def segment_skeleton(skel_img, mask=None):
""" Segment a skeleton image into pieces
Inputs:
skel_img = Skeletonized image
mask = (Optional) binary mask for debugging. If provided, debug image will be overlaid on the mask.
Returns:
segmented_img = Segmented debugging image
objects = list of contours
hierarchy = contour hierarchy list
:param skel_img: numpy.ndarray
:param mask: numpy.ndarray
:return segmented_img: numpy.ndarray
:return segment_objects: list
"return segment_hierarchies: numpy.ndarray
"""
# Store debug
debug = params.debug
params.debug = None
# Find branch points
bp = find_branch_pts(skel_img)
bp = dilate(bp, 3, 1)
# Subtract from the skeleton so that leaves are no longer connected
segments = image_subtract(skel_img, bp)
# Gather contours of leaves
segment_objects, _ = find_objects(segments, segments)
# Color each segment a different color
rand_color = color_palette(len(segment_objects))
if mask is None:
segmented_img = skel_img.copy()
else:
segmented_img = mask.copy()
segmented_img = cv2.cvtColor(segmented_img, cv2.COLOR_GRAY2RGB)
for i, cnt in enumerate(segment_objects):
cv2.drawContours(segmented_img, segment_objects, i, rand_color[i], params.line_thickness, lineType=8)
# Reset debug mode
params.debug = debug
# Auto-increment device
params.device += 1
if params.debug == 'print':
print_image(segmented_img, os.path.join(params.debug_outdir, str(params.device) + '_segmented.png'))
elif params.debug == 'plot':
plot_image(segmented_img)
return segmented_img, segment_objects
def split_lines(lines, max_scale):
labeled_lines, num_of_labeles = bwlabel(np.transpose(lines),
FULL_STRUCT)
labeled_lines = np.transpose(labeled_lines)
fitting = approximate_using_piecewise_linear_pca(
labeled_lines, num_of_labeles, [], 0)
indices = (fitting < 0.8 * max_scale) | (fitting == np.inf)
line_indices = np.argwhere(indices)[:, 0] + [1]
non_line_indices = np.argwhere(np.logical_not(indices))[:, 0] + [1]
intact_lines, intact_lines_num = bwlabel(
np.transpose(np.isin(labeled_lines, line_indices)), FULL_STRUCT)
intact_lines = np.transpose(intact_lines)
lines2split = np.isin(labeled_lines, non_line_indices)
labeled_lines_num = intact_lines_num
# TODO skeletonize loses data
skel = skeletonize(lines2split)
lines2split = np.isin(labeled_lines, non_line_indices)
# TODO find_branch_pts loses data
branch_pts = find_branch_pts(skel_img=skel)
branch_pts_fat = dilate(branch_pts, ksize=3, i=1)
broken_lines = np.logical_and(skel, np.logical_not(branch_pts_fat))
lines2split = 1 * lines2split
temp, labeled_lines_num = label_broken_lines(1 * lines2split,
1 * broken_lines,
labeled_lines_num)
intact_lines[temp > 0] = temp[temp > 0]
labeled_lines = intact_lines
for i in range(1, int(labeled_lines_num) + 1):
lbls, num = bwlabel(labeled_lines == i, FULL_STRUCT)
if num > 1:
props = regionprops(lbls)
areas = [p.area for p in props]
loc = np.argmax(areas)
lbls[lbls == loc + 1] = 0
labeled_lines[lbls > 0] = 0
return labeled_lines, labeled_lines_num, intact_lines_num
def segment_skeleton(skel_img, mask=None):
""" Segment a skeleton image into pieces
Inputs:
skel_img = Skeletonized image
mask = (Optional) binary mask for debugging. If provided, debug image will be overlaid on the mask.
Returns:
segmented_img = Segmented debugging image
segment_objects = list of contours
:param skel_img: numpy.ndarray
:param mask: numpy.ndarray
:return segmented_img: numpy.ndarray
:return segment_objects: list
"""
# Store debug
debug = params.debug
params.debug = None
# Find branch points
bp = find_branch_pts(skel_img)
bp = dilate(bp, 3, 1)
# Subtract from the skeleton so that leaves are no longer connected
segments = image_subtract(skel_img, bp)
# Gather contours of leaves
segment_objects, _ = find_objects(segments, segments)
# Reset debug mode
params.debug = debug
# Color each segment a different color, do not used a previously saved color scale
rand_color = color_palette(num=len(segment_objects), saved=False)
if mask is None:
segmented_img = skel_img.copy()
else:
segmented_img = mask.copy()
segmented_img = cv2.cvtColor(segmented_img, cv2.COLOR_GRAY2RGB)
for i, cnt in enumerate(segment_objects):
cv2.drawContours(segmented_img,
segment_objects,
i,
rand_color[i],
params.line_thickness,
lineType=8)
# Auto-increment device
params.device += 1
if params.debug == 'print':
print_image(
segmented_img,
os.path.join(params.debug_outdir,
str(params.device) + '_segmented.png'))
elif params.debug == 'plot':
plot_image(segmented_img)
return segmented_img, segment_objects
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 segment_insertion_angle(skel_img, segmented_img, leaf_objects,
leaf_hierarchies, 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
leaf_hierarchies = Leaf contour hierarchy NumPy array
stem_objects = List of stem segments
size = Size of inner leaf used to calculate slope lines
Returns:
insertion_angle_header = Leaf insertion angle headers
insertion_angle_data = Leaf insertion angle values
labeled_img = Debugging image with angles labeled
:param skel_img: numpy.ndarray
:param segmented_img: numpy.ndarray
:param leaf_objects: list
:param leaf_hierarchies: numpy.ndarray
:param stem_objects: list
:param size: int
:return insertion_angle_header: list
:return insertion_angle_data: list
: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 = []
# Create a list of tip tuples to use for sorting
tips = find_tips(skel_img)
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,
hierarchy=leaf_hierarchies)
cv2.drawContours(labeled_img,
leaf_objects,
i,
rand_color[i],
params.line_thickness,
lineType=8,
hierarchy=leaf_hierarchies)
# Prune back ends of leaves
pruned_segment = 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:
# 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)
# If none of the tips are within a segment_end then it's an insertion segment
if all(is_insertion_segment):
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])
# 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
while len(combined_stem) > 1:
stem_img = dilate(stem_img, 2, 1)
stem_img = closing(stem_img)
combined_stem, combined_stem_hier = find_objects(stem_img, stem_img)
# 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)
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)
insertion_angle_header = ['HEADER_INSERTION_ANGLE']
insertion_angle_data = ['INSERTION_ANGLE_DATA']
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=.55,
color=(150, 150, 150),
thickness=2)
segment_label = "ID" + str(i)
insertion_angle_header.append(segment_label)
insertion_angle_data.extend(intersection_angles)
if 'morphology_data' not in outputs.measurements:
outputs.measurements['morphology_data'] = {}
outputs.measurements['morphology_data'][
'segment_insertion_angles'] = intersection_angles
# 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 insertion_angle_header, insertion_angle_data, labeled_img
