Popravki
Spagnolo Gasper
parent
1742437304
commit
bd76aa0534
111
assignment4/]
111
assignment4/]
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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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@ -11,7 +11,7 @@ import os
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##############################################
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def ex1():
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one_a()
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#one_a()
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one_b()
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def one_a() -> None:
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@ -53,8 +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_b()
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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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@ -100,14 +100,12 @@ def two_b() -> None:
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a, b = uz_image.find_matches(graph_a_small, graph_b_small)
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print(a)
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print(b)
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uz_image.display_matches(graph_a_small, a, graph_b_small, b)
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def ex3():
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three_a()
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#three_b()
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#three_a()
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three_b()
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def three_a() -> None:
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"""
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@ -158,10 +156,10 @@ def three_b() -> None:
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"""
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Hi
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"""
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image_a = uz_image.imread_gray("data/graf/graf_a.jpg", uz_image.ImageType.float64)
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image_b = uz_image.imread_gray("data/graf/graf_b.jpg", uz_image.ImageType.float64)
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#image_a = uz_image.imread_gray("datam/img1.jpg", uz_image.ImageType.float64)
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#image_b = uz_image.imread_gray("datam/img2.jpg", uz_image.ImageType.float64)
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#image_a = uz_image.imread_gray("data/graf/graf_a.jpg", uz_image.ImageType.float64)
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#image_b = uz_image.imread_gray("data/graf/graf_b.jpg", uz_image.ImageType.float64)
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image_a = uz_image.imread_gray("datam/img1.jpg", uz_image.ImageType.float64)
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image_b = uz_image.imread_gray("datam/img2.jpg", uz_image.ImageType.float64)
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#image_a = uz_image.imread_gray("data/newyork/newyork_a.jpg", uz_image.ImageType.float64)
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#image_b = uz_image.imread_gray("data/newyork/newyork_b.jpg", uz_image.ImageType.float64)
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# Does not work for newyork dataset, becouse the keypoints are not reciprocal
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@ -1057,7 +1057,7 @@ def display_matches(I1, pts1, I2, pts2):
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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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sigma=6, treshold=1e-6):
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sigma=3, treshold=1e-6):
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"""
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Finds matches between two images.
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@ -1128,11 +1128,22 @@ def find_matches(image_a: npt.NDArray[np.float64],
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print("[+] Correspondences reciprocated")
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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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# Now map correspondences to the keypoints
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def map_indexes_to_points(a_points, b_points, corrs_a, corrs_b, img_a_k, img_b_k):
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for correspondence in corrs_a:
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ix = np.argwhere(correspondence[0] == corrs_b[:, 1])
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if ix.size > 0:
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a_points.append(np.flip(img_a_k[correspondence[0]]))
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b_points.append(np.flip(img_b_k[correspondence[1]]))
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return image_a_keypoints, image_b_keypoints
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image_a_points = []
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image_b_points = []
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map_indexes_to_points(image_a_points, image_b_points, correspondences_a, correspondences_b, image_a_keypoints, image_b_keypoints)
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map_indexes_to_points(image_b_points, image_a_points, correspondences_b, correspondences_a, image_b_keypoints, image_a_keypoints)
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print("[+] Correspondences mapped to points")
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return np.array(image_a_points),np.array(image_b_points)
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def estimate_homography(image_a: npt.NDArray[np.float64],
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def ransac(image_a: npt.NDArray[np.float64], correspondences_a: npt.NDArray[np.float64],
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image_b: npt.NDArray[np.float64], correspondences_b: npt.NDArray[np.float64],
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iterations: int = 10000,
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iterations: int = 1000,
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threshold: float = 1.5):
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"""
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RANSAC algorithm for estimating homography.
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