How to use the plantcv.plot_hist function in plantcv

# Read image
    device = 0
    img = cv2.imread(args.image, flags=0)
    path, img_name = os.path.split(args.image)
    # Read in image which is average of average of backgrounds
    img_bkgrd = cv2.imread("/home/mgehan/LemnaTec/plantcv/masks/nir_tv/background_nir_z3500.png", flags=0)

    # NIR images for burnin2 are up-side down. This may be fixed in later experiments
    img =  ndimage.rotate(img, 0)
    img_bkgrd =  ndimage.rotate(img_bkgrd, 0)

    # Subtract the image from the image background to make the plant more prominent
    device, bkg_sub_img = pcv.image_subtract(img, img_bkgrd, device, args.debug)
    if args.debug:
        pcv.plot_hist(bkg_sub_img, 'bkg_sub_img')
    device, bkg_sub_thres_img = pcv.binary_threshold(bkg_sub_img, 150, 255, 'dark', device, args.debug)
    bkg_sub_thres_img = cv2.inRange(bkg_sub_img, 50, 190)
    if args.debug:
        cv2.imwrite('bkgrd_sub_thres.png', bkg_sub_thres_img)
    
    #device, bkg_sub_thres_img = pcv.binary_threshold_2_sided(img_bkgrd, 50, 190, device, args.debug)
    
    # if a region of interest is specified read it in
    roi = cv2.imread(args.roi)
    
    
    # Start by examining the distribution of pixel intensity values
    if args.debug:
      pcv.plot_hist(img, 'hist_img')
      
    # Will intensity transformation enhance your ability to isolate object of interest by thesholding?

# Read image
    device = 0
    img = cv2.imread(args.image, flags=0)
    path, img_name = os.path.split(args.image)
    # Read in image which is average of average of backgrounds
    img_bkgrd = cv2.imread("/home/mgehan/LemnaTec/plantcv/masks/nir_tv/background_nir_z3500.png", flags=0)

    # NIR images for burnin2 are up-side down. This may be fixed in later experiments
    img =  ndimage.rotate(img, 0)
    img_bkgrd =  ndimage.rotate(img_bkgrd, 0)

    # Subtract the image from the image background to make the plant more prominent
    device, bkg_sub_img = pcv.image_subtract(img, img_bkgrd, device, args.debug)
    if args.debug:
        pcv.plot_hist(bkg_sub_img, 'bkg_sub_img')
    device, bkg_sub_thres_img = pcv.binary_threshold(bkg_sub_img, 150, 255, 'dark', device, args.debug)
    bkg_sub_thres_img = cv2.inRange(bkg_sub_img, 50, 190)
    if args.debug:
        cv2.imwrite('bkgrd_sub_thres.png', bkg_sub_thres_img)
    
    #device, bkg_sub_thres_img = pcv.binary_threshold_2_sided(img_bkgrd, 50, 190, device, args.debug)
    
    # if a region of interest is specified read it in
    roi = cv2.imread(args.roi)
    
    
    # Start by examining the distribution of pixel intensity values
    if args.debug:
      pcv.plot_hist(img, 'hist_img')
      
    # Will intensity transformation enhance your ability to isolate object of interest by thesholding?

# Sobel filtering  
    # 1st derivative sobel filtering along horizontal axis, kernel = 1, unscaled)
    device, sbx_img = pcv.sobel_filter(img, 1, 0, 1, 1, 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)

roi = cv2.imread(args.roi)
    
    
    # Start by examining the distribution of pixel intensity values
    if args.debug:
      pcv.plot_hist(img, 'hist_img')
      
    # Will intensity transformation enhance your ability to isolate object of interest by thesholding?
    device, he_img = pcv.HistEqualization(img, device, args.debug)
    if args.debug:
      pcv.plot_hist(he_img, 'hist_img_he')
    
    # Laplace filtering (identify edges based on 2nd derivative)
    device, lp_img = pcv.laplace_filter(img, 1, 1, device, args.debug)
    if args.debug:
      pcv.plot_hist(lp_img, 'hist_lp')
    
    # Lapacian image sharpening, this step will enhance the darkness of the edges detected
    device, lp_shrp_img = pcv.image_subtract(img, lp_img, device, args.debug)
    if args.debug:
      pcv.plot_hist(lp_shrp_img, 'hist_lp_shrp')
    
    # Sobel filtering  
    # 1st derivative sobel filtering along horizontal axis, kernel = 1, unscaled)
    device, sbx_img = pcv.sobel_filter(img, 1, 0, 1, 1, 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')

# Sobel filtering  
    # 1st derivative sobel filtering along horizontal axis, kernel = 1, unscaled)
    device, sbx_img = pcv.sobel_filter(img, 1, 0, 1, 1, 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)

cv2.imwrite('bkgrd_sub_thres.png', bkg_sub_thres_img)
    
    #device, bkg_sub_thres_img = pcv.binary_threshold_2_sided(img_bkgrd, 50, 190, device, args.debug)
    
