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full_histogram.py
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full_histogram.py
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from __future__ import division
import cv2
import numpy as np
from matplotlib import pyplot as plt
from scipy.signal import convolve2d
# FUNCTIONS
def RGB2YIQ(img_bgr):
"""
Conversion from BGR color space to YIQ color space
:param img_bgr: input BGR image
:return: image YIQ
"""
BGR = img_bgr.copy().astype(float)
R = BGR[:, :, 0]
G = BGR[:, :, 1]
B = BGR[:, :, 2]
Y = (0.299 * R) + (0.587 * G) + (0.114 * B)
I = (0.59590059 * R) + (-0.27455667 * G) + (-0.32134392 * B)
Q = (0.21153661 * R) + (-0.52273617 * G) + (0.31119955 * B)
YIQ = np.round(np.dstack((Y, I + 128, Q + 128))).astype(np.uint8)
return YIQ
def delta(img, center, x, y):
"""
Compares current position with the center of the lbp region
:param img: image in input
:param center: center of lbp region
:param x: current x coordinate
:param y: current y coordinate
:return: 1 if current position is greater or equal than the center position, otherwise 0
"""
new_value = 0
try:
# If local neighbourhood pixel value is greater than or equal to center pixel values then set it to 1
if img[x][y] >= center:
new_value = 1
except:
# Exception is required when neighbourhood value of a center pixel value is null (values present at boundaries).
pass
return new_value
def lbp_standard(img, x, y):
"""
Compute the lbp standard of an image
:param img: input image
:param x: current x coordinate
:param y: current y coordinate
:return: image in lbp form
"""
center = img[x][y]
#Values array, it is the 8bit LBP string
val_ar = []
#top_left
val_ar.append(delta(img, center, x - 1, y - 1))
#top_center
val_ar.append(delta(img, center, x - 1, y))
#top_right
val_ar.append(delta(img, center, x - 1, y + 1))
#right
val_ar.append(delta(img, center, x, y + 1))
#bottom_right
val_ar.append(delta(img, center, x + 1, y + 1))
#bottom_center
val_ar.append(delta(img, center, x + 1, y))
#bottom_left
val_ar.append(delta(img, center, x + 1, y - 1))
#left
val_ar.append(delta(img, center, x, y - 1))
#Conversion into decimal notation
power_val = [1, 2, 4, 8, 16, 32, 64, 128]
val = 0
for i in range(len(val_ar)):
val += val_ar[i] * power_val[i]
final_lbp = np.array(val)
print(final_lbp)
return val
def lbp_uniform(img, x, y):
"""
Compute the lbp uniform of an image (this is more efficient with faces)
:param img: input image
:param x: current x coordinate
:param y: current y coordinate
:return: image in lbp form
"""
value = 0 #result
#Center of the image and number of point (or pixel) that we will check
center = img[x][y]
p = 8
#Distribution of pixels and respective names, '--' is the center
# P0 P1 P2
# P7 -- P3
# P6 P5 P4
#Uniformity check, we check the difference on LBP value between each pixel
#Check between pixel P7(Left) and P0(TopLeft)
ue = abs(delta(img, center, x, y - 1) - delta(img, center, x - 1, y - 1))
#Check between pixel P1(Top) e P0(TopLeft)
us1 = abs(delta(img, center, x - 1, y) - delta(img, center, x - 1, y - 1))
#Check between pixel P2(TopRight) and P1(Top)
us2 = abs(delta(img, center, x - 1, y + 1) - delta(img, center, x - 1, y))
#Check between pixel P3(Right) and P2(TopRight)
us3 = abs(delta(img, center, x, y + 1) - delta(img, center, x - 1, y + 1))
#Check between pixel P4(BottomRight) and P3(Right)
us4 = abs(delta(img, center, x + 1, y + 1) - delta(img, center, x, y + 1))
#Check between pixel P5(Bottom) and P4(BottomRight)
us5 = abs(delta(img, center, x + 1, y) - delta(img, center, x + 1, y + 1))
#Check between pixel P6(BottomLeft) and P5(Bottom)
us6 = abs(delta(img, center, x + 1, y - 1) - delta(img, center, x + 1, y))
