"squash: continuing subpixel checking" (a8675137) · Commits · aflab / astrogeology / Autocnet

autocnet/matcher/naive_template.py

+37 −15

Original line number	Diff line number	Diff line
		@@ -2,6 +2,7 @@ from math import floor
		import cv2
		import numpy as np
		from scipy.ndimage.interpolation import zoom
		from skimage.transform import rescale
		from scipy.ndimage.measurements import center_of_mass


		@@ -80,7 +81,7 @@ def pattern_match_autoreg(template, image, subpixel_size=3, metric=cv2.TM_CCOEFF

		return x, y, max_corr, result

		def pattern_match(template, image, upsampling=16, metric=cv2.TM_CCOEFF_NORMED, error_check=False):
		def pattern_match(template, image, upsampling=8, metric=cv2.TM_CCOEFF_NORMED, error_check=False):
		"""
		Call an arbitrary pattern matcher using a subpixel approach where the template and image
		are upsampled using a third order polynomial.
		@@ -117,37 +118,58 @@ def pattern_match(template, image, upsampling=16, metric=cv2.TM_CCOEFF_NORMED, e
		if upsampling < 1:
		raise ValueError

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

		h, w = u_template.shape[:2]

		print(u_image)
		print(u_template)

		result = cv2.matchTemplate(u_image, u_template, method=metric)
		_, max_corr, min_loc, max_loc = cv2.minMaxLoc(result)
		print(result)

		print("min/max loc: ", min_loc, max_loc)
		_, max_corr, min_loc, max_loc = cv2.minMaxLoc(result)

		if metric == cv2.TM_SQDIFF or metric == cv2.TM_SQDIFF_NORMED:
		x, y = (min_loc[0], min_loc[1])
		x = (min_loc[0] + w//2) / upsampling
		y = (min_loc[1] + h//2) / upsampling
		else:
		x, y = (max_loc[0], max_loc[1])
		x = ((max_loc[0] + w//2)) / upsampling
		y = ((max_loc[1] + h//2)) / upsampling

		print('things', x, y, max_loc, h, w)
		return x, y, max_corr, result

		# -1 because the returned results array is W-w+1 and H-h+1 in shape,
		# where W, H are the width and height of the image and w,h are the
		# width and height of the template

		print(u_template.shape, u_image.shape, result.shape, x,y)
		print(u_image.shape, u_template.shape, max_loc)


		# the max_loc array is of shape W-w+1, H-h+1, where W, H are the width
		# and height of the image and w,h are the width and height of the template

		print(x / upsampling - 1, y / upsampling - 1)


		# Compute the idealized shift (image center)
		ideal_y = u_image.shape[0] / 2.
		ideal_x = u_image.shape[1] / 2.
		ideal_y = (u_image.shape[0] + 1) / 2. - 0.5
		ideal_x = (u_image.shape[1] + 1) / 2. - 0.5

		# Compute the shift from template upper left to template center
		y += (u_template.shape[0] / 2.)
		x += (u_template.shape[1] / 2.)

		print("x,ideal x, ", x, ideal_x, (x - ideal_x) / upsampling)
		print("y,ideal y, ", y, ideal_y, (y - ideal_y) / upsampling)

		#
		x = (x - ideal_x) / upsampling
		y = (y - ideal_y) / upsampling
		y = ((y - ideal_y) / upsampling) + 1
		print(x, y)
		return x, y, max_corr, result

autocnet/matcher/subpixel.py

+28 −16

Original line number	Diff line number	Diff line
		@@ -208,12 +208,15 @@ def subpixel_phase(reference_roi, moving_roi, affine=tf.AffineTransform(), **kwa
		walking_template,
		**kwargs)
		# get shifts in input pixel space
		shift_x, shift_y = affine.inverse([shift_x, shift_y])[0]
		new_affine = tf.AffineTransform(translation=(-shift_x, -shift_y))
		shift_x, shift_y = affine([shift_x, shift_y])[0]
		new_affine = tf.AffineTransform(translation=(-shift_y, -shift_x))
		return new_affine, error, diffphase


		def subpixel_template(reference_roi, moving_roi, affine=tf.AffineTransform(), mode='reflect',
		def subpixel_template(reference_roi,
		moving_roi,
		affine=tf.AffineTransform(),
		mode='reflect',
		func=pattern_match,
		**kwargs):
		"""
		@@ -260,7 +263,7 @@ def subpixel_template(reference_roi, moving_roi, affine=tf.AffineTransform(), mo

		ref_clip = reference_roi.clip()
		moving_clip = moving_roi.clip(affine)

		print(affine)
		if moving_clip.var() == 0:
		warnings.warn('Input ROI has no variance.')
		return [None] * 3
		@@ -270,7 +273,7 @@ def subpixel_template(reference_roi, moving_roi, affine=tf.AffineTransform(), mo
		shift_x, shift_y, metrics, corrmap = func(moving_clip, ref_clip, **kwargs)
		if shift_x is None:
		return None, None, None

