def prepare(rgb_img, gray_low, gray_high):
logger.debug('received rbg image')
gray_img = pcv.rgb2gray(rgb_img=rgb_img)
logger.debug('converted rbg image to grayscale image')
masked_img = cv2.inRange(gray_img, gray_low, gray_high)
logger.debug('filtered grayscale image')
fill_image = pcv.fill_holes(masked_img)
logger.debug('filled holes in binary mask')
logger.debug('returned binary image mask')
return fill_image
def segmentation(imgW, imgNIR, shape):
# VIS example from PlantCV with few modifications
# Higher value = more strict selection
s_threshold = 165
b_threshold = 200
# Read image
img = imread(imgW)
#img = cvtColor(img, COLOR_BGR2RGB)
imgNIR = imread(imgNIR)
#imgNIR = cvtColor(imgNIR, COLOR_BGR2RGB)
#img, path, img_filename = pcv.readimage(filename=imgW, mode="native")
#imgNIR, pathNIR, imgNIR_filename = pcv.readimage(filename=imgNIR, mode="native")
# Convert RGB to HSV and extract the saturation channel
s = pcv.rgb2gray_hsv(rgb_img=img, channel='s')
# Threshold the saturation image
s_thresh = pcv.threshold.binary(gray_img=s, threshold=s_threshold, max_value=255, object_type='light')
# Median Blur
s_mblur = pcv.median_blur(gray_img=s_thresh, ksize=5)
s_cnt = pcv.median_blur(gray_img=s_thresh, ksize=5)
# Convert RGB to LAB and extract the Blue channel
b = pcv.rgb2gray_lab(rgb_img=img, channel='b')
# Threshold the blue image ORIGINAL 160
b_thresh = pcv.threshold.binary(gray_img=b, threshold=b_threshold, max_value=255, object_type='light')
b_cnt = pcv.threshold.binary(gray_img=b, threshold=b_threshold, max_value=255, object_type='light')
# Join the thresholded saturation and blue-yellow images
bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_cnt)
# Apply Mask (for VIS images, mask_color=white)
masked = pcv.apply_mask(img=img, mask=bs, mask_color='white')
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
masked_a = pcv.rgb2gray_lab(rgb_img=masked, channel='a')
masked_b = pcv.rgb2gray_lab(rgb_img=masked, channel='b')
# Threshold the green-magenta and blue images
# 115
# 135
# 128
maskeda_thresh = pcv.threshold.binary(gray_img=masked_a, threshold=115, max_value=255, object_type='dark')
maskeda_thresh1 = pcv.threshold.binary(gray_img=masked_a, threshold=135, max_value=255, object_type='light')
maskedb_thresh = pcv.threshold.binary(gray_img=masked_b, threshold=128, max_value=255, object_type='light')
# Join the thresholded saturation and blue-yellow images (OR)
ab1 = pcv.logical_or(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh)
ab = pcv.logical_or(bin_img1=maskeda_thresh1, bin_img2=ab1)
# Fill small objects
ab_fill = pcv.fill(bin_img=ab, size=200)
# Apply mask (for VIS images, mask_color=white)
masked2 = pcv.apply_mask(img=masked, mask=ab_fill, mask_color='white')
# Identify objects
id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill)
# Define ROI
height = shape[0]
width = shape[1]
roi1, roi_hierarchy= pcv.roi.rectangle(img=masked2, x=0, y=0, h=height, w=width)
# Decide which objects to keep
roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img=img, roi_contour=roi1,
roi_hierarchy=roi_hierarchy,
object_contour=id_objects,
obj_hierarchy=obj_hierarchy,
roi_type='partial')
# Object combine kept objects
obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3)
# Filling holes in the mask, works great for alive plants, not so good for dead plants
filled_mask = pcv.fill_holes(mask)
final = pcv.apply_mask(img=imgNIR, mask=mask, mask_color='white')
pcv.print_image(final, "./segment/segment-temp.png")
def main():
# Create input arguments object
args = options()
# Set debug mode
pcv.params.debug = args.debug
# Open a single image
img, imgpath, imgname = pcv.readimage(filename=args.image)
# Visualize colorspaces
all_cs = pcv.visualize.colorspaces(rgb_img=img)
# Extract the Blue-Yellow ("b") channel from the LAB colorspace
gray_img = pcv.rgb2gray_lab(rgb_img=img, channel="b")
# Plot a histogram of pixel values for the Blue-Yellow ("b") channel.
hist_plot = pcv.visualize.histogram(gray_img=gray_img)
