Spaces:
Sleeping
Sleeping
File size: 2,412 Bytes
cf0b6b1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
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) |