		print(shift_x, shift_y)
		# get shifts in input pixel space
		shift_x, shift_y = affine.inverse([shift_x, shift_y])[0]
		new_affine = tf.AffineTransform(translation=(-shift_x, -shift_y))
		@@ -1715,8 +1718,8 @@ def subpixel_register_point_smart(pointid,
		destination_node.geodata,
		source.apriorisample,
		source.aprioriline,
		60,
		60)
		30,
		30)
		except Exception as e:
		print(e)
		m = {'id': measure.id,
		@@ -1737,8 +1740,16 @@ def subpixel_register_point_smart(pointid,
		if match_kwarg['template_size'][1] < size_y:
		size_y = match_kwarg['template_size'][1]

		reference_roi = roi.Roi(source_node.geodata, source.apriorisample, source.aprioriline, size_x=size_x, size_y=size_y)
		moving_roi = roi.Roi(destination_node.geodata, measure.apriorisample, measure.aprioriline, size_x=size_x, size_y=size_y)
		reference_roi = roi.Roi(source_node.geodata,
		source.apriorisample,
		source.aprioriline,
		size_x=size_x,
		size_y=size_y)
		moving_roi = roi.Roi(destination_node.geodata,
		measure.apriorisample,
		measure.aprioriline,
		size_x=size_x,
		size_y=size_y)

		_, baseline_corr, _ = subpixel_template(reference_roi,
		moving_roi,
		@@ -1766,6 +1777,7 @@ def subpixel_register_point_smart(pointid,
		baseline_mi = mutual_information(base_roi, dst_roi)"""
		baseline_mi = 0
		print(f'Baseline MI: {baseline_mi} \| Baseline Corr: {baseline_corr}')
		print(affine)
		for parameter in parameters:
		match_kwargs = parameter['match_kwargs']

		@@ -1782,6 +1794,7 @@ def subpixel_register_point_smart(pointid,
		updated_affine, maxcorr, temp_corrmap = subpixel_template(reference_roi,
		moving_roi,
		affine=affine)
		print(updated_affine)
		"""
		if x is None or y is None:
		print('Unable to match with this parameter set.')
		@@ -1803,10 +1816,9 @@ def subpixel_register_point_smart(pointid,
		else:
		metric = maxcorr
		new_x, new_y = updated_affine([measure.sample, measure.line])[0]
		dist = np.linalg.norm([measure.sample-new_x,
		measure.line-new_y])
		dist = np.linalg.norm([measure.line-new_x,
		measure.sample-new_y])
		cost = cost_func(dist, metric)
		print(new_x, new_y, dist)

		m = {'id': measure.id,
		'sample':new_x,

autocnet/transformation/roi.py

+1 −3

Original line number	Diff line number	Diff line
		@@ -130,14 +130,12 @@ class Roi():
		if left_x < 0 or top_y < 0 or right_x > raster_size[0] or bottom_y > raster_size[1]:
		raise IndexError(f"Input window size {(self.size_x, self.size_y)}) at center {(self.x, self.y)} is out of the image bounds")

		print("extents:", list(map(int, [left_x, right_x, top_y, bottom_y])))
		print("center:", self.x, self.y, self.size_x, self.size_y)
		return list(map(int, [left_x, right_x, top_y, bottom_y]))

		@property
		def center(self):
		ie = self.image_extent
		return (ie[1] - ie[0])/2.+0.5, (ie[3]-ie[2])/2.+0.5
		return (ie[1] - ie[0])/2., (ie[3]-ie[2])/2.

		@property
		def is_valid(self):

tests/test_subpixel_match.py

+6 −6

Original line number	Diff line number	Diff line
		@@ -36,12 +36,12 @@ def test_full_subpixel_registration(ncg, images, point):
		'chooser':'smart_subpixel_registration'}

		parameters = [
		{'match_kwargs': {'image_size':(121,121), 'template_size':(61,61)}},
		{'match_kwargs': {'image_size':(151,151), 'template_size':(67,67)}},
		{'match_kwargs': {'image_size':(181,181), 'template_size':(73,73)}},
		{'match_kwargs': {'image_size':(221,221), 'template_size':(81,81)}},
		{'match_kwargs': {'image_size':(251,251), 'template_size':(89,89)}},
		{'match_kwargs': {'image_size':(281,281), 'template_size':(98,98)}}
		{'match_kwargs': {'image_size':(61,61), 'template_size':(31,31)}},
		{'match_kwargs': {'image_size':(75,75), 'template_size':(33,33)}},
		{'match_kwargs': {'image_size':(91,91), 'template_size':(37,37)}},
		{'match_kwargs': {'image_size':(111,111), 'template_size':(41,41)}},
		{'match_kwargs': {'image_size':(125,125), 'template_size':(45,45)}},
		{'match_kwargs': {'image_size':(141,141), 'template_size':(49,49)}}
		]

		measures_to_update, measures_to_set_false = smart_register_point(1,