def plant_cv(img):
counter = 0
debug = None
counter, s = pcv.rgb2gray_hsv(img, 's', counter, debug)
counter, s_thresh = pcv.binary_threshold(s, 145, 255, 'light', counter,
debug)
counter, s_mblur = pcv.median_blur(s_thresh, 5, counter, debug)
# Convert RGB to LAB and extract the Blue channel
counter, b = pcv.rgb2gray_lab(img, 'b', counter, debug)
# Threshold the blue image
counter, b_thresh = pcv.binary_threshold(b, 145, 255, 'light', counter,
debug)
counter, b_cnt = pcv.binary_threshold(b, 145, 255, 'light', counter, debug)
# Join the thresholded saturation and blue-yellow images
counter, bs = pcv.logical_or(s_mblur, b_cnt, counter, debug)
counter, masked = pcv.apply_mask(img, bs, 'white', counter, debug)
#----------------------------------------
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
counter, masked_a = pcv.rgb2gray_lab(masked, 'a', counter, debug)
counter, masked_b = pcv.rgb2gray_lab(masked, 'b', counter, debug)
# Threshold the green-magenta and blue images
counter, maskeda_thresh = pcv.binary_threshold(masked_a, 115, 255, 'dark',
counter, debug)
counter, maskeda_thresh1 = pcv.binary_threshold(masked_a, 135, 255,
'light', counter, debug)
counter, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light',
counter, debug)
# Join the thresholded saturation and blue-yellow images (OR)
counter, ab1 = pcv.logical_or(maskeda_thresh, maskedb_thresh, counter,
debug)
counter, ab = pcv.logical_or(maskeda_thresh1, ab1, counter, debug)
counter, ab_cnt = pcv.logical_or(maskeda_thresh1, ab1, counter, debug)
# Fill small objects
counter, ab_fill = pcv.fill(ab, ab_cnt, 200, counter, debug)
# Apply mask (for vis images, mask_color=white)
counter, masked2 = pcv.apply_mask(masked, ab_fill, 'white', counter, debug)
zeros = np.zeros(masked2.shape[:2], dtype="uint8")
merged = cv2.merge([zeros, ab_fill, zeros])
return merged, masked2
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()
def generateMask(input, output, maskType=MASK_TYPES['BW']):
pcv.params.debug = True #set debug mode
# pcv.params.debug_outdir="./output.txt" #set output directory
# Read image (readimage mode defaults to native but if image is RGBA then specify mode='rgb')
# Inputs:
# filename - Image file to be read in
# mode - Return mode of image; either 'native' (default), 'rgb', 'gray', 'envi', or 'csv'
img, path, filename = pcv.readimage(filename=input, mode='rgb')
s = pcv.rgb2gray_hsv(rgb_img=img, channel='s')
# Threshold the saturation image
s_thresh = pcv.threshold.binary(gray_img=s,
threshold=85,
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
b_thresh = pcv.threshold.binary(gray_img=b,
threshold=160,
max_value=255,
object_type='light')
b_cnt = pcv.threshold.binary(gray_img=b,
threshold=160,
max_value=255,
object_type='light')
# Fill small objects
# b_fill = pcv.fill(b_thresh, 10)
# 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
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)
if maskType == MASK_TYPES['BW']:
pcv.print_image(ab, filename=output)
return (True, None)
# 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='black')
if maskType == MASK_TYPES['COLORED']:
pcv.print_image(masked2, filename=output)
return (True, None)
return (False, 'Unknown mask type.')
"""
def main():
# Get options
pcv.params.debug = args.debug #set debug mode
pcv.params.debug_outdir = args.outdir #set output directory
# Read image (readimage mode defaults to native but if image is RGBA then specify mode='rgb')
# Inputs:
# filename - Image file to be read in
# mode - Return mode of image; either 'native' (default), 'rgb', 'gray', or 'csv'
img, path, filename = pcv.readimage(filename=args.image, mode='rgb')
### SELECTING THE PLANT
### Attempt 5 combineren
# Parameters
hue_lower_tresh = 22 # 24
hue_higher_tresh = 50 # 50
saturation_lower_tresh = 138 # 140
saturation_higher_tresh = 230 # 230
value_lower_tresh = 120 # 125
value_higher_tresh = 255 # 255
# RGB color space
green_lower_tresh = 105 # 110
green_higher_tresh = 255 # 255
red_lower_tresh = 22 # 24
red_higher_thresh = 98 # 98
blue_lower_tresh = 85 # 85
blue_higher_tresh = 253 # 255
# CIELAB color space
#lab_blue_lower_tresh = 0 # Blue yellow channel
#lab_blue_higher_tresh = 255
s = pcv.rgb2gray_hsv(rgb_img=img, channel='h')
mask, masked_image = pcv.threshold.custom_range(
rgb_img=s,
lower_thresh=[hue_lower_tresh],
upper_thresh=[hue_higher_tresh],
channel='gray')
masked = pcv.apply_mask(rgb_img=img, mask=mask, mask_color='white')
# Filtered on Hue
s = pcv.rgb2gray_hsv(rgb_img=masked, channel='s')
mask, masked_image = pcv.threshold.custom_range(
rgb_img=s,
lower_thresh=[saturation_lower_tresh],
upper_thresh=[saturation_higher_tresh],
channel='gray')
masked = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white')
#filtered on saturation
s = pcv.rgb2gray_hsv(rgb_img=masked, channel='v')
mask, masked_image = pcv.threshold.custom_range(
rgb_img=s,
lower_thresh=[value_lower_tresh],
upper_thresh=[value_higher_tresh],
channel='gray')
masked = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white')
#filtered on value
mask, masked = pcv.threshold.custom_range(
rgb_img=masked,
lower_thresh=[0, green_lower_tresh, 0],
upper_thresh=[255, green_higher_tresh, 255],
channel='RGB')
masked = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white')
#filtered on green
mask, masked = pcv.threshold.custom_range(
rgb_img=masked,
lower_thresh=[red_lower_tresh, 0, 0],
upper_thresh=[red_higher_thresh, 255, 255],
channel='RGB')
masked = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white')
#filtered on red
mask_old, masked_old = pcv.threshold.custom_range(
rgb_img=masked,
lower_thresh=[0, 0, blue_lower_tresh],
upper_thresh=[255, 255, blue_higher_tresh],
channel='RGB')
masked = pcv.apply_mask(rgb_img=masked_old,
mask=mask_old,
mask_color='white')
#filtered on blue
#b = pcv.rgb2gray_lab(rgb_img = masked, channel = 'b') # Converting toe CIElab blue_yellow image
#b_thresh =pcv.threshold.binary(gray_img = b, threshold=lab_blue_lower_tresh, max_value = lab_blue_higher_tresh)
###_____________________________________ Now to identify objects
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=125, # original 115
max_value=255,
object_type='dark')
maskeda_thresh1 = pcv.threshold.binary(
gray_img=masked_a,
threshold=140, # original 135
max_value=255,
object_type='light')
maskedb_thresh = pcv.threshold.binary(gray_img=masked_b,
threshold=128,
max_value=255,
object_type='light')
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
# Inputs:
# bin_img - Binary image data
# size - Minimum object area size in pixels (must be an integer), and smaller objects will be filled
ab = pcv.median_blur(gray_img=ab, ksize=3)
ab_fill = pcv.fill(bin_img=ab, size=1000)
#print("filled")
# Apply mask (for VIS images, mask_color=white)
masked2 = pcv.apply_mask(rgb_img=masked, mask=ab_fill, mask_color='white')
# ID the objects
id_objects, obj_hierarchy = pcv.find_objects(masked2, ab_fill)
# Let's just take the largest
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2,
x=0,
y=0,
h=960,
w=1280) # Currently hardcoded
# Decide which objects to keep
# Inputs:
# img = img to display kept objects
# roi_contour = contour of roi, output from any ROI function
# roi_hierarchy = contour of roi, output from any ROI function
# object_contour = contours of objects, output from pcv.find_objects function
# obj_hierarchy = hierarchy of objects, output from pcv.find_objects function
# roi_type = 'partial' (default, for partially inside), 'cutto', or
# 'largest' (keep only largest contour)
with HiddenPrints():
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
# Inputs:
# img - RGB or grayscale image data for plotting
# contours - Contour list
# hierarchy - Contour hierarchy array
obj, mask = pcv.object_composition(img=img,
contours=roi_objects,
hierarchy=hierarchy3)
#print("final plant")
new_im = Image.fromarray(masked2)
new_im.save("output//" + args.filename + "last_masked.png")
