),(mcol-1,mrow-1),(0,mrow-1)], - refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1)]) - - assert np.sum(mask_warped_print)==83103814 - - -def test_plantcv_transform_warp_rgbimgerror(): - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR),-1) - gimg = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY),-1) - gimg_large = cv2.resize(gimg, (5000,7000)) - mrow, mcol = gimg_large.shape - vrow, vcol, vdepth = img.shape - - with pytest.raises(RuntimeError): - _ = pcv.transform.warp(img, img, - pts = [(0,0),(mcol-1,0),(mcol-1,mrow-1),(0,mrow-1)], - refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1)]) - - -def test_plantcv_transform_warp_4ptserror(): - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR),-1) - mrow, mcol, _ = img.shape - vrow, vcol, vdepth = img.shape - - with pytest.raises(RuntimeError): - _ = pcv.transform.warp(img[:,:,0], img, - pts = [(0,0),(mcol-1,0),(0,mrow-1)], - refpts = [(0,0),(vcol-1,0),(0,vrow-1)]) - - with pytest.raises(RuntimeError): - _ = pcv.transform.warp(img[:,:,1], img, - pts = [(0,0),(mcol-1,0),(0,mrow-1)], - refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1)]) - - with pytest.raises(RuntimeError): - _ = pcv.transform.warp(img[:,:,2], img, - pts = [(0,0),(mcol-1,0),(mcol-1,mrow-1),(0,mrow-1)], - refpts = [(0,0),(vcol-1,0),(vcol-1,vrow-1),(0,vrow-1),(0,vrow-1)]) - - def test_plantcv_acute(): # Read in test data mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL), -1) @@ -1122,22 +1232,10 @@ def test_plantcv_acute(): def test_plantcv_acute_vertex(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_acute_vertex") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_VIS_SMALL)) contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR), encoding="latin1") obj_contour = contours_npz['arr_0'] - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.acute_vertex(obj=obj_contour, win=5, thresh=15, sep=5, img=img, label="prefix") - _ = pcv.acute_vertex(obj=[], win=5, thresh=15, sep=5, img=img) - _ = pcv.acute_vertex(obj=[], win=.01, thresh=.01, sep=1, img=img) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.acute_vertex(obj=obj_contour, win=5, thresh=15, sep=5, img=img) # Test with debug = None pcv.params.debug = None acute = pcv.acute_vertex(obj=obj_contour, win=5, thresh=15, sep=5, img=img) @@ -1157,27 +1255,17 @@ def test_plantcv_acute_vertex_bad_obj(): def test_plantcv_analyze_bound_horizontal(): # Clear previous outputs pcv.outputs.clear() - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_bound_horizontal") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) img_above_bound_only = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL_PLANT)) mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") object_contours = contours_npz['arr_0'] - # Test with debug = "print" - pcv.params.debug = "print" + pcv.params.debug = None _ = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=300, label="prefix") pcv.outputs.clear() _ = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=100) _ = pcv.analyze_bound_horizontal(img=img_above_bound_only, obj=object_contours, mask=mask, line_position=1756) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=1756) - # Test with debug = None - pcv.params.debug = None _ = pcv.analyze_bound_horizontal(img=img, obj=object_contours, mask=mask, line_position=1756) assert len(pcv.outputs.observations["default"]) == 7 @@ -1217,21 +1305,11 @@ def test_plantcv_analyze_bound_horizontal_neg_y(): def test_plantcv_analyze_bound_vertical(): # Clear previous outputs pcv.outputs.clear() - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_bound_vertical") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") object_contours = contours_npz['arr_0'] - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.analyze_bound_vertical(img=img, obj=object_contours, mask=mask, line_position=1000, label="prefix") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.analyze_bound_vertical(img=img, obj=object_contours, mask=mask, line_position=1000) # Test with debug = None pcv.params.debug = None _ = pcv.analyze_bound_vertical(img=img, obj=object_contours, mask=mask, line_position=1000) @@ -1323,13 +1401,14 @@ def test_plantcv_analyze_color(): _ = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type='rgb') assert pcv.outputs.observations['default']['hue_median']['value'] == 84.0 + def test_plantcv_analyze_color_incorrect_image(): img_binary = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) with pytest.raises(RuntimeError): _ = pcv.analyze_color(rgb_img=img_binary, mask=mask, hist_plot_type=None) -# -# + + def test_plantcv_analyze_color_bad_hist_type(): img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) @@ -1484,6 +1563,7 @@ def test_plantcv_analyze_thermal_values(): thermal_hist = pcv.analyze_thermal_values(thermal_array=img, mask=mask, histplot=True) assert thermal_hist is not None and pcv.outputs.observations['default']['median_temp']['value'] == 33.20922 + def test_plantcv_apply_mask_white(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_apply_mask_white") @@ -1492,12 +1572,6 @@ def test_plantcv_apply_mask_white(): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.apply_mask(img=img, mask=mask, mask_color="white") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.apply_mask(img=img, mask=mask, mask_color="white") # Test with debug = None pcv.params.debug = None masked_img = pcv.apply_mask(img=img, mask=mask, mask_color="white") @@ -1512,12 +1586,6 @@ def test_plantcv_apply_mask_black(): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.apply_mask(img=img, mask=mask, mask_color="black") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.apply_mask(img=img, mask=mask, mask_color="black") # Test with debug = None pcv.params.debug = None masked_img = pcv.apply_mask(img=img, mask=mask, mask_color="black") @@ -1535,11 +1603,8 @@ def test_plantcv_apply_mask_hyperspectral(): img = np.ones((2056, 2454)) img_stacked = cv2.merge((img, img, img, img)) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.apply_mask(img=img_stacked, mask=img, mask_color="black") - # Test with debug = "plot" - pcv.params.debug = "plot" + # Test with debug = none + pcv.params.debug = None masked_array = pcv.apply_mask(img=hyper_array.array_data, mask=img, mask_color="black") assert np.mean(masked_array) == 13.97111260224949 @@ -1554,24 +1619,18 @@ def test_plantcv_apply_mask_bad_input(): def test_plantcv_auto_crop(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_auto_crop") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir # Read in test data img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI), -1) contours = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_OBJECT), encoding="latin1") roi_contours = [contours[arr_n] for arr_n in contours] - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.auto_crop(img=img1, obj=roi_contours[1], padding_x=(20, 10), padding_y=(20, 10), color='black') - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.auto_crop(img=img1, obj=roi_contours[1], color='image') - _ = pcv.auto_crop(img=img1, obj=roi_contours[1], padding_x=2000, padding_y=2000, color='image') # Test with debug = None pcv.params.debug = None - cropped = pcv.auto_crop(img=img1, obj=roi_contours[1], padding_x=20, padding_y=20, color='black') + # padding as tuple + _ = pcv.auto_crop(img=img1, obj=roi_contours[1], padding_x=(20, 10), padding_y=(20, 10), color='black') + # padding 0 so crop same as image + _ = pcv.auto_crop(img=img1, obj=roi_contours[1], color='image') + # padding as int + cropped = pcv.auto_crop(img=img1, obj=roi_contours[1], padding_x=20, padding_y=20, color='image') x, y, z = np.shape(img1) x1, y1, z1 = np.shape(cropped) assert x > x1 @@ -1624,14 +1683,9 @@ def test_plantcv_canny_edge_detect(): rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" _ = pcv.canny_edge_detect(img=rgb_img, mask=mask, mask_color='white') _ = pcv.canny_edge_detect(img=img, mask=mask, mask_color='black') - # Test with debug = "plot" - pcv.params.debug = "plot" _ = pcv.canny_edge_detect(img=img, thickness=2) - _ = pcv.canny_edge_detect(img=img) # Test with debug = None pcv.params.debug = None edge_img = pcv.canny_edge_detect(img=img) @@ -1666,12 +1720,7 @@ def test_plantcv_closing(): bin_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) # Test with debug=None pcv.params.debug = None - _ = pcv.closing(gray_img) - # Test with debug='plot' - pcv.params.debug = 'plot' - _ = pcv.closing(bin_img, np.ones((4, 4), np.uint8)) - # Test with debug='print' - pcv.params.debug = 'print' + _ = pcv.closing(gray_img, np.ones((4, 4), np.uint8)) filtered_img = pcv.closing(bin_img) assert np.sum(filtered_img) == 16261860 @@ -1694,13 +1743,9 @@ def test_plantcv_cluster_contours(): hierarchy = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_HIERARCHY), encoding="latin1") objs = [roi_objects[arr_n] for arr_n in roi_objects] obj_hierarchy = hierarchy['arr_0'] - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, nrow=4, ncol=6) + # Test with debug = 'plot' to cover plotting logic + pcv.params.debug = 'plot' _ = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, show_grid=True) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, nrow=4, ncol=6) # Test with debug = None pcv.params.debug = None clusters_i, contours, hierarchy = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, @@ -1721,12 +1766,9 @@ def test_plantcv_cluster_contours_grayscale_input(): hierachy = np.load(os.path.join(TEST_DATA, TEST_INPUT_MULTI_HIERARCHY), encoding="latin1") objs = [roi_objects[arr_n] for arr_n in roi_objects] obj_hierarchy = hierachy['arr_0'] - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, nrow=4, ncol=6) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, nrow=4, ncol=6) + # Test with debug = 'plot' to cover plotting logic + pcv.params.debug = 'plot' + _ = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, show_grid=True) # Test with debug = None pcv.params.debug = None clusters_i, contours, hierachy = pcv.cluster_contours(img=img1, roi_objects=objs, roi_obj_hierarchy=obj_hierarchy, @@ -1824,11 +1866,7 @@ def test_plantcv_crop(): os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir img, _, _ = pcv.readimage(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK), 'gray') - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.crop(img=img, x=10, y=10, h=50, w=50) - # Test with debug = "plot" - pcv.params.debug = "plot" + pcv.params.debug = None cropped = pcv.crop(img=img, x=10, y=10, h=50, w=50) assert np.shape(cropped) == (50, 50) @@ -1841,11 +1879,7 @@ def test_plantcv_crop_hyperspectral(): # Read in test data img = np.ones((2056, 2454)) img_stacked = cv2.merge((img, img, img, img)) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.crop(img=img_stacked, x=10, y=10, h=50, w=50) - # Test with debug = "plot" - pcv.params.debug = "plot" + pcv.params.debug = None cropped = pcv.crop(img=img_stacked, x=10, y=10, h=50, w=50) assert np.shape(cropped) == (50, 50, 4) @@ -1955,12 +1989,6 @@ def