Commit before my laptop burns
parent
f57399666d
commit
647ff20f61
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@ -10,9 +10,9 @@ import os
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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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#one_c()
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one_a()
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one_b()
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one_c()
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one_e()
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def one_a() -> None:
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@ -153,11 +153,11 @@ def one_e():
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"""
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hello
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"""
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museum = uz_image.imread('./images/museum.jpg', uz_image.ImageType.float64)
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#museum = uz_image.imread('./images/museum.jpg', uz_image.ImageType.float64)
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#uz_image.get_image_bins_gradient_magnitude_and_angles(museum)
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#ex2_naive('../assignment2/data/dataset/', 8)
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ex2_naive('../assignment2/data/dataset/', 8)
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ex2_optimized('../assignment2/data/dataset/', 8)
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@ -270,11 +270,10 @@ def ex2_optimized(directory: str, n_bins: int):
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############################################
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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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two_c()
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def two_a():
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"""
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Firstly, create a function findedges that accepts an image I, and the parameters
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@ -346,9 +345,10 @@ def ex3():
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#three_a()
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#three_b()
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#three_c()
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#three_d()
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#three_e()
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three_d()
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three_e()
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three_f()
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#three_g()
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def three_a():
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"""
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@ -507,33 +507,30 @@ def three_d():
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fig, axs = plt.subplots(1, 3)
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def select_best_pairs(image_line_params: npt.NDArray[np.float64], n =10):
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image_line_params = np.array(image_line_params)
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# Sorts just kth element so every eleement before kth element is lower than kth element
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# and every element after kth element is higher than kth element
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partition = np.argpartition(image_line_params, kth=len(image_line_params) - n - 1, axis=0)[-n:]
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image_line_params = image_line_params[partition.T[0]]
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return image_line_params
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# Plot synthetic image
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axs[0].imshow(synthetic_image, cmap='gray')
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neighbour_pairs = uz_image.retrieve_hough_pairs(synthetic_image, synthetic_image_hough_nonmax, np.max(synthetic_image_hough_nonmax)*0.99, N_BINS_THETA, N_BINS_RHO)
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for neighbour in neighbour_pairs:
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neighbour_pairs = uz_image.retrieve_hough_pairs(synthetic_image, synthetic_image_hough_nonmax, np.max(synthetic_image_hough_nonmax)*0.80, N_BINS_THETA, N_BINS_RHO)
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best_paris = uz_image.select_best_pairs(neighbour_pairs)
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for neighbour in best_paris:
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xs, ys = uz_image.get_line_to_plot(neighbour[0], neighbour[1], synthetic_image.shape[0], synthetic_image.shape[1])
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axs[0].plot(xs, ys, 'r', linewidth=0.7)
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# Plot oneline image
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axs[1].imshow(oneline_image, cmap='gray')
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neighbour_pairs = uz_image.retrieve_hough_pairs(oneline_image, oneline_image_hough_nonmax, np.max(oneline_image_hough_nonmax)*0.5, N_BINS_THETA, N_BINS_RHO)
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for neighbour in neighbour_pairs:
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neighbour_pairs = uz_image.retrieve_hough_pairs(oneline_image, oneline_image_hough_nonmax, np.max(oneline_image_hough_nonmax)*0.4, N_BINS_THETA, N_BINS_RHO)
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best_paris = uz_image.select_best_pairs(neighbour_pairs)
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for neighbour in best_paris:
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xs, ys = uz_image.get_line_to_plot(neighbour[0], neighbour[1], oneline_image.shape[0], oneline_image.shape[1])
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axs[1].plot(xs, ys, 'r', linewidth=0.7)
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# Plot rectangle image
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axs[2].imshow(rectangle_image, cmap='gray')
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neighbour_pairs = uz_image.retrieve_hough_pairs(rectangle_image, rectangle_image_hough_nonmax, np.max(rectangle_image_hough_nonmax)*0.35, N_BINS_THETA, N_BINS_RHO)
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best_paris = select_best_pairs(neighbour_pairs)
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best_paris = uz_image.select_best_pairs(neighbour_pairs)
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for neighbour in best_paris:
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xs, ys = uz_image.get_line_to_plot(neighbour[0], neighbour[1], rectangle_image.shape[0], rectangle_image.shape[1])
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@ -646,37 +643,82 @@ def three_f():
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the [−π/2, π/2] interval. Test the modified function on several images and compare
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the results with the original implementation.
