def segment_tangent_angle(segmented_img, objects, size):
""" Find 'tangent' angles in degrees of skeleton segments. Use `size` pixels on either end of
each segment to find a linear regression line, and calculate angle between the two lines
drawn per segment.
Inputs:
segmented_img = Segmented image to plot slope lines and intersection angles on
objects = List of contours
size = Size of ends used to calculate "tangent" lines
Returns:
labeled_img = Segmented debugging image with angles labeled
:param segmented_img: numpy.ndarray
:param objects: list
:param size: int
:return labeled_img: numpy.ndarray
"""
# Store debug
debug = params.debug
params.debug = None
labeled_img = segmented_img.copy()
intersection_angles = []
label_coord_x = []
label_coord_y = []
rand_color = color_palette(len(objects))
for i, cnt in enumerate(objects):
find_tangents = np.zeros(segmented_img.shape[:2], np.uint8)
cv2.drawContours(find_tangents, objects, i, 255, 1, lineType=8)
cv2.drawContours(labeled_img, objects, i, rand_color[i], params.line_thickness, lineType=8)
pruned_segment = _iterative_prune(find_tangents, size)
segment_ends = find_tangents - pruned_segment
segment_end_obj, segment_end_hierarchy = find_objects(segment_ends, segment_ends)
slopes = []
for j, obj in enumerate(segment_end_obj):
# Find bounds for regression lines to get drawn
rect = cv2.minAreaRect(cnt)
pts = cv2.boxPoints(rect)
df = pd.DataFrame(pts, columns=('x', 'y'))
x_max = int(df['x'].max())
x_min = int(df['x'].min())
# Find line fit to each segment
[vx, vy, x, y] = cv2.fitLine(obj, cv2.DIST_L2, 0, 0.01, 0.01)
slope = -vy / vx
left_list = int(((x - x_min) * slope) + y)
right_list = int(((x - x_max) * slope) + y)
slopes.append(slope)
if slope > 1000000 or slope < -1000000:
print("Slope of contour with ID#", i, "is", slope, "and cannot be plotted.")
else:
# Draw slope lines
cv2.line(labeled_img, (x_max - 1, right_list), (x_min, left_list), rand_color[i], 1)
if len(slopes) < 2:
# If size*2>len(obj) then pruning will remove the segment completely, and
# makes segment_end_objs contain just one contour.
print("Size too large, contour with ID#", i, "got pruned away completely.")
intersection_angles.append("NA")
else:
# Calculate intersection angles
slope1 = slopes[0][0]
slope2 = slopes[1][0]
intersection_angle = _slope_to_intesect_angle(slope1, slope2)
intersection_angles.append(intersection_angle)
# Store coordinates for labels
label_coord_x.append(objects[i][0][0][0])
label_coord_y.append(objects[i][0][0][1])
segment_ids = []
for i, cnt in enumerate(objects):
# Label slope lines
w = label_coord_x[i]
h = label_coord_y[i]
if type(intersection_angles[i]) is str:
text = "{}".format(intersection_angles[i])
else:
text = "{:.2f}".format(intersection_angles[i])
cv2.putText(img=labeled_img, text=text, org=(w, h), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=params.text_size, color=(150, 150, 150), thickness=params.text_thickness)
segment_label = "ID" + str(i)
segment_ids.append(i)
outputs.add_observation(variable='segment_tangent_angle', trait='segment tangent angle',
method='plantcv.plantcv.morphology.segment_tangent_angle', scale='degrees', datatype=list,
value=intersection_angles, label=segment_ids)
# Reset debug mode
params.debug = debug
# Auto-increment device
params.device += 1
if params.debug == 'print':
print_image(labeled_img, os.path.join(params.debug_outdir, str(params.device) + '_segment_tangent_angles.png'))
elif params.debug == 'plot':
plot_image(labeled_img)
return labeled_img
def segment_insertion_angle(skel_img, segmented_img, leaf_objects, stem_objects, size):
""" Find leaf insertion angles in degrees of skeleton segments. Fit a linear regression line to the stem.
Use `size` pixels on the portion of leaf next to the stem find a linear regression line,
and calculate angle between the two lines per leaf object.
