Problem-solving with a PlantCV update

plantcv_pipeline.py

#!/usr/bin/python

import sys, traceback
import cv2
import numpy as np
import argparse
import string
import matplotlib as mpl

# Import PlantCV
from plantcv import plantcv as pcv

### Parse command-line arguments
def 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("-D", "--debug", help="can be set to 'print' or None (or 'plot' if in jupyter) prints intermediate images.")
    args = parser.parse_args()
    return args

### Main pipeline

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(args.image, mode="rgb")

    # Rotate image
    img = pcv.rotate(img, -90, False)

    # Remove obvious area of no interest:
    maskeda, box1_img, rect_contour1, hierarchy1 = pcv.rectangle_mask(img, (3750,0), (5500,7000), color="white")
    maskedb, box2_img, rect_contour2, hierarchy2 = pcv.rectangle_mask(img, (0,0), (5500,300), color="white")
    maskedc, box3_img, rect_contour3, hierarchy3 = pcv.rectangle_mask(img, (0,3200), (5500,3700), color="white")
    maskedd, box4_img, rect_contour4, hierarchy4 = pcv.rectangle_mask(img, (0,0), (1400,7000), color="white")

    # combine boxes to filter the edges and car out of the photo
    bx12_img = pcv.logical_or(box1_img, box2_img)
    bx123_img = pcv.logical_or(bx12_img, box3_img)
    bx1234_img = pcv.logical_or(bx123_img, box4_img)

    # invert this mask and then apply it the masked image.
    inv_bx1234_img = pcv.invert(bx1234_img)
    img = pcv.apply_mask(img, inv_bx1234_img, 'white')

    # Convert RGB to HSV and extract the saturation channel
    s = pcv.rgb2gray_hsv(img, 'h')

    # Threshold
    s_thresh = pcv.threshold.binary(s, 14, 146, 'light')

    # Median Blur
    s_mblur = pcv.median_blur(s_thresh, 5)
    s_cnt = pcv.median_blur(s_thresh, 5)
    #gaussian_img = pcv.gaussian_blur(img=s_thresh, ksize=(5, 5), sigmax=0, sigmay=None)

    # Convert RGB to LAB and extract the blue channel ('b')
    b = pcv.rgb2gray_lab(img, 'b')

    # Threshold the blue channel image
    b_thresh = pcv.threshold.binary(b, 120, 255, 'dark')
    b_cnt = pcv.threshold.binary(b, 120, 255, 'light')

    # Fill small objects
    b_fill = pcv.fill(b_thresh, 10)

    # Remove specs of dirt from the image that will be processed as ROI (4,1 worked well)
    b_thresh = pcv.dilate(b_thresh,4,1)
    b_cnt = pcv.erode(b_cnt,4,1)

    # Join the threshold saturation and blue-yellow images
    bs = pcv.logical_or(s_mblur, b_cnt)

    # Appy Mask (for VIS images, mask_color='white')
    masked = pcv.apply_mask(img, bs, 'white')

    # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
    masked_a = pcv.rgb2gray_lab(masked, 'a')
    masked_b = pcv.rgb2gray_lab(masked, 'b')

    # Threshold the green-magenta and blue images
    maskeda_thresh = pcv.threshold.binary(masked_a, 128, 255, 'light')
    maskeda_thresh1 = pcv.threshold.binary(masked_a, 135, 255, 'light')
    maskedb_thresh = pcv.threshold.binary(masked_b, 126, 255, 'dark')

    # Join the thresholded saturation and blue-yellow images (OR)
    ab1 = pcv.logical_or(maskeda_thresh, maskedb_thresh)
    ab = pcv.logical_or(maskeda_thresh1, ab1)
    xor_img = pcv.logical_xor(maskeda_thresh, maskedb_thresh)

    # Fill small objects (reduce image noise)
    ab_fill = pcv.fill(ab, 2)

    # Apply mask (for VIS images, mask_color=white)
    masked2 = pcv.apply_mask(masked, ab_fill, 'white')

    # Identify objects
    id_objects, obj_hierarchy = pcv.find_objects(masked2, ab_fill)

    # Define the region of interest (ROI)
    roi1, roi_hierarchy= pcv.roi.rectangle(x=1450, y=250, h=2950, w=2350, img=masked2)

    # Decide which objects to keep
    #roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img, 'cutto', roi1, roi_hierarchy, id_objects, obj_hierarchy)
    roi_objects, hierarchy3, mask, obj_area = pcv.roi_objects(img, roi1, roi_hierarchy, id_objects, obj_hierarchy, roi_type='partial')

    # Object combine kept objects
    obj, mask = pcv.object_composition(img, roi_objects, 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, obj, mask)

    # Shape properties relative to user boundary line (optional)
    boundary_img1 = pcv.analyze_bound_horizontal(img, obj, mask, 1680)

    # Determine color properties: Histograms, Color Slices, output color analyzed histogram (optional)
    # color_histogram = pcv.analyze_color(img, kept_mask, 'all')

    # Pseudocolor the grayscale image
    pseudocolored_img = pcv.visualize.pseudocolor(gray_img=s, obj=None, mask=mask, axes=True, colorbar=True, cmap='jet')

    # Old
    #pseudocolored_img = pcv.analyze_color(img, mask, 256, hist_plot_type=None)

    # Write shape and color data to results file
    pcv.print_results(filename=args.result)

if __name__ == '__main__':
    main()

# End message
print("Complete")

z_errors.txt

Traceback (most recent call last):
  File "C:/Path/To/plantcv_pipeline.py", line 142, in <module>
    main()
  File "C:/Path/To/plantcv_pipeline.py", line 133, in main
    pseudocolored_img = pcv.visualize.pseudocolor(gray_img=s, obj=None, mask=mask, axes=True, colorbar=True, cmap='jet')
AttributeError: module 'plantcv.plantcv' has no attribute 'visualize'


Processing results...
Traceback (most recent call last):
  File "C:\Path\To\plantcv_pipeline.py", line 262, in <module>
    main()
  File "C:\Path\To\plantcv_pipeline.py", line 247, in main
    pcvp.process_results(valid_meta=args.valid_meta, job_dir=args.jobdir, json_file=args.json)
  File "C:\Path\To\plantcv_pipeline.py", line 42, in process_results
    obs = json.load(results)
  File "C:\Anaconda\envs\plantcv\lib\json\__init__.py", line 299, in load
    parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)
  File "C:\Anaconda\envs\plantcv\lib\json\__init__.py", line 354, in loads
    return _default_decoder.decode(s)
  File "C:\Anaconda\envs\plantcv\lib\json\decoder.py", line 339, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())
  File "C:\Anaconda\envs\plantcv\lib\json\decoder.py", line 357, in raw_decode
    raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)