def write_output(args, i):
"""Write Outputs for a single ROI
Parameters
----------
args : dict
commandline arguments and image metadata
i : int
ROI number
Returns
-------
Notes
-----
Side effect of printing a json file to disk
"""
# 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(args.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()
def main():
# Get options
args = options()
# Set variables
device = 0
pcv.params.debug = args.debug
img_file = args.image
# Read image
img, path, filename = pcv.readimage(filename=img_file, mode='rgb')
# Process saturation channel from HSV colour space
s = pcv.rgb2gray_hsv(rgb_img=img, channel='s')
lp_s = pcv.laplace_filter(s, 1, 1)
shrp_s = pcv.image_subtract(s, lp_s)
s_eq = pcv.hist_equalization(shrp_s)
s_thresh = pcv.threshold.binary(gray_img=s_eq,
threshold=215,
max_value=255,
object_type='light')
s_mblur = pcv.median_blur(gray_img=s_thresh, ksize=5)
# Process green-magenta channel from LAB colour space
b = pcv.rgb2gray_lab(rgb_img=img, channel='a')
b_lp = pcv.laplace_filter(b, 1, 1)
b_shrp = pcv.image_subtract(b, b_lp)
b_thresh = pcv.threshold.otsu(b_shrp, 255, object_type='dark')
# Create and apply mask
bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_thresh)
filled = pcv.fill_holes(bs)
masked = pcv.apply_mask(img=img, mask=filled, mask_color='white')
# Extract colour channels from masked image
masked_a = pcv.rgb2gray_lab(rgb_img=masked, channel='a')
masked_b = pcv.rgb2gray_lab(rgb_img=masked, channel='b')
# Threshold the green-magenta and blue images
maskeda_thresh = pcv.threshold.binary(gray_img=masked_a,
threshold=115,
max_value=255,
object_type='dark')
maskeda_thresh1 = pcv.threshold.binary(gray_img=masked_a,
threshold=140,
max_value=255,
object_type='light')
maskedb_thresh = pcv.threshold.binary(gray_img=masked_b,
threshold=128,
max_value=255,
object_type='light')
# Join the thresholded saturation and blue-yellow images (OR)
ab1 = pcv.logical_or(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh)
ab = pcv.logical_or(bin_img1=maskeda_thresh1, bin_img2=ab1)
# Produce and apply a mask
opened_ab = pcv.opening(gray_img=ab)
ab_fill = pcv.fill(bin_img=ab, size=200)
closed_ab = pcv.closing(gray_img=ab_fill)
masked2 = pcv.apply_mask(img=masked, mask=bs, mask_color='white')
# Identify objects
id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill)
# Define region of interest (ROI)
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2,
x=250,
y=100,
h=200,
w=200)
# Decide what objects to keep
roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img=img,
roi_contour=roi1,
roi_hierarchy=roi_hierarchy,
object_contour=id_objects,
obj_hierarchy=obj_hierarchy,
roi_type='partial')
# Object combine kept objects
obj, mask = pcv.object_composition(img=img,
contours=roi_objects,
hierarchy=hierarchy3)
############### Analysis ################
outfile = False
if args.writeimg == True:
outfile = args.outdir + "/" + filename
# Analyze the plant
analysis_image = pcv.analyze_object(img=img, obj=obj, mask=mask)
color_histogram = pcv.analyze_color(rgb_img=img,
mask=kept_mask,
hist_plot_type='all')
top_x, bottom_x, center_v_x = pcv.x_axis_pseudolandmarks(img=img,
obj=obj,
mask=mask)
top_y, bottom_y, center_v_y = pcv.y_axis_pseudolandmarks(img=img,
obj=obj,
mask=mask)
# Print results of the analysis
pcv.print_results(filename=args.result)
pcv.output_mask(img,
kept_mask,
filename,
outdir=args.outdir,
mask_only=True)
def main():
# Set variables
args = options()
pcv.params.debug = args.debug
# Read and rotate image
img, path, filename = pcv.readimage(filename=args.image)
img = pcv.rotate(img, -90, False)
# Create mask from LAB b channel
l = pcv.rgb2gray_lab(rgb_img=img, channel='b')
l_thresh = pcv.threshold.binary(gray_img=l,
threshold=115,
max_value=255,
object_type='dark')
l_mblur = pcv.median_blur(gray_img=l_thresh, ksize=5)
# Apply mask to image
masked = pcv.apply_mask(img=img, mask=l_mblur, mask_color='white')
ab_fill = pcv.fill(bin_img=l_mblur, size=50)
# Extract plant object from image
id_objects, obj_hierarchy = pcv.find_objects(img=img, mask=ab_fill)
roi1, roi_hierarchy = pcv.roi.rectangle(img=masked,
x=150,
y=270,
h=100,
w=100)
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')
obj, mask = pcv.object_composition(img=img,
contours=roi_objects,
hierarchy=hierarchy3)