Inputs:
skel_img = Skeletonized image
segmented_img = Segmented image to plot slope lines and intersection angles on
leaf_objects = List of leaf segments
stem_objects = List of stem segments
size = Size of inner leaf used to calculate slope lines
Returns:
labeled_img = Debugging image with angles labeled
:param skel_img: numpy.ndarray
:param segmented_img: numpy.ndarray
:param leaf_objects: list
:param stem_objects: list
:param size: int
:return labeled_img: numpy.ndarray
"""
# Store debug
debug = params.debug
params.debug = None
rows,cols = segmented_img.shape[:2]
labeled_img = segmented_img.copy()
segment_slopes = []
insertion_segments = []
insertion_hierarchies = []
intersection_angles = []
label_coord_x = []
label_coord_y = []
valid_segment = []
# Create a list of tip tuples to use for sorting
tips = find_tips(skel_img)
tips = dilate(tips, 3, 1)
tip_objects, tip_hierarchies = find_objects(tips, tips)
tip_tuples = []
for i, cnt in enumerate(tip_objects):
tip_tuples.append((cnt[0][0][0], cnt[0][0][1]))
rand_color = color_palette(len(leaf_objects))
for i, cnt in enumerate(leaf_objects):
# Draw leaf objects
find_segment_tangents = np.zeros(segmented_img.shape[:2], np.uint8)
cv2.drawContours(find_segment_tangents, leaf_objects, i, 255, 1, lineType=8)
# Prune back ends of leaves
pruned_segment = _iterative_prune(find_segment_tangents, size)
# Segment ends are the portions pruned off
segment_ends = find_segment_tangents - pruned_segment
segment_end_obj, segment_end_hierarchy = find_objects(segment_ends, segment_ends)
is_insertion_segment = []
if not len(segment_end_obj) == 2:
print("Size too large, contour with ID#", i, "got pruned away completely.")
else:
# The contour can have insertion angle calculated
valid_segment.append(cnt)
# Determine if a segment is leaf end or leaf insertion segment
for j, obj in enumerate(segment_end_obj):
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 np.sum(overlap_img) == 0:
insertion_segments.append(segment_end_obj[j])
insertion_hierarchies.append(segment_end_hierarchy[0][j])
# Store coordinates for labels
label_coord_x.append(leaf_objects[i][0][0][0])
label_coord_y.append(leaf_objects[i][0][0][1])
rand_color = color_palette(len(valid_segment))
for i, cnt in enumerate(valid_segment):
cv2.drawContours(labeled_img, valid_segment, i, rand_color[i], params.line_thickness, lineType=8)
# Plot stem segments
stem_img = np.zeros(segmented_img.shape[:2], np.uint8)
cv2.drawContours(stem_img, stem_objects, -1, 255, 2, lineType=8)
branch_pts = find_branch_pts(skel_img)
stem_img = stem_img + branch_pts
stem_img = closing(stem_img)
combined_stem, combined_stem_hier = find_objects(stem_img, stem_img)
# Make sure stem objects are a single contour
loop_count=0
while len(combined_stem) > 1 and loop_count<50:
loop_count += 1
stem_img = dilate(stem_img, 2, 1)
stem_img = closing(stem_img)
combined_stem, combined_stem_hier = find_objects(stem_img, stem_img)
if loop_count == 50:
fatal_error('Unable to combine stem objects.')
# Find slope of the stem
[vx, vy, x, y] = cv2.fitLine(combined_stem[0], cv2.DIST_L2, 0, 0.01, 0.01)
stem_slope = -vy / vx
stem_slope = stem_slope[0]
lefty = int((-x * vy / vx) + y)
righty = int(((cols - x) * vy / vx) + y)
cv2.line(labeled_img, (cols - 1, righty), (0, lefty), (150, 150, 150), 3)
rand_color = color_palette(len(insertion_segments))
for t, segment in enumerate(insertion_segments):
# Find line fit to each segment
[vx, vy, x, y] = cv2.fitLine(segment, cv2.DIST_L2, 0, 0.01, 0.01)
slope = -vy / vx
left_list = int((-x * vy / vx) + y)
right_list = int(((cols - x) * vy / vx) + y)
segment_slopes.append(slope[0])
# Draw slope lines if possible
if slope > 1000000 or slope < -1000000:
print("Slope of contour with ID#", t, "is", slope, "and cannot be plotted.")
