Merge pull request #1576 from danforthcenter/quick_cutto

Add "Quick Cutto" ROI function

plantcv/plantcv/roi/quick_filter.py

@@ -1,22 +1,29 @@
 """PlantCV quick_filter module."""
 import os
+import cv2
 import numpy as np
 from skimage.measure import label
+from skimage.color import label2rgb
 from plantcv.plantcv import params
 from plantcv.plantcv.roi import roi2mask
 from plantcv.plantcv._debug import _debug
+from plantcv.plantcv.logical_and import logical_and
 
 
 def quick_filter(mask, roi):
  """Quickly filter a binary mask using a region of interest.
- Inputs
- ------
- mask = binary mask
- roi = PlantCV ROI object
+
+ Parameters
+ ----------
+ mask : numpy.ndarray
+ Binary mask to filter.
+ roi : plantcv.plantcv.classes.Objects
+ PlantCV ROI object.
 
  Returns
  -------
- filtered_mask = Filtered binary mask
+ numpy.ndarray
+ Filtered binary mask.
  """
  # Increment the device counter
  params.device += 1
@@ -68,3 +75,46 @@ def quick_filter(mask, roi):
  _debug(visual=summed, filename=os.path.join(params.debug_outdir, f"{params.device}_roi_filter.png"), cmap="gray")
 
  return summed
+
+
+def _quick_cutto(mask, roi):
+ """Quickly filter a binary mask using a region of interest by cutting to each ROI.
+
+ Parameters
+ ----------
+ mask : numpy.ndarray
+ Binary mask to filter.
+ roi : plantcv.plantcv.classes.Objects
+ PlantCV ROI object.
+
+ Returns
+ -------
+ numpy.ndarray, numpy.ndarray, int
+ Filtered binary mask, labeled mask, number of labels.
+ """
+ # Increment the device counter
+ params.device += 1
+
+ # Store debug
+ debug = params.debug
+ params.debug = None
+
+ mask_copy = np.copy(mask).astype(np.int32)
+ labeled_mask = np.zeros(mask.shape, dtype=np.int32)
+ bin_mask = np.copy(labeled_mask)
+ num_labels = len(roi.contours)
+ for i in range(num_labels):
+ # Pixel intensity of (i+1) such that the first object has value
+ cv2.drawContours(labeled_mask, roi.contours[i], -1, (i+1), -1)
+ cv2.drawContours(bin_mask, roi.contours[i], -1, (255), -1)
+ cropped_mask = logical_and(mask_copy, bin_mask)
+ # Make a labeled mask from the cropped objects
+ label_mask_where = np.where(cropped_mask == 255, labeled_mask, 0)
+
+ # Print/plot debug image
+ colorful = label2rgb(label_mask_where)
+ colorful2 = (255*colorful).astype(np.uint8)
+ params.debug = debug
+ _debug(visual=colorful2, filename=os.path.join(params.debug_outdir, f"{params.device}_label_colored_mask.png"))
+
+ return cropped_mask.astype(np.uint8), label_mask_where, num_labels

tests/plantcv/roi/test_quick_filter.py

@@ -2,7 +2,7 @@
 import cv2
 import numpy as np
 from plantcv.plantcv import Objects
-from plantcv.plantcv.roi import quick_filter
+from plantcv.plantcv.roi.quick_filter import quick_filter, _quick_cutto
 
 
 def test_quick_filter(test_data):
@@ -19,3 +19,19 @@ def test_quick_filter(test_data):
  area = cv2.countNonZero(filtered_mask)
  # Assert that the contours were filtered as expected
  assert area == 221
+
+
+def test_quick_cutto(test_data):
+ """Test for PlantCV."""
+ # Read in test data
+ img = cv2.imread(test_data.small_rgb_img)
+ mask = np.zeros(np.shape(img)[:2], dtype=np.uint8)
+ cnt, cnt_str = test_data.load_contours(test_data.small_contours_file)
+ cv2.drawContours(mask, cnt, -1, (255), -1, lineType=8, hierarchy=cnt_str)
+ roi = [np.array([[[200, 200]], [[200, 190]], [[249, 190]], [[249, 200]]], dtype=np.int32)]
+ roi_str = np.array([[[-1, -1, -1, -1]]], dtype=np.int32)
+ roi_obj = Objects(contours=[roi], hierarchy=[roi_str])
+ filtered_mask, _, _ = _quick_cutto(mask=mask, roi=roi_obj)
+ area = cv2.countNonZero(filtered_mask)
+ # Assert that the contours were filtered as expected
+ assert area == 7