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| import cv2 | |
| def template_match(img_master, img_slave, method='cv2.TM_CCOEFF_NORMED', mlx=1, mly=1, show=True): | |
| # Apply image oversampling | |
| img_master = cv2.cvtColor(img_master, cv2.COLOR_BGR2GRAY) | |
| img_slave = cv2.cvtColor(img_slave, cv2.COLOR_BGR2GRAY) | |
| img_master = cv2.resize(img_master, None, fx=mlx, fy=mly, interpolation=cv2.INTER_CUBIC) | |
| img_slave = cv2.resize(img_slave, None, fx=mlx, fy=mly, interpolation=cv2.INTER_CUBIC) | |
| res = cv2.matchTemplate(img_slave, img_master, eval(method)) | |
| w, h = img_master.shape[::-1] | |
| min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) | |
| # Control if the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum value | |
| if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]: | |
| top_left = min_loc | |
| else: | |
| top_left = max_loc | |
| bottom_right = (top_left[0] + w, top_left[1] + h) | |
| # Retrieve center coordinates | |
| px = (top_left[0] + bottom_right[0]) / (2.0 * mlx) | |
| py = (top_left[1] + bottom_right[1]) / (2.0 * mly) | |
| # # Scale images for visualization | |
| # img_master_scaled = cv2.convertScaleAbs(img_master, alpha=(255.0 / 500)) | |
| # img_slave_scaled = cv2.convertScaleAbs(img_slave, alpha=(255.0 / 500)) | |
| # | |
| # cv2.rectangle(img_slave_scaled, top_left, bottom_right, 255, 2 * mlx) | |
| # | |
| # if show == True: | |
| # plt.figure(figsize=(20, 10)) | |
| # plt.subplot(131), plt.imshow(res, cmap='gray') | |
| # plt.title('Matching Result'), plt.xticks([]), plt.yticks([]) | |
| # plt.subplot(132), plt.imshow(img_master_scaled, cmap='gray') | |
| # plt.title('Detected Point'), plt.xticks([]), plt.yticks([]) | |
| # plt.subplot(133), plt.imshow(img_slave_scaled, cmap='gray') | |
| # plt.suptitle(method) | |
| # plt.show() | |
| return px, py, max_val | |
| # import numpy as np | |
| # example = np.arange(2000).reshape((100, 20)) | |
| # a_kernel = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]]) | |
| # def sliding_window(data, win_shape, **kwargs): | |
| # assert data.ndim == len(win_shape) | |
| # shape = tuple(dn - wn + 1 for dn, wn in zip(data.shape, win_shape)) + win_shape | |
| # strides = data.strides * 2 | |
| # return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides, **kwargs) | |
| # def arrays_from_kernel(a, a_kernel): | |
| # windows = sliding_window(a, a_kernel.shape) | |
| # return np.where(a_kernel, windows, 0) | |
| # sub_arrays = arrays_from_kernel(example, a_kernel) |