I Have coded this far and I think there is some problem and that is why I can not get the proper result:
Open image and discard alpha channel which makes wheel round rather than square
import numpy as np from skimage import color, io import matplotlib.pyplot as plt import imageio import cv2 import skimage import PIL
Open image and make Numpy arrays 'rgb' and 'Lab'
pic = imageio.imread('E:\JU_V2_DIGIT\RGB_Crop\RGB_P7_G0_8.png') pic2 = pic.copy() total_row, total_col, layers = pic2.shape mask= np.zeros_like(pic2) x,y = np.ogrid[:total_row, :total_col] Lab_pic = color.rgb2lab(pic) L_pic, A_pic, B_pic = cv2.split(Lab_pic)
cv2.imshow("L_Channel", L_pic) # For L Channel cv2.imshow("A_Channel", A_pic) # For A Channel cv2.imshow("B_Channel", B_pic) # For B Channel
cen_x, cen_y = total_row/2, total_col/2 distance_from_center = np.sqrt((x-cen_x+10)2+(y-cen_y-15)2) radius = (total_row/3) circular_pic = distance_from_center>radius pic2[circular_pic] = 0 pic2[mask] = 0
cv2.imwrite('D:\SHROUTI\Testpictures\opencvmasking_human.jpg',dst)
cv2.imshow("cir_mask",pic2) Lab = color.rgb2lab(pic2) L_mask,A_mask,B_mask = cv2.split(Lab)
cv2.imshow("L_Channel_mask", L_mask) # For L Channel cv2.imshow("A_Channel_mask", A_mask) # For A Channel cv2.imshow("B_Channel_mask", B_mask) # For B Channel
LMean = L_mask.mean() AMean = A_mask.mean() BMean = B_mask.mean()
'''LStandard = LMeannp.ones([total_row, total_col], dtype = int) AStandard = AMeannp.ones([total_row, total_col], dtype = int) BStandard = BMean*np.ones([total_row, total_col], dtype = int)'''
DeltaL = L_pic-LMean DeltaA = A_pic- AMean DeltaB = B_pic - BMean
DeltaE = np.sqrt(pow(DeltaA,2)+pow(DeltaB,2)+pow(DeltaL,2)) print(DeltaE) cv2.imshow("deltaE",DeltaE) cv2.waitKey()
I am using the code for the RGB image below
My moto is to measure Euclidean distance from the hand gesture to the whole image and so that we can Display the masked Delta E image - the delta E within the masked region only and also Display the Delta E image - the delta E over the entire image.