##################_________________ Analysis
outfile = args.outdir + "/" + filename
# Here come all the analyse functions.
# pcv.acute_vertex(img, obj, 30, 15, 100)
color_img = pcv.analyze_color(rgb_img=img,
mask=kept_mask,
hist_plot_type=None)
#new_im = Image.fromarray(color_img)
#new_im.save(args.filename + "color_img.png")
# Find shape properties, output shape image (optional)
# Inputs:
# img - RGB or grayscale image data
# obj- Single or grouped contour object
# mask - Binary image mask to use as mask for moments analysis
shape_img = pcv.analyze_object(img=img, obj=obj, mask=mask)
new_im = Image.fromarray(shape_img)
new_im.save("output//" + args.filename + "shape_img.png")
# Shape properties relative to user boundary line (optional)
# Inputs:
# img - RGB or grayscale image data
# obj - Single or grouped contour object
# mask - Binary mask of selected contours
# line_position - Position of boundary line (a value of 0 would draw a line
# through the bottom of the image)
boundary_img1 = pcv.analyze_bound_horizontal(img=img,
obj=obj,
mask=mask,
line_position=1680)
new_im = Image.fromarray(boundary_img1)
new_im.save("output//" + args.filename + "boundary_img.png")
# Determine color properties: Histograms, Color Slices, output color analyzed histogram (optional)
# Inputs:
# rgb_img - RGB image data
# mask - Binary mask of selected contours
# hist_plot_type - None (default), 'all', 'rgb', 'lab', or 'hsv'
# This is the data to be printed to the SVG histogram file
color_histogram = pcv.analyze_color(rgb_img=img,
mask=kept_mask,
hist_plot_type='all')
#new_im = Image.fromarray(color_histogram)
#new_im.save(args.filename + "color_histogram_img.png")
# Pseudocolor the grayscale image
# Inputs:
# gray_img - Grayscale image data
# obj - Single or grouped contour object (optional), if provided the pseudocolored image gets
# cropped down to the region of interest.
# mask - Binary mask (optional)
# background - Background color/type. Options are "image" (gray_img, default), "white", or "black". A mask
# must be supplied.
# cmap - Colormap
# min_value - Minimum value for range of interest
# max_value - Maximum value for range of interest
# dpi - Dots per inch for image if printed out (optional, if dpi=None then the default is set to 100 dpi).
# axes - If False then the title, x-axis, and y-axis won't be displayed (default axes=True).
# colorbar - If False then the colorbar won't be displayed (default colorbar=True)
pseudocolored_img = pcv.visualize.pseudocolor(gray_img=s,
mask=kept_mask,
cmap='jet')
#new_im = Image.fromarray(pseudocolored_img)
#new_im.save(args.filename + "pseudocolored.png")
# Write shape and color data to results file
pcv.print_results(filename=args.result)
def analyze(image_name):
# Read image
img, path, filename = pcv.readimage(filename=image_name)
# 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=85, 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
b_thresh = pcv.threshold.binary(gray_img=b, threshold=160, max_value=255, object_type='light')
b_cnt = pcv.threshold.binary(gray_img=b, threshold=160, max_value=255, object_type='light')
# Fill small objects
# b_fill = pcv.fill(b_thresh, 10)
# 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(rgb_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
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(rgb_img=masked, mask=ab_fill, mask_color='white')
# Identify objects
id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill)
# Define ROI
roi1, roi_hierarchy= pcv.roi.rectangle(img=masked2, x=0, y=500, h=2000, w=2000)
# 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)
############### Analysis ################
# Find shape properties
shape_imgs = pcv.analyze_object(img=img, obj=obj, mask=mask)
# Color
color_histogram = pcv.analyze_color(rgb_img=img, mask=kept_mask)
# Access data stored out from analyze_object
plant_observations = pcv.outputs.observations
return plant_observations
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)
# Threshold the blue channel image
b_thresh = pcv.threshold.binary(gray_img=b,
threshold=160,
max_value=255,
object_type='light')
pcv.print_image(img=b_thresh, filename="upload/output_imgs/Extracted_img.jpg")
pcv.print_image(img=b, filename="upload/output_imgs/Gray_img.jpg")
# In[9]:
# Join the threshold saturation and blue-yellow images with a logical or operation
# Inputs:
# bin_img1 - Binary image data to be compared to bin_img2
# bin_img2 - Binary image data to be compared to bin_img1
bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_thresh)
pcv.print_image(img=bs, filename="upload/output_imgs/BSature_img.jpg")
# In[10]:
# Appy Mask (for VIS images, mask_color='white')
# Inputs:
# rgb_img - RGB image data
# mask - Binary mask image data
# mask_color - 'white' or 'black'
rgb_img = img
masked = pcv.apply_mask(rgb_img, mask=bs, mask_color='white')
pcv.print_image(img=masked, filename="upload/output_imgs/VIS_img.jpg")
# In[11]:
def main():
# Get options
args = options()
# Set variables
pcv.params.debug = args.debug # Replace the hard-coded debug with the debug flag
img_file = args.image # Replace the hard-coded input image with image flag
############### Image read-in ################
# Read target image
img, path, filename = pcv.readimage(filename = img_file, mode = "rgb")
############### Find scale and crop ################
# find colour card in the image to be analysed
df, start, space = pcv.transform.find_color_card(rgb_img = img)
if int(start[0]) < 2000:
img = imutils.rotate_bound(img, -90)
rotated = 1
df, start, space = pcv.transform.find_color_card(rgb_img = img)
else: rotated = 0
#if img.shape[0] > 6000:
# rotated = 1
#else: rotated = 0
img_mask = pcv.transform.create_color_card_mask(rgb_img = img, radius = 10, start_coord = start, spacing = space, ncols = 4, nrows = 6)
# write the spacing of the colour card to file as size marker
with open(r'size_marker.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow([filename, space[0]])
# define a bounding rectangle around the colour card
x_cc,y_cc,w_cc,h_cc = cv2.boundingRect(img_mask)
x_cc = int(round(x_cc - 0.3 * w_cc))
y_cc = int(round(y_cc - 0.3 * h_cc))
h_cc = int(round(h_cc * 1.6))
w_cc = int(round(w_cc * 1.6))
# crop out colour card
start_point = (x_cc, y_cc)
end_point = (x_cc+w_cc, y_cc+h_cc)
colour = (0, 0, 0)
thickness = -1
crop_img = cv2.rectangle(img, start_point, end_point, colour, thickness)
############### Fine segmentation ################
# Threshold A and B channels of the LAB colourspace and the Hue channel of the HSV colourspace
l_thresh, _ = pcv.threshold.custom_range(img=crop_img, lower_thresh=[70,0,0], upper_thresh=[255,255,255], channel='LAB')
a_thresh, _ = pcv.threshold.custom_range(img=crop_img, lower_thresh=[0,0,0], upper_thresh=[255,145,255], channel='LAB')
b_thresh, _ = pcv.threshold.custom_range(img=crop_img, lower_thresh=[0,0,123], upper_thresh=[255,255,255], channel='LAB')
h_thresh_low, _ = pcv.threshold.custom_range(img=crop_img, lower_thresh=[0,0,0], upper_thresh=[130,255,255], channel='HSV')
h_thresh_high, _ = pcv.threshold.custom_range(img=crop_img, lower_thresh=[150,0,0], upper_thresh=[255,255,255], channel='HSV')
h_thresh = pcv.logical_or(h_thresh_low, h_thresh_high)
# Join the thresholded images to keep only consensus pixels
ab = pcv.logical_and(b_thresh, a_thresh)
lab = pcv.logical_and(l_thresh, ab)
labh = pcv.logical_and(lab, h_thresh)
# Fill small objects
labh_clean = pcv.fill(labh, 200)
# Dilate to close broken borders
#labh_dilated = pcv.dilate(labh_clean, 4, 1)
labh_dilated = labh_clean
# Apply mask (for VIS images, mask_color=white)
masked = pcv.apply_mask(crop_img, labh_dilated, "white")
# Identify objects
contours, hierarchy = pcv.find_objects(crop_img, labh_dilated)
# Define ROI
if rotated == 1:
roi_height = 3000
roi_lwr_bound = y_cc + (h_cc * 0.5) - roi_height
roi_contour, roi_hierarchy= pcv.roi.rectangle(x=1000, y=roi_lwr_bound, h=roi_height, w=2000, img=crop_img)
else:
roi_height = 1500
roi_lwr_bound = y_cc + (h_cc * 0.5) - roi_height
roi_contour, roi_hierarchy= pcv.roi.rectangle(x=2000, y=roi_lwr_bound, h=roi_height, w=2000, img=crop_img)
# Decide which objects to keep
filtered_contours, filtered_hierarchy, mask, area = pcv.roi_objects(img = crop_img,
roi_type = 'partial',
roi_contour = roi_contour,
roi_hierarchy = roi_hierarchy,
object_contour = contours,
obj_hierarchy = hierarchy)
# Combine kept objects
obj, mask = pcv.object_composition(crop_img, filtered_contours, filtered_hierarchy)
############### Analysis ################
outfile=False
if args.writeimg==True:
outfile_black=args.outdir+"/"+filename+"_black"
outfile_white=args.outdir+"/"+filename+"_white"
outfile_analysed=args.outdir+"/"+filename+"_analysed"
# analyse shape
shape_img = pcv.analyze_object(crop_img, obj, mask)
pcv.print_image(shape_img, outfile_analysed)
# analyse colour
colour_img = pcv.analyze_color(crop_img, mask, 'hsv')
# keep the segmented plant for visualisation
picture_mask = pcv.apply_mask(crop_img, mask, "black")
pcv.print_image(picture_mask, outfile_black)
picture_mask = pcv.apply_mask(crop_img, mask, "white")
pcv.print_image(picture_mask, outfile_white)
# print out results
pcv.outputs.save_results(filename=args.result, outformat="json")
def main():
# Get options
args = options()
pcv.params.debug = args.debug # set debug mode
pcv.params.debug_outdir = args.outdir # set output directory
# Read image
img, path, filename = pcv.readimage(filename=args.image)
# Convert RGB to HSV and extract the value channel
s = pcv.rgb2gray_hsv(rgb_img=img, channel='v')
# Threshold the saturation image removing highs and lows and join
s_thresh_1 = pcv.threshold.binary(gray_img=s,
threshold=10,
max_value=255,
object_type='light')
s_thresh_2 = pcv.threshold.binary(gray_img=s,
threshold=245,
max_value=255,
object_type='dark')
s_thresh = pcv.logical_and(bin_img1=s_thresh_1, bin_img2=s_thresh_2)
# Median Blur
s_mblur = 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
b_cnt = pcv.threshold.binary(gray_img=b,
threshold=128,
max_value=255,
object_type='light')
# Fill small objects
b_fill = pcv.fill(b_cnt, 10)
# Join the thresholded saturation and blue-yellow images
bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_fill)
# Apply Mask (for VIS images, mask_color=white)
masked = pcv.apply_mask(rgb_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
maskeda_thresh = pcv.threshold.binary(gray_img=masked_a,
threshold=127,
max_value=255,
object_type='dark')
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)
ab = pcv.logical_or(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh)
# Fill small objects
ab_fill = pcv.fill(bin_img=ab, size=200)
# Apply mask (for VIS images, mask_color=white)
masked2 = pcv.apply_mask(rgb_img=masked, mask=ab_fill, mask_color='white')
# Identify objects
id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill)
# Define ROI
w = 1600
h = 1200
pot = 230 #340
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2,
x=(2472 - w) / 2,
y=(3296 - h - pot),
h=h,
w=w)
# 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)
# Find shape properties, output shape image (optional)
shape_imgs = pcv.analyze_object(img=img, obj=obj, mask=mask)
if args.writeimg == True:
pcv.print_image(img=shape_imgs,
filename="{}_shape.png".format(args.result[:-5]))
pcv.print_image(img=masked2,
filename="{}_obj_on_img.png".format(args.result[:-5]))
# Shape properties relative to user boundary line (optional)
#boundary_img1 = pcv.analyze_bound_horizontal(img=img, obj=obj, mask=mask, line_position=1680)
# Determine color properties: Histograms, Color Slices, output color analyzed histogram (optional)
color_histogram = pcv.analyze_color(rgb_img=img,
mask=kept_mask,
hist_plot_type='all')
# Pseudocolor the grayscale image
pseudocolored_img = pcv.visualize.pseudocolor(gray_img=s,
mask=kept_mask,
cmap='jet')
# Write shape and color data to results file
pcv.print_results(filename=args.result)
def process_image(image_path, threshold):
# Read image
img, _, _ = pcv.readimage(filename=image_path)
# If image is not in Portrait rotate to ensure center of mass splits the plant correctly
if img.shape[0] < img.shape[1]:
img = np.array(Image.fromarray(img).transpose(Image.ROTATE_270))
# 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=85,
max_value=255,
object_type='light')
# Median Blur
s_mblur = 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
b_cnt = pcv.threshold.binary(gray_img=b,
threshold=160,
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
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
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2,
x=300,
y=300,
h=img.shape[0] - 600,
w=img.shape[1] - 600)
# Decide which objects to keep
roi_objects, hierarchy3, _, _ = 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 ----------------
# Find shape properties, output shape image (optional)
_ = pcv.analyze_object(img=img, obj=obj, mask=mask, label="default")
x, y = pcv.outputs.observations['default']['center_of_mass']['value']
width = pcv.outputs.observations['default']['width']['value']
height = pcv.outputs.observations['default']['height']['value']
x = int(x)
y = int(y)
top = max(int(y - (height / 2)), 0)
bottom = min(int(y + (height / 2)), img.shape[0])
left = max(int(x - (width / 2)), 0)
right = min(int(x + (width / 2)), img.shape[1])
left_half = mask[top:bottom, left:x]
right_half = mask[top:bottom, x:right]
left_half_count = cv2.countNonZero(left_half)
right_half_count = cv2.countNonZero(right_half)
left_percent = (left_half_count / (left_half_count + right_half_count))
right_percent = 1 - left_percent
print('Left Percentage: {:.2%} Right Percentage: {:.2%}'.format(
left_percent, right_percent))
if abs(left_percent - right_percent) > threshold:
print('Rotate plant')
else:
print('Do NOT rotate plant')
def main():
# Get options
args = options()
pcv.params.debug = args.debug # set debug mode
pcv.params.debug_outdir = args.outdir # set output directory
# Read metadata
with open(args.metadata, 'r', encoding='utf-8') as f:
md = json.load(f)
camera_label = md['camera_label']
# Read image
img, path, filename = pcv.readimage(filename=args.image)
# Convert RGB to HSV and extract the value channel
s = pcv.rgb2gray_hsv(rgb_img=img, channel='v')
# Threshold the saturation image removing highs and lows and join
s_thresh_1 = pcv.threshold.binary(gray_img=s,
threshold=10,
max_value=255,
object_type='light')
s_thresh_2 = pcv.threshold.binary(gray_img=s,
threshold=245,
max_value=255,
object_type='dark')
s_thresh = pcv.logical_and(bin_img1=s_thresh_1, bin_img2=s_thresh_2)
# Median Blur
s_mblur = 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
b_cnt = pcv.threshold.binary(gray_img=b,
threshold=128,
max_value=255,
object_type='light')
# Fill small objects
b_fill = pcv.fill(b_cnt, 10)
# Join the thresholded saturation and blue-yellow images
bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_fill)
# Apply Mask (for VIS images, mask_color=white)
masked = pcv.apply_mask(rgb_img=img, mask=bs, mask_color='white')
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
# Threshold the green-magenta and blue images
masked_a = pcv.rgb2gray_lab(rgb_img=masked, channel='a')
maskeda_thresh = pcv.threshold.binary(gray_img=masked_a,
threshold=127,
max_value=255,
object_type='dark')
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
# Threshold the green-magenta and blue images
masked_b = pcv.rgb2gray_lab(rgb_img=masked, channel='b')
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)
ab = pcv.logical_or(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh)