test_plantcv_dilate(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.dilate(gray_img=img, ksize=5, i=1) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.dilate(gray_img=img, ksize=5, i=1) # Test with debug = None pcv.params.debug = None dilate_img = pcv.dilate(gray_img=img, ksize=5, i=1) @@ -1991,12 +2019,6 @@ def test_plantcv_erode(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.erode(gray_img=img, ksize=5, i=1) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.erode(gray_img=img, ksize=5, i=1) # Test with debug = None pcv.params.debug = None erode_img = pcv.erode(gray_img=img, ksize=5, i=1) @@ -2027,12 +2049,6 @@ def test_plantcv_distance_transform(): pcv.params.debug_outdir = cache_dir # Read in test data mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_CROPPED_MASK), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.distance_transform(bin_img=mask, distance_type=1, mask_size=3) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.distance_transform(bin_img=mask, distance_type=1, mask_size=3) # Test with debug = None pcv.params.debug = None distance_transform_img = pcv.distance_transform(bin_img=mask, distance_type=1, mask_size=3) @@ -2047,18 +2063,8 @@ def test_plantcv_fatal_error(): def test_plantcv_fill(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_fill") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.fill(bin_img=img, size=63632) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.fill(bin_img=img, size=63632) # Test with debug = None pcv.params.debug = None fill_img = pcv.fill(bin_img=img, size=63632) @@ -2085,11 +2091,6 @@ def test_plantcv_fill_holes(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.fill_holes(bin_img=img) - pcv.params.debug = "plot" - _ = pcv.fill_holes(bin_img=img) # Test with debug = None pcv.params.debug = None fill_img = pcv.fill_holes(bin_img=img) @@ -2115,12 +2116,6 @@ def test_plantcv_find_objects(): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.find_objects(img=img, mask=mask) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.find_objects(img=img, mask=mask) # Test with debug = None pcv.params.debug = None contours, hierarchy = pcv.find_objects(img=img, mask=mask) @@ -2136,8 +2131,8 @@ def test_plantcv_find_objects_grayscale_input(): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), 0) mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "plot" - pcv.params.debug = "plot" + # Test with debug = None + pcv.params.debug = None contours, hierarchy = pcv.find_objects(img=img, mask=mask) # Assert the correct number of contours are found assert len(contours) == 2 @@ -2151,15 +2146,9 @@ def test_plantcv_flip(): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) img_binary = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.flip(img=img, direction="horizontal") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.flip(img=img, direction="vertical") - _ = pcv.flip(img=img_binary, direction="vertical") # Test with debug = None pcv.params.debug = None + _ = pcv.flip(img=img_binary, direction="vertical") flipped_img = pcv.flip(img=img, direction="horizontal") assert all([i == j] for i, j in zip(np.shape(flipped_img), TEST_COLOR_DIM)) @@ -2179,15 +2168,9 @@ def test_plantcv_gaussian_blur(): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) img_color = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.gaussian_blur(img=img, ksize=(51, 51), sigma_x=0, sigma_y=None) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.gaussian_blur(img=img, ksize=(51, 51), sigma_x=0, sigma_y=None) - _ = pcv.gaussian_blur(img=img_color, ksize=(51, 51), sigma_x=0, sigma_y=None) # Test with debug = None pcv.params.debug = None + _ = pcv.gaussian_blur(img=img_color, ksize=(51, 51), sigma_x=0, sigma_y=None) gaussian_img = pcv.gaussian_blur(img=img, ksize=(51, 51), sigma_x=0, sigma_y=None) imgavg = np.average(img) gavg = np.average(gaussian_img) @@ -2238,12 +2221,6 @@ def test_plantcv_hist_equalization(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.hist_equalization(gray_img=img) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.hist_equalization(gray_img=img) # Test with debug = None pcv.params.debug = None hist = pcv.hist_equalization(gray_img=img) @@ -2258,7 +2235,7 @@ def test_plantcv_hist_equalization_bad_input(): os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir # Read in test data - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), 1) + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR), 1) # Test with debug = None pcv.params.debug = None with pytest.raises(RuntimeError): @@ -2273,18 +2250,31 @@ def test_plantcv_image_add(): # Read in test data img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) img2 = np.copy(img1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.image_add(gray_img1=img1, gray_img2=img2) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.image_add(gray_img1=img1, gray_img2=img2) # Test with debug = None pcv.params.debug = None added_img = pcv.image_add(gray_img1=img1, gray_img2=img2) assert all([i == j] for i, j in zip(np.shape(added_img), TEST_BINARY_DIM)) +def test_plantcv_image_fusion(): + # Read in test data + # 16-bit image + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMAX), -1) + img2 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMIN)) + # 8-bit image + img2 = img_as_ubyte(img2) + fused_img = pcv.image_fusion(img1, img2, [480.0], [550.0, 640.0, 800.0]) + assert str(type(fused_img)) == "" + + +def test_plantcv_image_fusion_size_diff(): + img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), 0) + img2 = np.copy(img1) + img2 = img2[0:10, 0:10] + with pytest.raises(RuntimeError): + _ = pcv.image_fusion(img1, img2, [480.0, 550.0, 670.0], [480.0, 550.0, 670.0]) + + def test_plantcv_image_subtract(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_image_sub") @@ -2293,12 +2283,6 @@ def test_plantcv_image_subtract(): # read in images img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) img2 = np.copy(img1) - # Test with debug = "print" - pcv.params.debug = 'print' - _ = pcv.image_subtract(img1, img2) - # Test with debug = "plot" - pcv.params.debug = 'plot' - _ = pcv.image_subtract(img1, img2) # Test with debug = None pcv.params.debug = None new_img = pcv.image_subtract(img1, img2) @@ -2321,12 +2305,6 @@ def test_plantcv_invert(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.invert(gray_img=img) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.invert(gray_img=img) # Test with debug = None pcv.params.debug = None inverted_img = pcv.invert(gray_img=img) @@ -2368,12 +2346,6 @@ def test_plantcv_laplace_filter(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.laplace_filter(gray_img=img, ksize=1, scale=1) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.laplace_filter(gray_img=img, ksize=1, scale=1) # Test with debug = None pcv.params.debug = None lp_img = pcv.laplace_filter(gray_img=img, ksize=1, scale=1) @@ -2389,12 +2361,6 @@ def test_plantcv_logical_and(): # Read in test data img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) img2 = np.copy(img1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.logical_and(bin_img1=img1, bin_img2=img2) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.logical_and(bin_img1=img1, bin_img2=img2) # Test with debug = None pcv.params.debug = None and_img = pcv.logical_and(bin_img1=img1, bin_img2=img2) @@ -2409,12 +2375,6 @@ def test_plantcv_logical_or(): # Read in test data img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) img2 = np.copy(img1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.logical_or(bin_img1=img1, bin_img2=img2) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.logical_or(bin_img1=img1, bin_img2=img2) # Test with debug = None pcv.params.debug = None or_img = pcv.logical_or(bin_img1=img1, bin_img2=img2) @@ -2429,12 +2389,6 @@ def test_plantcv_logical_xor(): # Read in test data img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) img2 = np.copy(img1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.logical_xor(bin_img1=img1, bin_img2=img2) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.logical_xor(bin_img1=img1, bin_img2=img2) # Test with debug = None pcv.params.debug = None xor_img = pcv.logical_xor(bin_img1=img1, bin_img2=img2) @@ -2448,14 +2402,9 @@ def test_plantcv_median_blur(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.median_blur(gray_img=img, ksize=5) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.median_blur(gray_img=img, ksize=5) # Test with debug = None pcv.params.debug = None + _ = pcv.median_blur(gray_img=img, ksize=(5, 5)) blur_img = pcv.median_blur(gray_img=img, ksize=5) # Assert that the output image has the dimensions of the input image if all([i == j] for i, j in zip(np.shape(blur_img), TEST_BINARY_DIM)): @@ -2486,9 +2435,6 @@ def test_plantcv_naive_bayes_classifier(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS)) # Test with debug = "plot" pcv.params.debug = "plot" _ = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS)) @@ -2526,15 +2472,9 @@ def test_plantcv_object_composition(): object_contours = [object_contours_npz[arr_n] for arr_n in object_contours_npz] object_hierarchy_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_HIERARCHY), encoding="latin1") object_hierarchy = object_hierarchy_npz['arr_0'] - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.object_composition(img=img, contours=object_contours, hierarchy=object_hierarchy) - _ = pcv.object_composition(img=img, contours=[], hierarchy=object_hierarchy) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.object_composition(img=img, contours=object_contours, hierarchy=object_hierarchy) # Test with debug = None pcv.params.debug = None + _ = pcv.object_composition(img=img, contours=[], hierarchy=object_hierarchy) contours, mask = pcv.object_composition(img=img, contours=object_contours, hierarchy=object_hierarchy) # Assert that the objects have been combined contour_shape = np.shape(contours) # type: tuple @@ -2595,11 +2535,7 @@ def test_plantcv_opening(): # Test with debug=None pcv.params.debug = None _ = pcv.opening(gray_img) - # Test with debug='plot' - pcv.params.debug = 'plot' _ = pcv.opening(bin_img, np.ones((4, 4), np.uint8)) - # Test with debug='print' - pcv.params.debug = 'print' filtered_img = pcv.opening(bin_img) assert np.sum(filtered_img) == 16184595 @@ -2683,6 +2619,70 @@ def test_plantcv_plot_image_plotnine(): assert True +@pytest.mark.parametrize(['da', 'kwarg'], + [ + [psii_cropreporter('darkadapted').squeeze('measurement', drop=True), 'frame_label'] + ]) +def test_plantcv_show_dataarray(da, kwarg): + from plantcv.plantcv._show_dataarray import _show_dataarray + try: + _show_dataarray(da, col=kwarg) + except RuntimeError: + assert False + # Assert that the image was plotted without error + assert True + + +@pytest.mark.parametrize(['da', 'kwarg'], + [ + # missing col or row as kwarg + [psii_cropreporter('darkadapted').squeeze('measurement', drop=True), None], + # missing x or y dim + [psii_cropreporter('darkadapted').squeeze('measurement', drop=True)[0, :, :], 