Merge pull request #343 from danforthcenter/cluster_functions

Cluster function show_grid

docs/cluster_contours.md

@@ -2,7 +2,7 @@
 
 This function take a image with multiple contours and clusters them based on user input of rows and columns
 
-**platncv.cluster_contours**(*img, roi_objects, roi_obj_hierarchy, nrow=1,ncol=1*)
+**platncv.cluster_contours**(*img, roi_objects, roi_obj_hierarchy, nrow=1,ncol=1, show_grid=False*)
 
 **returns** grouped_contour_indexes, contours, hierarchy
 
@@ -12,6 +12,7 @@ This function take a image with multiple contours and clusters them based on use
  - roi_obj_hierarchy - object hierarchy
  - nrow - approximate number of rows (default nrow=1)
  - ncol - approximate number of columns (default ncol=1)
+ - show_grid - if True then a grid gets displayed in debug mode (default show_grid=False)
 - **Context:**
  - Cluster contours based on number of approximate rows and columns
 - **Example use:**
@@ -32,9 +33,14 @@ pcv.params.debug = "print"
 
 # clusters them based on user input of rows and columns
 clusters_i, contours, hierarchy = pcv.cluster_contours(img, roi_objects, roi_obj_hierarchy, 4, 6)
+clusters_i, contours, hierarchy = pcv.cluster_contours(img, roi_objects, roi_obj_hierarchy, 4, 6, show_grid=True)
 ```
 
 **Cluster Contour Image**
 
 ![Screenshot](img/documentation_images/cluster_contour/14_clusters.jpg)
 
+**Cluster Contour Image with Grid**
+
+![Screenshot](img/documentation_images/cluster_contour/show_grid.jpg)
+

docs/multi-plant_tutorial.md

@@ -325,6 +325,8 @@ roi_objects, roi_obj_hierarchy, kept_mask, obj_area = pcv.roi_objects(img1, 'par
 # roi_obj_hierarchy = object hierarchy
 # nrow = number of rows to cluster (this should be the approximate number of desired rows in the entire image even if there isn't a literal row of plants)
 # ncol = number of columns to cluster (this should be the approximate number of desired columns in the entire image even if there isn't a literal row of plants)
+# show_grid = if True then a grid gets displayed in debug mode (default show_grid=False)
+
 
 clusters_i, contours, hierarchies = pcv.cluster_contours(img1, roi_objects, roi_obj_hierarchy, 4, 6)
 ```
@@ -561,6 +563,7 @@ def main():
  # roi_obj_hierarchy = object hierarchy
  # nrow = number of rows to cluster (this should be the approximate number of desired rows in the entire image even if there isn't a literal row of plants)
  # ncol = number of columns to cluster (this should be the approximate number of desired columns in the entire image even if there isn't a literal row of plants)
+ # show_grid = if True then a grid gets displayed in debug mode (default show_grid=False)
 
  clusters_i, contours, hierarchies = pcv.cluster_contours(img1, roi_objects, roi_obj_hierarchy, 4, 6)
 

docs/updating.md

@@ -186,7 +186,7 @@ pages for more details on the input and output variable types.
 
 * pre v3.0dev2: device, cropped = **plantcv.auto_crop**(*device, img, objects, padding_x=0, padding_y=0, color='black', debug=None*)
 * post v3.0dev2: cropped = **plantcv.auto_crop**(*img, objects, padding_x=0, padding_y=0, color='black'*)
-* post v3.2: cropped = **plantcv.auto_crop**(*img, obj, padding_x=0, padding_y=0, color='black*) 
+* post v3.2: cropped = **plantcv.auto_crop**(*img, obj, padding_x=0, padding_y=0, color='black'*) 
 
 #### plantcv.background_subtraction
 
@@ -202,7 +202,7 @@ pages for more details on the input and output variable types.
 #### plantcv.canny_edge_detect
 
 ** pre v3.2: NA
-** post v3.2: bin_img = **plantcv.canny_edge_detect**(*img, mask = None, sigma=1.0, low_thresh=None, high_thresh=None, thickness=1, mask_color=None, use_quantiles=False*)
+** post v3.2: bin_img = **plantcv.canny_edge_detect**(*img, mask=None, sigma=1.0, low_thresh=None, high_thresh=None, thickness=1, mask_color=None, use_quantiles=False*)
 
 #### plantcv.cluster_contour_splitimg
 
@@ -213,6 +213,7 @@ pages for more details on the input and output variable types.
 
