Najsss
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import numpy as np
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import numpy.typing as npt
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from matplotlib import pyplot as plt
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import cv2
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import uz_framework.image as uz_image
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import uz_framework.text as uz_text
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import os
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##############################################
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# EXCERCISE 1: Exercise 1: Image derivatives #
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##############################################
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def ex1():
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one_a()
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one_b()
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def one_a() -> None:
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img = uz_image.imread_gray("data/graf/graf_a.jpg", uz_image.ImageType.float64)
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sigmas = [3, 6, 9, 12]
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# Plot the points
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fig, axs = plt.subplots(2, len(sigmas))
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fig.suptitle("Hessian corner detection")
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for i, sigma in enumerate(sigmas):
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determinant, hessian_points = uz_image.hessian_points(img, sigma, 0.004)
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# Plot determinant
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axs[0, i].imshow(determinant)
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axs[0, i].set_title(f"Sigma: {sigma}")
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# Plot grayscale image
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axs[1, i].imshow(img, cmap="gray")
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# Plot scatter hessian points (x, y)
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axs[1, i].scatter(hessian_points[:, 1], hessian_points[:, 0], s=20, c="r", marker="x")
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plt.show()
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def one_b() -> None:
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img = uz_image.imread_gray("data/graf/graf_a.jpg", uz_image.ImageType.float64)
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sigmas = [3, 6, 9]
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# Plot the points
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fig, axs = plt.subplots(2, len(sigmas))
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fig.suptitle("Harris corner detection")
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for i, sigma in enumerate(sigmas):
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determinant, harris_points = uz_image.harris_detector(img, sigma, treshold=1e-6)
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# Plot determinant
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axs[0, i].imshow(determinant)
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axs[0, i].set_title(f"Sigma: {sigma}")
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# Plot grayscale image
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axs[1, i].imshow(img, cmap="gray")
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# Plot scatter hessian points
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axs[1, i].scatter(harris_points[:, 1], harris_points[:, 0], s=20, c="r", marker="x")
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plt.show()
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def ex2():
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two_a()
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def two_a() -> None:
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"""
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Hello
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"""
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graph_a_small = uz_image.imread_gray("data/graf/graf_a_small.jpg", uz_image.ImageType.float64)
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graph_b_small = uz_image.imread_gray("data/graf/graf_b_small.jpg", uz_image.ImageType.float64)
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# Get the keypoints
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_, graph_a_keypoints = uz_image.harris_detector(graph_a_small, 3, treshold=1e-6)
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_, graph_b_keypoints = uz_image.harris_detector(graph_b_small, 3, treshold=1e-6)
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# Get the descriptors
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graph_a_descriptors = uz_image.simple_descriptors(graph_a_small, graph_a_keypoints[:,0], graph_a_keypoints[:,1])
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graph_b_descriptors = uz_image.simple_descriptors(graph_b_small, graph_b_keypoints[:,0], graph_b_keypoints[:,1])
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# Find the correspondences
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matches_a = uz_image.find_correspondences(graph_a_descriptors, graph_b_descriptors)
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matches_b = uz_image.find_correspondences(graph_b_descriptors, graph_a_descriptors)
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matches_a_coordinates = []
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matche_b_coordinates = []
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for i, match in enumerate(matches_a):
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if i % 2 == 0: # plot every second one
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if np.flip(match) in matches_b: # Check if the match is reciprocal
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print(match)
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print(np.flip(match))
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print(matches_b)
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print(np.argwhere(matches_b == np.flip(match)))
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matches_a_coordinates.append(np.flip(graph_a_keypoints[match[0]]))
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matche_b_coordinates.append(np.flip(graph_b_keypoints[match[1]]))
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else:
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print("Not reciprocal")
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# Plot the matches
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uz_image.display_matches(graph_a_small, matches_a_coordinates, graph_b_small, matche_b_coordinates)
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def two_b() -> None:
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"""
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jjjjj
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"""
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# ######## #
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# SOLUTION #
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# ######## #
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def main():
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#ex1()
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ex2()
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if __name__ == '__main__':
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main()
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@ -53,7 +53,8 @@ def one_b() -> None:
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plt.show()
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def ex2():
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two_a()
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#two_a()
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two_b()
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def two_a() -> None:
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"""
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@ -79,6 +80,7 @@ def two_a() -> None:
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for i, match in enumerate(matches_a):
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if i % 2 == 0: # plot every second one
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if np.flip(match) in matches_b: # Check if the match is reciprocal
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print(np.argwhere(matches_b == np.flip(match)))