'frame_label'] + ] + ) +def test_plantcv_show_dataarray_fatal_error(da, kwarg): + from plantcv.plantcv._show_dataarray import _show_dataarray + with pytest.raises(RuntimeError): + _show_dataarray(da, col=kwarg) + +@pytest.mark.parametrize(['da'], + [ + #too many dims + [psii_cropreporter('darkadapted')], + # not enough dims + [psii_cropreporter('darkadapted')[:, :, 0, 0]] + ]) +def test_plantcv_show_dataarray_value_error(da): + from plantcv.plantcv._show_dataarray import _show_dataarray + # pcolormesh() fails with ValueError if ndim != 2 in addition to row and/or col + with pytest.raises(ValueError): + _show_dataarray(img=da, col_wrap=4, row='frame_label') + + +@pytest.mark.parametrize(['da', 'kwarg'], + [[psii_cropreporter('darkadapted').squeeze('measurement', drop=True), 'frame_label']] + ) +def test_plantcv_plot_image_dataarray(da, kwarg): + try: + pcv.plot_image(da, col=kwarg) + except RuntimeError: + assert False + # Assert that the image was plotted without error + assert True + + +def test_plantcv_plot_image_psiidata(): + psii = pcv.PSII_data() + with pytest.raises(RuntimeError): + pcv.plot_image(psii) + + +def test_plantcv_plot_image_bad_type(): + with pytest.raises(RuntimeError): + pcv.plot_image(img=[], filename="/dev/null") + + def test_plantcv_print_image(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_print_image") @@ -2713,6 +2713,37 @@ def test_plantcv_print_image_plotnine(): assert os.path.exists(filename) is True +def test_plantcv_print_image_matplotlib(): + # Test cache directory + cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_print_image_plotnine") + os.mkdir(cache_dir) + # Input data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + plt.figure() + plt.imshow(img) + plot = plt.gcf() + filename = os.path.join(cache_dir, 'plantcv_print_image.png') + pcv.print_image(img=plot, filename=filename) + # Assert that the file was created + assert os.path.exists(filename) is True + + +def test_plantcv_print_image_dataarray(): + da = psii_cropreporter('darkadapted').squeeze('measurement', drop=True) + try: + pcv.print_image(img=da, col='frame_label', filename='/dev/null') + except RuntimeError: + assert False + # Assert that the image was plotted without error + assert True + + +def test_plantcv_print_image_psiidata(): + psii = pcv.PSII_data() + with pytest.raises(RuntimeError): + pcv.print_image(img=psii, filename='/dev/null') + + def test_plantcv_print_results(tmpdir): # Create a tmp directory cache_dir = tmpdir.mkdir("sub") @@ -2781,107 +2812,27 @@ def test_plantcv_readimage_bad_file(): _ = pcv.readimage(filename=TEST_INPUT_COLOR) -def test_plantcv_readbayer_default_bg(): +@pytest.mark.parametrize("alg, pattern", [["default", 'BG'], + ["default", 'GB'], + ["default", 'RG'], + ["default", 'GR'], + ["edgeaware", 'BG'], + ["edgeaware", 'GB'], + ["edgeaware", 'RG'], + ["edgeaware", 'GR'], + ["variablenumbergradients", 'BG'], + ["variablenumbergradients", 'GB'], + ["variablenumbergradients", 'RG'], + ["variablenumbergradients", 'GR']]) +def test_plantcv_readbayer(alg, pattern): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_readbayer_default_bg") os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir - # Test with debug = "print" - pcv.params.debug = "print" - _, _, _ = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="BG", alg="default") - # Test with debug = "plot" - pcv.params.debug = "plot" - img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="BG", alg="default") - assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) - - -def test_plantcv_readbayer_default_gb(): - # Test with debug = None - pcv.params.debug = None - img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="GB", alg="default") - assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) - - -def test_plantcv_readbayer_default_rg(): - # Test with debug = None - pcv.params.debug = None - img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="RG", alg="default") - assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) - - -def test_plantcv_readbayer_default_gr(): - # Test with debug = None - pcv.params.debug = None - img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="GR", alg="default") - assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) - - -def test_plantcv_readbayer_edgeaware_bg(): - # Test with debug = None - pcv.params.debug = None - img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="BG", alg="edgeaware") - assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) - - -def test_plantcv_readbayer_edgeaware_gb(): # Test with debug = None pcv.params.debug = None img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="GB", alg="edgeaware") - assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) - - -def test_plantcv_readbayer_edgeaware_rg(): - # Test with debug = None - pcv.params.debug = None - img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="RG", alg="edgeaware") - assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) - - -def test_plantcv_readbayer_edgeaware_gr(): - # Test with debug = None - pcv.params.debug = None - img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="GR", alg="edgeaware") - assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) - - -def test_plantcv_readbayer_variablenumbergradients_bg(): - # Test with debug = None - pcv.params.debug = None - img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="BG", alg="variablenumbergradients") - assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) - - -def test_plantcv_readbayer_variablenumbergradients_gb(): - # Test with debug = None - pcv.params.debug = None - img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="GB", alg="variablenumbergradients") - assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) - - -def test_plantcv_readbayer_variablenumbergradients_rg(): - # Test with debug = None - pcv.params.debug = None - img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="RG", alg="variablenumbergradients") - assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) - - -def test_plantcv_readbayer_variablenumbergradients_gr(): - # Test with debug = None - pcv.params.debug = None - img, path, img_name = pcv.readbayer(filename=os.path.join(TEST_DATA, TEST_INPUT_BAYER), - bayerpattern="GR", alg="variablenumbergradients") + bayerpattern=pattern, alg=alg) assert all([i == j] for i, j in zip(np.shape(img), (335, 400, 3))) @@ -2900,15 +2851,10 @@ def test_plantcv_rectangle_mask(): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) img_color = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.rectangle_mask(img=img, p1=(0, 0), p2=(2454, 2056), color="white") - _ = pcv.rectangle_mask(img=img, p1=(0, 0), p2=(2454, 2056), color="white") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.rectangle_mask(img=img_color, p1=(0, 0), p2=(2454, 2056), color="gray") # Test with debug = None pcv.params.debug = None + _ = pcv.rectangle_mask(img=img, p1=(0, 0), p2=(2454, 2056), color="white") + _ = pcv.rectangle_mask(img=img_color, p1=(0, 0), p2=(2454, 2056), color="gray") masked, hist, contour, heir = pcv.rectangle_mask(img=img, p1=(0, 0), p2=(2454, 2056), color="black") maskedsum = np.sum(masked) imgsum = np.sum(img) @@ -2939,14 +2885,6 @@ def test_plantcv_report_size_marker_detect(): roi_contour = [np.array([[[3550, 850]], [[3550, 1349]], [[4049, 1349]], [[4049, 850]]], dtype=np.int32)] roi_hierarchy = np.array([[[-1, -1, -1, -1]]], dtype=np.int32) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.report_size_marker_area(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, marker='detect', - objcolor='light', thresh_channel='s', thresh=120, label="prefix") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.report_size_marker_area(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, marker='detect', - objcolor='light', thresh_channel='s', thresh=120) # Test with debug = None pcv.params.debug = None images = pcv.report_size_marker_area(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, marker='detect', @@ -3032,12 +2970,6 @@ def test_plantcv_rgb2gray_hsv(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.rgb2gray_hsv(rgb_img=img, channel="s") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.rgb2gray_hsv(rgb_img=img, channel="s") # Test with debug = None pcv.params.debug = None s = pcv.rgb2gray_hsv(rgb_img=img, channel="s") @@ -3059,12 +2991,6 @@ def test_plantcv_rgb2gray_lab(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.rgb2gray_lab(rgb_img=img, channel='b') - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.rgb2gray_lab(rgb_img=img, channel='b') # Test with debug = None pcv.params.debug = None b = pcv.rgb2gray_lab(rgb_img=img, channel='b') @@ -3086,12 +3012,6 @@ def test_plantcv_rgb2gray(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.rgb2gray(rgb_img=img) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.rgb2gray(rgb_img=img) # Test with debug = None pcv.params.debug = None gray = pcv.rgb2gray(rgb_img=img) @@ -3108,9 +3028,7 @@ def test_plantcv_roi2mask(): img = cv2.imread(os.path.join(TEST_DATA, TEST_VIS_SMALL)) contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR), encoding="latin1") obj_contour = contours_npz['arr_0'] - pcv.params.debug = "plot" - _ = pcv.roi.roi2mask(img=img, contour=obj_contour) - pcv.params.debug = "print" + pcv.params.debug = None mask = pcv.roi.roi2mask(img=img, contour=obj_contour) assert np.shape(mask)[0:2] == np.shape(img)[0:2] and np.sum(mask) == 255 @@ -3130,16 +3048,10 @@ def test_plantcv_roi_objects(): object_contours = [object_contours_npz[arr_n] for arr_n in object_contours_npz] object_hierarchy_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_HIERARCHY), encoding="latin1") object_hierarchy = object_hierarchy_npz['arr_0'] - # Test with debug = "print" - pcv.params.debug = "print" + # Test with debug = None + pcv.params.debug = None _ = pcv.roi_objects(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, object_contour=object_contours, obj_hierarchy=object_hierarchy, roi_type="largest") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.roi_objects(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, - object_contour=object_contours, obj_hierarchy=object_hierarchy, roi_type="partial") - # Test with debug = None and roi_type = cutto - pcv.params.debug = None _ = pcv.roi_objects(img=img, roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, object_contour=object_contours, obj_hierarchy=object_hierarchy, roi_type="cutto") # Test with debug = None @@ -3183,8 +3095,7 @@ def test_plantcv_roi_objects_grayscale_input(): object_contours = [object_contours_npz[arr_n] for arr_n in object_contours_npz] object_hierarchy_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_OBJECT_HIERARCHY), encoding="latin1") object_hierarchy = object_hierarchy_npz['arr_0'] - # Test with debug = "plot" - pcv.params.debug = "plot" + pcv.params.debug = None kept_contours, kept_hierarchy, mask, area = pcv.roi_objects(img=img, roi_type="partial", roi_contour=roi_contour, roi_hierarchy=roi_hierarchy, object_contour=object_contours, @@ -3192,6 +3103,7 @@ def test_plantcv_roi_objects_grayscale_input(): # Assert that the contours were filtered as expected assert len(kept_contours) == 1891 + def test_plantcv_rotate(): img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) rotated = pcv.rotate(img=img, rotation_deg=45, crop=True) @@ -3199,6 +3111,7 @@ def test_plantcv_rotate(): rotateavg = np.average(rotated) assert rotateavg != imgavg + def test_plantcv_transform_rotate(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_rotate_img") @@ -3242,11 +3155,8 @@ def test_plantcv_scale_features(): mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL), -1) contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR), encoding="latin1") obj_contour = contours_npz['arr_0'] - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.scale_features(obj=obj_contour, mask=mask, points=TEST_ACUTE_RESULT, line_position=50) - # Test with debug = "plot" - pcv.params.debug = "plot" + # test with debug = 'plot' to cover plotting logic + pcv.params.debug = 'plot' _ = pcv.scale_features(obj=obj_contour, mask=mask, points=TEST_ACUTE_RESULT, line_position='NA') # Test with debug = None pcv.params.debug = None @@ -3271,12 +3181,6 @@ def test_plantcv_scharr_filter(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) - pcv.params.debug = "print" - # Test with debug = "print" - _ = pcv.scharr_filter(img=img, dx=1, dy=0, scale=1) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.scharr_filter(img=img, dx=1, dy=0, scale=1) # Test with debug = None pcv.params.debug = None scharr_img = pcv.scharr_filter(img=img, dx=1, dy=0, scale=1) @@ -3292,20 +3196,10 @@ def test_plantcv_shift_img(): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.shift_img(img=img, number=300, side="top") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.shift_img(img=img, number=300, side="top") - # Test with debug = "plot" + pcv.params.debug = None _ = pcv.shift_img(img=img, number=300, side="bottom") - # Test with debug = "plot" _ = pcv.shift_img(img=img, number=300, side="right") - # Test with debug = "plot" _ = pcv.shift_img(img=mask, number=300, side="left") - # Test with debug = None - pcv.params.debug = None rotated = pcv.shift_img(img=img, number=300, side="top") imgavg = np.average(img) shiftavg = np.average(rotated) @@ -3335,12 +3229,6 @@ def test_plantcv_sobel_filter(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.sobel_filter(gray_img=img, dx=1, dy=0, ksize=1) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.sobel_filter(gray_img=img, dx=1, dy=0, ksize=1) # Test with debug = None pcv.params.debug = None sobel_img = pcv.sobel_filter(gray_img=img, dx=1, dy=0, ksize=1) @@ -3355,9 +3243,7 @@ def test_plantcv_stdev_filter(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY_SMALL), -1) - pcv.params.debug = "plot" - _ = pcv.stdev_filter(img=img, ksize=11) - pcv.params.debug = "print" + pcv.params.debug = None filter_img = pcv.stdev_filter(img=img, ksize=11) assert (np.shape(filter_img) == np.shape(img)) @@ -3372,16 +3258,10 @@ def test_plantcv_watershed_segmentation(): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_CROPPED)) mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_CROPPED_MASK), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.watershed_segmentation(rgb_img=img, mask=mask, distance=10, label="prefix") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.watershed_segmentation(rgb_img=img, mask=mask, distance=10) # Test with debug = None pcv.params.debug = None - _ = pcv.watershed_segmentation(rgb_img=img, mask=mask, distance=10) - assert pcv.outputs.observations['default']['estimated_object_count']['value'] > 9 + _ = pcv.watershed_segmentation(rgb_img=img, mask=mask, distance=10, label='prefix') + assert pcv.outputs.observations['prefix']['estimated_object_count']['value'] > 9 def test_plantcv_white_balance_gray_16bit(): @@ -3391,17 +3271,11 @@ def test_plantcv_white_balance_gray_16bit(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.white_balance(img=img, mode='hist', roi=(5, 5, 80, 80)) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.white_balance(img=img, mode='max', roi=(5, 5, 80, 80)) # Test without an ROI pcv.params.debug = None - _ = pcv.white_balance(img=img, mode='hist', roi=None) + _ = pcv.white_balance(img=img, mode='max', roi=None) # Test with debug = None - white_balanced = pcv.white_balance(img=img, roi=(5, 5, 80, 80)) + white_balanced = pcv.white_balance(img=img, mode='hist', roi=(5, 5, 80, 80)) imgavg = np.average(img) balancedavg = np.average(white_balanced) assert balancedavg != imgavg @@ -3415,17 +3289,11 @@ def test_plantcv_white_balance_gray_8bit(): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK)) img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.white_balance(img=img, mode='hist', roi=(5, 5, 80, 80)) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.white_balance(img=img, mode='max', roi=(5, 5, 80, 80)) # Test without an ROI pcv.params.debug = None - _ = pcv.white_balance(img=img, mode='hist', roi=None) + _ = pcv.white_balance(img=img, mode='max', roi=None) # Test with debug = None - white_balanced = pcv.white_balance(img=img, roi=(5, 5, 80, 80)) + white_balanced = pcv.white_balance(img=img, mode='hist', roi=(5, 5, 80, 80)) imgavg = np.average(img) balancedavg = np.average(white_balanced) assert balancedavg != imgavg @@ -3438,47 +3306,26 @@ def test_plantcv_white_balance_rgb(): pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MARKER)) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.white_balance(img=img, mode='hist', roi=(5, 5, 80, 80)) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.white_balance(img=img, mode='max', roi=(5, 5, 80, 80)) - # Test without an ROI pcv.params.debug = None - _ = pcv.white_balance(img=img, mode='hist', roi=None) + # Test without an ROI + _ = pcv.white_balance(img=img, mode='max', roi=None) # Test with debug = None - white_balanced = pcv.white_balance(img=img, roi=(5, 5, 80, 80)) + white_balanced = pcv.white_balance(img=img, mode='hist', roi=(5, 5, 80, 80)) imgavg = np.average(img) balancedavg = np.average(white_balanced) assert balancedavg != imgavg -def test_plantcv_white_balance_bad_input(): - # Read in test data - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK), -1) - # Test with debug = None - with pytest.raises(RuntimeError): - pcv.params.debug = "plot" - _ = pcv.white_balance(img=img, mode='hist', roi=(5, 5, 5, 5, 5)) - - -def test_plantcv_white_balance_bad_mode_input(): - # Read in test data - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MARKER)) - # Test with debug = None - with pytest.raises(RuntimeError): - pcv.params.debug = "plot" - _ = pcv.white_balance(img=img, mode='histogram', roi=(5, 5, 80, 80)) - - -def test_plantcv_white_balance_bad_input_int(): +@pytest.mark.parametrize("mode, roi", [['hist', (5, 5, 5, 5, 5)], # too many points + ['hist', (5., 5, 5, 5)], # not all integers + ['histogram', (5, 5, 80, 80)]]) # bad mode +def test_plantcv_white_balance_bad_input(mode, roi): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_NIR_MASK), -1) # Test with debug = None with pytest.raises(RuntimeError): - pcv.params.debug = "plot" - _ = pcv.white_balance(img=img, mode='hist', roi=(5., 5, 5, 5)) + pcv.params.debug = None + _ = pcv.white_balance(img=img, mode=mode, roi=roi) def test_plantcv_x_axis_pseudolandmarks(): @@ -3490,18 +3337,14 @@ def test_plantcv_x_axis_pseudolandmarks(): mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL), -1) contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR), encoding="latin1") obj_contour = contours_npz['arr_0'] - pcv.params.debug = "print" - _ = pcv.x_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) - # Test with debug = "plot" - pcv.params.debug = "plot" + # Test with debug = None + pcv.params.debug = None _ = pcv.x_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img, label="prefix") _ = pcv.x_axis_pseudolandmarks(obj=np.array([[0, 0], [0, 0]]), mask=np.array([[0, 0], [0, 0]]), img=img) _ = pcv.x_axis_pseudolandmarks(obj=np.array(([[89, 222]], [[252, 39]], [[89, 207]])), mask=np.array(([[42, 161]], [[2, 47]], [[211, 222]])), img=img) _ = pcv.x_axis_pseudolandmarks(obj=(), mask=mask, img=img) - # Test with debug = None - pcv.params.debug = None top, bottom, center_v = pcv.x_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) pcv.outputs.clear() assert all([all([i == j] for i, j in zip(np.shape(top), (20, 1, 2))), @@ -3514,12 +3357,9 @@ def test_plantcv_x_axis_pseudolandmarks_small_obj(): mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL_PLANT), -1) contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR_SMALL_PLANT), encoding="latin1") obj_contour = contours_npz['arr_0'] - # Test with debug = "print" - pcv.params.debug = "print" - _, _, _ = pcv.x_axis_pseudolandmarks(obj=[], mask=mask, img=img) + # Test with debug = None + pcv.params.debug = None _, _, _ = pcv.x_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) - # Test with debug = "plot" - pcv.params.debug = "plot" _, _, _ = pcv.x_axis_pseudolandmarks(obj=[], mask=mask, img=img) top, bottom, center_v = pcv.x_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) assert all([all([i == j] for i, j in zip(np.shape(top), (20, 1, 2))), @@ -3551,20 +3391,14 @@ def test_plantcv_y_axis_pseudolandmarks(): mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL), -1) contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR), encoding="latin1") obj_contour = contours_npz['arr_0'] - pcv.params.debug = "print" - _ = pcv.y_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img, label="prefix") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.y_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) - pcv.outputs.clear() + # Test with debug = None + pcv.params.debug = None _ = pcv.y_axis_pseudolandmarks(obj=[], mask=mask, img=img) _ = pcv.y_axis_pseudolandmarks(obj=(), mask=mask, img=img) _ = pcv.y_axis_pseudolandmarks(obj=np.array(([[89, 222]], [[252, 39]], [[89, 207]])), mask=np.array(([[42, 161]], [[2, 47]], [[211, 222]])), img=img) _ = pcv.y_axis_pseudolandmarks(obj=np.array(([[21, 11]], [[159, 155]], [[237, 11]])), mask=np.array(([[38, 54]], [[144, 169]], [[81, 137]])), img=img) - # Test with debug = None - pcv.params.debug = None left, right, center_h = pcv.y_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) pcv.outputs.clear() assert all([all([i == j] for i, j in zip(np.shape(left), (20, 1, 2))), @@ -3579,12 +3413,8 @@ def test_plantcv_y_axis_pseudolandmarks_small_obj(): mask = cv2.imread(os.path.join(TEST_DATA, TEST_MASK_SMALL_PLANT), -1) contours_npz = np.load(os.path.join(TEST_DATA, TEST_VIS_COMP_CONTOUR_SMALL_PLANT), encoding="latin1") obj_contour = contours_npz['arr_0'] - # Test with debug = "print" - pcv.params.debug = "print" + pcv.params.debug = None _, _, _ = pcv.y_axis_pseudolandmarks(obj=[], mask=mask, img=img) - _, _, _ = pcv.y_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) - # Test with debug = "plot" - pcv.params.debug = "plot" pcv.outputs.clear() left, right, center_h = pcv.y_axis_pseudolandmarks(obj=obj_contour, mask=mask, img=img) pcv.outputs.clear() @@ -3634,27 +3464,6 @@ def test_plantcv_background_subtraction(): assert (all(truths)) -def test_plantcv_background_subtraction_debug(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_background_subtraction_debug") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir - # List to hold result of all tests. - truths = [] - fg_img = cv2.imread(os.path.join(TEST_DATA, TEST_FOREGROUND)) - bg_img = cv2.imread(os.path.join(TEST_DATA, TEST_BACKGROUND)) - # Test with debug = "print" - pcv.params.debug = "print" - fgmask = pcv.background_subtraction(background_image=bg_img, foreground_image=fg_img) - truths.append(np.sum(fgmask) > 0) - # Test with debug = "plot" - pcv.params.debug = "plot" - fgmask = pcv.background_subtraction(background_image=bg_img, foreground_image=fg_img) - truths.append(np.sum(fgmask) > 0) - # All of these should be true for the function to pass testing. - assert (all(truths)) - - def test_plantcv_background_subtraction_bad_img_type(): fg_color = cv2.imread(os.path.join(TEST_DATA, TEST_FOREGROUND)) bg_gray = cv2.imread(os.path.join(TEST_DATA, TEST_BACKGROUND), 0) @@ -3673,25 +3482,16 @@ def test_plantcv_background_subtraction_different_sizes(): assert np.sum(fgmask) > 0 -def test_plantcv_spatial_clustering_dbscan(): +@pytest.mark.parametrize("alg, min_size, max_size", [['DBSCAN', 10, None], + ['OPTICS', 100, 5000]] + ) +def test_plantcv_spatial_clustering(alg, min_size, max_size): cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_spatial_clustering_dbscan") os.mkdir(cache_dir) pcv.params.debug_outdir = cache_dir img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI_MASK), -1) - pcv.params.debug = "print" - _ = pcv.spatial_clustering(img, algorithm="DBSCAN", min_cluster_size=10, max_distance=None) - pcv.params.debug = "plot" - spmask = pcv.spatial_clustering(img, algorithm="DBSCAN", min_cluster_size=10, max_distance=None) - assert len(spmask[1]) == 2 - - -def test_plantcv_spatial_clustering_optics(): - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_spatial_clustering_optics") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MULTI_MASK), -1) pcv.params.debug = None - spmask = pcv.spatial_clustering(img, algorithm="OPTICS", min_cluster_size=100, max_distance=5000) + spmask = pcv.spatial_clustering(img, algorithm=alg, min_cluster_size=min_size, max_distance=max_size) assert len(spmask[1]) == 2 @@ -3904,7 +3704,7 @@ def test_plantcv_morphology_fill_segments_with_stem(): pcv.params.debug = None _ = pcv.morphology.fill_segments(mask, obj, stem_obj) num_objects = len(pcv.outputs.observations['default']['leaf_area']['value']) - assert num_objects == 70 + assert num_objects == 69 def test_plantcv_morphology_segment_angle(): @@ -4057,9 +3857,9 @@ def test_plantcv_morphology_segment_insertion_angle(): pcv.params.debug = "print" _ = pcv.morphology.segment_insertion_angle(pruned, segmented_img, leaf_obj, stem_obj, 10) assert pcv.outputs.observations['default']['segment_insertion_angle']['value'][:6] == ['NA', 'NA', 'NA', - 24.956918822001636, - 50.7313343343401, - 56.427712102130734] + 24.956918822001636, + 50.7313343343401, + 56.427712102130734] def test_plantcv_morphology_segment_insertion_angle_bad_stem(): @@ -4171,6 +3971,12 @@ def test_plantcv_hyperspectral_read_data_approx_pseudorgb(): assert np.shape(array_data.array_data) == (1, 1600, 978) +def test_plantcv_hyperspectral_read_data_bad_interleave(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA_BAD_INTERLEAVE) + with pytest.raises(RuntimeError): + _ = pcv.hyperspectral.read_data(filename=spectral_filename) + + def test_plantcv_spectral_index_ndvi(): cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_hyperspectral_index_ndvi") os.mkdir(cache_dir) @@ -4887,78 +4693,209 @@ def test_plantcv_hyperspectral_inverse_covariance(): # ######################################## # Tests for the photosynthesis subpackage -# ######################################## -def test_plantcv_photosynthesis_read_dat(): - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_photosynthesis_read_dat") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir - pcv.params.debug = "plot" - fluor_filename = os.path.join(FLUOR_TEST_DATA, FLUOR_IMG) - _, _, _ = pcv.photosynthesis.read_cropreporter(filename=fluor_filename) - pcv.params.debug = "print" - fdark, fmin, fmax = pcv.photosynthesis.read_cropreporter(filename=fluor_filename) - assert np.sum(fmin) < np.sum(fmax) +# ######################################## +def psii_walz(var): + """Create and return synthetic psii dataarrays from walz""" + # create darkadapted + if var == 'darkadapted': + i = 0 + fmin = np.ones((10, 10), dtype='uint8') * ((i+15)*2) + fmax = np.ones((10, 10), dtype='uint8') * (200-i*15) + data = np.stack([fmin, fmax], axis=2) + + frame_nums = range(0, 2) + indf = ['F0','Fm'] + ps_da = xr.DataArray( + data=data[..., None], + dims=('x', 'y', 'frame_label', 'measurement'), + coords={'frame_label': indf, + 'frame_num': ('frame_label', frame_nums), + 'measurement': ['t0']}, + name='darkadapted' + ) + + # create lightadapted + elif var == 'lightadapted': + da_list = [] + measurement = [] + + for i in np.arange(1, 3): + indf = ['Fp', 'Fmp'] + fmin = np.ones((10, 10), dtype='uint8') * ((i+15)*2) + fmax = np.ones((10, 10), dtype='uint8') * (200-i*15) + data = np.stack([fmin, fmax], axis=2) + + lightadapted = xr.DataArray( + data=data[..., None], + dims=('x', 'y', 'frame_label', 'measurement'), + coords={'frame_label': indf, + 'frame_num': ('frame_label', range(0, 2))} + ) + + measurement.append((f't{i*40}')) + da_list.append(lightadapted) + + prop_idx = pd.Index(measurement) + ps_da = xr.concat(da_list, 'measurement') + ps_da.name = 'lightadapted' + ps_da.coords['measurement'] = prop_idx + + return(ps_da) + + +def test_plantcv_classes_psii_data(): + psii = pcv.PSII_data() + psii.add_data(psii_cropreporter('darkadapted')) + assert repr(psii) == "PSII variables defined:\ndarkadapted" + + +def test_plantcv_photosynthesis_read_cropreporter(): + # Test with debug = None + pcv.params.debug = None + fluor_filename = os.path.join(PHOTOSYNTHESIS_TEST_DATA, PHOTOSYNTHESIS_NPQ_IMG_INF) + ps = pcv.photosynthesis.read_cropreporter(filename=fluor_filename) + assert isinstance(ps, pcv.PSII_data) and ps.darkadapted.shape == (966, 1296, 21, 1) -def test_plantcv_photosynthesis_analyze_fvfm(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_fvfm") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir - # filename = os.path.join(cache_dir, 'plantcv_fvfm_hist.png') - # Read in test data - fdark = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FDARK), -1) - fmin = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMIN), -1) - fmax = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMAX), -1) - fmask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMASK), -1) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.photosynthesis.analyze_fvfm(fdark=fdark, fmin=fmin, fmax=fmax, mask=fmask, bins=1000, label="prefix") - # Test with debug = "plot" - pcv.params.debug = "plot" - fvfm_images = pcv.photosynthesis.analyze_fvfm(fdark=fdark, fmin=fmin, fmax=fmax, mask=fmask, bins=1000) - assert len(fvfm_images) != 0 +def test_plantcv_photosynthesis_read_cropreporter_spc_only(tmpdir): + # Create a test tmp directory + cache_dir = tmpdir.mkdir("sub") + # Create dataset with only SPC + shutil.copyfile(os.path.join(PHOTOSYNTHESIS_TEST_DATA, PHOTOSYNTHESIS_NPQ_IMG_INF), + os.path.join(cache_dir, PHOTOSYNTHESIS_NPQ_IMG_INF)) + PHOTOSYNTHESIS_SPC_IMG_DAT = PHOTOSYNTHESIS_NPQ_IMG_INF.replace("HDR", "SPC") + PHOTOSYNTHESIS_SPC_IMG_DAT = PHOTOSYNTHESIS_SPC_IMG_DAT.replace("INF", "DAT") + shutil.copyfile(os.path.join(PHOTOSYNTHESIS_TEST_DATA, PHOTOSYNTHESIS_SPC_IMG_DAT), + os.path.join(cache_dir, PHOTOSYNTHESIS_SPC_IMG_DAT)) + # Test with debug = None + pcv.params.debug = None + fluor_filename = os.path.join(cache_dir, PHOTOSYNTHESIS_NPQ_IMG_INF) + ps = pcv.photosynthesis.read_cropreporter(filename=fluor_filename) + assert isinstance(ps, pcv.PSII_data) and ps.spectral.array_data.shape == (966, 1296, 3) + + +@pytest.mark.parametrize("mda, mlabels", + # test darkadapted control seq + [[psii_cropreporter("darkadapted"), None], + # test lightadapted control seq and measurement_labels arg + [psii_cropreporter("lightadapted"), ["Fq/Fm"]], + # test darkadapted walz + [psii_walz("darkadapted"), ["Fv/Fm"]], + # test lightadapted walz + [psii_walz("lightadapted"), [f't{i*40}' for i in np.arange(1, 3)]] + ] + ) +def test_plantcv_photosynthesis_analyze_yii(mda, mlabels): + # Test with debug = None + pcv.params.debug = None + _ = pcv.photosynthesis.analyze_yii(ps_da=mda, mask=ps_mask(), bins=100, + measurement_labels=mlabels, label="default") + if mlabels is None: + med = pcv.outputs.observations["default"]["yii_median_t0"]["value"] + pcv.outputs.clear() + assert med == 0.8 + elif "Fq/Fm" in mlabels: + med = pcv.outputs.observations["default"]["yii_median_Fq/Fm"]["value"] + pcv.outputs.clear() + assert med == 0.75 + elif "Fv/Fm" in mlabels: + med = pcv.outputs.observations["default"]["yii_median_Fv/Fm"]["value"] + pcv.outputs.clear() + assert med == float(np.around((200 - 30) / 200, decimals=4)) + elif "t40" in mlabels: + med = pcv.outputs.observations["default"]["yii_median_t40"]["value"] + pcv.outputs.clear() + assert med == float(np.around((185 - 32) / 185, decimals=4)) + + +@pytest.mark.parametrize("mlabels, tmask", + # test wrong mask shape + [[None, np.ones((2, 2))], + # test non binary mask + [None, np.random.random(ps_mask().shape)], + # test bad measurement_labels + [['f', 'm'], ps_mask()]]) +def test_plantcv_photosynthesis_analyze_yii_fatalerror(mlabels, tmask): + # Test with debug = None + pcv.params.debug = None + with pytest.raises(RuntimeError): + _ = pcv.photosynthesis.analyze_yii(ps_da=psii_cropreporter('darkadapted'), mask=tmask, + bins=100, measurement_labels=mlabels, label="default") -def test_plantcv_photosynthesis_analyze_fvfm_print_analysis_results(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_fvfm") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir - fdark = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FDARK), -1) - fmin = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMIN), -1) - fmax = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMAX), -1) - fmask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMASK), -1) - _ = pcv.photosynthesis.analyze_fvfm(fdark=fdark, fmin=fmin, fmax=fmax, mask=fmask, bins=1000) - result_file = os.path.join(cache_dir, "results.txt") - pcv.print_results(result_file) - pcv.outputs.clear() - assert os.path.exists(result_file) +@pytest.mark.parametrize("mda_light, mda_dark, mlabels", + [ + # test cropreporter with measurement_labels + [psii_cropreporter('lightadapted'), psii_cropreporter('darkadapted'), ["Fq/Fm"]], + # test walz + [psii_walz('lightadapted'), psii_walz('darkadapted'), None] + ] + ) +def test_plantcv_photosynthesis_analyze_npq(mda_dark, mda_light, mlabels): + # Test