# Fill small objects
ab_fill = pcv.fill(bin_img=ab, size=200)
# Apply mask (for VIS images, mask_color=white)
masked2 = pcv.apply_mask(rgb_img=masked, mask=ab_fill, mask_color='white')
# Identify objects
id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill)
# Define ROI
W = 2472
H = 3296
if "far" in camera_label:
# SIDE FAR
w = 1600
h = 1200
pot = 230 #340
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2,
x=(W - w) / 2,
y=(H - h - pot),
h=h,
w=w)
elif "lower" in camera_label:
# SIDE LOWER
w = 800
h = 2400
pot = 340
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2,
x=1000 - w / 2,
y=(H - h - pot),
h=h,
w=w)
elif "upper" in camera_label:
# SIDE UPPER
w = 600
h = 800
pot = 550
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2,
x=1400 - w / 2,
y=(H - h - pot),
h=h,
w=w)
elif "top" in camera_label:
# TOP
w = 450
h = 450
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2,
x=(H - h) / 2,
y=(W - w) / 2,
h=h,
w=w)
# 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)
#TODO: Update for plantCV metadata import
for key in md.keys():
if str(md[key]).isdigit():
pcv.outputs.add_observation(variable=key,
trait=key,
method='',
scale='',
datatype=int,
value=md[key],
label='')
else:
pcv.outputs.add_observation(variable=key,
trait=key,
method='',
scale='',
datatype=str,
value=md[key],
label='')
if obj is not None:
# Find shape properties, output shape image (optional)
shape_imgs = pcv.analyze_object(img=img, obj=obj, mask=mask)
# Shape properties relative to user boundary line (optional)
#boundary_img1 = pcv.analyze_bound_horizontal(img=img, obj=obj, mask=mask, line_position=1680)
# Determine color properties: Histograms, Color Slices, output color analyzed histogram (optional)
color_histogram = pcv.analyze_color(rgb_img=img,
mask=kept_mask,
hist_plot_type='all')
# Pseudocolor the grayscale image
#pseudocolored_img = pcv.visualize.pseudocolor(gray_img=s, mask=kept_mask, cmap='jet')
#print(pcv.outputs.images)
if args.writeimg == True:
for idx, item in enumerate(pcv.outputs.images[0]):
pcv.print_image(item,
"{}_{}.png".format(args.result[:-5], idx))
# Write shape and color data to results file
pcv.print_results(filename=args.result)
# Threshold the blue channel image
b_thresh = pcv.threshold.binary(gray_img=b,
threshold=160,
max_value=255,
object_type='light')
pcv.print_image(img=b_thresh, filename="upload/output_imgs2/Extracted_img.jpg")
pcv.print_image(img=b, filename="upload/output_imgs2/Gray_img.jpg")
# In[39]:
# Join the threshold saturation and blue-yellow images with a logical or operation
# Inputs:
# bin_img1 - Binary image data to be compared to bin_img2
# bin_img2 - Binary image data to be compared to bin_img1
bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_thresh)
pcv.print_image(img=bs, filename="upload/output_imgs2/BSature_img.jpg")
# In[40]:
# Appy Mask (for VIS images, mask_color='white')
# Inputs:
# rgb_img - RGB image data
# mask - Binary mask image data
# mask_color - 'white' or 'black'
rgb_img = img
masked = pcv.apply_mask(rgb_img, mask=bs, mask_color='white')
pcv.print_image(img=masked, filename="upload/output_imgs2/VIS_img.jpg")
# In[41]:
def main():
# Get options
args = options()
pcv.params.debug = args.debug # set debug mode
pcv.params.debug_outdir = args.outdir # set output directory
# Read image
img, path, filename = pcv.readimage(filename=args.image)
# Convert RGB to HSV and extract the saturation channel
h = pcv.rgb2gray_hsv(rgb_img=img, channel='h')
# Threshold the saturation image
h_thresh = pcv.threshold.binary(gray_img=h,
threshold=85,
max_value=255,
object_type='dark')
# Median Blur
h_mblur = pcv.median_blur(gray_img=h_thresh, ksize=20)
h_cnt = pcv.median_blur(gray_img=h_thresh, ksize=20)
# Convert RGB to LAB and extract the Blue channel
b = pcv.rgb2gray_lab(rgb_img=img, channel='b')
# Threshold the blue image
b_thresh = pcv.threshold.binary(gray_img=b,
threshold=160,
max_value=255,
object_type='light')
b_cnt = pcv.threshold.binary(gray_img=b,
threshold=160,
max_value=255,
object_type='light')
# Fill small objects
# b_fill = pcv.fill(b_thresh, 10)
# Join the thresholded saturation and blue-yellow images
bs = pcv.logical_or(bin_img1=h_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
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
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2,
x=400,
y=400,
h=200,
w=200)
# 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)
############### Analysis ################
outfile = False
if args.writeimg == True:
outfile = args.outdir + "/" + filename
# Find shape properties, output shape image (optional)
shape_imgs = pcv.analyze_object(img=img, obj=obj, mask=mask)
# Shape properties relative to user boundary line (optional)
boundary_img1 = pcv.analyze_bound_horizontal(img=img,
obj=obj,
mask=mask,
line_position=600)
# Determine color properties: Histograms, Color Slices, output color analyzed histogram (optional)
color_histogram = pcv.analyze_color(rgb_img=img,
mask=kept_mask,
hist_plot_type='all')
# Pseudocolor the grayscale image
pseudocolored_img = pcv.visualize.pseudocolor(gray_img=h,
mask=kept_mask,
cmap='jet')
# Write shape and color data to results file
pcv.print_results(filename=args.result)
def plantCVProcess(img, x, y, w, h):
# 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=85, 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
b_thresh = pcv.threshold.binary(gray_img=b, threshold=160, max_value=255, object_type='light')
b_cnt = pcv.threshold.binary(gray_img=b, threshold=160, max_value=255, object_type='light')
# Fill small objects
# b_fill = pcv.fill(b_thresh, 10)
# 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
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
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2, x=x, y=y, h=h, w=w)
# 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)
############### Analysis ################
# Find shape properties, output shape image (optional)
shape_imgs = pcv.analyze_object(img=img, obj=obj, mask=mask)
# Shape properties relative to user boundary line (optional)
boundary_img1 = pcv.analyze_bound_horizontal(img=img, obj=obj, mask=mask, line_position=1680)
# Determine color properties: Histograms, Color Slices, output color analyzed histogram (optional)
color_histogram = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type='all')
# Pseudocolor the grayscale image
pseudocolored_img = pcv.visualize.pseudocolor(gray_img=s, mask=mask, cmap='jet')
return print_results()
def shoot():
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
inp = Image.open(uploaded_file)
inp.save('input.jpg')
img, path, filename = pcv.readimage(filename='input.jpg')
image = Image.open('input.jpg')
st.image(image, caption='Original Image',use_column_width=True)
# Convert RGB to HSV and extract the saturation channel
# Inputs:
# rgb_image - RGB image data
# channel - Split by 'h' (hue), 's' (saturation), or 'v' (value) channel
s = pcv.rgb2gray_hsv(rgb_img=img, channel='s')
pcv.print_image(s, "plant/rgbtohsv.png")
image = Image.open('plant/rgbtohsv.png')
st.image(image, caption='RGB to HSV', use_column_width=True)
s_thresh = pcv.threshold.binary(gray_img=s, threshold=85, max_value=255, object_type='light')
pcv.print_image(s_thresh, "plant/binary_threshold.png")
image = Image.open('plant/binary_threshold.png')
st.image(image, caption='Binary Threshold',use_column_width=True)
# Median Blur to clean noise
# Inputs:
# gray_img - Grayscale image data
# ksize - Kernel size (integer or tuple), (ksize, ksize) box if integer input,
# (n, m) box if tuple input
s_mblur = pcv.median_blur(gray_img=s_thresh, ksize=5)
pcv.print_image(s_mblur, "plant/Median_blur.png")
image = Image.open('plant/Median_blur.png')
st.image(image, caption='Median Blur',use_column_width=True)
