How to use the plantcv.binary_threshold function in plantcv

device, brass_masked = pcv.apply_mask(masked, brass_inv, 'white', device, debug)

    # Further mask soil and car
    device, masked_a = pcv.rgb2gray_lab(brass_masked, 'a', device, debug)
    device, soil_car1 = pcv.binary_threshold(masked_a, 128, 255, 'dark', device, debug)
    device, soil_car2 = pcv.binary_threshold(masked_a, 128, 255, 'light', device, debug)
    device, soil_car = pcv.logical_or(soil_car1, soil_car2, device, debug)
    device, soil_masked = pcv.apply_mask(brass_masked, soil_car, 'white', device, debug)

    # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
    device, soil_a = pcv.rgb2gray_lab(soil_masked, 'a', device, debug)
    device, soil_b = pcv.rgb2gray_lab(soil_masked, 'b', device, debug)

    # Threshold the green-magenta and blue images
    device, soila_thresh = pcv.binary_threshold(soil_a, 124, 255, 'dark', device, debug)
    device, soilb_thresh = pcv.binary_threshold(soil_b, 148, 255, 'light', device, debug)

    # Join the thresholded saturation and blue-yellow images (OR)
    device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device, debug)
    device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device, debug)

    # Fill small objects
    device, soil_cnt = pcv.fill(soil_ab, soil_ab_cnt, 300, device, debug)

    # Apply mask (for vis images, mask_color=white)
    device, masked2 = pcv.apply_mask(soil_masked, soil_cnt, 'white', device, debug)

    # Identify objects
    device, id_objects, obj_hierarchy = pcv.find_objects(masked2, soil_cnt, device, debug)

    # Define ROI
    device, roi1, roi_hierarchy = pcv.define_roi(img, 'rectangle', device, None, 'default', debug, True, 600, 450, -600,

device, masked_b1 = pcv.rgb2gray_lab(masked3, 'b', device, args.debug)
  device, maskeda_thresh1 = pcv.binary_threshold(masked_a1, 110, 255, 'dark', device, args.debug)
  device, maskedb_thresh1 = pcv.binary_threshold(masked_b1, 200, 255, 'light', device, args.debug)
  device, ab1 = pcv.logical_or(maskeda_thresh1, maskedb_thresh1, device, args.debug)
  device, ab_cnt1 = pcv.logical_or(maskeda_thresh1, maskedb_thresh1, device, args.debug)
  device, ab_fill1 = pcv.fill(ab1, ab_cnt1, 300, device, args.debug)

  
  device, roi2, roi_hierarchy2= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 1900, 0,0,0)
  device, id_objects2,obj_hierarchy2 = pcv.find_objects(masked2, ab_fill, device, args.debug)
  device,roi_objects2, hierarchy2, kept_mask2, obj_area2 = pcv.roi_objects(masked2,'cutto',roi2,roi_hierarchy2,id_objects2,obj_hierarchy2,device, args.debug)
  device, masked4 = pcv.apply_mask(masked2, kept_mask2, 'white', device, args.debug)
  device, masked_a2 = pcv.rgb2gray_lab(masked4, 'a', device, args.debug)
  device, masked_b2 = pcv.rgb2gray_lab(masked4, 'b', device, args.debug)
  device, maskeda_thresh2 = pcv.binary_threshold(masked_a2, 122, 255, 'dark', device, args.debug)
  device, maskedb_thresh2 = pcv.binary_threshold(masked_b2, 170, 255, 'light', device, args.debug)
  device, ab2 = pcv.logical_or(maskeda_thresh2, maskedb_thresh2, device, args.debug)
  device, ab_cnt2 = pcv.logical_or(maskeda_thresh2, maskedb_thresh2, device, args.debug)
  device, ab_fill2 = pcv.fill(ab2, ab_cnt2, 200, device, args.debug)
  
  device, ab_cnt3 = pcv.logical_or(ab_fill1, ab_fill2, device, args.debug)
  device, masked3 = pcv.apply_mask(masked2, ab_cnt3, 'white', device, args.debug)
  