# Apply a binary threshold to the Blue-Yellow ("b") grayscale image.
thresh_img = pcv.threshold.binary(gray_img=gray_img,
threshold=140,
max_value=255,
object_type="light")
# Apply a dilation with a 5x5 kernel and 3 iterations
dil_img = pcv.dilate(gray_img=thresh_img, ksize=5, i=3)
# Fill in small holes in the leaves
closed_img = pcv.fill_holes(bin_img=dil_img)
# Erode the plant pixels using a 5x5 kernel and 3 iterations
er_img = pcv.erode(gray_img=closed_img, ksize=5, i=3)
# Apply a Gaussian blur with a 5 x 5 kernel.
blur_img = pcv.gaussian_blur(img=er_img, ksize=(5, 5))
# Set pixel values less than 255 to 0
blur_img[np.where(blur_img < 255)] = 0
# Fill/remove objects less than 300 pixels in area
cleaned = pcv.fill(bin_img=blur_img, size=300)
# Create a circular ROI
roi, roi_str = pcv.roi.circle(img=img, x=1725, y=1155, r=400)
# Identify objects in the binary image
cnts, cnts_str = pcv.find_objects(img=img, mask=cleaned)
# Filter objects by region of interest
plant_cnt, plant_str, plant_mask, plant_area = pcv.roi_objects(
img=img,
roi_contour=roi,
roi_hierarchy=roi_str,
object_contour=cnts,
obj_hierarchy=cnts_str)
# Combine objects into one
plant, mask = pcv.object_composition(img=img,
contours=plant_cnt,
hierarchy=plant_str)
# Measure size and shape properties
shape_img = pcv.analyze_object(img=img, obj=plant, mask=mask)
if args.writeimg:
pcv.print_image(img=shape_img,
filename=os.path.join(args.outdir,
"shapes_" + imgname))
# Analyze color properties
color_img = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type="hsv")
if args.writeimg:
pcv.print_image(img=color_img,
filename=os.path.join(args.outdir,
"histogram_" + imgname))
# Save the measurements to a file
pcv.print_results(filename=args.result)
def main():
# Get options
args = options()
# Set variables
device = 0
pcv.params.debug = args.debug
img_file = args.image
# Read image
img, path, filename = pcv.readimage(filename=img_file, mode='rgb')
# Process saturation channel from HSV colour space
s = pcv.rgb2gray_hsv(rgb_img=img, channel='s')
lp_s = pcv.laplace_filter(s, 1, 1)
shrp_s = pcv.image_subtract(s, lp_s)
s_eq = pcv.hist_equalization(shrp_s)
s_thresh = pcv.threshold.binary(gray_img=s_eq,
threshold=215,
max_value=255,
object_type='light')
s_mblur = pcv.median_blur(gray_img=s_thresh, ksize=5)
# Process green-magenta channel from LAB colour space
b = pcv.rgb2gray_lab(rgb_img=img, channel='a')
b_lp = pcv.laplace_filter(b, 1, 1)
b_shrp = pcv.image_subtract(b, b_lp)
b_thresh = pcv.threshold.otsu(b_shrp, 255, object_type='dark')
# Create and apply mask
bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_thresh)
filled = pcv.fill_holes(bs)
masked = pcv.apply_mask(img=img, mask=filled, mask_color='white')
# Extract colour channels from masked image
masked_a = pcv.rgb2gray_lab(rgb_img=masked, channel='a')
masked_b = pcv.rgb2gray_lab(rgb_img=masked, channel='b')
# Threshold the green-magenta and blue images
maskeda_thresh = pcv.threshold.binary(gray_img=masked_a,
threshold=115,
max_value=255,
object_type='dark')
maskeda_thresh1 = pcv.threshold.binary(gray_img=masked_a,
threshold=140,
max_value=255,
object_type='light')
maskedb_thresh = pcv.threshold.binary(gray_img=masked_b,
threshold=128,
max_value=255,
object_type='light')
# Join the thresholded saturation and blue-yellow images (OR)
ab1 = pcv.logical_or(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh)
ab = pcv.logical_or(bin_img1=maskeda_thresh1, bin_img2=ab1)
# Produce and apply a mask
opened_ab = pcv.opening(gray_img=ab)
ab_fill = pcv.fill(bin_img=ab, size=200)
closed_ab = pcv.closing(gray_img=ab_fill)
masked2 = pcv.apply_mask(img=masked, mask=bs, mask_color='white')
# Identify objects
id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill)
# Define region of interest (ROI)
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2,
x=250,
y=100,
h=200,
w=200)
# Decide what objects to keep
roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img=img,
roi_contour=roi1,
roi_hierarchy=roi_hierarchy,
object_contour=id_objects,
obj_hierarchy=obj_hierarchy,
roi_type='partial')
