add show_grid option to cluster_contours

- Added an option to show the grid that is getting used to cluster contours
- Added onto unit test
- Simplified plotting part of code
- Added an example to the documentation
- Appended new argument to updating.md
- Added the new argument to the multi-plant tutorial

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
 
@@ -360,6 +361,7 @@ post v3.0: new_img = **plantcv.image_subtract**(*gray_img1, gray_img2*)
 
 * pre v3.0dev2: bins, hist = **plantcv.plot_hist**(*img, name=False*)
 * post v3.0dev2: bins, hist = **plantcv.plot_hist**(*img, name=False*)
+* post v3.2: header, hist_data, hist_figure = **pcv.plot_hist**(*gray_img, mask=None, bins=256*)
 
 #### plantcv.plot_image
 
@@ -538,7 +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)