  # Identify objects
  device, id_objects3,obj_hierarchy3 = pcv.find_objects(masked2, ab_fill, device, args.debug)

  # Define ROI
  device, roi3, roi_hierarchy3= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 500, 0,-450,-750)
 
  # Decide which objects to keep and combine with objects overlapping with black bars
  device,roi_objects3, hierarchy3, kept_mask3, obj_area1 = pcv.roi_objects(img,'cutto',roi3,roi_hierarchy3,id_objects3,obj_hierarchy3,device, args.debug)
  device, kept_mask4_1 = pcv.logical_or(ab_cnt3, kept_mask3, device, args.debug)

device, brass_mask1 = pcv.rgb2gray_hsv(brass_mask, 'v', device, args.debug)
  device, brass_thresh = pcv.binary_threshold(brass_mask1, 0, 255, 'light', device, args.debug)
  device, brass_inv=pcv.invert(brass_thresh, device, args.debug)
  device, brass_masked = pcv.apply_mask(masked, brass_inv, 'white', device, args.debug)
  
  # Further mask soil and car
  device, masked_a = pcv.rgb2gray_lab(brass_masked, 'a', device, args.debug)
  device, soil_car = pcv.binary_threshold(masked_a, 128, 255, 'dark', device, args.debug)
  device, soil_masked = pcv.apply_mask(brass_masked, soil_car, 'white', device, args.debug)
  
  # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
  device, soil_a = pcv.rgb2gray_lab(soil_masked, 'a', device, args.debug)
  device, soil_b = pcv.rgb2gray_lab(soil_masked, 'b', device, args.debug)
  
  # Threshold the green-magenta and blue images
  device, soila_thresh = pcv.binary_threshold(soil_a, 118, 255, 'dark', device, args.debug)
  device, soilb_thresh = pcv.binary_threshold(soil_b, 155, 255, 'light', device, args.debug)

  # Join the thresholded saturation and blue-yellow images (OR)
  device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)
  device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)

  # Fill small objects
  device, soil_fill = pcv.fill(soil_ab, soil_ab_cnt, 75, device, args.debug)

  # Median Filter
  device, soil_mblur = pcv.median_blur(soil_fill, 5, device, args.debug)
  device, soil_cnt = pcv.median_blur(soil_fill, 5, device, args.debug)
  
  # Apply mask (for vis images, mask_color=white)
  device, masked2 = pcv.apply_mask(soil_masked, soil_cnt, 'white', device, args.debug)

# Threshold the Saturation image
  device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device, args.debug)
  
  # Median Filter
  device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug)
  device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug)
  
  # Fill small objects
  device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug)
  
  # Convert RGB to LAB and extract the Blue channel
  device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug)
  
  # Threshold the blue image
  device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug)
  device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug)
  
  # Fill small objects
  device, b_fill = pcv.fill(b_thresh, b_cnt, 150, device, args.debug)
  
  # Join the thresholded saturation and blue-yellow images
  device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug)
  
  # Apply Mask (for vis images, mask_color=white)
  device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug)
  
  # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
  device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug)
  device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, args.debug)
  
  # Threshold the green-magenta and blue images
  device, maskeda_thresh = pcv.binary_threshold(masked_a, 122, 255, 'dark', device, args.debug)

device, brass_masked = pcv.apply_mask(masked, brass_inv, 'white', device, args.debug)
  
  # Further mask soil and car
  device, masked_a = pcv.rgb2gray_lab(brass_masked, 'a', device, args.debug)
  device, soil_car1 = pcv.binary_threshold(masked_a, 128, 255, 'dark', device, args.debug)
  device, soil_car2 = pcv.binary_threshold(masked_a, 128, 255, 'light', device, args.debug)
  device, soil_car=pcv.logical_or(soil_car1, soil_car2,device, args.debug)
  device, soil_masked = pcv.apply_mask(brass_masked, soil_car, 'white', device, args.debug)
  
  # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
  device, soil_a = pcv.rgb2gray_lab(soil_masked, 'a', device, args.debug)
  device, soil_b = pcv.rgb2gray_lab(soil_masked, 'b', device, args.debug)
  
  # Threshold the green-magenta and blue images
  device, soila_thresh = pcv.binary_threshold(soil_a, 124, 255, 'dark', device, args.debug)
  device, soilb_thresh = pcv.binary_threshold(soil_b, 148, 255, 'light', device, args.debug)