# Object combine kept objects
obj, mask = pcv.object_composition(img=img,
contours=roi_objects,
hierarchy=hierarchy3)
############### Analysis ################
outfile = False
if args.writeimg == True:
outfile = args.outdir + "/" + filename
# Analyze the plant
analysis_image = pcv.analyze_object(img=img, obj=obj, mask=mask)
color_histogram = pcv.analyze_color(rgb_img=img,
mask=kept_mask,
hist_plot_type='all')
top_x, bottom_x, center_v_x = pcv.x_axis_pseudolandmarks(img=img,
obj=obj,
mask=mask)
top_y, bottom_y, center_v_y = pcv.y_axis_pseudolandmarks(img=img,
obj=obj,
mask=mask)
# Print results of the analysis
pcv.print_results(filename=args.result)
pcv.output_mask(img,
kept_mask,
filename,
outdir=args.outdir,
mask_only=True)
def main():
# Initialize options
args = options()
# Set PlantCV debug mode to input debug method
pcv.params.debug = args.debug
# Use PlantCV to read in the input image. The function outputs an image as a NumPy array, the path to the file,
# and the image filename
img, path, filename = pcv.readimage(filename=args.image)
# ## Segmentation
# ### Saturation channel
# Convert the RGB image to HSV colorspace and extract the saturation channel
s = pcv.rgb2gray_hsv(rgb_img=img, channel='s')
# Use a binary threshold to set an inflection value where all pixels in the grayscale saturation image below the
# threshold get set to zero (pure black) and all pixels at or above the threshold get set to 255 (pure white)
s_thresh = pcv.threshold.binary(gray_img=s, threshold=80, max_value=255, object_type='light')
# ### Blue-yellow channel
# Convert the RGB image to LAB colorspace and extract the blue-yellow channel
b = pcv.rgb2gray_lab(rgb_img=img, channel='b')
# Use a binary threshold to set an inflection value where all pixels in the grayscale blue-yellow image below the
# threshold get set to zero (pure black) and all pixels at or above the threshold get set to 255 (pure white)
b_thresh = pcv.threshold.binary(gray_img=b, threshold=134, max_value=255, object_type='light')
# ### Green-magenta channel
# Convert the RGB image to LAB colorspace and extract the green-magenta channel
a = pcv.rgb2gray_lab(rgb_img=img, channel='a')
# In the green-magenta image the plant pixels are darker than the background. Setting object_type="dark" will
# invert the image first and then use a binary threshold to set an inflection value where all pixels in the
# grayscale green-magenta image below the threshold get set to zero (pure black) and all pixels at or above the
# threshold get set to 255 (pure white)
a_thresh = pcv.threshold.binary(gray_img=a, threshold=122, max_value=255, object_type='dark')
# Combine the binary images for the saturation and blue-yellow channels. The "or" operator returns a binary image
# that is white when a pixel was white in either or both input images
bs = pcv.logical_or(bin_img1=s_thresh, bin_img2=b_thresh)
# Combine the binary images for the combined saturation and blue-yellow channels and the green-magenta channel.
# The "or" operator returns a binary image that is white when a pixel was white in either or both input images
bsa = pcv.logical_or(bin_img1=bs, bin_img2=a_thresh)
# The combined binary image labels plant pixels well but the background still has pixels labeled as foreground.
# Small white noise (salt) in the background can be removed by filtering white objects in the image by size and
# setting a size threshold where smaller objects can be removed
bsa_fill1 = pcv.fill(bin_img=bsa, size=15) # Fill small noise
# Before more stringent size filtering is done we want to connect plant parts that may still be disconnected from
# the main plant. Use a dilation to expand the boundary of white regions. Ksize is the size of a box scanned
# across the image and i is the number of times a scan is done
bsa_fill2 = pcv.dilate(gray_img=bsa_fill1, ksize=3, i=3)
# Remove small objects by size again but use a higher threshold
bsa_fill3 = pcv.fill(bin_img=bsa_fill2, size=250)
# Use the binary image to identify objects or connected components.
id_objects, obj_hierarchy = pcv.find_objects(img=img, mask=bsa_fill3)