#Check between pixel P7(Left) and P6(BottomLeft)
us7 = abs(delta(img, center, x, y - 1) - delta(img, center, x + 1, y - 1))
transitions_number = ue + us1 + us2 + us3 + us4 + us5 + us6 + us7
#Verification of LBP, if we have 2 or less transitions on the LBP binary string, then it is uniform
if transitions_number <= 2:
val_ar = [delta(img, center, x - 1, y - 1), delta(img, center, x - 1, y), # top_left - top
delta(img, center, x - 1, y + 1), delta(img, center, x, y + 1), # top_right - right
delta(img, center, x + 1, y + 1), delta(img, center, x + 1, y), # bottom_right - bottom
delta(img, center, x + 1, y - 1), delta(img, center, x, y - 1)] # bottom_left - left
#Compute the correct decimal LBP value for that pixel
power_val = [1, 2, 4, 8, 16, 32, 64, 128]
for i in range(len(val_ar)):
value += val_ar[i] * power_val[i]
#else if we have more than two transitions then the lbp is not uniform (we assign a fixed decimal value)
else:
value = p*(p-1)+2
return value
def split_region(img, region_factor):
"""
Splits the input image into a certain number of regions
:param img: input image
:param region_factor: length and width of how to factorize the image
:return:
"""
#Divide the image in equal region given the wanted number of regions
height = int(img.shape[0] / region_factor)
width = int(img.shape[1] / region_factor)
#Create a region list and fill it with the image's regions (with height and width computed previously)
region = []
i = 0
for x in range(region_factor):
for y in range(region_factor):
temp_frame = img[0 + (x * height):height + (x * height), 0 + (y * width):width + (y * width)]
region.append(temp_frame)
i += 1
return region
def lbp_region_image_compute(region_list):
"""
Compute the lbp of every region obtained
:param region_list: list of regions obtained with split_region method
:return: list of lbp regions
"""
#Create a list of empty regions all with the same height and width as the input regions
lbp_region = []
height, width = region_list[0].shape
#Initialize new regions, then compute the LBP for each region and add the LBP to the list lbp_region
for v in region_list:
temp_region = np.zeros((height, width), np.uint8)
for x in range(height):
for y in range(width):
temp_region[x, y] = lbp_uniform(v, x, y)
lbp_region.append(temp_region)
return lbp_region
def histogram(img, v_min, v_max, scale):
"""
Creates histogram of a given image
:param img: input image
:param v_min: lower bound of admitted values
:param v_max: upper bound of admitted values
:param scale: Scale can be used to reduce the resolution and limit the computation phase during test
:return: histogram
"""
#Initialize a histogram
hist = []
#Initialize the histogram with the correct len
for x in range(v_min, v_max):
hist.append(0)
#For each pixel in height x and width y, add it to the right column of the histogram
#Scale can be used to reduce the resolution and limit the computation phase during test
range_x = int(img.shape[0]*scale)
range_y = int(img.shape[1]*scale)
total_pixel = range_x * range_y
for x in range(range_x):
for y in range(range_y):
error = 1
for v in range(v_min, v_max):
if img[x][y] == v:
hist[v] = hist[v] + 1
error = 0
#Check if the pixel under analysis has been assigned to no column of the histogram
if error == 1:
print("WARNING: pixel (", x, y, ") NOT ADDED TO ANY COLUMN")
#Histogram element number check
histogram_elements = 0
for x in range(v_min, v_max):
histogram_elements = histogram_elements + hist[x]
print("Histogram total element:", histogram_elements, "/", total_pixel,
", Missing elements:", total_pixel - histogram_elements)
if histogram_elements != total_pixel:
print("MISSING ELEMENTS, CHECK IF v_min and v_max are correct!")
