How to use the plantcv.find_objects function in plantcv

To help you get started, we’ve selected a few plantcv examples, based on popular ways it is used in public projects.

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terraref / computing-pipeline / scripts / plantcv / extractor_with_avg_traits / PlantcvClowderIndoorAnalysis.py View on Github

# 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,
                                                 -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, debug)

    # Object combine kept objects
    device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, debug)

    # Find shape properties, output shape image (optional)
    device, shape_header, shape_data, shape_img = pcv.analyze_object(img, vis_img, obj, mask, device, debug)

    # Determine color properties

danforthcenter / plantcv / scripts / dev / lifter_vis_sv_z1_L0.py View on Github

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, 650, 0,-650,-120)
 
  # 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 = pcv.logical_or(ab_cnt3, kept_mask3, device, args.debug)
  device, masked5 = pcv.apply_mask(masked2, kept_mask4, 'white', device, args.debug)
  device, masked5_a = pcv.rgb2gray_lab(masked5, 'a', device, args.debug)
  device, masked5_a_thresh = pcv.binary_threshold(masked5_a, 130, 255, 'light', device, args.debug)
  device, masked5_a_cnt = pcv.binary_threshold(masked5_a, 130, 255, 'light', device, args.debug)
  device, masked5_a_fill = pcv.fill(masked5_a_thresh, masked5_a_cnt, 200, device, args.debug)
  device, masked5_mblur = pcv.median_blur(masked5_a_fill, 7, device, args.debug)

  device, id_objects4,obj_hierarchy4 = pcv.find_objects(masked5, masked5_mblur, device, args.debug)
  device, roi4, roi_hierarchy4= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,False, 0, 0,0,0)

danforthcenter / plantcv / scripts / dev / lifter_vis_tv_z1_L0.py View on Github

device, soila_thresh = pcv.binary_threshold(soil_a, 133, 255, 'light', device, args.debug)
  device, soila_cnt = pcv.binary_threshold(soil_a, 133, 255, 'light', device, args.debug)

#
#   Fill small objects
  device, soil_fill = pcv.fill(soila_thresh, soila_cnt, 200, device, args.debug)
#
#   Median Filter
  device, soil_mblur = pcv.median_blur(soil_fill, 13, device, args.debug)
  device, soil_cnt = pcv.median_blur(soil_fill, 13, device, args.debug)
#  
#   Apply mask (for vis images, mask_color=white)
  device, masked2 = pcv.apply_mask(soil_mblur, 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, 400,400,-400,-400)
#  
#   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)
#  
############## 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)
   
  # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)

danforthcenter / plantcv / scripts / dev / gehan_brachy / vis-sv / vis_sv_z1500_frame0_L2_e82.py View on Github

# 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)
  
  # 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 ################
  
  outfile=False
  if args.writeimg==True:
    outfile=args.outdir+"/"+filename

danforthcenter / plantcv / scripts / dev / gehan_brachy / vis-sv / vis_sv_z3500_L2_e82.py View on Github

# 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 ################
  
  outfile=False
  if args.writeimg==True:
    outfile=args.outdir+"/"+filename

danforthcenter / plantcv / scripts / dev / vis_sv_trip_kt.py View on Github

# 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, 30, 25,-10,-15)
  
  # 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)
  
############## 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)

danforthcenter / plantcv / scripts / dev / vis_sv_z1_L1_e82.py View on Github

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, 650, 0,-450,-250)
 
  # 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)

danforthcenter / plantcv / scripts / dev / gehan_brachy / vis-nir-sv / vis_nir_sv_z500_frame0_h2_e82_brachy_drought.py View on Github

# Find matching NIR image
  device, nirpath=pcv.get_nir(path,filename,device,args.debug)
  nir, path1, filename1=pcv.readimage(nirpath)
  nir2=cv2.imread(nirpath,-1)
  
  # Flip mask
  device, f_mask= pcv.flip(mask,"vertical",device,args.debug)
  
  # Reize mask
  device, nmask = pcv.resize(f_mask, 0.116148,0.116148, device, args.debug)
  
  # position, and crop mask
  device,newmask=pcv.crop_position_mask(nir,nmask,device,33,3,"top","right",args.debug)
  
  # Identify objects
  device, nir_objects,nir_hierarchy = pcv.find_objects(nir, newmask, device, args.debug)
  
  # Object combine kept objects
  device, nir_combined, nir_combinedmask = pcv.object_composition(nir, nir_objects, nir_hierarchy, device, args.debug)

####################################### Analysis #############################################
  outfile1=False
  if args.writeimg==True:
    outfile1=args.outdir+"/"+filename1

  device,nhist_header, nhist_data,nir_imgs= pcv.analyze_NIR_intensity(nir2, filename1, nir_combinedmask, 256, device,False, args.debug, outfile1)
  device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(nir2, filename1, nir_combined, nir_combinedmask, device, args.debug, outfile1)
  
  coresult=open(args.coresult,"a")
  coresult.write('\t'.join(map(str,nhist_header)))
  coresult.write("\n")
  coresult.write('\t'.join(map(str,nhist_data)))

danforthcenter / plantcv / scripts / dev / vis_sv_trip_kt2.py View on Github

# 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)
#  
############### 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)

danforthcenter / plantcv / scripts / dev / gehan_brachy / vis-nir-sv / vis_nir_sv_z500_h2_e82_brachy_drought.py View on Github

# Find matching NIR image
  device, nirpath=pcv.get_nir(path,filename,device,args.debug)
  nir, path1, filename1=pcv.readimage(nirpath)
  nir2=cv2.imread(nirpath,-1)
  
  # Flip mask
  device, f_mask= pcv.flip(mask,"vertical",device,args.debug)
  
  # Reize mask
  device, nmask = pcv.resize(f_mask, 0.116148,0.116148, device, args.debug)
  
  # position, and crop mask
  device,newmask=pcv.crop_position_mask(nir,nmask,device,36,2,"top","right",args.debug)
  
  # Identify objects
  device, nir_objects,nir_hierarchy = pcv.find_objects(nir, newmask, device, args.debug)
  
  # Object combine kept objects
  device, nir_combined, nir_combinedmask = pcv.object_composition(nir, nir_objects, nir_hierarchy, device, args.debug)

####################################### Analysis #############################################
  outfile1=False
  if args.writeimg==True:
    outfile1=args.outdir+"/"+filename1

  device,nhist_header, nhist_data,nir_imgs= pcv.analyze_NIR_intensity(nir2, filename1, nir_combinedmask, 256, device,False, args.debug, outfile1)
  device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(nir2, filename1, nir_combined, nir_combinedmask, device, args.debug, outfile1)
  
  coresult=open(args.coresult,"a")
  coresult.write('\t'.join(map(str,nhist_header)))
  coresult.write("\n")
  coresult.write('\t'.join(map(str,nhist_data)))

How to use the plantcv.find_objects function in plantcv

To help you get started, we’ve selected a few plantcv examples, based on popular ways it is used in public projects.

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