# Because the background still contains pixels labeled as foreground, the object list contains background.
# Because these images were collected in an automated system the plant is always centered in the image at the
# same position each time. Define a region of interest (ROI) to set the area where we expect to find plant
# pixels. PlantCV can make simple ROI shapes like rectangles, circles, etc. but here we use a custom ROI to fit a
# polygon around the plant area
roi_custom, roi_hier_custom = pcv.roi.custom(img=img, vertices=[[1085, 1560], [1395, 1560], [1395, 1685],
[1890, 1744], [1890, 25], [600, 25], [615, 1744],
[1085, 1685]])
# Use the ROI to filter out objects found outside the ROI. When `roi_type = "cutto"` objects outside the ROI are
# cropped out. The default `roi_type` is "partial" which allows objects to overlap the ROI and be retained
roi_objects, hierarchy, kept_mask, obj_area = pcv.roi_objects(img=img, roi_contour=roi_custom,
roi_hierarchy=roi_hier_custom,
object_contour=id_objects,
obj_hierarchy=obj_hierarchy, roi_type='cutto')
# Filter remaining objects by size again to remove any remaining background objects
filled_mask1 = pcv.fill(bin_img=kept_mask, size=350)
# Use a closing operation to first dilate (expand) and then erode (shrink) the plant to fill in any additional
# gaps in leaves or stems
filled_mask2 = pcv.closing(gray_img=filled_mask1)
# Remove holes or dark spot noise (pepper) in the plant binary image
filled_mask3 = pcv.fill_holes(filled_mask2)
# With the clean binary image identify the contour of the plant
id_objects, obj_hierarchy = pcv.find_objects(img=img, mask=filled_mask3)
# Because a plant or object of interest may be composed of multiple contours, it is required to combine all
# remaining contours into a single contour before measurements can be done
obj, mask = pcv.object_composition(img=img, contours=id_objects, hierarchy=obj_hierarchy)
# ## Measurements PlantCV has several built-in measurement or analysis methods. Here, basic measurements of size
# and shape are done. Additional typical modules would include plant height (`pcv.analyze_bound_horizontal`) and
# color (`pcv.analyze_color`)
shape_img = pcv.analyze_object(img=img, obj=obj, mask=mask)
# Save the shape image if requested
if args.writeimg:
outfile = os.path.join(args.outdir, filename[:-4] + "_shapes.png")
pcv.print_image(img=shape_img, filename=outfile)
# ## Morphology workflow
# Update a few PlantCV parameters for plotting purposes
pcv.params.text_size = 1.5
pcv.params.text_thickness = 5
pcv.params.line_thickness = 15
# Convert the plant mask into a "skeletonized" image where each path along the stem and leaves are a single pixel
# wide
skel = pcv.morphology.skeletonize(mask=mask)
# Sometimes wide parts of leaves or stems are skeletonized in the direction perpendicular to the main path. These
# "barbs" or "spurs" can be removed by pruning the skeleton to remove small paths. Pruning will also separate the
# individual path segments (leaves and stem parts)
pruned, segmented_img, segment_objects = pcv.morphology.prune(skel_img=skel, size=30, mask=mask)
pruned, segmented_img, segment_objects = pcv.morphology.prune(skel_img=pruned, size=3, mask=mask)
# Leaf and stem segments above are separated but only into individual paths. We can sort the segments into stem
# and leaf paths by identifying primary segments (stems; those that end in a branch point) and secondary segments
# (leaves; those that begin at a branch point and end at a tip point)
leaf_objects, other_objects = pcv.morphology.segment_sort(skel_img=pruned, objects=segment_objects, mask=mask)
# Label the segment unique IDs
segmented_img, labeled_id_img = pcv.morphology.segment_id(skel_img=pruned, objects=leaf_objects, mask=mask)
# Measure leaf insertion angles. Measures the angle between a line fit through the stem paths and a line fit
# through the first `size` points of each leaf path
labeled_angle_img = pcv.morphology.segment_insertion_angle(skel_img=pruned, segmented_img=segmented_img,
leaf_objects=leaf_objects, stem_objects=other_objects,
size=22)
# Save leaf angle image if requested
if args.writeimg:
outfile = os.path.join(args.outdir, filename[:-4] + "_leaf_insertion_angles.png")
pcv.print_image(img=labeled_angle_img, filename=outfile)