    # if a region of interest is specified read it in
    roi = cv2.imread(args.roi)
    
    
    # Start by examining the distribution of pixel intensity values
    if args.debug:
      pcv.plot_hist(img, 'hist_img')
      
    # Will intensity transformation enhance your ability to isolate object of interest by thesholding?
    device, he_img = pcv.HistEqualization(img, device, args.debug)
    if args.debug:
      pcv.plot_hist(he_img, 'hist_img_he')
    
    # Laplace filtering (identify edges based on 2nd derivative)
    device, lp_img = pcv.laplace_filter(img, 1, 1, device, args.debug)
    if args.debug:
      pcv.plot_hist(lp_img, 'hist_lp')
    
    # Lapacian image sharpening, this step will enhance the darkness of the edges detected
    device, lp_shrp_img = pcv.image_subtract(img, lp_img, device, args.debug)
    if args.debug:
      pcv.plot_hist(lp_shrp_img, 'hist_lp_shrp')
    
    # Sobel filtering  
    # 1st derivative sobel filtering along horizontal axis, kernel = 1, unscaled)
    device, sbx_img = pcv.sobel_filter(img, 1, 0, 1, 1, device, args.debug)
    if args.debug:
      pcv.plot_hist(sbx_img, 'hist_sbx')

pcv.plot_hist(img, 'hist_img')
      
    # Will intensity transformation enhance your ability to isolate object of interest by thesholding?
    device, he_img = pcv.HistEqualization(img, device, args.debug)
    if args.debug:
      pcv.plot_hist(he_img, 'hist_img_he')
    
    # Laplace filtering (identify edges based on 2nd derivative)
    device, lp_img = pcv.laplace_filter(img, 1, 1, device, args.debug)
    if args.debug:
      pcv.plot_hist(lp_img, 'hist_lp')
    
    # Lapacian image sharpening, this step will enhance the darkness of the edges detected
    device, lp_shrp_img = pcv.image_subtract(img, lp_img, device, args.debug)
    if args.debug:
      pcv.plot_hist(lp_shrp_img, 'hist_lp_shrp')
      
    # Sobel filtering  
    # 1st derivative sobel filtering along horizontal axis, kernel = 1, unscaled)
    device, sbx_img = pcv.sobel_filter(img, 1, 0, 1, 1, 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:

def main():
   
    # Get options
    args = options()
    if args.debug:
      print("Analyzing your image dude...")
    # Read image
    img = cv2.imread(args.image, flags=0)
    # if a region of interest is specified read it in
    roi = cv2.imread(args.roi)
    # Pipeline step
    device = 0
    
    # Start by examining the distribution of pixel intensity values
    if args.debug:
      pcv.plot_hist(img, 'hist_img')
      
    # Will intensity transformation enhance your ability to isolate object of interest by thesholding?
    device, he_img = pcv.HistEqualization(img, device, args.debug)
    if args.debug:
      pcv.plot_hist(he_img, 'hist_img_he')
    
    # Laplace filtering (identify edges based on 2nd derivative)
    device, lp_img = pcv.laplace_filter(img, 1, 1, device, args.debug)
    if args.debug:
      pcv.plot_hist(lp_img, 'hist_lp')
    
    # Lapacian image sharpening, this step will enhance the darkness of the edges detected
    device, lp_shrp_img = pcv.image_subtract(img, lp_img, device, args.debug)
    if args.debug:
      pcv.plot_hist(lp_shrp_img, 'hist_lp_shrp')

# 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)
    kern2[1,0:2]=1
    kern3 = np.copy(kern)
    kern3[0:2,1]=1
    kern4 = np.copy(kern)
    kern4[1:3,1]=1
    
    # Prepare a larger kernel for dilation

cv2.imwrite('bkgrd_sub_thres.png', bkg_sub_thres_img)
  
    #device, bkg_sub_thres_img = pcv.binary_threshold_2_sided(img_bkgrd, 50, 190, device, args.debug)

    # if a region of interest is specified read it in
    roi = cv2.imread(args.roi)

    
    # Start by examining the distribution of pixel intensity values
    if args.debug:
      pcv.plot_hist(img, 'hist_img')
      
    # Will intensity transformation enhance your ability to isolate object of interest by thesholding?
    device, he_img = pcv.HistEqualization(img, device, args.debug)
    if args.debug:
      pcv.plot_hist(he_img, 'hist_img_he')
    
    # Laplace filtering (identify edges based on 2nd derivative)
    device, lp_img = pcv.laplace_filter(img, 1, 1, device, args.debug)
    if args.debug:
      pcv.plot_hist(lp_img, 'hist_lp')
    
    # Lapacian image sharpening, this step will enhance the darkness of the edges detected
    device, lp_shrp_img = pcv.image_subtract(img, lp_img, device, args.debug)
    if args.debug:
      pcv.plot_hist(lp_shrp_img, 'hist_lp_shrp')

    # Sobel filtering  
    # 1st derivative sobel filtering along horizontal axis, kernel = 1, unscaled)
    device, sbx_img = pcv.sobel_filter(img, 1, 0, 1, 1, device, args.debug)
    if args.debug:
      pcv.plot_hist(sbx_img, 'hist_sbx')