  # Join the thresholded saturation and blue-yellow images (OR)
  device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)
  device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)

  # Fill small objects
  device, soil_cnt = pcv.fill(soil_ab, soil_ab_cnt, 150, device, args.debug)

  # Median Filter
  #device, soil_mblur = pcv.median_blur(soil_fill, 5, device, args.debug)
  #device, soil_cnt = pcv.median_blur(soil_fill, 5, device, args.debug)
  
  # Apply mask (for vis images, mask_color=white)
  device, masked2 = pcv.apply_mask(soil_masked, soil_cnt, 'white', device, args.debug)
  
  # Identify objects

def main():
  # Get options
  args = options()
  
  # Read image
  img, path, filename = pcv.readimage(args.image)
  #roi = cv2.imread(args.roi)
  
  # Pipeline step
  device = 0

  # Convert RGB to HSV and extract the Saturation channel
  device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug)
  
  # Threshold the Saturation image
  device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device, args.debug)
  
  # Median Filter
  device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug)
  device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug)
  
  # Fill small objects
  device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug)
  
  # Convert RGB to LAB and extract the Blue channel
  device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug)
  
  # Threshold the blue image
  device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug)
  device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug)
  
  # Fill small objects

# Fill small objects
  #device, b_fill = pcv.fill(b_thresh, b_cnt, 150, device, args.debug)
  
  # Join the thresholded saturation and blue-yellow images
  device, bs = pcv.logical_and(s_mblur, b_cnt, device, args.debug)
  
  # Apply Mask (for vis images, mask_color=white)
  device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug)
  
  # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
  device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug)
  device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, args.debug)
  
  # Threshold the green-magenta and blue images
  device, maskeda_thresh = pcv.binary_threshold(masked_a, 127, 255, 'dark', device, args.debug)
  device, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light', device, args.debug)
  
  # Join the thresholded saturation and blue-yellow images (OR)
  device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug)
  device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug)
  
  # Fill small objects
  device, ab_fill = pcv.fill(ab, ab_cnt, 200, device, args.debug)
  
  # Apply mask (for vis images, mask_color=white)
  device, masked2 = pcv.apply_mask(masked, ab_fill, 'white', device, args.debug)
  
  # Select area with black bars and find overlapping plant material
  device, roi1, roi_hierarchy1= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 0, 0,-1700,0)
  device, id_objects1,obj_hierarchy1 = pcv.find_objects(masked2, ab_fill, device, args.debug)
  device,roi_objects1, hierarchy1, kept_mask1, obj_area1 = pcv.roi_objects(masked2,'cutto',roi1,roi_hierarchy1,id_objects1,obj_hierarchy1,device, args.debug)

# Dilate to join small objects with larger ones
  device, ab_cnt1=pcv.dilate(ab_fill1, 3, 2, device, args.debug)
  device, ab_cnt2=pcv.dilate(ab_fill1, 3, 2, device, args.debug)
  
  # Fill dilated image mask
  device, ab_cnt3=pcv.fill(ab_cnt2,ab_cnt1,150,device,args.debug)
  device, masked2 = pcv.apply_mask(masked, ab_cnt3, 'white', device, args.debug)
  
  # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
  device, masked2_a = pcv.rgb2gray_lab(masked2, 'a', device, args.debug)
  device, masked2_b = pcv.rgb2gray_lab(masked2, 'b', device, args.debug)
  
  # Threshold the green-magenta and blue images
  device, masked2a_thresh = pcv.binary_threshold(masked2_a, 127, 255, 'dark', device, args.debug)
  device, masked2b_thresh = pcv.binary_threshold(masked2_b, 128, 255, 'light', device, args.debug)
  device, ab_fill = pcv.logical_or(masked2a_thresh, masked2b_thresh, device, args.debug)
  
  # Identify objects
  device, id_objects,obj_hierarchy = pcv.find_objects(masked2, ab_fill, device, args.debug)
  
  # Define ROI
  device, roi1, roi_hierarchy= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 500, 0,-600,-885)
  
  # Decide which objects to keep
  device,roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img,'partial',roi1,roi_hierarchy,id_objects,obj_hierarchy,device, args.debug)
  