# ## Other potential morphological measurements There are many other functions that extract data from within the
# morphology sub-package of PlantCV. For our purposes, we are most interested in the relative angle between each
# leaf and the stem which we measure with `plantcv.morphology.segment_insertion_angle`. However, the following
# cells show some of the other traits that we are able to measure from images that can be succesfully sorted into
# primary and secondary segments.
# Segment the plant binary mask using the leaf and stem segments. Allows for the measurement of individual leaf
# areas
# filled_img = pcv.morphology.fill_segments(mask=mask, objects=leaf_objects)
# Measure the path length of each leaf (geodesic distance)
# labeled_img2 = pcv.morphology.segment_path_length(segmented_img=segmented_img, objects=leaf_objects)
# Measure the straight-line, branch point to tip distance (Euclidean) for each leaf
# labeled_img3 = pcv.morphology.segment_euclidean_length(segmented_img=segmented_img, objects=leaf_objects)
# Measure the curvature of each leaf (Values closer to 1 indicate that a segment is a straight line while larger
# values indicate the segment has more curvature)
# labeled_img4 = pcv.morphology.segment_curvature(segmented_img=segmented_img, objects=leaf_objects)
# Measure absolute leaf angles (angle of linear regression line fit to each leaf object) Note: negative values
# signify leaves to the left of the stem, positive values signify leaves to the right of the stem
# labeled_img5 = pcv.morphology.segment_angle(segmented_img=segmented_img, objects=leaf_objects)
# Measure leaf curvature in degrees
# labeled_img6 = pcv.morphology.segment_tangent_angle(segmented_img=segmented_img, objects=leaf_objects, size=35)
# Measure stem characteristics like stem angle and length
# stem_img = pcv.morphology.analyze_stem(rgb_img=img, stem_objects=other_objects)
# Remove unneeded observations (hack)
_ = pcv.outputs.observations.pop("tips")
_ = pcv.outputs.observations.pop("branch_pts")
angles = pcv.outputs.observations["segment_insertion_angle"]["value"]
remove_indices = []
for i, value in enumerate(angles):
if value == "NA":
remove_indices.append(i)
remove_indices.sort(reverse=True)
for i in remove_indices:
_ = pcv.outputs.observations["segment_insertion_angle"]["value"].pop(i)
# ## Save the results out to file for downsteam analysis
pcv.print_results(filename=args.result)
def initcrop(imagePath):
dire = dir
path = dire + '/Classifyer_dump'
try:
os.makedirs(path)
except OSError:
pass
image = cv2.imread(imagePath)
blue_image = pcv.rgb2gray_lab(image, 'l')
Gaussian_blue = cv2.adaptiveThreshold(blue_image, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 981,
-1) # 241 is good 981
cv2.imwrite(os.path.join(path, "blue_test.png"), Gaussian_blue)
fill = pcv.fill_holes(Gaussian_blue)
fill_again = pcv.fill(fill, 100000)
id_objects, obj_hierarchy = pcv.find_objects(
img=image,
mask=fill_again) # lazy way to findContours and draw them
roi1, roi_hierarchy = pcv.roi.rectangle(img=image,
x=3000,
y=1000,
h=200,
w=300)
roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img=image,
roi_contour=roi1,
roi_hierarchy=roi_hierarchy,
object_contour=id_objects,
obj_hierarchy=obj_hierarchy,
roi_type='partial')
cv2.imwrite(os.path.join(path, "plate_mask.png"), kept_mask)
mask = cv2.imread(os.path.join(path, "plate_mask.png"))
result = image * (mask.astype(image.dtype))
result = cv2.bitwise_not(result)
cv2.imwrite(os.path.join(path, "AutoCrop.png"), result)
output = cv2.connectedComponentsWithStats(kept_mask, connectivity=8)
stats = output[2]
left = (stats[1, cv2.CC_STAT_LEFT])
# print(stats[1, cv2.CC_STAT_TOP])
# print(stats[1, cv2.CC_STAT_HEIGHT])
# exit(2)
L, a, b = cv2.split(result)
# cv2.imwrite("gray_scale.png", L)
plate_threshold = cv2.adaptiveThreshold(b, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 87,
-1) # 867 is good 241
cv2.imwrite(os.path.join(path, "plate_threshold.png"), plate_threshold)
fill_again2 = pcv.fill(plate_threshold, 1000)
cv2.imwrite(os.path.join(path, "fill_test.png"), fill_again2)
# fill = pcv.fill_holes(fill_again2)
# cv2.imwrite(os.path.join(path, "fill_test2.png"), fill)
blur_image = pcv.median_blur(fill_again2, 10)
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(
blur_image, connectivity=8)
sizes = stats[1:, -1]
nb_components = nb_components - 1
min_size = 20000
img2 = np.zeros((output.shape))
for i in range(0, nb_components):
if sizes[i] <= min_size:
img2[output == i + 1] = 255
cv2.imwrite(os.path.join(path, "remove_20000.png"),
img2) # this can be made better to speed it up
thresh_image = img2.astype(
np.uint8) # maybe crop to the roi below then do it
thresh_image = pcv.fill_holes(thresh_image)
cv2.imwrite("NEWTEST.jpg", thresh_image)
id_objects, obj_hierarchy = pcv.find_objects(img=image,
mask=thresh_image)
roi1, roi_hierarchy = pcv.roi.rectangle(img=image,
x=(left + 380),
y=750,
h=175,
w=100)
try:
where_cell = 0
roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img=image,
roi_contour=roi1,
roi_hierarchy=roi_hierarchy,
object_contour=id_objects,
obj_hierarchy=obj_hierarchy,
roi_type='partial')
cv2.imwrite(os.path.join(path, "test_mask.png"), kept_mask)
mask = cv2.imread(os.path.join(path, "test_mask.png"))
result = image * (mask.astype(image.dtype))
result = cv2.bitwise_not(result)
cv2.imwrite(os.path.join(path, "TEST.png"), result)
output = cv2.connectedComponentsWithStats(kept_mask,
connectivity=8)
stats = output[2]
centroids = output[3]
centroids_x = (int(centroids[1][0]))
centroids_y = (int(centroids[1][1]))
except:
where_cell = 1
print("did this work?")