# An alternative to using median_blur is gaussian_blur, which applies
# a gaussian blur filter to the image. Depending on the image, one
# technique may be more effective than others.
# Inputs:
# img - RGB or grayscale image data
# ksize - Tuple of kernel size
# sigma_x - Standard deviation in X direction; if 0 (default),
# calculated from kernel size
# sigma_y - Standard deviation in Y direction; if sigmaY is
# None (default), sigmaY is taken to equal sigmaX
gaussian_img = pcv.gaussian_blur(img=s_thresh, ksize=(5, 5), sigma_x=0, sigma_y=None)
# Convert RGB to LAB and extract the blue channel ('b')
# Input:
# rgb_img - RGB image data
# channel- Split by 'l' (lightness), 'a' (green-magenta), or 'b' (blue-yellow) channel
b = pcv.rgb2gray_lab(rgb_img=img, channel='b')
b_thresh = pcv.threshold.binary(gray_img=b, threshold=160, max_value=255,
object_type='light')
# Join the threshold saturation and blue-yellow images with a logical or operation
# Inputs:
# bin_img1 - Binary image data to be compared to bin_img2
# bin_img2 - Binary image data to be compared to bin_img1
bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_thresh)
pcv.print_image(bs, "plant/threshold comparison.png")
image = Image.open('plant/threshold comparison.png')
st.image(image, caption='Threshold Comparision',use_column_width=True)
# Appy Mask (for VIS images, mask_color='white')
# Inputs:
# img - RGB or grayscale image data
# mask - Binary mask image data
# mask_color - 'white' or 'black'
masked = pcv.apply_mask(img=img, mask=bs, mask_color='white')
pcv.print_image(masked, "plant/Apply_mask.png")
image = Image.open('plant/Apply_mask.png')
st.image(image, caption='Applied Mask',use_column_width=True)
# 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
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')
pcv.print_image( maskeda_thresh, "plant/maskeda_thresh.png")
pcv.print_image(maskeda_thresh1, "plant/maskeda_thresh1.png")
pcv.print_image(maskedb_thresh, "plant/maskedb_thresh1.png")
image = Image.open('plant/maskeda_thresh.png')
st.image(image, caption='Threshold green-magneta and blue image',use_column_width=True)
# 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)
def test(true_positive_file, test_parameters):
hue_lower_tresh = test_parameters[0]
hue_higher_tresh = test_parameters[1]
saturation_lower_tresh = test_parameters[2]
saturation_higher_tresh = test_parameters[3]
value_lower_tresh = test_parameters[4]
value_higher_tresh = test_parameters[5]
green_lower_tresh = test_parameters[6]
green_higher_tresh = test_parameters[7]
red_lower_tresh = test_parameters[8]
red_higher_thresh = test_parameters[9]
blue_lower_tresh = test_parameters[10]
blue_higher_tresh = test_parameters[11]
blur_k = test_parameters[12]
fill_k = test_parameters[13]
class args:
#image = "C:\\Users\\RensD\\OneDrive\\studie\\Master\\The_big_project\\top_perspective\\0214_2018-03-07 08.55 - 26_cam9.png"
image = true_positive_file
outdir = "C:\\Users\\RensD\\OneDrive\\studie\\Master\\The_big_project\\top_perspective\\output"
debug = debug_setting
result = "results.txt"
# Get options
pcv.params.debug=args.debug #set debug mode
pcv.params.debug_outdir=args.outdir #set output directory
# Read image (readimage mode defaults to native but if image is RGBA then specify mode='rgb')
# Inputs:
# filename - Image file to be read in
# mode - Return mode of image; either 'native' (default), 'rgb', 'gray', or 'csv'
img, path, filename = pcv.readimage(filename=args.image, mode='rgb')
s = pcv.rgb2gray_hsv(rgb_img=img, channel='h')
mask, masked_image = pcv.threshold.custom_range(rgb_img=s, lower_thresh=[hue_lower_tresh], upper_thresh=[hue_higher_tresh], channel='gray')
masked = pcv.apply_mask(rgb_img=img, mask = mask, mask_color = 'white')
#print("filtered on hue")
s = pcv.rgb2gray_hsv(rgb_img=masked, channel='s')
mask, masked_image = pcv.threshold.custom_range(rgb_img=s, lower_thresh=[saturation_lower_tresh], upper_thresh=[saturation_higher_tresh], channel='gray')
masked = pcv.apply_mask(rgb_img=masked, mask = mask, mask_color = 'white')
#print("filtered on saturation")
s = pcv.rgb2gray_hsv(rgb_img=masked, channel='v')
mask, masked_image = pcv.threshold.custom_range(rgb_img=s, lower_thresh=[value_lower_tresh], upper_thresh=[value_higher_tresh], channel='gray')
masked = pcv.apply_mask(rgb_img=masked, mask = mask, mask_color = 'white')
#print("filtered on value")
mask, masked = pcv.threshold.custom_range(rgb_img=masked, lower_thresh=[0,green_lower_tresh,0], upper_thresh=[255,green_higher_tresh,255], channel='RGB')
masked = pcv.apply_mask(rgb_img=masked, mask = mask, mask_color = 'white')
#print("filtered on green")
mask, masked = pcv.threshold.custom_range(rgb_img=masked, lower_thresh=[red_lower_tresh,0,0], upper_thresh=[red_higher_thresh,255,255], channel='RGB')
masked = pcv.apply_mask(rgb_img=masked, mask = mask, mask_color = 'white')
#print("filtered on red")
mask_old, masked_old = pcv.threshold.custom_range(rgb_img=masked, lower_thresh=[0,0,blue_lower_tresh], upper_thresh=[255,255,blue_higher_tresh], channel='RGB')
masked = pcv.apply_mask(rgb_img=masked_old, mask = mask_old, mask_color = 'white')
#print("filtered on blue")
###____________________________________ Blur to minimize
try:
s_mblur = pcv.median_blur(gray_img = masked_old, ksize = blur_k)
s = pcv.rgb2gray_hsv(rgb_img=s_mblur, channel='v')
mask, masked_image = pcv.threshold.custom_range(rgb_img=s, lower_thresh=[0], upper_thresh=[254], channel='gray')
except:
print("failed blur step")
try:
mask = pcv.fill(mask, fill_k)
except:
pass
masked = pcv.apply_mask(rgb_img=masked, mask = mask, mask_color = 'white')
###_____________________________________ Now to identify objects
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=135,
max_value=255, object_type='light')
maskedb_thresh = pcv.threshold.binary(gray_img=masked_b, threshold=128,
max_value=255, object_type='light')
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
# Inputs:
# bin_img - Binary image data
# size - Minimum object area size in pixels (must be an integer), and smaller objects will be filled
ab_fill = pcv.fill(bin_img=ab, size=200)
#print("filled")
# Apply mask (for VIS images, mask_color=white)
masked2 = pcv.apply_mask(rgb_img=masked, mask=ab_fill, mask_color='white')
id_objects, obj_hierarchy = pcv.find_objects(masked, ab_fill)
# Let's just take the largest
roi1, roi_hierarchy= pcv.roi.rectangle(img=masked, x=0, y=0, h=960, w=1280) # Currently hardcoded
with HiddenPrints():
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=roi_type)
obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3)
if use_mask == True:
return(mask)
else:
masked2 = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white')
return(masked2)
def root():
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
inp = Image.open(uploaded_file)
inp.save('input.jpg')
img, path, filename = pcv.readimage(filename='input.jpg')
image = Image.open('input.jpg')
st.image(image, caption='Original Image',use_column_width=True)
# Convert RGB to HSV and extract the saturation channel
# Inputs:
# rgb_image - RGB image data
# channel - Split by 'h' (hue), 's' (saturation), or 'v' (value) channel
s = pcv.rgb2gray_hsv(rgb_img=img, channel='s')
pcv.print_image(s, "plant/rgbtohsv.png")
image = Image.open('plant/rgbtohsv.png')
st.image(image, caption='RGB to HSV', use_column_width=True)
s_thresh = pcv.threshold.binary(gray_img=s, threshold=85, max_value=255, object_type='light')
pcv.print_image(s_thresh, "plant/binary_threshold.png")
image = Image.open('plant/binary_threshold.png')
st.image(image, caption='Binary Threshold',use_column_width=True)
# Median Blur to clean noise
# Inputs:
# gray_img - Grayscale image data
# ksize - Kernel size (integer or tuple), (ksize, ksize) box if integer input,
# (n, m) box if tuple input
s_mblur = pcv.median_blur(gray_img=s_thresh, ksize=5)
pcv.print_image(s_mblur, "plant/Median_blur.png")
image = Image.open('plant/Median_blur.png')
st.image(image, caption='Median Blur',use_column_width=True)