else:
cv2.line(labeled_img, (cols - 1, right_list), (0, left_list), rand_color[t], 1)
# Store intersection angles between insertion segment and stem line
intersection_angle = _slope_to_intesect_angle(slope[0], stem_slope)
# Function measures clockwise but we want the acute angle between stem and leaf insertion
if intersection_angle > 90:
intersection_angle = 180 - intersection_angle
intersection_angles.append(intersection_angle)
segment_ids = []
for i, cnt in enumerate(insertion_segments):
# Label slope lines
w = label_coord_x[i]
h = label_coord_y[i]
text = "{:.2f}".format(intersection_angles[i])
cv2.putText(img=labeled_img, text=text, org=(w, h), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=params.text_size, color=(150, 150, 150), thickness=params.text_thickness)
segment_label = "ID" + str(i)
segment_ids.append(i)
outputs.add_observation(variable='segment_insertion_angle', trait='segment insertion angle',
method='plantcv.plantcv.morphology.segment_insertion_angle', scale='degrees', datatype=list,
value=intersection_angles, label=segment_ids)
# Reset debug mode
params.debug = debug
# Auto-increment device
params.device += 1
if params.debug == 'print':
print_image(labeled_img,
os.path.join(params.debug_outdir, str(params.device) + '_segment_insertion_angles.png'))
elif params.debug == 'plot':
plot_image(labeled_img)
return labeled_img
def prune(skel_img, size=0, mask=None):
"""
The pruning algorithm proposed by https://github.com/karnoldbio
Segments a skeleton into discrete pieces, prunes off all segments less than or
equal to user specified size. Returns the remaining objects as a list and the
pruned skeleton.
Inputs:
skel_img = Skeletonized image
size = Size to get pruned off each branch
mask = (Optional) binary mask for debugging. If provided, debug image will be overlaid on the mask.
Returns:
pruned_img = Pruned image
segmented_img = Segmented debugging image
segment_objects = List of contours
:param skel_img: numpy.ndarray
:param size: int
:param mask: numpy.ndarray
:return pruned_img: numpy.ndarray
:return segmented_img: numpy.ndarray
:return segment_objects: list
"""
# Store debug
debug = params.debug
params.debug = None
pruned_img = skel_img.copy()
# Check to see if the skeleton has multiple objects
skel_objects, _ = find_objects(skel_img, skel_img)
_, objects = segment_skeleton(skel_img)
kept_segments = []
removed_segments = []
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)
# Keep segments longer than specified size
for i in range(0, len(secondary_objects)):
if len(secondary_objects[i]) > size:
kept_segments.append(secondary_objects[i])
else:
removed_segments.append(secondary_objects[i])
# Draw the contours that got removed
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)
# Reset debug mode
params.debug = debug
# Make debugging image
if mask is None:
pruned_plot = np.zeros(skel_img.shape[:2], np.uint8)
else:
pruned_plot = mask.copy()
pruned_plot = cv2.cvtColor(pruned_plot, cv2.COLOR_GRAY2RGB)
pruned_obj, pruned_hierarchy = find_objects(pruned_img, pruned_img)
cv2.drawContours(pruned_plot,
removed_segments,
-1, (0, 0, 255),
params.line_thickness,
lineType=8)
cv2.drawContours(pruned_plot,
pruned_obj,
-1, (150, 150, 150),
params.line_thickness,
lineType=8)
# Auto-increment device
params.device += 1
if params.debug == 'print':
print_image(
pruned_img,
os.path.join(params.debug_outdir,
str(params.device) + '_pruned.png'))
print_image(
pruned_plot,
os.path.join(params.debug_outdir,
str(params.device) + '_pruned_debug.png'))
elif params.debug == 'plot':
plot_image(pruned_img, cmap='gray')
plot_image(pruned_plot)
# Segment the pruned skeleton
segmented_img, segment_objects = segment_skeleton(pruned_img, mask)
return pruned_img, segmented_img, segment_objects