roi1, roi_hierarchy = pcv.roi.rectangle(img=image,
x=(left + 380),
y=3200,
h=100,
w=100)
roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img=image,
roi_contour=roi1,
roi_hierarchy=roi_hierarchy,
object_contour=id_objects,
obj_hierarchy=obj_hierarchy,
roi_type='partial')
cv2.imwrite(os.path.join(path, "test_mask.png"), kept_mask)
mask = cv2.imread(os.path.join(path, "test_mask.png"))
result = image * (mask.astype(image.dtype))
result = cv2.bitwise_not(result)
cv2.imwrite(os.path.join(path, "TEST.png"), result)
output = cv2.connectedComponentsWithStats(kept_mask,
connectivity=8)
stats = output[2]
centroids = output[3]
centroids_x = (int(centroids[1][0]))
centroids_y = (int(centroids[1][1]))
flag = 0
# print(stats[1, cv2.CC_STAT_AREA])
if ((stats[1, cv2.CC_STAT_AREA]) > 4000):
flag = 30
# print(centroids_x)
# print(centroids_y)
# print(centroids)
if (where_cell == 0):
left = (centroids_x - 70)
right = (centroids_x + 3695 + flag) # was 3715
top = (centroids_y - 80)
bottom = (centroids_y + 2462)
if (where_cell == 1):
left = (centroids_x - 70)
right = (centroids_x + 3715 + flag)
top = (centroids_y - 2480)
bottom = (centroids_y + 62)
# print(top)
# print(bottom)
image = Image.open(imagePath)
img_crop = image.crop((left, top, right, bottom))
# img_crop.show()
img_crop.save(os.path.join(path, 'Cropped_full_yeast.png'))
circle_me = cv2.imread(os.path.join(path, "Cropped_full_yeast.png"))
cropped_img = cv2.imread(
os.path.join(path, "Cropped_full_yeast.png"
)) # changed from Yeast_Cluster.%d.png %counter
L, a, b = cv2.split(cropped_img) # can do l a or b
Gaussian_blue = cv2.adaptiveThreshold(b, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 241,
-1) # For liz's pictures 241
cv2.imwrite(os.path.join(path, "blue_test.png"), Gaussian_blue)
blur_image = pcv.median_blur(Gaussian_blue, 10)
heavy_fill_blue = pcv.fill(blur_image, 1000) # value 400
hole_fill = pcv.fill_holes(heavy_fill_blue)
cv2.imwrite(os.path.join(path, "Cropped_Threshold.png"), hole_fill)
def initcrop(imagePath):
dire = os.getcwd()
path = dire + '/Classifyer_dump'
try:
os.makedirs(path)
except OSError:
pass
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imwrite(os.path.join(path, "gray.png"), gray)
ret, thres = cv2.threshold(gray, 127, 255,
cv2.THRESH_BINARY) # 127 137
cv2.imwrite(os.path.join(path, 'Wholepic.png'), thres)
fill = pcv.fill(thres, 100000)
cv2.imwrite(os.path.join(path, "noiseReduced.png"), fill)
im2, contours, hierarchy = cv2.findContours(fill, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
test = []
for c in contours:
peri = cv2.arcLength(c, True)
test.append(peri)
index_max = np.argmax(test)
x, y, w, h = cv2.boundingRect(contours[index_max])
c = max(contours, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(c)
# draw the biggest contour (c) in green
# cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),5)
image = image[y:y + h, x:x + w]
cv2.imwrite(os.path.join(path, "cropped.png"), image)
L, a, b = cv2.split(image) # can do l a or b
thres = cv2.adaptiveThreshold(b, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 255,
-1) # For liz's pictures 241
cv2.imwrite(os.path.join(path, "cropped_thres.png"), thres)
fill = pcv.fill(thres, 1000)
cv2.imwrite(os.path.join(path, "cropped_thres_filled.png"), fill)
blur = cv2.blur(fill, (15, 15), 0)
fill_hole = cv2.morphologyEx(fill,
cv2.MORPH_CLOSE,
cv2.getStructuringElement(
cv2.MORPH_RECT, (21, 21)),
iterations=2)
cv2.imwrite(os.path.join(path, "cropped_thres_filled.png"), fill_hole)
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(
fill_hole, connectivity=8)
new_centroids = []
sizes = stats[1:, -1]
nb_components = nb_components - 1
min_size = 20000
img2 = np.zeros((output.shape))
for i in range(0, nb_components):
if sizes[i] <= min_size:
img2[output == i + 1] = 255
new_centroids.append(centroids[i])
cv2.imwrite(os.path.join(path, "filter.png"), img2)
# print(len(centroids))
# print(len(new_centroids))
# print(new_centroids[0])
# print(new_centroids[0][1])
y = []
x = []
for i in range(len(new_centroids)):
y.append(new_centroids[i][1])
x.append(new_centroids[i][0])
# print(len(x))
XARRAY = sorted(x)
YARRAY = sorted(y)
smallX = int(XARRAY[4]) - 80
bigX = int(XARRAY[len(XARRAY) - 4]) + 80
smallY = int(YARRAY[4]) - 80
bigY = int(YARRAY[len(YARRAY) - 4]) + 80