# An alternative to using median_blur is gaussian_blur, which applies
# a gaussian blur filter to the image. Depending on the image, one
# technique may be more effective than others.
# Inputs:
# img - RGB or grayscale image data
# ksize - Tuple of kernel size
# sigma_x - Standard deviation in X direction; if 0 (default),
# calculated from kernel size
# sigma_y - Standard deviation in Y direction; if sigmaY is
# None (default), sigmaY is taken to equal sigmaX
gaussian_img = pcv.gaussian_blur(img=s_thresh, ksize=(5, 5), sigma_x=0, sigma_y=None)
# Convert RGB to LAB and extract the blue channel ('b')
# Input:
# rgb_img - RGB image data
# channel- Split by 'l' (lightness), 'a' (green-magenta), or 'b' (blue-yellow) channel
b = pcv.rgb2gray_lab(rgb_img=img, channel='b')
b_thresh = pcv.threshold.binary(gray_img=b, threshold=160, max_value=255,
object_type='light')
# Join the threshold saturation and blue-yellow images with a logical or operation
# Inputs:
# bin_img1 - Binary image data to be compared to bin_img2
# bin_img2 - Binary image data to be compared to bin_img1
bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_thresh)
pcv.print_image(bs, "plant/threshold comparison.png")
image = Image.open('plant/threshold comparison.png')
st.image(image, caption='Threshold Comparision',use_column_width=True)
# Appy Mask (for VIS images, mask_color='white')
# Inputs:
# img - RGB or grayscale image data
# mask - Binary mask image data
# mask_color - 'white' or 'black'
masked = pcv.apply_mask(img=img, mask=bs, mask_color='white')
pcv.print_image(masked, "plant/Apply_mask.png")
image = Image.open('plant/Apply_mask.png')
st.image(image, caption='Applied Mask',use_column_width=True)
# 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
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')
pcv.print_image( maskeda_thresh, "plant/maskeda_thresh.png")
pcv.print_image(maskeda_thresh1, "plant/maskeda_thresh1.png")
pcv.print_image(maskedb_thresh, "plant/maskedb_thresh1.png")
image = Image.open('plant/maskeda_thresh.png')
st.image(image, caption='Threshold green-magneta and blue image',use_column_width=True)
# 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)
# Opening filters out bright noise from an image.
# Inputs:
# gray_img - Grayscale or binary image data
# kernel - Optional neighborhood, expressed as an array of 1's and 0's. If None (default),
# uses cross-shaped structuring element.
opened_ab = pcv.opening(gray_img=ab)
# Depending on the situation it might be useful to use the
# exclusive or (pcv.logical_xor) function.
# Inputs:
# bin_img1 - Binary image data to be compared to bin_img2
# bin_img2 - Binary image data to be compared to bin_img1
xor_img = pcv.logical_xor(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh)
# Fill small objects (reduce image noise)
# Inputs:
# bin_img - Binary image data
# size - Minimum object area size in pixels (must be an integer), and smaller objects will be filled
ab_fill = pcv.fill(bin_img=ab, size=200)
# Closing filters out dark noise from an image.
# Inputs:
# gray_img - Grayscale or binary image data
# kernel - Optional neighborhood, expressed as an array of 1's and 0's. If None (default),
# uses cross-shaped structuring element.
closed_ab = pcv.closing(gray_img=ab_fill)
# Apply mask (for VIS images, mask_color=white)
masked2 = pcv.apply_mask(img=masked, mask=ab_fill, mask_color='white')
# Identify objects
# Inputs:
# img - RGB or grayscale image data for plotting
# mask - Binary mask used for detecting contours
id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill)
# Define the region of interest (ROI)
# Inputs:
# img - RGB or grayscale image to plot the ROI on
# x - The x-coordinate of the upper left corner of the rectangle
# y - The y-coordinate of the upper left corner of the rectangle
# h - The height of the rectangle
# w - The width of the rectangle
roi1, roi_hierarchy= pcv.roi.rectangle(img=masked2, x=50, y=50, h=100, w=100)
# Decide which objects to keep
# Inputs:
# img = img to display kept objects
# roi_contour = contour of roi, output from any ROI function
# roi_hierarchy = contour of roi, output from any ROI function
# object_contour = contours of objects, output from pcv.find_objects function
# obj_hierarchy = hierarchy of objects, output from pcv.find_objects function
# roi_type = 'partial' (default, for partially inside the ROI), 'cutto', or
# 'largest' (keep only largest contour)
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
# Inputs:
# img - RGB or grayscale image data for plotting
# contours - Contour list
# hierarchy - Contour hierarchy array
obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3)
############### Analysis ################
# Find shape properties, data gets stored to an Outputs class automatically
# Inputs:
# img - RGB or grayscale image data
# obj- Single or grouped contour object
# mask - Binary image mask to use as mask for moments analysis
analysis_image = pcv.analyze_object(img=img, obj=obj, mask=mask)
pcv.print_image(analysis_image, "plant/analysis_image.png")
image = Image.open('plant/analysis_image.png')
st.image(image, caption='Analysis_image',use_column_width=True)
# Shape properties relative to user boundary line (optional)
# Inputs:
# img - RGB or grayscale image data
# obj - Single or grouped contour object
# mask - Binary mask of selected contours
# line_position - Position of boundary line (a value of 0 would draw a line
# through the bottom of the image)
boundary_image2 = pcv.analyze_bound_horizontal(img=img, obj=obj, mask=mask,
line_position=370)
pcv.print_image(boundary_image2, "plant/boundary_image2.png")
image = Image.open('plant/boundary_image2.png')
st.image(image, caption='Boundary Image',use_column_width=True)
# Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)
# Inputs:
# rgb_img - RGB image data
# mask - Binary mask of selected contours
# hist_plot_type - None (default), 'all', 'rgb', 'lab', or 'hsv'
# This is the data to be printed to the SVG histogram file
color_histogram = pcv.analyze_color(rgb_img=img, mask=kept_mask, hist_plot_type='all')
# Print the histogram out to save it
pcv.print_image(img=color_histogram, filename="plant/vis_tutorial_color_hist.jpg")
image = Image.open('plant/vis_tutorial_color_hist.jpg')
st.image(image, caption='Color Histogram',use_column_width=True)
