How to use the plantcv.median_blur function in plantcv

# Read image
  img, path, filename = pcv.readimage(args.image)
  brass_mask = 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, 49, 255, 'light', device, args.debug)
  
   #Median Filter
  device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug)
  
   #Apply Mask (for vis images, mask_color=white)
  device, masked = pcv.apply_mask(img, s_mblur, 'white', device, args.debug)
  
#   Convert RGB to LAB and extract the Green-Magenta 
  device, soil_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug)
#  
#   Threshold the green-magenta 
  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

# Read image
  img, path, filename = pcv.readimage(args.image)
  brass_mask = 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, 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, 130, 255, 'light', device, args.debug)
  device, b_cnt = pcv.binary_threshold(b, 130, 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)

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, 50, 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,'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)

# Read image
  img, path, filename = pcv.readimage(args.image)
  brass_mask = 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, 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, 100, device, args.debug)
  
  # Join the thresholded saturation and blue-yellow images
  device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug)

# 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
  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)

# 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
  device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, args.debug)
  
  # Join the thresholded saturation and blue-yellow images

if args.debug:
      pcv.plot_hist(sbx_img, 'hist_sbx')
      
    # 1st derivative sobel filtering along vertical axis, kernel = 1, unscaled)
    device, sby_img = pcv.sobel_filter(img, 0, 1, 1, 1, device, args.debug)
    if args.debug:
      pcv.plot_hist(sby_img, 'hist_sby')
      
    # Combine the effects of both x and y filters through matrix addition
    # This will capture edges identified within each plane and emphesize edges found in both images
    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, 150, 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)

# Read image
  img, path, filename = pcv.readimage(args.image)
  brass_mask = 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, 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, 100, device, args.debug)
  
  # Join the thresholded saturation and blue-yellow images
  device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug)

if args.debug:
      pcv.plot_hist(sbx_img, 'hist_sbx')
      
    # 1st derivative sobel filtering along vertical axis, kernel = 1, unscaled)
    device, sby_img = pcv.sobel_filter(img, 0, 1, 1, 1, device, args.debug)
    if args.debug:
      pcv.plot_hist(sby_img, 'hist_sby')
      
    # Combine the effects of both x and y filters through matrix addition
    # This will capture edges identified within each plane and emphesize edges found in both images
    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, 145, 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)

# 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
  device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, args.debug)
  
  # Join the thresholded saturation and blue-yellow images