37_Find_Similarities_between_Images_1.py

"""
For some reason, the SURF and SIFT algorithms are protected and not install into the opencv-python package since OpenCV 3.x. They are included in opencv-contrib-python, so in order to run this file, you need run the following command:
pip uninstall opencv-contrib-python
pip uninstall opencv-python
pip install opencv-contrib-python==3.3.0.10
pip install opencv-python==3.4.2.16

Please download the images from:
https: // pysource.com/2018/07/27/check-if-a-set-of-images-match-the-original-one-with-opencv-and-python/
"""

import cv2
import numpy as np

original = cv2.imread("./images/original_golden_bridge.jpg")
original = cv2.resize(original, dsize=None, fx=1/3, fy=1/3)
# image_to_compare = cv2.imread("./images/Golden_Bridge/george-washington-bridge.jpg")
image_to_compare = cv2.imread("images/Golden_Bridge/mixed_colors.jpg")
# image_to_compare = cv2.imread("./images/Golden_Bridge/different-golden-gate-bridge.jpg")
image_to_compare = cv2.resize(image_to_compare, dsize=None, fx=1/3, fy=1/3)


# 1) Check if 2 images are equals
if original.shape == image_to_compare.shape:
    print("Checking using absolute difference: ")
    print("The images have same size and channels")
    difference = cv2.subtract(original, image_to_compare)
    b, g, r = cv2.split(difference)

    if cv2.countNonZero(b) == 0 and cv2.countNonZero(g) == 0 and cv2.countNonZero(r) == 0:
        print("The images are completely Equal")
    else:
        print("The images are NOT equal")

# 2) Check for similarities between the 2 images
sift = cv2.xfeatures2d.SIFT_create()
kp_1, desc_1 = sift.detectAndCompute(original, None)
kp_2, desc_2 = sift.detectAndCompute(image_to_compare, None)

index_params = dict(algorithm=0, trees=5)
search_params = dict()
flann = cv2.FlannBasedMatcher(index_params, search_params)

matches = flann.knnMatch(desc_1, desc_2, k=2)

good_points = []
ratio = 0.9
for m, n in matches:
    if m.distance < ratio*n.distance:
        good_points.append(m)

len_good_points = len(good_points)

# Define how similar they are
number_keypoints = 0
if len(kp_1) <= len(kp_2):
    number_keypoints = len(kp_1)
else:
    number_keypoints = len(kp_2)
print("Keypoints 1ST Image: " + str(len(kp_1)))
print("Keypoints 2ND Image: " + str(len(kp_2)))


print("Checking using feature matching methods: ")
print("Good points number is: ", len_good_points)
print("How good is the match? : {:.2f}%".format(len(
    good_points) / number_keypoints * 100))

result = cv2.drawMatches(
    original, kp_1, image_to_compare, kp_2, good_points, None)

cv2.imshow("result", result)
cv2.imshow("Original", original)
cv2.imshow("Duplicate", image_to_compare)
cv2.waitKey(0)
cv2.destroyAllWindows()