Updates for trying to get a local transformation going (e1b653a1) · Commits · aflab / astrogeology / Autocnet

autocnet/matcher/naive_template.py

+20 −12

Original line number	Diff line number	Diff line
		@@ -126,25 +126,33 @@ def pattern_match(template, image, upsampling=8, metric=cv2.TM_CCOEFF_NORMED, er
		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)
		print(result)

		_, max_corr, min_loc, max_loc = cv2.minMaxLoc(result)

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

		print('things', x, y, max_loc, h, w)
		return x, y, max_corr, result
		# Transform from the results array shape to the template shape
		x = x - (result.shape[1] - u_template.shape[1]) // 2
		y = y - (result.shape[0] - u_template.shape[0]) // 2

		# Recenter the origin from the upper left to the center of the template
		ideal_x = u_template.shape[1] // 2
		ideal_y = u_template.shape[0] // 2

		x -= ideal_x
		y -= ideal_y

		y /= upsampling
		x /= upsampling


		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

autocnet/matcher/subpixel.py

+25 −7

Original line number	Diff line number	Diff line
		@@ -263,7 +263,6 @@ def subpixel_template(reference_roi,

		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
		@@ -273,10 +272,29 @@ def subpixel_template(reference_roi,
		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))

		# Shift x and shift y are computed in the affine space.
		moving_clip_center = (np.array(moving_clip.shape[:2][::-1])-1)/2.
		print(moving_clip_center)

		new_x = moving_clip_center[0] + shift_x
		new_y = moving_clip_center[1] + shift_y
		print('Pre-transform', shift_x, shift_y, moving_clip.shape, moving_clip_center, new_x, new_y, affine)

		# convert shifts in input pixel space
		image_space_new_x, image_space_new_y = affine([new_x, new_y])[0]
		print('Image Space NEW', image_space_new_x, image_space_new_y)

		x_shift_in_image = image_space_new_x - moving_roi.center[0]
		y_shift_in_image = image_space_new_y - moving_roi.center[1]

		new_affine = tf.AffineTransform(affine.params)
		new_affine.translation = (x_shift_in_image,y_shift_in_image)

		"""new_affine = tf.AffineTransform(translation=(x_shift_in_image,
		y_shift_in_image),
		rotation=tf.AffineTransform(affine).rotation,
		shear=tf.AffineTransform(affine).shear)"""

		return new_affine, metrics, corrmap

		@@ -1815,7 +1833,7 @@ def subpixel_register_point_smart(pointid,
		'status': False}
		else:
		metric = maxcorr
		new_x, new_y = updated_affine([measure.sample, measure.line])[0]
		new_x, new_y = updated_affine.inverse([measure.sample, measure.line])[0]
		dist = np.linalg.norm([measure.line-new_x,
		measure.sample-new_y])
		cost = cost_func(dist, metric)

autocnet/transformation/affine.py

+6 −4

Original line number	Diff line number	Diff line
		@@ -141,10 +141,12 @@ def estimate_local_affine(reference_image, moving_image, center_x, center_y, siz

		# This rotates about the center of the image
		shift_x, shift_y = moving_roi.center
		tf_shift = tf.SimilarityTransform(translation=[-shift_x, -shift_y])
		tf_shift_inv = tf.SimilarityTransform(translation=[shift_x, shift_y])
		tf_shift = tf.SimilarityTransform(translation=[shift_x, shift_y])
		tf_shift_inv = tf.SimilarityTransform(translation=[-shift_x, -shift_y])

		# Define the full chain
		trans = (tf_shift + (tf_rotate + tf_shift_inv))
		# Define the full chain multiplying the transformations (read right to left),
		# this is 'shift to the center', apply the rotation, shift back
		# to the origin.
		trans = tf_shift_inv + tf_rotate + tf_shift

		return trans
		No newline at end of file

autocnet/transformation/roi.py

+3 −5

Original line number	Diff line number	Diff line
		@@ -135,7 +135,7 @@ class Roi():
		@property
		def center(self):
		ie = self.image_extent
		return (ie[1] - ie[0])/2., (ie[3]-ie[2])/2.
		return ((ie[1] - ie[0])-1)/2., ((ie[3]-ie[2])-1)/2.

		@property
		def is_valid(self):
		@@ -165,7 +165,7 @@ class Roi():
		data = self.data.read_array(pixels=pixels)
		return img_as_float32(data)

		def clip(self, affine=None, dtype=None, mode="reflect"):
		def clip(self, affine=None, dtype=None, mode="constant"):
		"""
		Compatibility function that makes a call to the array property.
		Warning: The dtype passed in via this function resets the dtype attribute of this
		@@ -191,9 +191,7 @@ class Roi():
		# f"{((self.size_y * 2) + 1, (self.size_x * 2) + 1)} was asked for. Select, " +
		# "a smaller region of interest" )


		#Affine transform the larger, moving array
		transformed_array = tf.warp(array_to_warp, affine, order=3, mode=mode)
		transformed_array = tf.warp(array_to_warp, affine.inverse, order=3, mode=mode, cval=0)
		return transformed_array

		return img_as_float32(self.array)