How to use the plantcv.fill function in plantcv
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danforthcenter / plantcv / scripts / image_analysis / flu_tv / fluor_z600_L1.py View on Github 
# Mask pesky track autofluor
device, track1= pcv.rgb2gray_hsv(track, 'v', device, args.debug)
device, track_thresh = pcv.binary_threshold(track1, 0, 255, 'light', device, args.debug)
device, track_inv=pcv.invert(track_thresh, device, args.debug)
device, track_masked = pcv.apply_mask(mask1, track_inv, 'black', device, args.debug)
# Threshold the Saturation image
device, fmax_thresh = pcv.binary_threshold(track_masked, 20, 255, 'light', device, args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(fmax_thresh, 5, device, args.debug)
device, s_cnt = pcv.median_blur(fmax_thresh, 5, device, args.debug)
# Fill small objects
device, s_fill = pcv.fill(s_mblur, s_cnt, 110, device, args.debug)
device, sfill_cnt = pcv.fill(s_mblur, s_cnt, 110, device, args.debug)
# Identify objects
device, id_objects,obj_hierarchy = pcv.find_objects(mask, sfill_cnt, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy= pcv.define_roi(mask,'circle', device, None, 'default', args.debug,True, 0,0,-50,-50)
# Decide which objects to keep
device,roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(mask,'partial',roi1,roi_hierarchy,id_objects,obj_hierarchy,device, args.debug)
# Object combine kept objects
device, obj, masked = pcv.object_composition(mask, roi_objects, hierarchy3, device, args.debug)
################ Analysis ################
# Find shape properties, output shape image (optional)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 noise
device, ab_fill1 = pcv.fill(ab, ab_cnt, 2, 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, 550, 0,-600,-907)# 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)
device, masked3 = pcv.apply_mask(masked2, kept_mask1, 'white', device, args.debug)
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, 300, device, args.debug)
device, roi2, roi_hierarchy2= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 1700, 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)device, masked = pcv.apply_mask(img, s_cnt, '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, 20, device, args.debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(masked, ab_fill, 'white', 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(img,'rectangle', device, None, 'default', args.debug,True, 25, 25,-10,-25)
# 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)
# # 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, maskedb_thresh = pcv.binary_threshold(masked_b, 133, 255, 'light', device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)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, 125, 255, 'dark', device, args.debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 133, 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, 10, device, args.debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(masked, ab_fill, 'white', 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, 0, 0,0,-925)
# 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)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, 250, 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
device, id_objects,obj_hierarchy = pcv.find_objects(masked2, soil_cnt, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy= pcv.define_roi(img,'rectangle', device, None, 'default', args.debug,True, 600,450,-600,-350)
# 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)# 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, 10, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_or(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, 125, 255, 'dark', device, args.debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 133, 255, 'light', device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)# 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, 49, 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, 150, 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)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,-900)
# 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, kept_cnt = pcv.logical_or(ab_cnt3, kept_mask3, device, args.debug)
device, kept_mask4 = pcv.fill(kept_mask4_1, kept_cnt, 200, device, args.debug)
device, masked5 = pcv.apply_mask(masked2, kept_mask4, 'white', device, args.debug)
device, id_objects4,obj_hierarchy4 = pcv.find_objects(masked5, kept_mask4, device, args.debug)
device, roi4, roi_hierarchy4= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,False, 0, 0,0,0)
device,roi_objects4, hierarchy4, kept_mask4, obj_area = pcv.roi_objects(img,'partial',roi4,roi_hierarchy4,id_objects4,obj_hierarchy4,device, args.debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects4, hierarchy4, device, args.debug)
############## Analysis ################
# Find shape properties, output shape image (optional)
device, shape_header,shape_data,shape_img = pcv.analyze_object(img, args.image, obj, mask, device,args.debug,args.outdir+'/'+filename)
# Shape properties relative to user boundary line (optional)
device, boundary_header,boundary_data, boundary_img1= pcv.analyze_bound(img, args.image,obj, mask, 950, device,args.debug,args.outdir+'/'+filename)plantcv
An image processing package for plant phenotyping.
Package Health Score
75 / 100Popular plantcv functions
- plantcv.analyze_color
- plantcv.analyze_object
- plantcv.apply_mask
- plantcv.binary_threshold
- plantcv.define_roi
- plantcv.fill
- plantcv.find_objects
- plantcv.logical_and
- plantcv.logical_or
- plantcv.median_blur
- plantcv.object_composition
- plantcv.plantcv.params
- plantcv.plantcv.params.debug
- plantcv.plantcv.params.device
- plantcv.plot_hist
- plantcv.print_results
- plantcv.readimage
- plantcv.rgb2gray_hsv
- plantcv.rgb2gray_lab
- plantcv.roi_objects