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"""
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rectangle_image = uz_image.imread_gray('./images/rectangle.png', uz_image.ImageType.float64)
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SIGMA = 1
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THETA = 0.02
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T_LOW = 0.04
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T_HIGH = 0.16
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image_with_edges_n, derivative_magnitude_n, gradient_angles_n, hough_image_n, hough_image_nms_n, pairs_n, best_pairs_n = uz_image.find_lines_in_image_naive(
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rectangle_image
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)
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img = cv2.imread('images/rectangle.png')
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image_with_edges_i, derivative_magnitude_i, gradient_angles_i, hough_image_i, hough_image_nms_i, pairs_i, best_pairs_i = uz_image.find_lines_in_image_improved(
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rectangle_image
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)
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# Get gradient magntude and gradient angle
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t_lower = 70
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t_upper = 200
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edge_detected_image = cv2.Canny(img, t_lower, t_upper)
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gm, ga = uz_image.gradient_magnitude(img, 1)
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print('Edge detected:', edge_detected_image.shape)
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# Transform image into hough space
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image_transformed_into_hough_space = uz_image.hough_find_lines_i(edge_detected_image, ga, gm, 360, 360, 0.2)
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print('Hough lines drawn:', image_transformed_into_hough_space.shape)
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hugh_pairs = uz_image.retrieve_hough_pairs(img, image_transformed_into_hough_space, np.max(image_transformed_into_hough_space) * 0.2, 360, 360)
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best_pairs = hugh_pairs
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fig, axs = plt.subplots(2, 2)
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axs[0, 0].imshow(hough_image_n)
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axs[0, 0].imshow(edge_detected_image)
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axs[0, 0].set(title='normal')
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axs[0, 1].imshow(hough_image_i)
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axs[0, 1].imshow(image_transformed_into_hough_space)
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axs[0, 1].set(title='normal')
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axs[1, 0].imshow(rectangle_image, cmap='gray')
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for param in best_pairs_n:
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xs, ys = uz_image.get_line_to_plot(param[0], param[1], rectangle_image.shape[0], rectangle_image.shape[1])
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axs[1, 0].imshow(edge_detected_image, cmap='gray')
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for param in best_pairs:
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xs, ys = uz_image.get_line_to_plot(param[0], param[1], img.shape[0], img.shape[1])
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axs[1, 0].plot(xs, ys, 'r', linewidth=0.7)
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axs[1,1].imshow(rectangle_image, cmap='gray')
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for param in best_pairs_i:
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xs, ys = uz_image.get_line_to_plot(param[0], param[1], rectangle_image.shape[0], rectangle_image.shape[1])
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axs[1, 1].plot(xs, ys, 'r', linewidth=0.7)
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axs[1, 1].imshow(image_transformed_into_hough_space)
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#axs[1,1].imshow(rectangle_image, cmap='gray')
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#for param in best_pairs_i:
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# xs, ys = uz_image.get_line_to_plot(param[0], param[1], rectangle_image.shape[0], rectangle_image.shape[1])
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# axs[1, 1].plot(xs, ys, 'r', linewidth=0.7)
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#plt.show()
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plt.show()
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def three_g():
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"""
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F (5 points) Implement a Hough transform that detects circles of a fixed radius.
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You can test the algorithm on image eclipse.jpg. Try using a radius somewhere
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between 45 and 50 pixels.
