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| def hough_transform( | |
| img: np.ndarray, theta: float, rho: float | |
| ) -> (np.ndarray, list, list): | |
| thetas = np.deg2rad(np.linspace(0, 180, int(180 / theta + 1))) | |
| width, height = img.shape | |
| diag_len = int(np.ceil(np.sqrt(width * width + height * height))) # max_dist | |
| rhos = np.linspace(-diag_len, diag_len, int(diag_len / rho + 1)) | |
| # Cache some resuable values | |
| cos_t = np.cos(thetas) | |
| sin_t = np.sin(thetas) | |
| num_thetas = len(thetas) | |
| # Hough accumulator array of theta vs rho | |
| accumulator = np.zeros((len(rhos), num_thetas), dtype=np.uint64) | |
| y_idxs, x_idxs = np.nonzero(img) # (row, col) indexes to edges | |
| # Vote in the hough accumulator | |
| for i in tqdm(range(len(x_idxs))): | |
| x = x_idxs[i] | |
| y = y_idxs[i] | |
| for t_idx in range(num_thetas): | |
| # Calculate rho. diag_len is added for a positive index | |
| rho = int(x * cos_t[t_idx] + y * sin_t[t_idx]) + diag_len | |
| accumulator[rho, t_idx] += 1 | |
| return accumulator, thetas, list(rhos) |
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