Image matching

image_matcher.py

import cv2
import numpy as np


def calculate_differences(img1, img2):
    # Initialization
    detector = cv2.ORB_create()
    descriptor = cv2.ORB_create()
    matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)

    # First image objects
    img1_keypoints, img1_descriptors = detector.detectAndCompute(img1, None)

    # Second image objects
    img2_keypoints, img2_descriptors = detector.detectAndCompute(img2, None)

    # Match KeyPoints
    mat_of_d_match = matcher.match(img1_descriptors, img2_descriptors)

    # Filtering the matches
    d_match_list = mat_of_d_match
    min_dist = 100
    max_dist = 0

    for i in range(len(img1_descriptors)):
        dist = d_match_list[i].distance
        min_dist = min(min_dist, dist)
        max_dist = max(max_dist, dist)

    good_matches = [m for m in d_match_list if m.distance < 3 * min_dist]

    # Converting to MatOfPoint2f format
    img1_points_list = [img1_keypoints[m.queryIdx].pt for m in good_matches]
    img2_points_list = [img2_keypoints[m.trainIdx].pt for m in good_matches]

    img1_point2f_mat = np.float32(img1_points_list)
    img2_point2f_mat = np.float32(img2_points_list)

    # Computing the affine transform matrix
    result, _ = cv2.estimateAffine2D(img1_point2f_mat, img2_point2f_mat)
    print_mat(result)  # Printing the optimal affine transformation 2x3 array

    # The following variables correspond to the estimateRigidTransform result as shown here: https://stackoverflow.com/a/29511091/5165833
    a = result[0, 0]
    b = result[0, 1]
    d = result[1, 1]
    c = result[1, 0]
    tx = result[0, 2]
    ty = result[1, 2]

    # Solving for scale, translation, and rotation as shown in the link above
    scale_x = np.sign(a) * np.sqrt(a * a + b * b)  # Axis x scale difference
    scale_y = np.sign(d) * np.sqrt(c * c + d * d)  # Axis y scale difference
    translation = ty  # The translation difference
    rotation_angle = np.arctan2(c, d)  # Rotation difference

    # Printing results
    print(f"Scale_x diff: {scale_x}")
    print(f"Scale_y diff: {scale_y}")
    print(f"Translation diff: {translation}")
    print(f"Rotation diff: {rotation_angle}")


def print_mat(m):
    for x in range(m.shape[0]):
        for y in range(m.shape[1]):
            print(f"{m[x, y]:.6f}", end=" ")
        print()


# Example usage:
img1 = cv2.imread("path/to/image1.jpg", cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread("path/to/image2.jpg", cv2.IMREAD_GRAYSCALE)
calculate_differences(img1, img2)