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"""
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circle_image = uz_image.imread('images/eclipse.jpg', uz_image.ImageType.uint8)
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#img = cv2.imread('images/rectangle.png')
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t_lower = 100
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t_upper = 150
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edge_detected_image = cv2.Canny(circle_image, t_lower, t_upper)
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hugh_transformed_circle = uz_image.hough_transform_a_circle(edge_detected_image, 45, 50, 0.2)
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fig, axs = plt.subplots(1, 2)
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axs[0].imshow(circle_image, cmap='gray')
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axs[0].set(title='Original')
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axs[1].imshow(edge_detected_image, cmap='gray')
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axs[1].set(title='Edges')
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plt.show()
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for i in range(hugh_transformed_circle.shape[2]):
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plt.imshow(hugh_transformed_circle[:, :, i])
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plt.show()
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# ######## #
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@ -684,9 +726,9 @@ def three_f():
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# ######## #
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def main():
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ex1()
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#ex2()
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#ex3()
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#ex1() # everything K
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#ex2() # everything OK
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ex3()
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if __name__ == '__main__':
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main()
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@ -241,7 +241,13 @@ def get_image_bins_ND(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint
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return hist / np.sum(hist)
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def get_image_bins_gradient_magnitude_and_angles(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]) -> npt.NDArray[np.float64]:
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def get_image_bins_gradient_magnitude_and_angles(image: Union[npt.NDArray[np.float64],
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npt.NDArray[np.uint8]]) -> npt.NDArray[np.float64]:
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"""
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Accepts: image,
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Returns: 1D histogram of image using gradient magnitude and gradient angles
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Works OK on many dimensions
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"""
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WIDTH = image.shape[0]
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HEIGHT = image.shape[1]
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WIDTH_8 = WIDTH // 8
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@ -721,6 +727,30 @@ def hough_transform_a_point(x: int, y: int, n_bins: int) -> npt.NDArray[np.float
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return accumlator
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def hough_transform_a_circle(edged_image: Union[npt.NDArray[np.float64] , npt.NDArray[np.uint8]],
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r_start: int, r_end: int, treshold: float) -> npt.NDArray[np.float64]:
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"""
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Accepts: image, r_start, r_end
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Returns: hough space
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"""
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image = edged_image.copy()
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image[image < treshold] = 0
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accumlator = np.zeros((edged_image.shape[0], edged_image.shape[1], r_end - r_start))
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indices = np.argwhere(image)
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sine_value = np.sin(np.linspace(0, np.pi, 360))
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cosine_value = np.cos(np.linspace(0, np.pi, 360))
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# Loop through all nonzero pixels above treshold
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for i in tqdm(range(len(indices)), desc='Hough transform'):
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for r in range(0, r_end - r_start):
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x, y = indices[i]
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for svcv in range(sine_value.shape[0]):
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a = x - r * cosine_value[svcv]
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b = y - r * sine_value[svcv]
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accumlator[int(a), int(b), r ] += 1
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return accumlator
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def hough_find_lines(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]],
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n_bins_theta: int, n_bins_rho: int, treshold: float) -> npt.NDArray[np.uint64]:
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@ -758,38 +788,37 @@ def hough_find_lines(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]
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return accumulator
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def hough_find_lines_i(image_with_lines: npt.NDArray[np.float64], gradient_angles: npt.NDArray[np.float64],
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def hough_find_lines_i(image_with_lines: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], gradient_angles: npt.NDArray[np.float64],
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gradient_magnitude: npt.NDArray[np.float64],
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n_bins_theta: int, n_bins_rho: int, treshold: float) -> npt.NDArray[np.uint64]:
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n_bins_theta: int, n_bins_rho: int, treshold: float) -> Union[npt.NDArray[np.uint64], npt.NDArray[np.float64]]:
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""""
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Accepts: bw image with lines, n_bins_theta, n_bins_rho, treshold
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Returns: image points above treshold transformed into hough space
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"""
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image = image_with_lines.copy()
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#image[image < treshold] = 0