# Divide plant object into twenty equidistant bins and assign pseudolandmark points based upon their
# actual (not scaled) position. Once this data is scaled this approach may provide some information
# regarding shape independent of size.
# Inputs:
# img - RGB or grayscale image data
# obj - Single or grouped contour object
# mask - Binary mask of selected contours
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)
# The print_results function will take the measurements stored when running any (or all) of these functions, format,
# and print an output text file for data analysis. The Outputs class stores data whenever any of the following functions
# are ran: analyze_bound_horizontal, analyze_bound_vertical, analyze_color, analyze_nir_intensity, analyze_object,
# fluor_fvfm, report_size_marker_area, watershed. If no functions have been run, it will print an empty text file
pcv.print_results(filename='vis_tutorial_results.txt')
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 main():
# Get options
args = options()
pcv.params.debug = args.debug # set debug mode
pcv.params.debug_outdir = args.outdir # set output directory
# Read image
img, path, filename = pcv.readimage(filename=args.image)
# 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=85,
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
b_thresh = pcv.threshold.binary(gray_img=b,
threshold=160,
max_value=255,
object_type='light')
b_cnt = pcv.threshold.binary(gray_img=b,
threshold=160,
max_value=255,
object_type='light')
# Fill small objects
# b_fill = pcv.fill(b_thresh, 10)
# 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')
cv2.imwrite("masked.jpeg", masked)
# 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=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')
cv2.imwrite("masked2.jpeg", masked2)
# Collect the cropped image
cropped_img = image[int(roi[1]):int(roi[1] + roi[3]),
int(roi[0]):int(roi[0] + roi[2])]
# Convert RGB to HSV and extract saturation channel
# The HSV value can be changed to be h, s, or v depending on the colour of the flower
saturation_img = pcv.rgb2gray_hsv(cropped_img, 'h')
# Threshold the saturation img
# Depending on the output of the saturation image, the value can be light or dark
# Light or dark is what defines what part of the image its to be removed
saturation_thresh = pcv.threshold.binary(saturation_img, 85, 255, 'light')
# Apply median blur
saturation_mblur = pcv.median_blur(saturation_thresh, 5)
# Convert RGB to LAB and extract blue channel
# Like the HSV function this can be l, a, or b depending on the colour of the flower
blue_channel_img = pcv.rgb2gray_lab(cropped_img, 'l')
blue_channel_cnt = pcv.threshold.binary(blue_channel_img, 160, 255,
'light')
# Join thresholded saturated and blue channel imgs
blue_saturated = pcv.logical_or(saturation_mblur, blue_channel_cnt)
# Apply black colour mask
masked = pcv.apply_mask(cropped_img, blue_saturated, 'black')
# Save image
cv2.imwrite('tmp/' + image, masked)
def main_side():
# Setting "args"
# Get options
pcv.params.debug = args.debug #set debug mode
pcv.params.debug_outdir = args.outdir #set output directory
# Read image (readimage mode defaults to native but if image is RGBA then specify mode='rgb')
# Inputs:
# filename - Image file to be read in
# mode - Return mode of image; either 'native' (default), 'rgb', 'gray', or 'csv'
filename = args.image
img = cv2.imread(args.image, flags=0)
#img = pcv.invert(img)
path, img_name = os.path.split(args.image)
img_bkgrd = cv2.imread("background.png", flags=0)
#print(img)
#print(img_bkgrd)
bkg_sub_img = pcv.image_subtract(img_bkgrd, img)
bkg_sub_thres_img, masked_img = pcv.threshold.custom_range(
rgb_img=bkg_sub_img,
lower_thresh=[50],
upper_thresh=[255],
channel='gray')
# Laplace filtering (identify edges based on 2nd derivative)
# Inputs:
# gray_img - Grayscale image data
# ksize - Aperture size used to calculate the second derivative filter,
# specifies the size of the kernel (must be an odd integer)
# scale - Scaling factor applied (multiplied) to computed Laplacian values
# (scale = 1 is unscaled)
lp_img = pcv.laplace_filter(gray_img=img, ksize=1, scale=1)
# Plot histogram of grayscale values
pcv.visualize.histogram(gray_img=lp_img)
# Lapacian image sharpening, this step will enhance the darkness of the edges detected
lp_shrp_img = pcv.image_subtract(gray_img1=img, gray_img2=lp_img)
# Plot histogram of grayscale values, this helps to determine thresholding value
pcv.visualize.histogram(gray_img=lp_shrp_img)
# Sobel filtering
# 1st derivative sobel filtering along horizontal axis, kernel = 1)
# Inputs:
# gray_img - Grayscale image data
# dx - Derivative of x to analyze
# dy - Derivative of y to analyze
# ksize - Aperture size used to calculate 2nd derivative, specifies the size of the kernel and must be an odd integer
# NOTE: Aperture size must be greater than the largest derivative (ksize > dx & ksize > dy)
sbx_img = pcv.sobel_filter(gray_img=img, dx=1, dy=0, ksize=1)
# 1st derivative sobel filtering along vertical axis, kernel = 1)
sby_img = pcv.sobel_filter(gray_img=img, dx=0, dy=1, ksize=1)
# Combine the effects of both x and y filters through matrix addition
# This will capture edges identified within each plane and emphasize edges found in both images
# Inputs:
# gray_img1 - Grayscale image data to be added to gray_img2
# gray_img2 - Grayscale image data to be added to gray_img1
sb_img = pcv.image_add(gray_img1=sbx_img, gray_img2=sby_img)
# Use a lowpass (blurring) filter to smooth sobel image
# Inputs:
# gray_img - Grayscale image data
# ksize - Kernel size (integer or tuple), (ksize, ksize) box if integer input,
# (n, m) box if tuple input
mblur_img = pcv.median_blur(gray_img=sb_img, ksize=1)
# Inputs:
# gray_img - Grayscale image data
mblur_invert_img = pcv.invert(gray_img=mblur_img)
# combine the smoothed sobel image with the laplacian sharpened image
# combines the best features of both methods as described in "Digital Image Processing" by Gonzalez and Woods pg. 169
edge_shrp_img = pcv.image_add(gray_img1=mblur_invert_img,
gray_img2=lp_shrp_img)
# Perform thresholding to generate a binary image
tr_es_img = pcv.threshold.binary(gray_img=edge_shrp_img,
threshold=145,
max_value=255,
object_type='dark')
# Do erosion with a 3x3 kernel (ksize=3)
# Inputs:
# gray_img - Grayscale (usually binary) image data
# ksize - The size used to build a ksize x ksize
# matrix using np.ones. Must be greater than 1 to have an effect
# i - An integer for the number of iterations
e1_img = pcv.erode(gray_img=tr_es_img, ksize=3, i=1)
# Bring the two object identification approaches together.
# Using a logical OR combine object identified by background subtraction and the object identified by derivative filter.
# Inputs:
# bin_img1 - Binary image data to be compared in bin_img2
# bin_img2 - Binary image data to be compared in bin_img1
comb_img = pcv.logical_or(bin_img1=e1_img, bin_img2=bkg_sub_thres_img)
