# -- coding: utf-8 -- """ Created on Sun Feb 9 00:09:31 2020
@author: Shrouti """
import cv2 import numpy as np from PIL import Image, ImageCms from skimage import color
f = 500 rotXval = 90 rotYval = 90 rotZval = 90 distXval = 500 distYval = 500 distZval = 500
def onFchange(val): global f f = val
def onRotXChange(val): global rotXval rotXval = val
def onRotYChange(val): global rotYval rotYval = val
def onRotZChange(val): global rotZval rotZval = val
def onDistXChange(val): global distXval distXval = val
def onDistYChange(val): global distYval distYval = val
def onDistZChange(val): global distZval distZval = val
if __name__ == '__main__':
# Read input image, and create output image
src = cv2.imread("D:\SHROUTI\Testpictures\hand_gesture3.jpg")
src = cv2.resize(src, (640, 480))
dst = np.zeros_like(src)
h, w = src.shape[:2]
# Create user interface with trackbars that will allow to modify the parameters of the transformation
wndname1 = "Source:"
wndname2 = "WarpPerspective: "
# Show original image
cv2.imshow(wndname1, src)
k = -1
while k != 27:
if f <= 0: f = 1
rotX = (rotXval - 90) * np.pi / 180
rotY = (rotYval - 90) * np.pi / 180
rotZ = (rotZval - 90) * np.pi / 180
distX = distXval - 500
distY = distYval - 500
distZ = distZval - 500
# Camera intrinsic matrix
K = np.array([[f, 0, w / 2, 0],
[0, f, h / 2, 0],
[0, 0, 1, 0]])
# K inverse
Kinv = np.zeros((4, 3))
Kinv[:3, :3] = np.linalg.inv(K[:3, :3]) * f
Kinv[-1, :] = [0, 0, 1]
# Rotation matrices around the X,Y,Z axis
RX = np.array([[1, 0, 0, 0],
[0, np.cos(rotX), -np.sin(rotX), 0],
[0, np.sin(rotX), np.cos(rotX), 0],
[0, 0, 0, 1]])
RY = np.array([[np.cos(rotY), 0, np.sin(rotY), 0],
[0, 1, 0, 0],
[-np.sin(rotY), 0, np.cos(rotY), 0],
[0, 0, 0, 1]])
RZ = np.array([[np.cos(rotZ), -np.sin(rotZ), 0, 0],
[np.sin(rotZ), np.cos(rotZ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]])
# Composed rotation matrix with (RX,RY,RZ)
R = np.linalg.multi_dot([RX, RY, RZ])
# Translation matrix
T = np.array([[1, 0, 0, distX],
[0, 1, 0, distY],
[0, 0, 1, distZ],
[0, 0, 0, 1]])
# Overall homography matrix
H = np.linalg.multi_dot([K, R, T, Kinv])
# Apply matrix transformation
cv2.warpPerspective(src, H, (w, h), dst, cv2.INTER_NEAREST, cv2.BORDER_CONSTANT, 0)
# Show the image
cv2.imshow(wndname2, dst)
k = cv2.waitKey(1)
# convert image to gray scale
gray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
# blur the image
blur = cv2.blur(gray, (3, 3))
# binary thresholding of the image
ret, thresh = cv2.threshold(blur, 200, 255, cv2.THRESH_BINARY)
# ret, thresh = cv2.threshold(gray, 127, 255,0)
# find contours
# contours, hierarchy = cv2.findContours(thresh,2,1)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# cc
cnt = sorted(contours, key=cv2.contourArea, reverse=True)
mask = contours[0]
# Convert to Lab colourspace
Lab = color.rgb2lab(dst)
L, A, B = cv2.split(Lab)
# cv2.imshow("L_Channel", L) # For L Channel
# cv2.imshow("A_Channel", A) # For A Channel (Here's what You need)
# cv2.imshow("B_Channel", B) # For B Channel
LMean = L.mean()
AMean = A.mean()
BMean = B.mean()
cv2.waitKey(0)
cv2.destroyAllWindows()
#print(Lab)
#print(LMean)
#print(AMean)
#print(BMean)
[row,column,no_of_ColorBands]= src.shape
#make uniform images with lab colors
LStandard = LMean*np.ones([row, column], dtype = int)
AStandard = AMean*np.ones([row, column], dtype = int)
BStandard = BMean*np.ones([row, column], dtype = int)
#determine delta values
DeltaL = L-LStandard
DeltaA = A- AStandard
DeltaB = B - BStandard
DeltaE = np.sqrt(pow(DeltaA,2)+pow(DeltaB,2)+pow(DeltaL,2))
print(DeltaE)
print(mask)
#euclidian distance(delta E) for only the masked region only
maskedDeltaE = DeltaE*mask
It is showing the following error:
File "C:/Users/Shrouti/PycharmProjects/homography/homography.py", line 175, in <module> maskedDeltaE = DeltaE*mask ValueError: operands could not be broadcast together with shapes (480,640) (6,1,2)
I want to get the Delta E for the region in the image which has largest contour and nearest from the camera considering the depth value. Please help.
