cv2_facerecognition/test.py at main · malam1050-dev/cv2_facerecognition · GitHub

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import cv2
import numpy as np
import matplotlib.pyplot as plt

img1 = cv2.imread('panda.jpg')
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
h, w = img1.shape

img2 = cv2.imread('panda1.jpg')
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
img3 = cv2.imread('bike.jpg')
img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)

def error(img1, img2):
   diff = cv2.subtract(img1, img2)
   err = np.sum(diff**2)
   mse = err/(float(h*w))
   msre = np.sqrt(mse)
   return mse, diff

match_error12, diff12 = error(img1, img2)
match_error13, diff13 = error(img1, img3)
match_error23, diff23 = error(img2, img3)

print("Image matching Error between image 1 and image 2:",match_error12)
print("Image matching Error between image 1 and image 3:",match_error13)
print("Image matching Error between image 2 and image 3:",match_error23)

plt.subplot(221), plt.imshow(diff12,'gray'),plt.title("image1 - Image2"),plt.axis('off')
plt.subplot(222), plt.imshow(diff13,'gray'),plt.title("image1 - Image3"),plt.axis('off')
plt.subplot(223), plt.imshow(diff23,'gray'),plt.title("image2 - Image3"),plt.axis('off')
plt.show()

# import required libraries
import cv2
import numpy as np

# load the input images
img1 = cv2.imread('panda.jpg')
img2 = cv2.imread('bike.jpg')

cv2.imshow("image", img1)
cv2.imshow("image", img2)

# convert the images to grayscale
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

# define the function to compute MSE between two images
def mse(img1, img2):
   h, w = img1.shape
   diff = cv2.subtract(img1, img2)
   err = np.sum(diff**2)
   mse = err/(float(h*w))
   return mse, diff

error, diff = mse(img1, img2)
print("Image matching Error between the two images:",error)

cv2.imshow("difference", diff)
cv2.waitKey(0)
cv2.destroyAllWindows()