with debug = None + pcv.params.debug = None + _ = pcv.photosynthesis.analyze_npq(ps_da_light=mda_light, ps_da_dark=mda_dark, + mask=ps_mask(), bins=100, measurement_labels=mlabels, label="prefix") + if mlabels is not None: + med = pcv.outputs.observations["prefix"]["npq_median_Fq/Fm"]["value"] + pcv.outputs.clear() + assert med == 0.25 + else: + med = pcv.outputs.observations["prefix"]["npq_median_t40"]["value"] + pcv.outputs.clear() + assert med == float(np.around(200 / 185 - 1, decimals=4)) + + +@pytest.mark.parametrize("mlabels, tmask", + # test wrong mask shape + [[None, np.ones((2, 2))], + # test non binary mask or not uint8 + [None, np.random.random(ps_mask().shape)], + # test bad measurement_labels + ['fm', ps_mask()]]) +def test_plantcv_photosynthesis_analyze_npq_fatalerror(mlabels, tmask): + # Test with debug = None + pcv.params.debug = None -def test_plantcv_photosynthesis_analyze_fvfm_bad_fdark(): - # Clear previous outputs - pcv.outputs.clear() - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_analyze_fvfm") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir - # Read in test data - fdark = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FDARK), -1) - fmin = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMIN), -1) - fmax = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMAX), -1) - fmask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMASK), -1) - _ = pcv.photosynthesis.analyze_fvfm(fdark=fdark + 3000, fmin=fmin, fmax=fmax, mask=fmask, bins=1000) - check = pcv.outputs.observations['default']['fdark_passed_qc']['value'] is False - assert check + with pytest.raises(RuntimeError): + _ = pcv.photosynthesis.analyze_npq(ps_da_dark=psii_cropreporter('darkadapted'), ps_da_light=psii_cropreporter( + 'lightadapted'), mask=tmask, bins=100, measurement_labels=mlabels, label="default") -def test_plantcv_photosynthesis_analyze_fvfm_bad_input(): - fdark = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - fmin = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMIN), -1) - fmax = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMAX), -1) - fmask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_FMASK), -1) +@pytest.mark.parametrize("da", + [ + # test darkadapted + psii_cropreporter("darkadapted"), + # test lightadapted + psii_cropreporter("lightadapted") + ] + ) +def test_plantcv_photosynthesis_reassign_frame_labels(da): + # Test with debug = None + pcv.params.debug = None + _ = pcv.photosynthesis.reassign_frame_labels(ps_da=da, mask=ps_mask()) + + +@pytest.mark.parametrize("da, tmask", + [ + # test not PSII_data + [pcv.PSII_data(), ps_mask()], + # test input is dataarray + ['nope', ps_mask()], + # test input is dataarray with correct name + [psii_cropreporter('darkadapted').rename('test'), ps_mask()], + # test mask shape + [psii_cropreporter('darkadapted'), np.ones((2, 2))], + # test mask is binary + [psii_cropreporter('lightadapted'), np.random.random(ps_mask().shape)] + ] + ) +def test_plantcv_photosynthesis_reassign_frame_labels_fatalerror(da, tmask): + # Test with debug = None + pcv.params.debug = None with pytest.raises(RuntimeError): - _ = pcv.photosynthesis.analyze_fvfm(fdark=fdark, fmin=fmin, fmax=fmax, mask=fmask, bins=1000) + _, _ = pcv.photosynthesis.reassign_frame_labels(ps_da=da, mask=tmask) # ############################## @@ -5724,28 +5661,90 @@ def test_plantcv_transform_nonuniform_illumination_gray(): assert np.shape(corrected) == np.shape(gray_img) +def test_plantcv_transform_warp_default(): + pcv.params.debug = "plot" + img = create_test_img((12, 10, 3)) + refimg = create_test_img((12, 10, 3)) + pts = [(0, 0), (1, 0), (0, 3), (4, 4)] + refpts = [(0, 0), (1, 0), (0, 3), (4, 4)] + warped_img, mat = pcv.transform.warp(img, refimg, pts, refpts, method="default") + assert mat.shape == (3, 3) + + +def test_plantcv_transform_warp_lmeds(): + pcv.params.debug = "plot" + img = create_test_img((10, 10, 3)) + refimg = create_test_img((11, 11)) + pts = [(0, 0), (1, 0), (0, 3), (4, 4)] + refpts = [(0, 0), (1, 0), (0, 3), (4, 4)] + warped_img, mat = pcv.transform.warp(img, refimg, pts, refpts, method="lmeds") + assert mat.shape == (3, 3) + + +def test_plantcv_transform_warp_rho(): + pcv.params.debug = "plot" + img = create_test_img_bin((10, 10)) + refimg = create_test_img((11, 11)) + pts = [(0, 0), (1, 0), (0, 3), (4, 4)] + refpts = [(0, 0), (1, 0), (0, 3), (4, 4)] + warped_img, mat = pcv.transform.warp(img, refimg, pts, refpts, method="rho") + assert mat.shape == (3, 3) + + +def test_plantcv_transform_warp_ransac(): + pcv.params.debug = "plot" + img = create_test_img((100, 150)) + refimg = create_test_img((10, 15)) + pts = [(0, 0), (149, 0), (99, 149), (0, 99), (3, 3)] + refpts = [(0, 0), (0, 14), (9, 14), (0, 9), (3, 3)] + warped_img, mat = pcv.transform.warp(img, refimg, pts, refpts, method="ransac") + assert mat.shape == (3, 3) + + +@pytest.mark.parametrize("pts, refpts", [ + [[(0, 0)], [(0, 0), (0, 1)]], # different # of points provided for img and refimg + [[(0, 0)], [(0, 0)]], # not enough pairs of points provided + [[(0, 0), (0, 14), (9, 14), (0, 9), (3, 3)], + [(0, 0), (149, 0), (99, 149), (0, 99), (3, 3)]] # homography not able to be calculated (cannot converge) +]) +def test_plantcv_transform_warp_err(pts, refpts): + img = create_test_img((10, 15)) + refimg = create_test_img((100, 150)) + method = "rho" + with pytest.raises(RuntimeError): + pcv.transform.warp(img, refimg, pts, refpts, method=method) + + +def test_plantcv_transform_warp_align(): + img = create_test_img((10, 10, 3)) + refimg = create_test_img((11, 11)) + mat = np.array([[1.00000000e+00, 1.04238500e-15, -7.69185075e-16], + [1.44375646e-16, 1.00000000e+00, 0.00000000e+00], + [-5.41315251e-16, 1.78930521e-15, 1.00000000e+00]]) + warp_img = pcv.transform.warp_align(img=img, mat=mat, refimg=refimg) + assert warp_img.shape == (11, 11, 3) + + +def test_plantcv_transform_gamma_correct(): + # Read in test data + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MARKER)) + # Test + gamma_corrected = pcv.transform.gamma_correct(img=img, gamma=2, gain=1) + imgavg = np.average(img) + correctedavg = np.average(gamma_corrected) + assert correctedavg != imgavg + + # ############################## # Tests for the threshold subpackage # ############################## -def test_plantcv_threshold_binary(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_binary") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir +@pytest.mark.parametrize("objtype", ["dark", "light"]) +def test_plantcv_threshold_binary(objtype): # Read in test data gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) # Test with object type = dark pcv.params.debug = None - _ = pcv.threshold.binary(gray_img=gray_img, threshold=25, max_value=255, object_type="dark") - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.threshold.binary(gray_img=gray_img, threshold=25, max_value=255, object_type="light") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.threshold.binary(gray_img=gray_img, threshold=25, max_value=255, object_type="light") - # Test with debug = None - pcv.params.debug = None - binary_img = pcv.threshold.binary(gray_img=gray_img, threshold=25, max_value=255, object_type="light") + binary_img = pcv.threshold.binary(gray_img=gray_img, threshold=25, max_value=255, object_type=objtype) # Assert that the output image has the dimensions of the input image if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): # Assert that the image is binary @@ -5764,25 +5763,13 @@ def test_plantcv_threshold_binary_incorrect_object_type(): _ = pcv.threshold.binary(gray_img=gray_img, threshold=25, max_value=255, object_type="lite") -def test_plantcv_threshold_gaussian(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_gaussian") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir +@pytest.mark.parametrize("objtype", ["dark", "light"]) +def test_plantcv_threshold_gaussian(objtype): # Read in test data gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) # Test with object type = dark pcv.params.debug = None - _ = pcv.threshold.gaussian(gray_img=gray_img, max_value=255, object_type="dark") - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.threshold.gaussian(gray_img=gray_img, max_value=255, object_type="light") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.threshold.gaussian(gray_img=gray_img, max_value=255, object_type="light") - # Test with debug = None - pcv.params.debug = None - binary_img = pcv.threshold.gaussian(gray_img=gray_img, max_value=255, object_type="light") + binary_img = pcv.threshold.gaussian(gray_img=gray_img, max_value=255, object_type=objtype) # Assert that the output image has the dimensions of the input image if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): # Assert that the image is binary @@ -5801,25 +5788,13 @@ def test_plantcv_threshold_gaussian_incorrect_object_type(): _ = pcv.threshold.gaussian(gray_img=gray_img, max_value=255, object_type="lite") -def test_plantcv_threshold_mean(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_mean") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir +@pytest.mark.parametrize("objtype", ["dark", "light"]) +def test_plantcv_threshold_mean(objtype): # Read in test data gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) # Test with object type = dark pcv.params.debug = None - _ = pcv.threshold.mean(gray_img=gray_img, max_value=255, object_type="dark") - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.threshold.mean(gray_img=gray_img, max_value=255, object_type="light") - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.threshold.mean(gray_img=gray_img, max_value=255, object_type="light") - # Test with debug = None - pcv.params.debug = None - binary_img = pcv.threshold.mean(gray_img=gray_img, max_value=255, object_type="light") + binary_img = pcv.threshold.mean(gray_img=gray_img, max_value=255, object_type=objtype) # Assert that the output image has the dimensions of the input image if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): # Assert that the image is binary @@ -5838,25 +5813,13 @@ def test_plantcv_threshold_mean_incorrect_object_type(): _ = pcv.threshold.mean(gray_img=gray_img, max_value=255, object_type="lite") -def test_plantcv_threshold_otsu(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_otsu") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir +@pytest.mark.parametrize("objtype", ["dark", "light"]) +def test_plantcv_threshold_otsu(objtype): # Read in test data gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GREENMAG), -1) # Test with object set to light pcv.params.debug = None - _ = pcv.threshold.otsu(gray_img=gray_img, max_value=255, object_type="light") - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.threshold.otsu(gray_img=gray_img, max_value=255, object_type='dark') - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.threshold.otsu(gray_img=gray_img, max_value=255, object_type='dark') - # Test with debug = None - pcv.params.debug = None - binary_img = pcv.threshold.otsu(gray_img=gray_img, max_value=255, object_type='dark') + binary_img = pcv.threshold.otsu(gray_img=gray_img, max_value=255, object_type=objtype) # Assert that the output image has the dimensions of the input image if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): # Assert that the image is binary @@ -5875,27 +5838,35 @@ def test_plantcv_threshold_otsu_incorrect_object_type(): _ = pcv.threshold.otsu(gray_img=gray_img, max_value=255, object_type="lite") -def test_plantcv_threshold_custom_range(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir +@pytest.mark.parametrize("channel,lower_thresh,upper_thresh", [["HSV", [0, 0, 0], [255, 255, 255]], + ["LAB", [0, 0, 0], [255, 255, 255]], + ["RGB", [0, 0, 0], [255, 255, 255]], + ["GRAY", [0], [255]]]) +def test_plantcv_threshold_custom_range_rgb(channel, lower_thresh, upper_thresh): # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + # Test with debug = None + pcv.params.debug = None + mask, binary_img = pcv.threshold.custom_range(img, lower_thresh=lower_thresh, upper_thresh=upper_thresh, + channel=channel) + # Assert that the output image has the dimensions of the input image + if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): + # Assert that the image is binary + if all([i == j] for i, j in zip(np.unique(binary_img), [0, 255])): + assert 1 + else: + assert 0 + else: + assert 0 + + +def test_plantcv_threshold_custom_range_grayscale(): + # Read in test data gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) - # Test with debug = "print" - pcv.params.debug = 'print' - # Test channel='gray' - _, _ = pcv.threshold.custom_range(img, lower_thresh=[0], upper_thresh=[255], channel='gray') - _, _ = pcv.threshold.custom_range(gray_img, lower_thresh=[0], upper_thresh=[255], channel='gray') - # Test channel='HSV' - _, _ = pcv.threshold.custom_range(img, lower_thresh=[0, 0, 0], upper_thresh=[255, 255, 255], channel='HSV') - # Test channel='LAB' - _, _ = pcv.threshold.custom_range(img, lower_thresh=[0, 0, 0], upper_thresh=[255, 255, 255], channel='LAB') - pcv.params.debug = 'plot' - # Test channel='RGB' - mask, binary_img = pcv.threshold.custom_range(img, lower_thresh=[0, 0, 0], upper_thresh=[255, 255, 255], - channel='RGB') + # Test with debug = None + pcv.params.debug = None + # # Test channel='gray' + mask, binary_img = pcv.threshold.custom_range(gray_img, lower_thresh=[0], upper_thresh=[255], channel='gray') # Assert that the output image has the dimensions of the input image if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): # Assert that the image is binary @@ -5908,10 +5879,6 @@ def test_plantcv_threshold_custom_range(): def test_plantcv_threshold_custom_range_bad_input_hsv(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) with pytest.raises(RuntimeError): @@ -5919,21 +5886,14 @@ def test_plantcv_threshold_custom_range_bad_input_hsv(): def test_plantcv_threshold_custom_range_bad_input_rgb(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir # Read in test data + pcv.params.debug = None img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) with pytest.raises(RuntimeError): _, _ = pcv.threshold.custom_range(img, lower_thresh=[0, 0], upper_thresh=[2, 2, 2, 2], channel='RGB') def test_plantcv_threshold_custom_range_bad_input_lab(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) with pytest.raises(RuntimeError): @@ -5941,10 +5901,6 @@ def test_plantcv_threshold_custom_range_bad_input_lab(): def test_plantcv_threshold_custom_range_bad_input_gray(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) with pytest.raises(RuntimeError): @@ -5952,37 +5908,23 @@ def test_plantcv_threshold_custom_range_bad_input_gray(): def test_plantcv_threshold_custom_range_bad_input_channel(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_range") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir # Read in test data img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) with pytest.raises(RuntimeError): _, _ = pcv.threshold.custom_range(img, lower_thresh=[0], upper_thresh=[2], channel='CMYK') -def test_plantcv_threshold_saturation(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_saturation") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir +@pytest.mark.parametrize("channel", ["all", "any"]) +def test_plantcv_threshold_saturation(channel): # Read in test data rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.threshold.saturation(rgb_img=rgb_img, threshold=254, channel="all") - # Test with debug = "plot" - pcv.params.debug = "plot" - thresh = pcv.threshold.saturation(rgb_img=rgb_img, threshold=254, channel="any") - assert np.sum(thresh) == 920050455 and len(np.unique(thresh)) == 2 + # Test with debug = None + pcv.params.debug = None + thresh = pcv.threshold.saturation(rgb_img=rgb_img, threshold=254, channel=channel) + assert len(np.unique(thresh)) == 2 def test_plantcv_threshold_saturation_bad_input(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_saturation_bad_input") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir # Read in test data rgb_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) with pytest.raises(RuntimeError): @@ -5996,14 +5938,12 @@ def test_plantcv_threshold_triangle(): pcv.params.debug_outdir = cache_dir # Read in test data gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) - # Test with debug = "print" - pcv.params.debug = "print" + + pcv.params.debug = None _ = pcv.threshold.triangle(gray_img=gray_img, max_value=255, object_type="dark", xstep=10) - # Test with debug = "plot" pcv.params.debug = "plot" _ = pcv.threshold.triangle(gray_img=gray_img, max_value=255, object_type="light", xstep=10) - # Test with debug = None - pcv.params.debug = None + pcv.params.debug = "print" binary_img = pcv.threshold.triangle(gray_img=gray_img, max_value=255, object_type="light", xstep=10) # Assert that the output image has the dimensions of the input image if all([i == j] for i, j in zip(np.shape(binary_img), TEST_GRAY_DIM)): @@ -6024,10 +5964,8 @@ def test_plantcv_threshold_triangle_incorrect_object_type(): def test_plantcv_threshold_texture(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_threshold_texture") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir + # Test with debug = None + pcv.params.debug = None gray_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY_SMALL), -1) binary_img = pcv.threshold.texture(gray_img, ksize=6, threshold=7, offset=3, texture_method='dissimilarity', borders='nearest', max_value=255) @@ -6042,59 +5980,115 @@ def test_plantcv_threshold_texture(): assert 0 +def create_test_img(sz_img): + img = np.random.randint(np.prod(sz_img), size=sz_img) * 255 + img = img.astype(np.uint8) + return img + + +def create_test_img_bin(sz_img): + img = np.zeros(sz_img) + img[3:7, 2:8] = 1 + return img + + +@pytest.mark.parametrize("bad_type", ["native", "nan", "inf"]) +def test_plantcv_threshold_mask_bad(bad_type): + # Create a synthetic bad image + bad_img = np.reshape(np.random.rand(25), (5, 5)) + bad_img[2, 2] = np.inf + bad_img[2, 3] = np.nan + sz = np.shape(bad_img) + pcv.params.debug = None + mask = pcv.threshold.mask_bad(bad_img, bad_type=bad_type) + assert((np.shape(mask) == sz) and (len(np.unique(mask)) == 2)) + + +def test_plantcv_threshold_mask_bad_native_bad_input(): + # Create a synthetic bad image + bad_img = np.reshape(np.random.rand(25), (5, 5)) + sz = np.shape(bad_img) + mask10 = pcv.threshold.mask_bad(bad_img, bad_type='native') + + assert mask10.all() == np.zeros(sz, dtype='uint8').all() + + +def test_plantcv_threshold_mask_bad_nan_bad_input(): + # Create a synthetic bad image + bad_img = np.reshape(np.random.rand(25), (5, 5)) + bad_img[2, 2] = np.inf + sz = np.shape(bad_img) + mask11 = pcv.threshold.mask_bad(bad_img, bad_type='nan') + + assert mask11.all() == np.zeros(sz, dtype='uint8').all() + + +def test_plantcv_threshold_mask_bad_input_color_img(): + # Read in test data + bad_img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) + with pytest.raises(RuntimeError): + pcv.threshold.mask_bad(bad_img, bad_type='nan') + + # ################################### # Tests for the visualize subpackage # ################################### def test_plantcv_visualize_auto_threshold_methods_bad_input(): - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_auto_threshold_methods") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir + # Test with debug = None + pcv.params.debug = None img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) with pytest.raises(RuntimeError): _ = pcv.visualize.auto_threshold_methods(gray_img=img) def test_plantcv_visualize_auto_threshold_methods(): - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_auto_threshold_methods") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir + # Test with debug = None + pcv.params.debug = None img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) - pcv.params.debug = "print" - _ = pcv.visualize.auto_threshold_methods(gray_img=img) - pcv.params.debug = "plot" labeled_imgs = pcv.visualize.auto_threshold_methods(gray_img=img) assert len(labeled_imgs) == 5 and np.shape(labeled_imgs[0])[0] == np.shape(img)[0] -def test_plantcv_visualize_pseudocolor(): - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_pseudocolor") - os.mkdir(cache_dir) +@pytest.mark.parametrize("debug,axes", [["print", True], ["plot", False]]) +def test_plantcv_visualize_pseudocolor(debug, axes, tmpdir): + # Create a tmp directory + cache_dir = tmpdir.mkdir("sub") pcv.params.debug_outdir = cache_dir + # Input image img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) - contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") - obj_contour = contours_npz['arr_0'] - filename = os.path.join(cache_dir, 'plantcv_pseudo_image.png') - # Test with debug = "print" - pcv.params.debug = "print" - _ = pcv.visualize.pseudocolor(gray_img=img, mask=None) - _ = pcv.visualize.pseudocolor(gray_img=img, mask=None) + r, c = img.shape + # generate 200 "bad" pixels + mask_bad = np.zeros((r, c), dtype=np.uint8) + mask_bad = np.reshape(mask_bad, (-1, 1)) + mask_bad[0:100] = 255 + mask_bad = np.reshape(mask_bad, (r, c)) + # Debug mode + pcv.params.debug = debug + pseudo_img = pcv.visualize.pseudocolor(gray_img=img, mask=None, title="Pseudocolored image", axes=axes, + bad_mask=mask_bad) + # Assert that the output image has the dimensions of the input image + assert all([i == j] for i, j in zip(np.shape(pseudo_img), TEST_BINARY_DIM)) - pimg = pcv.visualize.pseudocolor(gray_img=img, mask=mask, min_value=10, max_value=200) - pcv.print_image(pimg, filename) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.visualize.pseudocolor(gray_img=img, mask=mask, background="image") - _ = pcv.visualize.pseudocolor(gray_img=img, mask=mask, background="image", title="customized title") - _ = pcv.visualize.pseudocolor(gray_img=img, mask=None) - _ = pcv.visualize.pseudocolor(gray_img=img, mask=mask, background="black", obj=obj_contour, axes=False, - colorbar=False) - _ = pcv.visualize.pseudocolor(gray_img=img, mask=mask, background="image", obj=obj_contour, obj_padding=15) - _ = pcv.visualize.pseudocolor(gray_img=img, mask=None, axes=False, colorbar=False) + +@pytest.mark.parametrize("bkgrd,axes,pad", [["image", True, "auto"], ["white", False, 1], ["black", True, "auto"]]) +def test_plantcv_visualize_pseudocolor_mask(bkgrd, axes, pad): # Test with debug = None pcv.params.debug = None - _ = pcv.visualize.pseudocolor(gray_img=img, mask=None) - pseudo_img = pcv.visualize.pseudocolor(gray_img=img, mask=mask, background="white") + # Input image + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Input mask + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1) + # Input contours + contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS), encoding="latin1") + obj_contour = contours_npz['arr_0'] + r, c = img.shape + # generate 200 "bad" pixels + mask_bad = np.zeros((r, c), dtype=np.uint8) + mask_bad = np.reshape(mask_bad, (-1, 1)) + mask_bad[0:100] = 255 + mask_bad = np.reshape(mask_bad, (r, c)) + pseudo_img = pcv.visualize.pseudocolor(gray_img=img, obj=obj_contour, mask=mask, background=bkgrd, + bad_mask=mask_bad, title="Pseudocolored image", axes=axes, obj_padding=pad) # Assert that the output image has the dimensions of the input image if all([i == j] for i, j in zip(np.shape(pseudo_img), TEST_BINARY_DIM)): assert 1 @@ -6133,26 +6127,21 @@ def test_plantcv_visualize_pseudocolor_bad_padding(): _ = pcv.visualize.pseudocolor(gray_img=img, mask=mask, obj=obj_contour, obj_padding="pink") -def test_plantcv_visualize_colorize_masks(): - # Test cache directory - cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_naive_bayes_classifier") - os.mkdir(cache_dir) - pcv.params.debug_outdir = cache_dir - # Read in test data - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - # Test with debug = "print" - pcv.params.debug = "print" - mask = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS)) - _ = pcv.visualize.colorize_masks(masks=[mask['plant'], mask['background']], - colors=[(0, 0, 0), (1, 1, 1)]) - # Test with debug = "plot" - pcv.params.debug = "plot" - _ = pcv.visualize.colorize_masks(masks=[mask['plant'], mask['background']], - colors=[(0, 0, 0), (1, 1, 1)]) +def test_plantcv_visualize_pseudocolor_bad_mask(): + # Test with debug = None + pcv.params.debug = None + + +@pytest.mark.parametrize('colors', [['red', 'blue'], [(0, 0, 255), (255, 0, 0)]]) +def test_plantcv_visualize_colorize_masks(colors): # Test with debug = None pcv.params.debug = None - colored_img = pcv.visualize.colorize_masks(masks=[mask['plant'], mask['background']], - colors=['red', 'blue']) + # Create test data + mask1 = np.zeros((100, 100), dtype=np.uint8) + mask2 = np.copy(mask1) + mask1[0:50, 0:50] = 255 + mask2[50:100, 50:100] = 255 + colored_img = pcv.visualize.colorize_masks(masks=[mask1, mask2], colors=colors) # Assert that the output image has the dimensions of the input image assert not np.average(colored_img) == 0 @@ -6163,23 +6152,30 @@ def test_plantcv_visualize_colorize_masks_bad_input_empty(): def test_plantcv_visualize_colorize_masks_bad_input_mismatch_number(): - # Read in test data - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - # Test with debug = "print" - pcv.params.debug = "print" - mask = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS)) + # Create test data + mask1 = np.zeros((100, 100), dtype=np.uint8) + mask2 = np.copy(mask1) + mask1[0:50, 0:50] = 255 + mask2[50:100, 50:100] = 255 with pytest.raises(RuntimeError): - _ = pcv.visualize.colorize_masks(masks=[mask['plant'], mask['background']], colors=['red', 'green', 'blue']) + _ = pcv.visualize.colorize_masks(masks=[mask1, mask2], colors=['red', 'green', 'blue']) def test_plantcv_visualize_colorize_masks_bad_color_input(): - # Read in test data - img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR)) - # Test with debug = "print" - pcv.params.debug = "print" - mask = pcv.naive_bayes_classifier(rgb_img=img, pdf_file=os.path.join(TEST_DATA, TEST_PDFS)) + # Create test data + mask1 = np.zeros((100, 100), dtype=np.uint8) + mask2 = np.copy(mask1) + mask1[0:50, 0:50] = 255 + mask2[50:100, 50:100] = 255 with pytest.raises(RuntimeError): - _ = pcv.visualize.colorize_masks(masks=[mask['plant'], mask['background']], colors=['red', 1.123]) + _ = pcv.visualize.colorize_masks(masks=[mask1, mask2], colors=['red', 1.123]) + + +def test_plantcv_visualize_colorize_label_img(): + label_img = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + pcv.params.debug = None + colored_img = pcv.visualize.colorize_label_img(label_img) + assert (colored_img.shape[0:-1] == label_img.shape) and colored_img.shape[-1] == 3 @pytest.mark.parametrize("bins,lb,ub,title", [[200, 0, 255, "Include Title"], [100, None, None, None]]) @@ -6234,6 +6230,46 @@ def test_plantcv_visualize_histogram_array(): _ = pcv.visualize.histogram(img=img[0, :]) +@pytest.mark.parametrize("wavelengths", [[], [390, 500, 640, 992, 990]]) +def test_plantcv_visualize_hyper_histogram(wavelengths): + # Test with debug = None + pcv.params.debug = None + + # Read in test data + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array = pcv.hyperspectral.read_data(filename=spectral_filename) + mask = np.ones((array.lines, array.samples)) + + fig_hist = pcv.visualize.hyper_histogram(array, mask, wvlengths=wavelengths, title="Hyper Histogram Test") + assert isinstance(fig_hist, ggplot) + + +def test_plantcv_visualize_hyper_histogram_wv_out_range(): + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array = pcv.hyperspectral.read_data(filename=spectral_filename) + with pytest.raises(RuntimeError): + _ = pcv.visualize.hyper_histogram(array, wvlengths=[200, 550]) + + +def test_plantcv_visualize_hyper_histogram_extreme_wvs(): + # Test with debug = None + pcv.params.debug = None + + # Read in test data + spectral_filename = os.path.join(HYPERSPECTRAL_TEST_DATA, HYPERSPECTRAL_DATA) + array = pcv.hyperspectral.read_data(filename=spectral_filename) + mask = np.ones((array.lines, array.samples)) + + wv_keys = list(array.wavelength_dict.keys()) + wavelengths = [250, 270, 1800, 2500] + # change first 4 keys + for (k_, k) in zip(wv_keys[0:5], wavelengths): + array.wavelength_dict[k] = array.wavelength_dict.pop(k_) + array.min_wavelength, array.max_wavelength = min(array.wavelength_dict), max(array.wavelength_dict) + fig_hist = pcv.visualize.hyper_histogram(array, mask, wvlengths=wavelengths) + assert isinstance(fig_hist, ggplot) + + def test_plantcv_visualize_clustered_contours(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_plot_hist") @@ -6253,13 +6289,13 @@ def test_plantcv_visualize_clustered_contours(): # Reset the saved color scale (can be saved between tests) pcv.params.saved_color_scale = None _ = pcv.visualize.clustered_contours(img=img1, grouped_contour_indices=cluster, roi_objects=objs, - roi_obj_hierarchy=obj_hierarchy, bounding=False) + roi_obj_hierarchy=obj_hierarchy, bounding=False) # Test in print mode pcv.params.debug = "print" # Reset the saved color scale (can be saved between tests) pcv.params.saved_color_scale = None cluster_img = pcv.visualize.clustered_contours(img=img, grouped_contour_indices=cluster, roi_objects=objs, - roi_obj_hierarchy=obj_hierarchy, nrow=2, ncol=2, bounding=True) + roi_obj_hierarchy=obj_hierarchy, nrow=2, ncol=2, bounding=True) assert np.sum(cluster_img) > np.sum(img) @@ -6396,9 +6432,28 @@ def test_plantcv_visualize_click_count(): counter.onclick(e2_) assert len(counter.events) == 4 + +@pytest.mark.parametrize("num,expected", [[100, 35], [30, 33]]) +def test_plantcv_visualize_size(num, expected): + pcv.params.debug = None + img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_LEAF_MASK), -1) + visualization = pcv.visualize.obj_sizes(img=img, mask=img, num_objects=num) + # Output unique colors are the 32 objects, the gray text, the black background, and white unlabeled leaves + assert len(np.unique(visualization.reshape(-1, visualization.shape[2]), axis=0)) == expected + + +@pytest.mark.parametrize("title", ["Include Title", None]) +def test_plantcv_visualize_obj_size_ecdf(title): + pcv.params.debug = None + mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_MASK), -1) + fig_ecdf = plantcv.plantcv.visualize.obj_size_ecdf(mask=mask, title=title) + assert isinstance(fig_ecdf, ggplot) + + # ############################## # Tests for the utils subpackage # ############################## + def test_plantcv_utils_json2csv(): # Test cache directory cache_dir = os.path.join(TEST_TMPDIR, "test_plantcv_utils_json2csv") From 2fbdd9ee11505c25c08d1ab53e624d17b7d668a5 Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Tue, 21 Sep 2021 10:28:15 -0500 Subject: [PATCH 033/178] revert back to integer conversion code debugged testing image, so changing back to int conversion on centers --- plantcv/plantcv/detect_discs.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/plantcv/plantcv/detect_discs.py b/plantcv/plantcv/detect_discs.py index 280378181..f5975f400 100644 --- a/plantcv/plantcv/detect_discs.py +++ b/plantcv/plantcv/detect_discs.py @@ -38,9 +38,8 @@ def detect_discs(bin_img, ecc_thresh=0): for i,obj in enumerate(obj_measures): if obj.eccentricity < ecc_thresh: # Convert coord values to int - #coords = tuple(map(int, obj.centroid)) - #discs_coor.append(coords) - discs_coor.append(obj.centroid) + coords = tuple(map(int, obj.centroid)) + discs_coor.append(coords) discs_mask = discs_mask + (labeled_img == i+1) _debug(visual=255*discs_mask, filename=os.path.join(params.debug_outdir, From dbfe6423a032b2f240abd04cb019802540e57aa0 Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Tue, 21 Sep 2021 10:44:10 -0500 Subject: [PATCH 034/178] Create interactive_ClickCount_tutorial.md binder tutorials don't exist yet, so i might need to rename links once I actually create them --- docs/tutorials/interactive_ClickCount_tutorial.md | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 docs/tutorials/interactive_ClickCount_tutorial.md diff --git a/docs/tutorials/interactive_ClickCount_tutorial.md b/docs/tutorials/interactive_ClickCount_tutorial.md new file mode 100644 index 000000000..5dbdbfdd3 --- /dev/null +++ b/docs/tutorials/interactive_ClickCount_tutorial.md @@ -0,0 +1,6 @@ +## Tutorial: Seed Analysis Workflow + + +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/danforthcenter/plantcv-binder.git/master?filepath=notebooks/interactive_seed_count/interactive_seed_count_workflow.ipynb) Check out our interactive seed analysis tutorial! + + From f7ec1bf284a669aabe6da880c37142f4081e8a46 Mon Sep 17 00:00:00 2001 From: HaleySchuhl Date: Tue, 21 Sep 2021 10:48:53 -0500 Subject: [PATCH 035/178] add seed count tutorial --- docs/tutorials.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/docs/tutorials.md b/docs/tutorials.md index 71e95958c..1b8985fb3 100644 --- a/docs/tutorials.md +++ b/docs/tutorials.md @@ -278,6 +278,22 @@ + +     +
    + + Interactive Seed Count tutorial + +
    +
    Interactive Seed Count Tutorial
    +
    +
    + Tags: seed, seed analysis, human-in-the-loop, RGB, seed count, camilina +
    +
    + +
    +