 * pre v3.0dev2: device, grouped_contour_indexes, contours, roi_obj_hierarchy = **plantcv.cluster_contours**(*device, img, roi_objects,roi_obj_hierarchy, nrow=1, ncol=1, debug=None*)
 * post v3.0dev2: grouped_contour_indexes, contours, roi_obj_hierarchy = **plantcv.cluster_contours**(*img, roi_objects, roi_obj_hierarchy, nrow=1, ncol=1*)
+* post v3.2: grouped_contour_indexes, contours, roi_obj_hierarchy = **plantcv.cluster_contours**(*img, roi_objects, roi_object_hierarchy, nrow=1, ncol=1, show_grid=False*)
 
 #### plantcv.color_palette
 
@@ -539,7 +540,7 @@ post v3.0: new_img = **plantcv.image_subtract**(*gray_img1, gray_img2*)
 #### plantcv.transform.find_color_card
 
 * pre v3.0: NA
-* post v3.0: df, start_coord, spacing = **plantcv.transofrm.find_color_card**(*rgb_img, threshold='adaptgauss', threshvalue=125, blurry=False, background='dark'*)
+* post v3.0: df, start_coord, spacing = **plantcv.transform.find_color_card**(*rgb_img, threshold='adaptgauss', threshvalue=125, blurry=False, background='dark'*)
 
 #### plantcv.transform.get_color_matrix
 

plantcv/plantcv/cluster_contours.py

@@ -7,7 +7,7 @@
 from plantcv.plantcv import params
 
 
-def cluster_contours(img, roi_objects, roi_obj_hierarchy, nrow=1, ncol=1):
+def cluster_contours(img, roi_objects, roi_obj_hierarchy, nrow=1, ncol=1, show_grid=False):
 
  """
  This function take a image with multiple contours and clusters them based on user input of rows and columns
@@ -20,8 +20,7 @@ def cluster_contours(img, roi_objects, roi_obj_hierarchy, nrow=1, ncol=1):
  in the entire image (even if there isn't a literal row of plants)
  ncol = number of columns to cluster (this should be the approximate number of desired columns
  in the entire image (even if there isn't a literal row of plants)
- file = output of filename from read_image function
- filenames = input txt file with list of filenames in order from top to bottom left to right
+ show_grid = if True then the grid will get plot to show how plants are being clustered
 
  Returns:
  grouped_contour_indexes = contours grouped
@@ -31,6 +30,7 @@ def cluster_contours(img, roi_objects, roi_obj_hierarchy, nrow=1, ncol=1):
  :param roi_objects: list
  :param nrow: int
  :param ncol: int
+ :param show_grid: bool
  :return grouped_contour_indexes: list
  :return contours: list
  :return roi_obj_hierarchy: list
@@ -123,7 +123,7 @@ def digitize(a, step):
 
  # Debug image is rainbow printed contours
 
- if params.debug == 'print':
+ if params.debug is not None:
  if len(np.shape(img)) == 3:
  img_copy = np.copy(img)
  else:
@@ -137,22 +137,14 @@ def digitize(a, step):
  pass
  else:
  cv2.drawContours(img_copy, roi_objects, a, rand_color[i], -1, hierarchy=roi_obj_hierarchy)
- print_image(img_copy, os.path.join(params.debug_outdir, str(params.device) + '_clusters.png'))
-
- elif params.debug == 'plot':
- if len(np.shape(img)) == 3:
- img_copy = np.copy(img)
- else:
- iy, ix = np.shape(img)
- img_copy = np.zeros((iy, ix, 3), dtype=np.uint8)
-
- rand_color = color_palette(len(coordlist))
- for i, x in enumerate(coordlist):
- for a in x:
- if roi_obj_hierarchy[0][a][3] > -1:
- pass
- else:
- cv2.drawContours(img_copy, roi_objects, a, rand_color[i], -1, hierarchy=roi_obj_hierarchy)
- plot_image(img_copy)
+ if show_grid:
+ for y in rbreaks:
+ cv2.line(img_copy, (0, y), (ix, y), (255, 0, 0), params.line_thickness)
+ for x in cbreaks:
+ cv2.line(img_copy, (x, 0), (x, iy), (255, 0, 0), params.line_thickness)
+ if params.debug=='print':
+ print_image(img_copy, os.path.join(params.debug_outdir, str(params.device) + '_clusters.png'))
+ elif params.debug=='plot':
+ plot_image(img_copy)
 
  return grouped_contour_indexes, contours, roi_obj_hierarchy

tests/tests.py

@@ -637,7 +637,7 @@ def test_plantcv_cluster_contours():
  # 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)
+ _ = 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)