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matches_a_coordinates.append(np.flip(graph_a_keypoints[match[0]]))
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matche_b_coordinates.append(np.flip(graph_b_keypoints[match[1]]))
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else:
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@ -91,8 +93,12 @@ def two_b() -> None:
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"""
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jjjjj
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"""
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graph_a_small = uz_image.imread_gray("datam/img1.jpg", uz_image.ImageType.float64)
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graph_b_small = uz_image.imread_gray("datam/img2.jpg", uz_image.ImageType.float64)
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a, b = uz_image.find_matches(graph_a_small, graph_b_small)
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uz_image.display_matches(graph_a_small, a, graph_b_small, b)
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# ######## #
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@ -1,4 +1,3 @@
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import numpy as np
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import cv2 as cv2
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from matplotlib import pyplot as plt
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@ -33,7 +32,6 @@ def imread(path: str, type: ImageType) -> Union[npt.NDArray[np.float64], npt.ND
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raise Exception(f"Unrecognized image format! {type}")
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def imread_gray(path: str, type: ImageType) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
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"""
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Reads an image in gray. Image type is transformed from uint8 to float, and
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@ -289,7 +287,6 @@ def get_image_bins_gradient_magnitude_and_angles(image: Union[npt.NDArray[np.flo
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histogram = np.array(histogram)
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return histogram / np.sum(histogram)
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def compare_two_histograms(h1: npt.NDArray[np.float64], h2: npt.NDArray[np.float64],
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method: DistanceMeasure) -> float:
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"""
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@ -1034,8 +1031,6 @@ def find_correspondences(img_a_descriptors: npt.NDArray[np.float64],
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return np.array(correspondances)
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def display_matches(I1, pts1, I2, pts2):
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"""
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Displays matches between images.
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plt.show()
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def find_matches(image_a: npt.NDArray[np.float64],
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image_b: npt.NDArray[np.float64]):
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"""
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Finds matches between two images.
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image_a, image_b: Image in grayscale.
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"""
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# Get the keypoints
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_, image_a_keypoints = harris_detector(image_a, 6, treshold=1e-6)
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_, image_b_keypoints = harris_detector(image_b, 6, treshold=1e-6)
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# Get the descriptors
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image_a_descriptors = simple_descriptors(image_a, image_a_keypoints[:, 0], image_a_keypoints[:, 1])
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image_b_descriptors = simple_descriptors(image_b, image_b_keypoints[:, 0], image_b_keypoints[:, 1])
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# Find correspondences
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correspondences_a = find_correspondences(image_a_descriptors, image_b_descriptors)
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correspondences_b = find_correspondences(image_b_descriptors, image_a_descriptors)
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def select_best_correspondences(c_a, d_a, c_b, d_b):
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#Find correspondances that map into same index a->b (b)
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unique, counts = np.unique(c_a[:, 1], return_counts=True)
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# Find all b's
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duplicates = unique[counts > 1]
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for duplicate in duplicates:
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# Find the indices of the duplicates find all a's
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indices = np.where(c_a[:, 1] == duplicate)[0]
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# Extract those from the descriptors
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candidates = d_a[indices]
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# Extract comparing distance from b's descriptors
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comparing_distance = d_b[duplicate]
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# Find the closest one using hellingers distance
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dists = np.sqrt(0.5 * np.sum(np.square(np.sqrt(comparing_distance) - np.sqrt(candidates)), axis=1))
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# Select the best one
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min_dist_idx = np.argmin(dists)
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# and map that candidate back to the indces index
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candidate = indices[min_dist_idx]
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candidate = c_a[candidate]
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#if np.flip(candidate) in c_b:
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# # Remove it from the indices so it does not get removed in the next step
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# indices = np.delete(indices, min_dist_idx)
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#else:
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# print(f'[i] select_best_correspondences -> Not reciprocal {np.flip(candidate)}')
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#Remove remainig from the correspondences
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c_a = np.delete(c_a, indices, axis=0)
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return c_a
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correspondences_a = select_best_correspondences(correspondences_a, image_a_descriptors, correspondences_b, image_b_descriptors)
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correspondences_b = select_best_correspondences(correspondences_b, image_b_descriptors, correspondences_a, image_a_descriptors)
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def check_repciporacvbillity(c_a, c_b):
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for _, match in enumerate(c_a):
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if np.flip(match) not in c_b:
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print(f'[i] check_repciporacvbillity -> Not reciprocal {match}')
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index = np.where((c_a == match).all(axis=1))[0][0]
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c_a = np.delete(c_a, index, axis=0)
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return c_a
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correspondences_a = check_repciporacvbillity(correspondences_a, correspondences_b)
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correspondences_b = check_repciporacvbillity(correspondences_b, correspondences_a)
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# Map correspondences to keypoints
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image_a_keypoints = np.flip(image_a_keypoints[correspondences_a[:, 0]])
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image_b_keypoints = np.flip(image_b_keypoints[correspondences_b[:, 0]])
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return image_a_keypoints, image_b_keypoints
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