np_hist = np.array(hist)
return np_hist
def histogram_region(lbp_region, v_min, v_max, scale):
"""
Compute the histogram of all the regions
:param lbp_region: regions list obtained with lbp_region_image_compute
:param v_min: lower bound of admitted values
:param v_max: upper bound of admitted values
:param scale: Scale can be used to reduce the resolution and limit the computation phase during test
:return: histogram list
"""
#Create a list of histogram, then compute the histogram for each region and save the result in the list
hist = []
for x in lbp_region:
temp_hist = histogram(x, v_min, v_max, scale)
hist.append(temp_hist)
np_hist = np.array(hist)
return np_hist
def histogram_full_image_print(hist, name):
"""
Prints the final histogram
:param hist: input histogram to print
:param name: name of the histogram
:return: printed histogram
"""
plt.figure()
plt.bar(np.arange(len(hist)), hist, width=10, color='b')
plt.title(name)
plt.show()
return
def histogram_concatenate(hist_list, chart=False, name="NAME"):
"""
Concatenate all the histograms of the different regions coming from histogram_region
:param hist_list: input list of histograms
:param chart: tells you whether you want to plot the final histogram or not (default value FALSE)
:param name: name of a single histogram plot
:return: final concatenated histogram
"""
final_hist = []
single_hist_len = len(hist_list[0])
final_hist_len = len(hist_list) * single_hist_len
#Initialize the final histogram with the correct len
for x in range(final_hist_len):
final_hist.append(0)
#Populate the final histogram concatenating all the histogram from the input list in one single histogram
for x in range(len(hist_list)):
temp_hist = hist_list[x]
for y in range(single_hist_len):
final_hist[y + x*single_hist_len] = temp_hist[y]
final_np_hist = np.array(final_hist)
#Prints the histogram
if chart:
histogram_full_image_print(final_np_hist, 'LBP Histogram for channel: %(name)s' % {"name": name})
print("LBP descriptor for channel", name, "successfully computed. It is a fusion of:", len(hist_list),
"histograms, with length:", single_hist_len, ",total length of:", final_hist_len)
return final_np_hist
def CoALBP(image, chart=False, name="NAME", lbp_r=1, co_r=2):
"""
Compute the Co-occurrence of Adjacent Local Binary Patterns of an image
:param image: input image
:param chart: tells you whether you want to plot the final histogram or not (default value FALSE)
:param name: name of a single CoALBP plot
:param lbp_r: radius for adjacent local binary patterns
:param co_r: radius for co-occurence of the patterns
:return: CoALBP descriptor with length 1024 * number of channels
"""
height, width, channels = image.shape
#albp and co-occurrence per channel in image
histogram = np.empty(0, dtype=int)
for i in range(image.shape[2]):
C = image[lbp_r:height - lbp_r, lbp_r:width - lbp_r, i].astype(float)
X = np.zeros((4, height - 2 * lbp_r, width - 2 * lbp_r))
# adjacent local binary patterns
X[0, :, :] = image[lbp_r:height - lbp_r, lbp_r + lbp_r:width - lbp_r + lbp_r, i] - C
X[1, :, :] = image[lbp_r - lbp_r:height - lbp_r - lbp_r, lbp_r:width - lbp_r, i] - C
X[2, :, :] = image[lbp_r:height - lbp_r, lbp_r - lbp_r:width - lbp_r - lbp_r, i] - C
X[3, :, :] = image[lbp_r + lbp_r:height - lbp_r + lbp_r, lbp_r:width - lbp_r, i] - C
X = (X > 0).reshape(4, -1)
# co-occurrence of the patterns
A = np.dot(np.array([1, 2, 4, 8]), X)
A = A.reshape(height - 2 * lbp_r, width - 2 * lbp_r) + 1
hh, ww = A.shape
D = (A[co_r:hh - co_r, co_r:ww - co_r] - 1) * 16 - 1
Y1 = A[co_r:hh - co_r, co_r + co_r:ww - co_r + co_r] + D
Y2 = A[co_r - co_r:hh - co_r - co_r, co_r + co_r:ww - co_r + co_r] + D
Y3 = A[co_r - co_r:hh - co_r - co_r, co_r:ww - co_r] + D
Y4 = A[co_r - co_r:hh - co_r - co_r, co_r - co_r:ww - co_r - co_r] + D
Y1 = np.bincount(Y1.ravel(), minlength=256)
Y2 = np.bincount(Y2.ravel(), minlength=256)
Y3 = np.bincount(Y3.ravel(), minlength=256)
Y4 = np.bincount(Y4.ravel(), minlength=256)
pattern = np.concatenate((Y1, Y2, Y3, Y4))
histogram = np.concatenate((histogram, pattern))
CoALBPdesc = histogram
# Prints the histogram of the descriptor
if chart:
plt.figure()
plt.bar(np.arange(len(CoALBPdesc)), CoALBPdesc, width=10)
plt.title('CoALBP histogram for channel: %(name)s, with lbp_r: %(lbpr)d and co_r: %(cor)d' % {"lbpr": lbp_r, "cor": co_r, "name": name})
plt.show()
print("CoALBP descriptor for channel", name, "successfully computed with lbp_r:", lbp_r, ", and co_r:", co_r, "total length of:", len(CoALBPdesc))
return CoALBPdesc
def lpq(image, chart=False, name="NAME", winSize=3, freqestim=1, mode='nh'):
"""
Compute the Local Phase Quantization of an image
:param image: input image
:param chart: tells you whether you want to plot the final histogram or not (default value FALSE)
:param name: name of a single CoALBP plot
:param winSize:
:param freqestim:
:param mode:
:return: descriptor with length 256
"""
rho = 0.90
STFTalpha = 1/winSize # alpha in STFT approaches (for Gaussian derivative alpha=1)
sigmaS = (winSize-1)/4 # Sigma for STFT Gaussian window (applied if freqestim==2)
sigmaA = 8/(winSize-1) # Sigma for Gaussian derivative quadrature filters (applied if freqestim==3)
convmode = 'valid' # Compute descriptor responses only on part that have full neigborhood. Use 'same' if all pixels are included (extrapolates np.image with zeros).