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image[image < treshold] = 0
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theta_values = np.linspace(-np.pi/2, np.pi/2, n_bins_theta)
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D = np.sqrt(image.shape[0]**2 + image.shape[1]**2)
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rho_values = np.linspace(-D, D, n_bins_rho)
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accumulator = np.zeros((n_bins_rho, n_bins_theta), dtype=np.uint64)
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accumulator = np.zeros((n_bins_rho, n_bins_theta), dtype=np.float64)
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cos_precalculated = np.cos(theta_values)
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sin_precalculated = np.sin(theta_values)
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indices = np.argwhere(image)
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# Loop through all nonzero pixels above treshold
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for i in tqdm(range(len(indices)), desc='Hough transform'):
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y, x = indices[i]
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angle = (np.mod(gradient_angles[y, x] + np.pi/2 , np.pi)) - np.pi/2
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theta = np.digitize(gradient_angles[y, x] / 2, theta_values) -1
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rho = np.round(x* cos_precalculated[theta] + y* sin_precalculated[theta]).astype(np.int64)
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theta = np.digitize(angle, theta_values) -1
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rho = np.round(x* cos_precalculated[theta] + y* sin_precalculated[theta]).astype(np.float64)
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binned_rho = np.digitize(rho, rho_values) - 1 # cuz digitize is returning bin number + 1
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# Add to accumulator
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print(gradient_magnitude[y, x])
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accumulator[binned_rho, theta] += 1
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accumulator[binned_rho, theta] += gradient_magnitude[y, x]
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return accumulator
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break
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return image
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def retrieve_hough_pairs(original_image: npt.NDArray[np.float64], hough_image: npt.NDArray[np.uint64],
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treshold: int, n_bins_theta: int, n_bins_rho: int) -> list[tuple[int, int]]:
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"""
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image_line_params = image_line_params[partition.T[0]]
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return image_line_params
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def find_lines_in_image_naive(image: npt.NDArray[np.float64], SIGMA=1, THETA=0.02, T_LOW=0.04,
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T_HIGH=0.16, N_BINS_THETA=360, N_BINS_RHO=360, TRESHOLD=0.2):
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"""
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Aplies all methods to transform image into hough space and find lines
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"""
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image = image.copy()
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# First step: apply canny edge detector
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image_with_edges = find_edges_canny(image, SIGMA, THETA, T_LOW, T_HIGH)
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# Second step: Retrieve gradient angles
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derivative_magnitude, gradient_angles = gradient_magnitude(image, SIGMA)
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# Third step: Transform image into hough space
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hough_image = hough_find_lines(image_with_edges, int(N_BINS_THETA), int(N_BINS_RHO), TRESHOLD)
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# Fourth step: Apply nonmaxima suppression
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hough_image_nms = nonmaxima_suppression_box(hough_image)
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# Fifth step: Retrieve sigma and theta pairs
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pairs = retrieve_hough_pairs(image, hough_image_nms, np.max(hough_image_nms) *0.5, int(N_BINS_THETA), int(N_BINS_RHO))
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# Sixth step: select best pairs
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best_pairs = select_best_pairs(pairs, 10)
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return image_with_edges, derivative_magnitude, gradient_angles, hough_image, hough_image_nms, pairs, best_pairs
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def find_lines_in_image_improved(image: npt.NDArray[np.float64], SIGMA=1, THETA=0.02, T_LOW=0.04, T_HIGH=0.16, N_BINS_THETA=360, N_BINS_RHO=360, TRESHOLD=0.2):
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"""
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Aplies all methods to transform image into hough space and find lines
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"""
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image = image.copy()
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# First step: apply canny edge detector
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image_with_edges = find_edges_canny(image, SIGMA, THETA, T_LOW, T_HIGH)
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# Second step: Retrieve gradient angles
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derivative_magnitude, gradient_angles = gradient_magnitude(image, SIGMA)
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# Third step: Transform image into hough space
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hough_image = hough_find_lines_i(image_with_edges, gradient_angles, derivative_magnitude, N_BINS_THETA, N_BINS_RHO, TRESHOLD)
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# Fourth step: Apply nonmaxima suppression
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hough_image_nms = nonmaxima_suppression_box(hough_image)
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# Fifth step: Retrieve sigma and theta pairs
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pairs = retrieve_hough_pairs(image, hough_image_nms, 0, N_BINS_THETA, N_BINS_RHO)
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# Sixth step: select best pairs
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#best_pairs = select_best_pairs(pairs, 10)
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best_pairs = pairs
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return image_with_edges, derivative_magnitude, gradient_angles, hough_image, hough_image_nms, pairs, best_pairs
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def get_line_to_plot(rho, theta, h, w):
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"""
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Accepts: rho, theta, image height h, image width w
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