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)