How to use the plantcv.analyze_object function in plantcv

device, roi1, roi_hierarchy= pcv.define_roi(img,'circle', device, None, 'default', args.debug,True, 0,0,-200,-200)
  
  # 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
  
  # 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,outfile)
    
  # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)
  device, color_header,color_data,color_img= pcv.analyze_color(img, args.image, mask, 256, device, args.debug,None,'v','img',300,outfile)
  
  # Output shape and color data

  result=open(args.result,"a")
  result.write('\t'.join(map(str,shape_header)))
  result.write("\n")
  result.write('\t'.join(map(str,shape_data)))
  result.write("\n")
  for row in shape_img:
    result.write('\t'.join(map(str,row)))
    result.write("\n")
  result.write('\t'.join(map(str,color_header)))
  result.write("\n")

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)
  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, 270, device,args.debug,args.outdir+'/'+filename)
  
  # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)
  device, color_header,color_data,norm_slice= pcv.analyze_color(img, args.image, mask, 256, device, args.debug,'all','rgb','v','img',300,args.outdir+'/'+filename)
  
  # Output shape and color data
  pcv.print_results(args.image, shape_header, shape_data)
  pcv.print_results(args.image, color_header, color_data)
  pcv.print_results(args.image, boundary_header, boundary_data)

# 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)
  
  # 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)
  device, color_header,color_data,color_img= pcv.analyze_color(img, args.image, kept_mask, 256, device, args.debug,None,'v','img',300,args.outdir+'/'+filename)
  
  # Output shape and color data
  pcv.print_results(args.image, shape_header, shape_data)
  pcv.print_results(args.image, color_header, color_data)

# 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)
  
  # 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)
  device, color_header,color_data,norm_slice= pcv.analyze_color(img, args.image, kept_mask, 256, device, args.debug,'all','rgb','v','img',300,args.outdir+'/'+filename)
  
  # Output shape and color data
  pcv.print_results(args.image, shape_header, shape_data)
  pcv.print_results(args.image, color_header, color_data)

# 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)
  device, boundary_header,boundary_data, boundary_img1= pcv.analyze_bound(img, args.image,obj, mask, 25, device,args.debug,args.outdir+'/'+filename)
  
  # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)
  device, color_header,color_data,norm_slice= pcv.analyze_color(img, args.image, kept_mask, 256, device, args.debug,'all','rgb','v','img',300,args.outdir+'/'+filename)
  
  # Output shape and color data
  pcv.print_results(args.image, shape_header, shape_data)
  pcv.print_results(args.image, color_header, color_data)
  pcv.print_results(args.image, boundary_header, boundary_data)
#  

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, 290, device,args.debug,args.outdir+'/'+filename)
  
  # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)
  device, color_header,color_data,norm_slice= pcv.analyze_color(img, args.image, kept_mask4, 256, device, args.debug,'all','rgb','v','img',300,args.outdir+'/'+filename)
  
  # Output shape and color data
  pcv.print_results(args.image, shape_header, shape_data)
  pcv.print_results(args.image, color_header, color_data)
  pcv.print_results(args.image, boundary_header, boundary_data)

# 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,'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(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)
  device, color_header,color_data,color_img= pcv.analyze_color(img, args.image, kept_mask, 256, device, args.debug,'all','v','img',300,args.outdir+'/'+filename)
  
  # Output shape and color data
  pcv.print_results(args.image, shape_header, shape_data)
  pcv.print_results(args.image, color_header, color_data)

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
  
  # 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,outfile)
  
  # Shape properties relative to user boundary line (optional)
  device, boundary_header,boundary_data, boundary_img1= pcv.analyze_bound(img, args.image,obj, mask, 935, device,args.debug,outfile)
  
  # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)
  device, color_header,color_data,color_img= pcv.analyze_color(img, args.image, mask, 256, device, args.debug,None,'v','img',300,outfile)
  
  # Output shape and color data

  result=open(args.result,"a")
  result.write('\t'.join(map(str,shape_header)))
  result.write("\n")
  result.write('\t'.join(map(str,shape_data)))
  result.write("\n")
  result.write('\t'.join(map(str,shape_img)))
  result.write("\n")

device, roi_objects, hierarchy5, kept_mask, obj_area = pcv.roi_objects(img, 'partial', roi_contour, roi_hierarchy, plant_objects, plant_hierarchy, device, args.debug)
    
      
    # Apply the box mask to the image
    # device, masked_img = pcv.apply_mask(masked_erd_dil, inv_bx1234_img, 'black', device, args.debug)
    device, masked_img = pcv.apply_mask(kept_mask, inv_bx1234_img, 'black', device, args.debug)
    rgb = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
    # Generate a binary to send to the analysis function
    device, mask = pcv.binary_threshold(masked_img, 1, 255, 'light', device, args.debug)
    mask3d = np.copy(mask)
    plant_objects_2, plant_hierarchy_2 = cv2.findContours(mask3d,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
    device, o, m = pcv.object_composition(rgb, roi_objects, hierarchy5, device, args.debug)
    
    ### Analysis ###
    device, hist_header, hist_data, h_norm = pcv.analyze_NIR_intensity(img, args.image, mask, 256, device, args.debug, args.outdir + '/' + img_name)
    device, shape_header, shape_data, ori_img = pcv.analyze_object(rgb, args.image, o, m, device, args.debug, args.outdir + '/' + img_name)
    
    pcv.print_results(args.image, hist_header, hist_data)
    pcv.print_results(args.image, shape_header, shape_data)

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, 380, device,args.debug,args.outdir+'/'+filename)
  
  # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)
  device, color_header,color_data,color_img= pcv.analyze_color(img, args.image, kept_mask4, 256, device, args.debug,'all','v','img',300,args.outdir+'/'+filename)
  
  # Output shape and color data
  pcv.print_results(args.image, shape_header, shape_data)
  pcv.print_results(args.image, color_header, color_data)
  pcv.print_results(args.image, boundary_header, boundary_data)