Merge pull request #433 from danforthcenter/misc_function_improvements

Misc function improvements

docs/segment_sort.md

@@ -2,14 +2,15 @@
 
 Sort segments from a skeletonized image into two categories: leaf objects and other objects. 
 
-**plantcv.morphology.segment_sort**(*skel_img, objects, mask=None*)
+**plantcv.morphology.segment_sort**(*skel_img, objects, mask=None, first_stem=True*)
 
 **returns** Secondary objects, primary objects
 
 - **Parameters:**
     - skel_img - Skeleton image (output from [plantcv.morphology.skeletonize](skeletonize.md))
     - objects - Segment objects (output from [plantcv.morphology.prune](prune.md), or [plantcv.morphology.segment_skeleton](segment_skeleton.md))
     - mask - Binary mask for debugging. If provided, debug image will be overlaid on the mask.
+    - first_stem - When True, the first segment (the bottom segment) gets classified as stem. If False, then the algorithm classification is applied to each segment. 
 - **Context:**
     - Sorts skeleton segments into two categories: primary and secondary segments. Segments get classified as primary 
     if both end points of the segment coincide with branch points. Segments get classified as secondary if at least one of their
@@ -34,12 +35,10 @@ pcv.params.debug = "print"
 pcv.params.line_thickness = 3 
 
 leaf_obj, other_obj = pcv.morphology.segment_sort(skel_img=skeleton,
-                                                  objects=obj,
-                                                  hierarchies=hier)
+                                                  objects=obj)
 