############### Analysis ################
# Analyze shape properties
analysis_image = pcv.analyze_object(img=img, obj=obj, mask=mask)
boundary_image2 = pcv.analyze_bound_horizontal(img=img,
obj=obj,
mask=mask,
line_position=370)
# Analyze colour properties
color_histogram = pcv.analyze_color(rgb_img=img,
mask=kept_mask,
hist_plot_type='all')
# Analyze shape independent of size
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
pcv.print_results(filename='{}'.format(args.result))
pcv.print_image(img=color_histogram,
filename='{}_color_hist.jpg'.format(args.outdir))
pcv.print_image(img=kept_mask, filename='{}_mask.jpg'.format(args.outdir))
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)
obj=obj,
mask=mask)
# In[28]:
top_y, bottom_y, center_v_y = pcv.y_axis_pseudolandmarks(img=img,
obj=obj,
mask=mask)
# In[29]:
# 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='upload/Results/VIS_results.txt')
# In[30]:
# Python program to convert text
# file to JSON
import json
# the file to be converted to
# json format
filename = 'upload/Results/VIS_results.txt'
# dictionary where the lines from
# text will be stored
dict1 = {}
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)
def silhouette_top():
"First we draw the picture from the 3D data"
########################################################################################################################################################################
x = []
y = []
z = []
image_top = Image.new("RGB", (width, height), color='white')
draw = ImageDraw.Draw(image_top)
data_3d = open(args.image, "r")
orignal_file = args.image
for line in data_3d:
line = line.split(",")
y.append(int(line[0]))
x.append(int(line[1]))
z.append(int(line[2]))
i = 0
for point_x in x:
point_y = y[i]
draw.rectangle([point_x, point_y, point_x + 1, point_y + 1],
fill="black")
#rectange takes input [x0, y0, x1, y1]
i += 1
image_top.save("top_temp.png")
image_side = Image.new("RGB", (1280, 960), color='white')
draw = ImageDraw.Draw(image_side)
i = 0
for point_y in y:
point_z = z[i]
draw.rectangle([point_z, point_y, point_z + 1, point_y + 1],
fill="black")
#rectange takes input [x0, y0, x1, y1]
i += 1
image_side.save("side_temp.png")
########################################################################################################################################################################
args.image = "top_temp.png"
# Get options
pcv.params.debug = args.debug #set debug mode
pcv.params.debug_outdir = args.outdir #set output directory
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)
v = pcv.rgb2gray_hsv(rgb_img=img, channel='v')
v_thresh, maskedv_image = pcv.threshold.custom_range(rgb_img=v,
lower_thresh=[0],
upper_thresh=[200],
channel='gray')
id_objects, obj_hierarchy = pcv.find_objects(img=maskedv_image,
mask=v_thresh)
# Define ROI
roi1, roi_hierarchy = pcv.roi.rectangle(img=maskedv_image,
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')
obj, mask = pcv.object_composition(img=img,
contours=roi_objects,
hierarchy=hierarchy3)
outfile = args.outdir + "/" + filename
# Shape properties relative to user boundary line (optional)
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 + "_top_boundary.png")
# Find shape properties, output shape image (optional)
shape_img = pcv.analyze_object(img=img, obj=obj, mask=mask)
new_im = Image.fromarray(shape_img)
new_im.save("output//" + args.filename + "_top_shape.png")
new_im.save("output//" + args.filename + "shape_img.png")
GT = re.sub(pattern, replacement, files_names[file_counter])
pcv.outputs.add_observation(variable="genotype",
trait="genotype",
method="Regexed from the filename",
scale=None,
datatype=str,
value=int(GT),
label="GT")
# Write shape and color data to results file
pcv.print_results(filename=args.result)
##########################################################################################################################################
args.image = "side_temp.png"
# Get options
pcv.params.debug = args.debug #set debug mode
pcv.params.debug_outdir = args.outdir #set output directory
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)
v = pcv.rgb2gray_hsv(rgb_img=img, channel='v')
v_thresh, maskedv_image = pcv.threshold.custom_range(rgb_img=v,
lower_thresh=[0],
upper_thresh=[200],
