Merge pull request #933 from danforthcenter/morphology-segment_combin…

…e-update

Morphology segment combine update

docs/segment_combine.md

@@ -7,14 +7,14 @@ Manually combine segments from a segmented skeleton
 **returns** Labeled image, new segment objects list 
 
 - **Parameters:**
-    - segment_list - List of segments to get combined, or list of lists of segments to get combined
+    - segment_list - List of segments to get combined
     - objects - Segment objects (output from either [plantcv.morphology.prune](prune.md),,
     [plantcv.morphology.segment_skeleton](segment_skeleton.md), or
     [plantcv.morphology.segment_sort](segment_sort.md)).
     - mask - Binary mask for debugging images.
 
 - **Context:**
-    - Images with more complexity will often cause issues with the algorithms used in functions within the 
+    - Objects with more complexity will often cause issues with the algorithms used in functions within the 
     `plantcv.morphology` sub-package. However, it is still possible to collect accurate data from such images
     with a manual step such as `plantcv.morphology.segment_combine`. An example workflow would use 
     [plantcv.morphology.check_cycles](check_cycles.md) to identify images that have parts of plant that occlude 
@@ -37,13 +37,13 @@ pcv.params.text_thickness = 3
 
 # Plot an image with ID's labeled so the specific segment ID's can get used
 # to combine segments 
-segment_img, id_labeled_img = pcv.morphology.segment_id(pruned_img, segment_objects, mask)
+segment_img, id_labeled_img = pcv.morphology.segment_id(skel_img=pruned_img, objects=segment_objects, mask=mask)
 
 # Combine the segments of the really floppy leaf
-labeled_img, new_objects = pcv.morphology.segment_combine([12,6], segment_objects, mask)
+labeled_img, new_objects = pcv.morphology.segment_combine(segment_list=[12,6], objects=segment_objects, mask=mask)
 
 # Combine the segments of the leaf obscuring the floppy leaf 
-labeled_img2, new_objects = pcv.morphology.segment_combine([7,6], new_objects, mask)
+labeled_img2, new_objects = pcv.morphology.segment_combine(segment_list=[7,6], objects=new_objects, mask=mask)
 
 # Optionally, if all ID's were legible we could combine multiple segments in one step. 
 # Users can provide a list of lists in which each list contains segments that should 

plantcv/plantcv/morphology/segment_combine.py

@@ -13,15 +13,13 @@ def segment_combine(segment_list, objects, mask):
     """Combine user specified segments together.
 
     Inputs:
-    segment_list  = List of segments to get combined, or list of lists of segments to get combined
+    segment_list  = List of segment indices to get combined
     objects       = List of contours
-    hierarchy     = Contour hierarchy NumPy array
     mask          = Binary mask for debugging image
 
     Returns:
     segmented_img = Segmented image
     objects       = Updated list of contours
-    hierarchy     = Updated contour hierarchy NumPy array
 
     :param segment_list: list
     :param objects: list
@@ -32,54 +30,33 @@ def segment_combine(segment_list, objects, mask):
     label_coord_x = []
     label_coord_y = []
     all_objects = objects[:]
+    if type(segment_list[0]) is not int:
+        fatal_error("segment_list must be a list of object ID's")
+    segment_list_copy = sorted(segment_list, reverse=True)
 
     # If user provides a single list of objects to combine
-    if type(segment_list[0]) is int:
-        num_contours = len(segment_list)
-        count = 1
-
-        # Store the first object into the new object array
-        new_objects = all_objects[segment_list[0]]
-        # Remove the objects getting combined from the list of all objects
-        all_objects.remove(objects[segment_list[0]])
-
-        while count < num_contours:
-            # Combine objects into a single array
-            new_objects = np.append(new_objects, objects[segment_list[count]], 0)
-            # Remove the objects getting combined from the list of all objects
-            all_objects.remove(objects[segment_list[count]])
-            count += 1
-        # Replace with the combined object
-        all_objects.append(new_objects)
-
-    # If user provides a list of lists of objects to combine
-    elif type(segment_list[0]) is list:
-        # For each list provided
-        for lists in segment_list:
-            num_contours = len(lists)
-            count = 1
-            # Store the first object into the new object array
-            new_objects = all_objects[lists[0]]
-            # Remove the objects getting combined from the list of all objects
-            all_objects.remove(objects[lists[0]])
-
-            while count < num_contours:
-                # Combine objects into a single array
-                new_objects = np.append(new_objects, objects[lists[count]], 0)
-                # Remove the objects getting combined from the list of all objects
-                all_objects.remove(objects[lists[count]])
-                count += 1
-            # Add combined contour to list of all contours
-            all_objects.append(new_objects)
-    else:
-        fatal_error("segment_list must be a list of object ID's or a list of lists of ID's!")
+    num_contours = len(segment_list)
+    count = 1
+
+    # Store the first object into the new object array
+    combined_object = objects[segment_list_copy[0]]
+    # Remove the objects getting combined from the list of all objects
+    all_objects.pop(segment_list_copy[0])
+
+    while count < num_contours:
+        # Combine segments into a single object
+        combined_object = np.append(combined_object, objects[segment_list_copy[count]], 0)
+        # Remove the segment that was combined from the list of all objects
+        all_objects.pop(segment_list_copy[count])
+        count += 1
+    # Replace with the combined object
+    all_objects.append(combined_object)
 
     labeled_img = mask.copy()
     labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_GRAY2RGB)
 
     # Color each segment a different color, use a previously saved scale if available
     rand_color = color_palette(num=len(all_objects), saved=True)
-
     # Plot all segment contours
     for i, cnt in enumerate(all_objects):
         cv2.drawContours(labeled_img, all_objects[i], -1, rand_color[i], params.line_thickness, lineType=8)

tests/plantcv/morphology/test_segment_combine.py

@@ -12,14 +12,6 @@ def test_segment_combine(morphology_test_data):
     assert len(new_objects) + 1 == len(edges)
 
 
-def test_segment_combine_lists(morphology_test_data):
-    """Test for PlantCV."""
-    skel = cv2.imread(morphology_test_data.skel_img, -1)
-    edges = morphology_test_data.load_segments(morphology_test_data.segments_file, "edges")
-    # Test with list of lists input
-    _, new_objects = segment_combine(segment_list=[[0, 1, 2], [3, 4]], objects=edges, mask=skel)
-    assert len(new_objects) + 3 == len(edges)
-
 
 def test_segment_combine_bad_input(morphology_test_data):
     """Test for PlantCV."""