How to use the plantcv.print_results function in plantcv

# Apply the box mask to the image
    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)

# Apply the box mask to the image
    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)

# 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)

# 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)

# 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)

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)

# 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)

############## 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, 320, 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)

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)