channel='gray')
id_objects, obj_hierarchy = pcv.find_objects(img=maskedv_image,
mask=v_thresh)
# Define ROI
roi1, roi_hierarchy = pcv.roi.rectangle(img=maskedv_image,
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')
obj, mask = pcv.object_composition(img=img,
contours=roi_objects,
hierarchy=hierarchy3)
outfile = args.outdir + "/" + filename
# Shape properties relative to user boundary line (optional)
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 + "_side_boundary.png")
# Find shape properties, output shape image (optional)
shape_img = pcv.analyze_object(img=img, obj=obj, mask=mask)
new_im = Image.fromarray(shape_img)
new_im.save("output//" + args.filename + "_side_shape.png")
GT = re.sub(pattern, replacement, files_names[file_counter])
pcv.outputs.add_observation(variable="genotype",
trait="genotype",
method="Regexed from the filename",
scale=None,
datatype=str,
value=int(GT),
label="GT")
# Write shape and color data to results file
pcv.print_results(filename=args.result_side)
obj=obj,
mask=mask)
# In[86]:
top_y, bottom_y, center_v_y = pcv.y_axis_pseudolandmarks(img=img,
obj=obj,
mask=mask)
# In[87]:
# 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='upload/Results1/NIR_results.txt')
# In[88]:
# Python program to convert text
# file to JSON
import json
# the file to be converted to
# json format
filename = 'upload/Results1/NIR_results.txt'
# dictionary where the lines from
# text will be stored
dict1 = {}
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 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 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(gray_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=100,
y=100,
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=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 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 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():
# Get options
args = options()
pcv.params.debug = args.debug #set debug mode
pcv.params.debug_outdir = args.outdir #set output directory
# Read image (converting fmax and track to 8 bit just to create a mask, use 16-bit for all the math)
fmax, path, filename = pcv.readimage(args.fmax)
# Threshold the image
mask = pcv.threshold.binary(gray_img=fmax,
threshold=20,
max_value=255,
object_type='light')
mask = pcv.fill(mask, 100)
# Identify objects
id_objects, obj_hierarchy = pcv.find_objects(img=fmax, mask=mask)
# Define ROI
roi1, roi_hierarchy = pcv.roi.rectangle(img=mask,
x=180,
y=90,
h=200,
w=200)
# Decide which objects to keep
roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img=mask,
roi_contour=roi1,
roi_hierarchy=roi_hierarchy,
object_contour=id_objects,
obj_hierarchy=obj_hierarchy,
roi_type='partial')
# Object combine kept objects
obj, masked = pcv.object_composition(img=mask,
contours=roi_objects,
hierarchy=hierarchy3)
################ Analysis ################
outfile = False
if args.writeimg == True:
outfile = args.outdir + "/" + filename
# Find shape properties, output shape image (optional)
shape_img = pcv.analyze_object(img=mask, obj=obj, mask=masked)
# Fluorescence Measurement (read in 16-bit images)
fmin = cv2.imread(args.fmin, -1)
fmax = cv2.imread(args.fmax, -1)
fvfm_images = pcv.fluor_fvfm(fdark=np.zeros_like(fmax),
fmin=fmin,
fmax=fmax,
mask=kept_mask,
bins=256)
# Store the two images
fv_img = fvfm_images[0]
fvfm_hist = fvfm_images[1]
fvfm = np.divide(fv_img,
fmax,
out=np.zeros_like(fmax, dtype='float'),
where=np.logical_and(mask > 1, fmax > 0))
# Pseudocolor the Fv/Fm grayscale image that is calculated inside the fluor_fvfm function
pseudocolored_img = pcv.visualize.pseudocolor(gray_img=fvfm,
mask=kept_mask,
cmap='viridis',
min_value=0,
max_value=1)
# Write shape and nir data to results file
pcv.print_results(filename=args.result)
def main():
# Create input arguments object
args = options()
# Set debug mode
pcv.params.debug = args.debug
# Open a single image
img, imgpath, imgname = pcv.readimage(filename=args.image)
# Visualize colorspaces
all_cs = pcv.visualize.colorspaces(rgb_img=img)
# Extract the Blue-Yellow ("b") channel from the LAB colorspace
gray_img = pcv.rgb2gray_lab(rgb_img=img, channel="b")
# Plot a histogram of pixel values for the Blue-Yellow ("b") channel.
hist_plot = pcv.visualize.histogram(gray_img=gray_img)