image = np.float64(image) # Convert np.image to double
r = (winSize-1)/2 # Get radius from window size
x = np.arange(-r, r+1)[np.newaxis] # Form spatial coordinates in window
w0 = w1 = w2 = 0
if freqestim == 1: # STFT uniform window
# Basic STFT filters
w0 = np.ones_like(x)
w1 = np.exp(-2*np.pi*x*STFTalpha*1j)
w2 = np.conj(w1)
# print("Vector w0:", x)
## Run filters to compute the frequency response in the four points. Store np.real and np.imaginary parts separately
# Run first filter
filterResp1 = convolve2d(convolve2d(image, w0.T, convmode), w1, convmode)
filterResp2 = convolve2d(convolve2d(image, w1.T, convmode), w0, convmode)
filterResp3 = convolve2d(convolve2d(image, w1.T, convmode), w1, convmode)
filterResp4 = convolve2d(convolve2d(image, w1.T, convmode), w2, convmode)
# Initilize frequency domain matrix for four frequency coordinates (np.real and np.imaginary parts for each frequency).
freqResp = np.dstack([filterResp1.real, filterResp1.imag,
filterResp2.real, filterResp2.imag,
filterResp3.real, filterResp3.imag,
filterResp4.real, filterResp4.imag])
## Perform quantization and compute LPQ codewords
inds = np.arange(freqResp.shape[2])[np.newaxis,np.newaxis,:]
LPQdesc = ((freqResp>0)*(2**inds)).sum(2)
## Switch format to uint8 if LPQ code np.image is required as output
if mode == 'im':
LPQdesc = np.uint8(LPQdesc)
## Histogram if needed
if mode == 'nh' or mode == 'h':
LPQdesc = np.histogram(LPQdesc.flatten(), range(256))[0]
## Normalize histogram if needed
if mode == 'nh':
LPQdesc = LPQdesc/LPQdesc.sum()
if chart:
plt.figure()
plt.bar(np.arange(len(LPQdesc)), LPQdesc, width=5)
plt.title('LPQ histogram for channel: %(name)s' % {"name": name})
plt.show()
LPQdesc = np.concatenate((LPQdesc, np.zeros(1)))
print("LPQ descriptor for channel", name, "successfully computed, total length of:", len(LPQdesc))
return LPQdesc
def channel_descriptor(channel, channel_3D, channel_name="NAME", n_split=3, v_min=0, v_max=256, scale=1.0, LBP_multiplier=1, CoALBP_multiplier=1, LPQ_multiplier=1, single_charts=False, final_charts=False):
"""
Given a channel we carry out the full analysis composed of LBP, CoALBP (run 3 times with different parameters) and LPQ
:param channel: input channel (bi-dimensional image)
:param channel_3D: input 3D channel (three dimensions matrix with only one channel, used for CoALBP)
:param channel_name: name of the channel we are working with
:param n_split: length and width of how to factorize the image
:param v_min: lower bound of admitted values
:param v_max: upper bound of admitted values
:param scale: Scale can be used to reduce the resolution and limit the computation phase during test
:param LBP_multiplier: scale value used in order to see the columns of the histogram
:param CoALBP_multiplier: scale value used in order to see the columns of the histogram
:param LPQ_multiplier: scale value used in order to see the columns of the histogram
:param single_charts: tells you whether you want to plot all the single histograms or not (default value FALSE)
:param final_charts: plot the final descriptor for the current channel (default value FALSE)
:return: the complete descriptor for the channel
"""
print("---------- STARTING COMPUTATION OF FINAL DESCRIPTOR FOR CHANNEL", channel_name, "----------")
#Compute LBP descriptor (Split channel, compute single split LBP, compute single histograms, concatenate, print)