# Get masked image, Essentially identify pixels corresponding to plant and keep those.
# Inputs:
# rgb_img - RGB image data
# mask - Binary mask image data
# mask_color - 'black' or 'white'
masked_erd = pcv.apply_mask(rgb_img=img, mask=comb_img, mask_color='black')
# Need to remove the edges of the image, we did that by generating a set of rectangles to mask the edges
# img is (1280 X 960)
# mask for the bottom of the image
# Inputs:
# img - RGB or grayscale image data
# p1 - Point at the top left corner of the rectangle (tuple)
# p2 - Point at the bottom right corner of the rectangle (tuple)
# color 'black' (default), 'gray', or 'white'
#
masked1, box1_img, rect_contour1, hierarchy1 = pcv.rectangle_mask(img=img,
p1=(500,
875),
p2=(720,
960))
# mask the edges
masked2, box2_img, rect_contour2, hierarchy2 = pcv.rectangle_mask(img=img,
p1=(1,
1),
p2=(1279,
959))
bx12_img = pcv.logical_or(bin_img1=box1_img, bin_img2=box2_img)
inv_bx1234_img = bx12_img # we dont invert
inv_bx1234_img = bx12_img
#inv_bx1234_img = pcv.invert(gray_img=bx12_img)
edge_masked_img = pcv.apply_mask(rgb_img=masked_erd,
mask=inv_bx1234_img,
mask_color='black')
#print("here we create a mask")
mask, masked = pcv.threshold.custom_range(rgb_img=edge_masked_img,
lower_thresh=[25],
upper_thresh=[175],
channel='gray')
masked = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white')
#print("end")
# Identify objects
# Inputs:
# img - RGB or grayscale image data for plotting
# mask - Binary mask used for detecting contours
id_objects, obj_hierarchy = pcv.find_objects(img=edge_masked_img,
mask=mask)
# Define ROI
# Inputs:
# img - RGB or grayscale image to plot the ROI on
# x - The x-coordinate of the upper left corner of the rectangle
# y - The y-coordinate of the upper left corner of the rectangle
# h - The height of the rectangle
# w - The width of the rectangle
roi1, roi_hierarchy = pcv.roi.rectangle(img=edge_masked_img,
x=100,
y=100,
h=800,
w=1000)
# Decide which objects to keep
# Inputs:
# img = img to display kept objects
# roi_contour = contour of roi, output from any ROI function
# roi_hierarchy = contour of roi, output from any ROI function
# object_contour = contours of objects, output from pcv.find_objects function
# obj_hierarchy = hierarchy of objects, output from pcv.find_objects function
# roi_type = 'partial' (default, for partially inside), 'cutto', or
# 'largest' (keep only largest contour)
with HiddenPrints():
roi_objects, hierarchy5, kept_mask, obj_area = pcv.roi_objects(
img=edge_masked_img,
roi_contour=roi1,
roi_hierarchy=roi_hierarchy,
object_contour=id_objects,
obj_hierarchy=obj_hierarchy,
roi_type='largest')
rgb_img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
# Inputs:
# img - RGB or grayscale image data for plotting
# contours - Contour list
# hierarchy - Contour hierarchy array
o, m = pcv.object_composition(img=rgb_img,
contours=roi_objects,
hierarchy=hierarchy5)
### Analysis ###
outfile = False
if args.writeimg == True:
outfile = args.outdir + "/" + filename
# Perform signal analysis
# Inputs:
# img - RGB or grayscale image data
# obj- Single or grouped contour object
# mask - Binary image mask to use as mask for moments analysis
shape_img = pcv.analyze_object(img=img, obj=o, mask=m)
new_im = Image.fromarray(shape_img)
new_im.save("output//" + args.filename + "shape_img_side.png")
# Inputs:
# gray_img - 8 or 16-bit grayscale image data
# mask - Binary mask made from selected contours
# bins - Number of classes to divide the spectrum into
# histplot - If True, plots the histogram of intensity values
nir_hist = pcv.analyze_nir_intensity(gray_img=img,
mask=kept_mask,
bins=256,
histplot=True)
# Pseudocolor the grayscale image to a colormap
# Inputs:
# gray_img - Grayscale image data
# obj - Single or grouped contour object (optional), if provided the pseudocolored image gets cropped down to the region of interest.
# mask - Binary mask (optional)
# background - Background color/type. Options are "image" (gray_img), "white", or "black". A mask must be supplied.
# cmap - Colormap
# min_value - Minimum value for range of interest
# max_value - Maximum value for range of interest
# dpi - Dots per inch for image if printed out (optional, if dpi=None then the default is set to 100 dpi).
# axes - If False then the title, x-axis, and y-axis won't be displayed (default axes=True).
# colorbar - If False then the colorbar won't be displayed (default colorbar=True)
pseudocolored_img = pcv.visualize.pseudocolor(gray_img=img,
mask=kept_mask,
cmap='viridis')
# Perform shape analysis
# Inputs:
# img - RGB or grayscale image data
# obj- Single or grouped contour object
# mask - Binary image mask to use as mask for moments analysis
shape_imgs = pcv.analyze_object(img=rgb_img, obj=o, mask=m)
# Write shape and nir data to results file
pcv.print_results(filename=args.result)
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():
# Get options
#args = options()
parser = argparse.ArgumentParser(
description="Imaging processing with opencv")
parser.add_argument("-i",
"--image",
help="Input image file.",
required=True)
parser.add_argument("-o",
"--outdir",
help="Output directory for image files.",
required=False)
parser.add_argument("-r", "--result", help="result file.", required=False)
parser.add_argument("-w",
"--writeimg",
help="write out images.",
default=False,
action="store_true")
parser.add_argument("-f",
"--fileout",
help="output mask file path",
required=True)
parser.add_argument(
"-D",
"--debug",
help=
"can be set to 'print' or None (or 'plot' if in jupyter) prints intermediate images.",
default=None)
args = parser.parse_args()
pcv.params.debug = args.debug # set debug mode
pcv.params.debug_outdir = args.outdir # set output directory
# Read image
img, path, filename = pcv.readimage(filename=args.image)
img = cv2.resize(img, (1280, 960), interpolation=cv2.INTER_AREA)
# 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=35,
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
b_thresh = pcv.threshold.binary(gray_img=b,
threshold=180,
max_value=255,
object_type='light')
b_cnt = pcv.threshold.binary(gray_img=b,
threshold=180,
max_value=255,
object_type='light')
# Fill small objects
# b_fill = pcv.fill(b_thresh, 10)
# 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(rgb_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
maskeda_thresh = pcv.threshold.binary(gray_img=masked_a,
threshold=95,
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(rgb_img=masked, mask=ab_fill, mask_color='white')
# Identify objects
id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill)
# Define ROI
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2,
x=0,
y=0,
h=img.shape[0],
w=img.shape[1])
# 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)
############### Analysis ################
outfile = False
if args.writeimg == True:
outfile = args.outdir + "/" + filename
# Find shape properties, output shape image (optional)
shape_imgs = pcv.analyze_object(img=img, obj=obj, mask=mask)
# Shape properties relative to user boundary line (optional)
boundary_img1 = pcv.analyze_bound_horizontal(img=img,
obj=obj,
mask=mask,
line_position=1680)
# Determine color properties: Histograms, Color Slices, output color analyzed histogram (optional)
color_histogram = pcv.analyze_color(rgb_img=img,
mask=kept_mask,
hist_plot_type='all')
# Pseudocolor the grayscale image
pseudocolored_img = pcv.visualize.pseudocolor(gray_img=s,
mask=kept_mask,
cmap='jet')
# Write shape and color data to results file
#pcv.print_results(filename=args.result)
count = 0
[rows, cols] = mask.shape
for i in range(rows):
for j in range(cols):
if mask[i, j] > 128:
count += 1
re = float(count) / (rows * cols)
text = "rec_rate:" + str(round(re, 4))
cv2.putText(mask, text, (40, 50), cv2.FONT_HERSHEY_PLAIN, 2.0, 255, 2)
#cv2.imshow("tt",mask)
#cv2.waitKey(0)
cv2.imwrite(args.fileout, mask)
print(str(round(re, 4)))