# Apply a binary threshold to the Blue-Yellow ("b") grayscale image.
thresh_img = pcv.threshold.binary(gray_img=gray_img,
threshold=140,
max_value=255,
object_type="light")
# Apply a dilation with a 5x5 kernel and 3 iterations
dil_img = pcv.dilate(gray_img=thresh_img, ksize=5, i=3)
# Fill in small holes in the leaves
closed_img = pcv.fill_holes(bin_img=dil_img)
# Erode the plant pixels using a 5x5 kernel and 3 iterations
er_img = pcv.erode(gray_img=closed_img, ksize=5, i=3)
# Apply a Gaussian blur with a 5 x 5 kernel.
blur_img = pcv.gaussian_blur(img=er_img, ksize=(5, 5))
# Set pixel values less than 255 to 0
blur_img[np.where(blur_img < 255)] = 0
# Fill/remove objects less than 300 pixels in area
cleaned = pcv.fill(bin_img=blur_img, size=300)
# Create a circular ROI
roi, roi_str = pcv.roi.circle(img=img, x=1725, y=1155, r=400)
# Identify objects in the binary image
cnts, cnts_str = pcv.find_objects(img=img, mask=cleaned)
# Filter objects by region of interest
plant_cnt, plant_str, plant_mask, plant_area = pcv.roi_objects(
img=img,
roi_contour=roi,
roi_hierarchy=roi_str,
object_contour=cnts,
obj_hierarchy=cnts_str)
# Combine objects into one
plant, mask = pcv.object_composition(img=img,
contours=plant_cnt,
hierarchy=plant_str)
# Measure size and shape properties
shape_img = pcv.analyze_object(img=img, obj=plant, mask=mask)
if args.writeimg:
pcv.print_image(img=shape_img,
filename=os.path.join(args.outdir,
"shapes_" + imgname))
# Analyze color properties
color_img = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type="hsv")
if args.writeimg:
pcv.print_image(img=color_img,
filename=os.path.join(args.outdir,
"histogram_" + imgname))
# Save the measurements to a file
pcv.print_results(filename=args.result)
obj_hierarchy=roi_obj_hierarchy)
# Combine objects together in each plant
plant_contour, plant_mask = pcv.object_composition(
img=img_copy, contours=filtered_contours, hierarchy=filtered_hierarchy)
# Analyze the shape of each plant
analysis_images = pcv.analyze_object(img=img_copy,
obj=plant_contour,
mask=plant_mask)
# Save the image with shape characteristics
img_copy = analysis_images
# Print out a text file with shape data for each plant in the image
pcv.print_results(filename='prefix_' + str(i) + '.txt')
# Clear the measurements stored globally into the Ouptuts class
pcv.outputs.clear()
# Plot out the image with shape analysis on each plant in the image
pcv.plot_image(img_copy)
# To view and/or download the text file output (saved in JSON format)...
# 1) To see the text file with data that got saved out, click “File” tab in top left corner.
# 2) Click “Open…”
# 3) Open the multi-plant text files
#
# Check out documentation on how to [convert JSON](https://plantcv.readthedocs.io/en/latest/tools/#convert-output-json-data-files-to-csv-tables) format output into table formatted output. Depending on the analysis steps a PlantCV user may have two CSV files (single value traits and multivalue traits).
#
#
#
filled_img = fill_segments(mask=cropped_mask, objects=roi_objects)
# Access data stored out from fill_segments
segments_area = pcv.outputs.observations['segment_area']['value']
# In[116]:
#Length
# Measure path lengths of segments
# Inputs:
# segmented_img = Segmented image to plot lengths on
# objects = List of contours
labeled_img = pcv.morphology.segment_path_length(segmented_img=filled_img,
objects=roi_objects)
# In[117]:
# Write morphological data to results file
# 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=args.result)
# In[100]:
#CLEARS OUTPUTS
#pcv.outputs.clear()
object, mask = pcv.object_composition(img, roi_objects, roi_obj_hierarchy)
shape_img = pcv.analyze_object(img, object, mask)
pcv.outputs.add_observation(variable='roi',
trait='roi',
method='roi',
scale='int',
datatype=int,
value=i,
label='#')
filename = str(i) + "_" + args.result
with open(filename, "w") as r:
r.write(metadata)
pcv.print_results(filename=filename)
pcv.outputs.clear()
#Print out a text file with shape data for each plant in the image <-- removed code
#Old code would create a separate txt file for each plant in the picture
#i.e. if you had 18 recognized plants you would get 18 text files
#new code takes opens those 18 text files one by one and re-writes them into a "final file"
#all the data from each text file gets put into one file and the 18 text files get removed except
#the last one
# pcv.print_results(filename = str(i)+ args.result)
# with open(args.result, "a") as finalfile:
# with open('prefix_' +str(i)+ '.txt') as tempfile:
# for x in tempfile.readlines():
# finalfile.write(x)
# finalfile.write("\n")
# tempfile.close()