channel_splits = split_region(channel, n_split)
channel_splits_lbp = lbp_region_image_compute(channel_splits)
channel_splits_lbp_hist = histogram_region(channel_splits_lbp, v_min, v_max, scale)
channel_lbp_descriptor = histogram_concatenate(channel_splits_lbp_hist, single_charts, channel_name) * LBP_multiplier
#Compute CoALBP descriptors (each one automatically printed)
channel_CoALBP_descriptor_1 = CoALBP(channel_3D, single_charts, channel_name, lbp_r=1, co_r=2) * CoALBP_multiplier
channel_CoALBP_descriptor_2 = CoALBP(channel_3D, single_charts, channel_name, lbp_r=2, co_r=4) * CoALBP_multiplier
channel_CoALBP_descriptor_3 = CoALBP(channel_3D, single_charts, channel_name, lbp_r=4, co_r=8) * CoALBP_multiplier
#Compute LPQ descriptor (automatically printed)
channel_LPQ_descriptor = lpq(channel, single_charts, channel_name) * LPQ_multiplier
#Compute FINAL DESCRIPTOR (and print)
channel_final_descriptor = np.concatenate((channel_lbp_descriptor, channel_CoALBP_descriptor_1,
channel_CoALBP_descriptor_2, channel_CoALBP_descriptor_3,
channel_LPQ_descriptor))
print("FINAL DESCRIPTOR for channel", channel_name, "successfully computed, total length of:", len(channel_final_descriptor))
if final_charts:
histogram_full_image_print(channel_final_descriptor, 'FINAL DESCRIPTOR for channel: %(name)s' % {"name": channel_name})
return channel_final_descriptor
def channel_descriptor_concatenate(desc1, desc2, desc3=[], desc4=[], desc5=[], desc6=[], n_descriptor=2, final_chart=False):
"""
Concatenate all the channel descriptors (from 2 to 6 channels)
:param desc1-6: descriptor for the given channel (from 1 to 6)
:param n_descriptor: number of descriptor to concatenate
:param final_chart: plot the final concatenated descriptor (default value FALSE)
:return: final image descriptor on the given channel
"""
final_descriptor = []
if n_descriptor < 2:
print("ERROR, minimum descriptor number = 2")
return
print("---------- STARTING COMPUTATION OF FINAL DESCRIPTOR ----------")
if n_descriptor == 2:
final_descriptor = np.concatenate((desc1, desc2))
elif n_descriptor == 3:
final_descriptor = np.concatenate((desc1, desc2, desc3))
elif n_descriptor == 4:
final_descriptor = np.concatenate((desc1, desc2, desc3, desc4))
elif n_descriptor == 5:
final_descriptor = np.concatenate((desc1, desc2, desc3, desc4, desc5))
elif n_descriptor == 6:
final_descriptor = np.concatenate((desc1, desc2, desc3, desc4, desc5, desc6))
print("FINAL DESCRIPTOR successfully computed, total length of:", len(final_descriptor))
if final_chart:
histogram_full_image_print(final_descriptor, 'FINAL DESCRIPTOR')
return final_descriptor
def final_function(path, plot_channels, plot_all_descriptors, plot_final_descriptor):
"""
Function that merges all the previous methods. First computes the different color spaces and then processes the final
descriptor
:param path: path of the image
:return: final histogram
"""
yiq = 0
ycrcb = 1
hsv = 1
if plot_final_descriptor:
plot_final_descriptors = True
# Read image in BGR
img_bgr = cv2.imread(path, 1)
print("BGR image created, dimension:", img_bgr.shape)
# Convert image to RGB
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
if plot_channels:
plt.figure()
plt.imshow(img_rgb)
plt.title('RGB IMAGE')
plt.show()
print("RGB image created")
if yiq == 1:
# Convert image to YIQ
img_YIQ = RGB2YIQ(img_bgr)
