Updates to be correct. (b3dd2a91) · Commits · aflab / astrogeology / Autocnet

autocnet/matcher/naive_template.py

+53 −16

Original line number	Diff line number	Diff line
		@@ -3,6 +3,7 @@ import cv2
		import numpy as np
		from scipy.ndimage.interpolation import zoom
		from skimage.transform import rescale
		from skimage.util import img_as_float32
		from scipy.ndimage.measurements import center_of_mass


		@@ -86,6 +87,43 @@ def pattern_match(template, image, upsampling=8, metric=cv2.TM_CCOEFF_NORMED, er
		Call an arbitrary pattern matcher using a subpixel approach where the template and image
		are upsampled using a third order polynomial.

		This function assumes that the image center (0,0) and template center (0,0) are the a priori
		coordinates. This function returns the offset to the center of the template such that the
		template is brought into alignment with the image.

		For example, if the image to be searched appears as follow:

		0, 0, 0, 0, 0, 0, 0, 1, 0
		0, 0, 0, 0, 0, 0, 0, 1, 0
		1, 1, 1, 0, 0, 0, 0, 1, 0
		0, 1, 0, 0, 0, 0, 0, 0, 0
		0, 1, 0, 0, 0, 0, 0, 0, 0
		0, 0, 0, 0, 0, 0, 0, 0, 0
		0, 0, 0, 0, 0, 0, 0, 0, 0
		0, 0, 0, 0, 0, 0, 0, 0, 0
		0, 0, 0, 0, 0, 0, 1, 1, 1
		0, 1, 1, 1, 0, 0, 1, 0, 1
		0, 1, 0, 1, 0, 0, 1, 0, 1
		0, 1, 1, 1, 0, 0, 1, 0, 1
		0, 0, 0, 0, 0, 0, 1, 1, 1

		and the template image is

		1, 1, 1
		0, 1, 0
		0, 1, 0

		the expected shift from the center of the search image to have template image align is -3 in
		the y-direction and -3 in the x-direction. Conversely, if the search image is:

		1, 1, 1
		1, 0, 1
		1, 0, 1
		1, 0, 1
		1, 1, 1

		the expected shift is 4 in the y-direction and 3 in the x-direction.

		Parameters
		----------
		template : ndarray
		@@ -120,32 +158,31 @@ def pattern_match(template, image, upsampling=8, metric=cv2.TM_CCOEFF_NORMED, er

		# Fit a 3rd order polynomial to upsample the images
		if upsampling != 1:
		u_template = rescale(template, upsampling, order=1, mode='edge', preserve_range=True)
		u_image = rescale(image, upsampling, order=1, mode='edge', preserve_range=True)
		u_template = img_as_float32(rescale(template, upsampling, order=3, mode='edge'))
		u_image = img_as_float32(rescale(image, upsampling, order=3, mode='edge'))
		else:
		u_template = template
		u_image = image

		corrmap = cv2.matchTemplate(u_image, u_template, method=metric)


		width = (np.asarray(u_image.shape) - np.asarray(corrmap.shape)) // 2 # This needs to be an integer

		# Pad the result array with values outside the valid correlation range
		width = (np.asarray(u_template.shape) - np.asarray(corrmap.shape)) // 2
		result = np.pad(corrmap,
		((width[0], width[0]),(width[1], width[1])),
		mode='constant') # pads zeros

		result = np.pad(corrmap, width, mode='constant') # pads zeros
		if metric == cv2.TM_SQDIFF or metric == cv2.TM_SQDIFF_NORMED:
		matched_y, matched_x = np.unravel_index(np.argmin(result), result.shape)
		else:
		matched_x, matched_y = np.unravel_index(np.argmax(result), result.shape)

		# The center of the template is the origin
		original_y = (u_template.shape[0] - 1) // 2
		original_x = (u_template.shape[1] - 1) // 2

		# Compute the shift, if any, between the template center and the
		# best correlation index
		shift_x = (original_x - matched_x) / upsampling
		shift_y = (original_y - matched_y) / upsampling
		matched_y, matched_x = np.unravel_index(np.argmax(result), result.shape)

		max_corr = result[matched_y, matched_x]

		# Since the center of the image is (0,0), shift the datum from the upper left to the center.
		u_image_center_y, u_image_center_x = (np.asarray(u_image.shape) - 1) // 2
		shift_y = (matched_y - u_image_center_y) / upsampling
		shift_x = (matched_x - u_image_center_x) / upsampling

		return shift_x, shift_y , max_corr, corrmap