  # Object combine kept objects
  device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug)
  
  ############## VIS Analysis ################

device, sb_img = pcv.image_add(sbx_img, sby_img, device, args.debug)
    if args.debug:
      pcv.plot_hist(sb_img, 'hist_sb_comb_img')
    
    # Use a lowpass (blurring) filter to smooth sobel image
    device, mblur_img = pcv.median_blur(sb_img, 1, device, args.debug)
    device, mblur_invert_img = pcv.invert(mblur_img, device, args.debug)
    
    # 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 
    device, edge_shrp_img = pcv.image_add(mblur_invert_img, lp_shrp_img, device, args.debug)
    if args.debug:
      pcv.plot_hist(edge_shrp_img, 'hist_edge_shrp_img')
      
    # Perform thresholding to generate a binary image
    device, tr_es_img = pcv.binary_threshold(edge_shrp_img, 155, 255, 'dark', device, args.debug)
    
    # Prepare a few small kernels for morphological filtering
    kern = np.zeros((3,3), dtype=np.uint8)
    kern1 = np.copy(kern)
    kern1[1,1:3]=1
    kern2 = np.copy(kern)
    kern2[1,0:2]=1
    kern3 = np.copy(kern)
    kern3[0:2,1]=1
    kern4 = np.copy(kern)
    kern4[1:3,1]=1
    
    # Prepare a larger kernel for dilation
    kern[1,0:3]=1
    kern[0:3,1]=1

device, masked_a1 = pcv.rgb2gray_lab(masked3, 'a', device, args.debug)
  device, masked_b1 = pcv.rgb2gray_lab(masked3, 'b', device, args.debug)
  device, maskeda_thresh1 = pcv.binary_threshold(masked_a1, 122, 255, 'dark', device, args.debug)
  device, maskedb_thresh1 = pcv.binary_threshold(masked_b1, 170, 255, 'light', device, args.debug)
  device, ab1 = pcv.logical_or(maskeda_thresh1, maskedb_thresh1, device, args.debug)
  device, ab_cnt1 = pcv.logical_or(maskeda_thresh1, maskedb_thresh1, device, args.debug)
  device, ab_fill1 = pcv.fill(ab1, ab_cnt1, 200, device, args.debug)

  
  device, roi2, roi_hierarchy2= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 1900, 0,0,0)
  device, id_objects2,obj_hierarchy2 = pcv.find_objects(masked2, ab_fill, device, args.debug)
  device,roi_objects2, hierarchy2, kept_mask2, obj_area2 = pcv.roi_objects(masked2,'cutto',roi2,roi_hierarchy2,id_objects2,obj_hierarchy2,device, args.debug)
  device, masked4 = pcv.apply_mask(masked2, kept_mask2, 'white', device, args.debug)
  device, masked_a2 = pcv.rgb2gray_lab(masked4, 'a', device, args.debug)
  device, masked_b2 = pcv.rgb2gray_lab(masked4, 'b', device, args.debug)
  device, maskeda_thresh2 = pcv.binary_threshold(masked_a2, 122, 255, 'dark', device, args.debug)
  device, maskedb_thresh2 = pcv.binary_threshold(masked_b2, 170, 255, 'light', device, args.debug)
  device, ab2 = pcv.logical_or(maskeda_thresh2, maskedb_thresh2, device, args.debug)
  device, ab_cnt2 = pcv.logical_or(maskeda_thresh2, maskedb_thresh2, device, args.debug)
  device, ab_fill2 = pcv.fill(ab2, ab_cnt2, 200, device, args.debug)
  
  device, ab_cnt3 = pcv.logical_or(ab_fill1, ab_fill2, device, args.debug)
  device, masked3 = pcv.apply_mask(masked2, ab_cnt3, 'white', device, args.debug)
  
  # Identify objects
  device, id_objects3,obj_hierarchy3 = pcv.find_objects(masked2, ab_fill, device, args.debug)

  # Define ROI
  device, roi3, roi_hierarchy3= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 525, 0,-500,-110)
 
  # Decide which objects to keep and combine with objects overlapping with black bars
  device,roi_objects3, hierarchy3, kept_mask3, obj_area1 = pcv.roi_objects(img,'cutto',roi3,roi_hierarchy3,id_objects3,obj_hierarchy3,device, args.debug)