 leaf_obj, other_obj = pcv.morphology.segment_sort(skel_img=skeleton,
                                                   objects=obj,
-                                                  hierarchies=hier, 
                                                   mask=plant_mask)
 
 ```

plantcv/plantcv/morphology/__init__.py

@@ -1,12 +1,12 @@
 from plantcv.plantcv.morphology.find_branch_pts import find_branch_pts
-from plantcv.plantcv.morphology.segment_skeleton import segment_skeleton
 from plantcv.plantcv.morphology.find_tips import find_tips
+from plantcv.plantcv.morphology._iterative_prune import _iterative_prune
+from plantcv.plantcv.morphology.segment_skeleton import segment_skeleton
 from plantcv.plantcv.morphology.segment_sort import segment_sort
 from plantcv.plantcv.morphology.prune import prune
 from plantcv.plantcv.morphology.skeletonize import skeletonize
 from plantcv.plantcv.morphology.check_cycles import check_cycles
 from plantcv.plantcv.morphology.segment_skeleton import segment_skeleton
-from plantcv.plantcv.morphology._iterative_prune import _iterative_prune
 from plantcv.plantcv.morphology.segment_angle import segment_angle
 from plantcv.plantcv.morphology.segment_path_length import segment_path_length
 from plantcv.plantcv.morphology.segment_euclidean_length import segment_euclidean_length
@@ -18,4 +18,4 @@
 
 __all__ = ["find_branch_pts", "find_tips", "prune", "skeletonize", "check_cycles", "segment_skeleton", "segment_angle",
            "segment_path_length", "segment_euclidean_length", "segment_curvature", "segment_sort", "segment_id",
-           "segment_tangent_angle", "segment_insertion_angle", "segment_combine", "_iterative_prune"]
+           "segment_tangent_angle", "segment_insertion_angle", "segment_combine", "_iterative_prune"]

plantcv/plantcv/morphology/_iterative_prune.py

@@ -26,8 +26,6 @@ def _iterative_prune(skel_img, size):
 
     # Check to see if the skeleton has multiple objects
     objects, _ = find_objects(pruned_img, pruned_img)
-    if not len(objects) == 1:
-        print("Warning: Multiple objects detected! Pruning will further separate the difference pieces.")
 
     # Iteratively remove endpoints (tips) from a skeleton
     for i in range(0, size):

plantcv/plantcv/morphology/prune.py

@@ -8,9 +8,9 @@
 from plantcv.plantcv import print_image
 from plantcv.plantcv import find_objects
 from plantcv.plantcv import image_subtract
-from plantcv.plantcv.morphology import find_tips
 from plantcv.plantcv.morphology import segment_sort
 from plantcv.plantcv.morphology import segment_skeleton
+from plantcv.plantcv.morphology import _iterative_prune
 
 
 def prune(skel_img, size=0, mask=None):
@@ -46,8 +46,6 @@ def prune(skel_img, size=0, mask=None):
 
     # Check to see if the skeleton has multiple objects
     skel_objects, _ = find_objects(skel_img, skel_img)
-    if not len(skel_objects) == 1:
-        print("Warning: Multiple objects detected! Pruning will further separate the difference pieces.")
 
     _, objects = segment_skeleton(skel_img)
     kept_segments = []
@@ -56,7 +54,6 @@ def prune(skel_img, size=0, mask=None):
     if size>0:
         # If size>0 then check for segments that are smaller than size pixels long
 
-
         # Sort through segments since we don't want to remove primary segments
         secondary_objects, primary_objects = segment_sort(skel_img, objects)
 
@@ -71,8 +68,10 @@ def prune(skel_img, size=0, mask=None):
         removed_barbs = np.zeros(skel_img.shape[:2], np.uint8)
         cv2.drawContours(removed_barbs, removed_segments, -1, 255, 1,
                          lineType=8)
+
         # Subtract all short segments from the skeleton image
         pruned_img = image_subtract(pruned_img, removed_barbs)
+        pruned_img = _iterative_prune(pruned_img, 1)
 
     # Make debugging image
     if mask is None:

plantcv/plantcv/morphology/segment_id.py

@@ -41,7 +41,7 @@ def segment_id(skel_img, objects, mask=None):
 
     # Plot all segment contours
     for i, cnt in enumerate(objects):
-        cv2.drawContours(segmented_img, objects, i, rand_color[i], params.line_thickness, lineType=8)
+        cv2.drawContours(segmented_img, cnt, -1, rand_color[i], params.line_thickness, lineType=8)
         # Store coordinates for labels
         label_coord_x.append(objects[i][0][0][0])
         label_coord_y.append(objects[i][0][0][1])

plantcv/plantcv/morphology/segment_insertion_angle.py

@@ -8,6 +8,7 @@
 from plantcv.plantcv import closing
 from plantcv.plantcv import outputs
 from plantcv.plantcv import plot_image
+from plantcv.plantcv import logical_and
 from plantcv.plantcv import fatal_error
 from plantcv.plantcv import print_image
 from plantcv.plantcv import find_objects
@@ -86,26 +87,12 @@ def segment_insertion_angle(skel_img, segmented_img, leaf_objects, stem_objects,
             # Determine if a segment is leaf end or leaf insertion segment
             for j, obj in enumerate(segment_end_obj):
 
-                cnt_as_tuples = []
-                num_pixels = len(obj)
-                count = 0
-
-                # Turn each contour into a list of tuples (can't search for list of coords, so reformat)
-                while num_pixels > count:
-                    x_coord = obj[count][0][0]
-                    y_coord = obj[count][0][1]
-                    cnt_as_tuples.append((x_coord, y_coord))
-                    count += 1
-
-                for tip_tups in tip_tuples:
-                    # If a tip is inside the list of contour tuples then it is a leaf end segment
-                    if tip_tups in cnt_as_tuples:
-                        is_insertion_segment.append(False)
-                    else:
-                        is_insertion_segment.append(True)
+                segment_plot = np.zeros(segmented_img.shape[:2], np.uint8)
+                cv2.drawContours(segment_plot, obj, -1, 255, 1, lineType=8)
+                overlap_img = logical_and(segment_plot, tips)
 
                 # If none of the tips are within a segment_end then it's an insertion segment
-                if all(is_insertion_segment):
+                if np.sum(overlap_img) == 0:
                     insertion_segments.append(segment_end_obj[j])
                     insertion_hierarchies.append(segment_end_hierarchy[0][j])
 

plantcv/plantcv/morphology/segment_sort.py

@@ -10,13 +10,14 @@
 from plantcv.plantcv.morphology import find_tips
 
 
-def segment_sort(skel_img, objects, mask=None):
+def segment_sort(skel_img, objects, mask=None, first_stem=True):
     """ Calculate segment curvature as defined by the ratio between geodesic and euclidean distance
 
         Inputs:
         skel_img          = Skeletonized image
         objects           = List of contours
         mask              = (Optional) binary mask for debugging. If provided, debug image will be overlaid on the mask.
+        first_stem        = (Optional) if True, then the first (bottom) segment always gets classified as stem
 
         Returns:
         labeled_img       = Segmented debugging image with lengths labeled
@@ -25,8 +26,8 @@ def segment_sort(skel_img, objects, mask=None):
 
         :param skel_img: numpy.ndarray
         :param objects: list
-        :param labeled_img: numpy.ndarray
         :param mask: numpy.ndarray
+        :param first_stem: bool
         :return secondary_objects: list
         :return other_objects: list
         """
@@ -52,7 +53,7 @@ def segment_sort(skel_img, objects, mask=None):
         overlap_img = logical_and(segment_plot, tips_img)
 
         # The first contour is the base, and while it contains a tip, it isn't a leaf
-        if i == 0:
+        if i == 0 and first_stem:
             primary_objects.append(cnt)
 
         # Sort segments
@@ -81,4 +82,4 @@ def segment_sort(skel_img, objects, mask=None):
     elif params.debug == 'plot':
         plot_image(labeled_img)
 
-    return secondary_objects, primary_objects
+    return secondary_objects, primary_objects

tests/tests.py

@@ -3419,7 +3419,16 @@ def test_plantcv_morphology_segment_insertion_angle():
     pcv.params.debug = "print"
     insert_angles = pcv.morphology.segment_insertion_angle(pruned, segmented_img, leaf_obj, stem_obj, 10)
     pcv.print_results(os.path.join(cache_dir, "results.txt"))
-    assert len(pcv.outputs.observations['segment_insertion_angle']['value']) == 14
+    assert pcv.outputs.observations['segment_insertion_angle']['value'] == [24.97999120101794, 50.75442037373474,
+                                                                            56.45078448114704, 64.19513117863062,
+                                                                            45.146799092975584, 57.80220388909291,
+                                                                            66.1559145648012, 77.57112958360631,
+                                                                            39.245580536881675, 84.24558178912076,
+                                                                            84.24558178912076, 50.75442037373474,
+                                                                            26.337516798081822, 58.46112771993523,
+                                                                            39.245580536881675, 28.645972294617223,
+                                                                            35.371548466069214, 20.64797104069403,
+                                                                            62.89851538735208]
     pcv.outputs.clear()