# print(smallX, smallY, bigX, bigY)
image = cv2.imread(os.path.join(path, "cropped.png"))
image = image[smallY:bigY, smallX:bigX]
#print(image.shape)
cv2.imwrite(os.path.join(path, "final.png"), image)
L, a, b = cv2.split(image) # can do l a or b
blur_image = pcv.median_blur(a, 10)
Gaussian_blue = cv2.adaptiveThreshold(a, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 281,
-1) # For liz's pictures 241
heavy_fill_blue = pcv.fill(Gaussian_blue, 1000) # value 400
hole_fill = pcv.fill_holes(heavy_fill_blue)
cv2.imwrite(os.path.join(path, "Cropped_Threshold.png"), hole_fill)
# cv2.imwrite(os.path.join(test1, "Cropped_thres%d.png" % image_counter),hole_fill)
image = cv2.resize(image, (800, 600))
cv2.imwrite(os.path.join(path, "scaled.png"), image)
image = os.path.join(path, "scaled.png")
msg = "Does this crop look good"
choices = ["Yes", "No"]
reply = easygui.indexbox(msg, image=image, choices=choices)
if reply == 0:
df = pd.DataFrame([smallY, bigY, smallX, bigX])
df.to_pickle('pickled_setup')
def main():
# Get options
args = options()
if args.debug:
pcv.params.debug = args.debug # set debug mode
if args.debugdir:
pcv.params.debug_outdir = args.debugdir # set debug directory
os.makedirs(args.debugdir, exist_ok=True)
# pixel_resolution
# mm
# see pixel_resolution.xlsx for calibration curve for pixel to mm translation
pixelresolution = 0.052
# The result file should exist if plantcv-workflow.py was run
if os.path.exists(args.result):
# Open the result file
results = open(args.result, "r")
# The result file would have image metadata in it from plantcv-workflow.py, read it into memory
metadata = json.load(results)
# Close the file
results.close()
# Delete the file, we will create new ones
os.remove(args.result)
plantbarcode = metadata['metadata']['plantbarcode']['value']
print(plantbarcode,
metadata['metadata']['timestamp']['value'],
sep=' - ')
else:
# If the file did not exist (for testing), initialize metadata as an empty string
metadata = "{}"
regpat = re.compile(args.regex)
plantbarcode = re.search(regpat, args.image).groups()[0]
# read images and create mask
img, _, fn = pcv.readimage(args.image)
imagename = os.path.splitext(fn)[0]
# create mask
# taf=filters.try_all_threshold(s_img)
## remove background
s_img = pcv.rgb2gray_hsv(img, 's')
min_s = filters.threshold_minimum(s_img)
thresh_s = pcv.threshold.binary(s_img, min_s, 255, 'light')
rm_bkgrd = pcv.fill_holes(thresh_s)
## low greenness
thresh_s = pcv.threshold.binary(s_img, min_s + 15, 255, 'dark')
# taf = filters.try_all_threshold(s_img)
c = pcv.logical_xor(rm_bkgrd, thresh_s)
cinv = pcv.invert(c)
cinv_f = pcv.fill(cinv, 500)
cinv_f_c = pcv.closing(cinv_f, np.ones((5, 5)))
cinv_f_c_e = pcv.erode(cinv_f_c, 2, 1)
## high greenness
a_img = pcv.rgb2gray_lab(img, channel='a')
# taf = filters.try_all_threshold(a_img)
t_a = filters.threshold_isodata(a_img)
thresh_a = pcv.threshold.binary(a_img, t_a, 255, 'dark')
thresh_a = pcv.closing(thresh_a, np.ones((5, 5)))
thresh_a_f = pcv.fill(thresh_a, 500)
## combined mask
lor = pcv.logical_or(cinv_f_c_e, thresh_a_f)
close = pcv.closing(lor, np.ones((2, 2)))
fill = pcv.fill(close, 800)
erode = pcv.erode(fill, 3, 1)
fill2 = pcv.fill(erode, 1200)
# dilate = pcv.dilate(fill2,2,2)
mask = fill2
final_mask = np.zeros_like(mask)
# Compute greenness
# split color channels
b, g, r = cv2.split(img)
# print green intensity
# g_img = pcv.visualize.pseudocolor(g, cmap='Greens', background='white', min_value=0, max_value=255, mask=mask, axes=False)
# convert color channels to int16 so we can add them (values will be greater than 255 which is max of current uint8 format)
g = g.astype('uint16')
r = r.astype('uint16')
b = b.astype('uint16')
denom = g + r + b
# greenness index
out_flt = np.zeros_like(denom, dtype='float32')
# divide green by sum of channels to compute greenness index with values 0-1
gi = np.divide(g,
denom,
out=out_flt,
where=np.logical_and(denom != 0, mask > 0))
# find objects
c, h = pcv.find_objects(img, mask)
rc, rh = pcv.roi.multi(img, coord=[(1300, 900), (1300, 2400)], radius=350)
# Turn off debug temporarily, otherwise there will be a lot of plots
pcv.params.debug = None
# Loop over each region of interest
i = 0
rc_i = rc[i]
for i, rc_i in enumerate(rc):
rh_i = rh[i]