if plot_channels:
plt.figure()
plt.imshow(img_YIQ)
plt.title('YIQ image created')
plt.show()
print("YIQ image created")
# Extract single channels Y, I, Q from YIQ
channel_Y_fromYIQ = img_YIQ[:, :, 0]
channel_Y_fromYIQ_3D = img_YIQ[:, :, 0:1]
if plot_channels:
plt.figure()
plt.imshow(channel_Y_fromYIQ)
plt.title('Channel Y of YIQ')
plt.show()
print("Channel Y extracted")
channel_I = img_YIQ[:, :, 1]
channel_I_3D = img_YIQ[:, :, 1:2]
if plot_channels:
plt.figure()
plt.imshow(channel_I)
plt.title('Channel I of YIQ')
plt.show()
print("Channel I extracted")
channel_Q = img_YIQ[:, :, 2]
channel_Q_3D = img_YIQ[:, :, 2:3]
if plot_channels:
plt.figure()
plt.imshow(channel_Q)
plt.title('Channel Q of YIQ')
plt.show()
print("Channel Q extracted")
if ycrcb == 1:
# Convert image to YCrCb
img_YCrCb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2YCrCb)
if plot_channels:
plt.figure()
plt.imshow(img_YCrCb)
plt.title('YCrCb IMAGE')
plt.show()
print("YCrCb image created")
# Extract single channels Y, Cr, Cb from YCrCb
channel_Y = img_YCrCb[:, :, 0]
channel_Y_3D = img_YCrCb[:, :, 0:1]
if plot_channels:
plt.figure()
plt.imshow(channel_Y)
plt.title('Channel Y of YCrCb')
plt.show()
print("Channel Y extracted")
channel_Cr = img_YCrCb[:, :, 1]
channel_Cr_3D = img_YCrCb[:, :, 1:2]
if plot_channels:
plt.figure()
plt.imshow(channel_Cr)
plt.title('Channel Cr of YCrCb')
plt.show()
print("Channel Cr extracted")
channel_Cb = img_YCrCb[:, :, 2]
channel_Cb_3D = img_YCrCb[:, :, 2:3]
if plot_channels:
plt.figure()
plt.imshow(channel_Cb)
plt.title('Channel Cb of YCrCb')
plt.show()
print("Channel Cb extracted")
if hsv == 1:
# Convert image to HSV
img_HSV = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)
if plot_channels:
plt.figure()
plt.imshow(img_HSV)
plt.title('HSV IMAGE')
plt.show()
print("HSV image created")
# Extract single channels Y, Cr, Cb from YCrCb
channel_H = img_HSV[:, :, 0]
channel_H_3D = img_HSV[:, :, 0:1]
if plot_channels:
plt.figure()
plt.imshow(channel_H)
plt.title('Channel H of HSV')
plt.show()
print("Channel H extracted")
channel_S = img_HSV[:, :, 1]
channel_S_3D = img_HSV[:, :, 1:2]
if plot_channels:
plt.figure()
plt.imshow(channel_S)
plt.title('Channel S of HSV')
plt.show()
print("Channel S extracted")
channel_V = img_HSV[:, :, 2]
channel_V_3D = img_HSV[:, :, 2:3]
if plot_channels:
plt.figure()
plt.imshow(channel_V)
plt.title('Channel V of HSV')
plt.show()
print("Channel V extracted")
# DESCRIPTORS COMPUTATION AND CONCATENATION
Y_descriptor = channel_descriptor(channel_Y, channel_Y_3D, "Y", 3, 0, 256, final_charts=plot_all_descriptors)
Cr_descriptor = channel_descriptor(channel_Cr, channel_Cr_3D, "Cr", 3, 0, 256, final_charts=plot_all_descriptors)
Cb_descriptor = channel_descriptor(channel_Cb, channel_Cb_3D, "Cb", 3, 0, 256, final_charts=plot_all_descriptors)
H_descriptor = channel_descriptor(channel_H, channel_H_3D, "H", 3, 0, 256, final_charts=plot_all_descriptors)
S_descriptor = channel_descriptor(channel_S, channel_S_3D, "S", 3, 0, 256, final_charts=plot_all_descriptors)
V_descriptor = channel_descriptor(channel_V, channel_V_3D, "V", 3, 0, 256, final_charts=plot_all_descriptors)
final_result = channel_descriptor_concatenate(Y_descriptor, Cr_descriptor, Cb_descriptor, H_descriptor, S_descriptor,
V_descriptor, 6, final_chart=plot_final_descriptor)
return final_result