Here is the following code that I have.
-- 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\handgesture2.png")
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 to Lab colourspace
Lab = color.rgb2lab(dst)
print(Lab)
# Convert to Lab colourspace
''' cv2.namedWindow(wndname1, 1) cv2.namedWindow(wndname2, 1) cv2.createTrackbar("f", wndname2, f, 1000, onFchange) cv2.createTrackbar("Rotation X", wndname2, rotXval, 180, onRotXChange) cv2.createTrackbar("Rotation Y", wndname2, rotYval, 180, onRotYChange) cv2.createTrackbar("Rotation Z", wndname2, rotZval, 180, onRotZChange) cv2.createTrackbar("Distance X", wndname2, distXval, 1000, onDistXChange) cv2.createTrackbar("Distance Y", wndname2, distYval, 1000, onDistYChange) cv2.createTrackbar("Distance Z", wndname2, distZval, 1000, onDistZChange) '''
Now what I have tried to do here is to apply homography transformation on an RGB image and also tried to transform it to CIE-L*ab colour space. What I need is, to calculate the Euclidean distances among one pixel selected from the object of interest and the rest of the points in the image. This can be done by calculating the level of similarity among the pixels. We also need to compute the delta values of the image yielding only one chromatic difference on an image.
There are delta values associated with this colour scale.ΔL, Δa andΔb indicate how much a standard and simple difference from one another in L, a, and b, respectively. Please help.