# Add ROI number to output. Before roi_objects so result has NA if no object.
pcv.outputs.add_observation(variable='roi',
trait='roi',
method='roi',
scale='int',
datatype=int,
value=i,
label='#')
roi_obj, hierarchy_obj, submask, obj_area = pcv.roi_objects(
img,
roi_contour=rc_i,
roi_hierarchy=rh_i,
object_contour=c,
obj_hierarchy=h,
roi_type='partial')
if obj_area == 0:
print('\t!!! No object found in ROI', str(i))
pcv.outputs.add_observation(
variable='plantarea',
trait='plant area in sq mm',
method='observations.area*pixelresolution^2',
scale=pixelresolution,
datatype="<class 'float'>",
value=0,
label='sq mm')
else:
# Combine multiple objects
# ple plant objects within an roi together
plant_object, plant_mask = pcv.object_composition(
img=img, contours=roi_obj, hierarchy=hierarchy_obj)
final_mask = pcv.image_add(final_mask, plant_mask)
# Save greenness for individual ROI
grnindex = np.mean(gi[np.where(plant_mask > 0)])
pcv.outputs.add_observation(
variable='greenness_index',
trait='mean normalized greenness index',
method='g/sum(b+g+r)',
scale='[0,1]',
datatype="<class 'float'>",
value=float(grnindex),
label='/1')
# Analyze all colors
hist = pcv.analyze_color(img, plant_mask, 'all')
# Analyze the shape of the current plant
shape_img = pcv.analyze_object(img, plant_object, plant_mask)
plant_area = pcv.outputs.observations['area'][
'value'] * pixelresolution**2
pcv.outputs.add_observation(
variable='plantarea',
trait='plant area in sq mm',
method='observations.area*pixelresolution^2',
scale=pixelresolution,
datatype="<class 'float'>",
value=plant_area,
label='sq mm')
# end if-else
# At this point we have observations for one plant
# We can write these out to a unique results file
# Here I will name the results file with the ROI ID combined with the original result filename
basename, ext = os.path.splitext(args.result)
filename = basename + "-roi" + str(i) + ext
# Save the existing metadata to the new file
with open(filename, "w") as r:
json.dump(metadata, r)
pcv.print_results(filename=filename)
# The results are saved, now clear out the observations so the next loop adds new ones for the next plant
pcv.outputs.clear()
if args.writeimg and obj_area != 0:
imgdir = os.path.join(args.outdir, 'shape_images', plantbarcode)
os.makedirs(imgdir, exist_ok=True)
pcv.print_image(
shape_img,
os.path.join(imgdir,
imagename + '-roi' + str(i) + '-shape.png'))
imgdir = os.path.join(args.outdir, 'colorhist_images',
plantbarcode)
os.makedirs(imgdir, exist_ok=True)
pcv.print_image(
hist,
os.path.join(imgdir,
imagename + '-roi' + str(i) + '-colorhist.png'))
# end roi loop
if args.writeimg:
# save grnness image of entire tray
imgdir = os.path.join(args.outdir, 'pseudocolor_images', plantbarcode)
os.makedirs(imgdir, exist_ok=True)
gi_img = pcv.visualize.pseudocolor(gi,
obj=None,
mask=final_mask,
cmap='viridis',
axes=False,
min_value=0.3,
max_value=0.6,
background='black',
obj_padding=0)
gi_img = add_scalebar(gi_img,
pixelresolution=pixelresolution,
barwidth=20,
barlocation='lower left')
gi_img.set_size_inches(6, 6, forward=False)
gi_img.savefig(os.path.join(imgdir, imagename + '-greenness.png'),
bbox_inches='tight')
gi_img.clf()