refactor: add initial fixes for new subpixel template api (857f0912) · Commits · aflab / astrogeology / Autocnet

autocnet/matcher/subpixel.py

+36 −33

Original line number	Diff line number	Diff line
		@@ -36,7 +36,7 @@ from autocnet.spatial import isis
		from autocnet.io.db.model import Measures, Points, Images, JsonEncoder
		from autocnet.graph.node import NetworkNode
		from autocnet.transformation import roi
		from autocnet.transformation.affine import estimate_affine_from_sensors
		from autocnet.transformation.affine import estimate_local_affine
		from autocnet import spatial
		from autocnet.utils.utils import bytescale

		@@ -1711,10 +1711,12 @@ def subpixel_register_point_smart(pointid,
		print('geom_func', geom_func)

		try:
		affine = estimate_affine_from_sensors(source_node.geodata,
		affine = estimate_local_affine(source_node.geodata,
		destination_node.geodata,
		source.apriorisample,
		source.aprioriline)
		source.aprioriline,
		60,
		60)
		except Exception as e:
		print(e)
		m = {'id': measure.id,
		@@ -1725,11 +1727,6 @@ def subpixel_register_point_smart(pointid,
		updated_measures.append([None, None, m])
		continue

		# Here is where I need to get the two ROIs extracted. Then I need to get the destination ROI affine transformed to the source ROI
		base_arr, dst_arr = affine_warp_image(source_node.geodata,
		destination_node.geodata,
		affine)

		# Compute the baseline metrics using the smallest window
		size_x = np.inf
		size_y = np.inf
		@@ -1740,10 +1737,14 @@ def subpixel_register_point_smart(pointid,
		if match_kwarg['template_size'][1] < size_y:
		size_y = match_kwarg['template_size'][1]

		base_roi = roi.Roi(base_arr, source.apriorisample, source.aprioriline, size_x=size_x, size_y=size_y).array
		dst_roi = roi.Roi(dst_arr, source.apriorisample, source.aprioriline, size_x=size_x, size_y=size_y).array
		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,
		affine=affine)

		if np.isnan(base_roi).any() or np.isnan(dst_roi).any():
		"""if np.isnan(base_roi).any() or np.isnan(dst_roi).any():
		print('Unable to process due to NaN values in the input data.')
		m = {'id': measure.id,
		'status': False,
		@@ -1762,26 +1763,26 @@ def subpixel_register_point_smart(pointid,
		base_roi = img_as_float32(base_roi)
		dst_roi = img_as_float32(dst_roi)

		baseline_mi = mutual_information(base_roi, dst_roi)


		# Refactor this call to module
		result = cv2.matchTemplate(base_roi, dst_roi, method=cv2.TM_CCOEFF_NORMED)
		baseline_corr = result[0][0]
		baseline_mi = mutual_information(base_roi, dst_roi)"""
		baseline_mi = 0
		print(f'Baseline MI: {baseline_mi} \| Baseline Corr: {baseline_corr}')
		for parameter in parameters:
		match_kwargs = parameter['match_kwargs']

		restemplate = match_func(source.apriorisample, source.aprioriline, source.apriorisample, source.aprioriline, base_arr, dst_arr, **match_kwargs)

		try:
		x,y,maxcorr,temp_corrmap = restemplate
		except:
		# did not return a corrmap
		x,y,maxcorr = restemplate
		temp_corrmap = np.empty((size_x, size_y))
		temp_corrmap[:] = np.nan

		reference_roi = roi.Roi(source_node.geodata,
		source.apriorisample,
		source.aprioriline,
		size_x=match_kwargs['image_size'][0],
		size_y=match_kwargs['image_size'][1])
		moving_roi = roi.Roi(destination_node.geodata,
		measure.apriorisample,
		measure.aprioriline,
		size_x=match_kwargs['template_size'][0],
		size_y=match_kwargs['template_size'][1])
		updated_affine, maxcorr, temp_corrmap = subpixel_template(reference_roi,
		moving_roi,
		affine=affine)
		"""
		if x is None or y is None:
		print('Unable to match with this parameter set.')
		continue
		@@ -1793,17 +1794,19 @@ def subpixel_register_point_smart(pointid,
		base_roi = img_as_float32(base_roi)
		dst_roi = img_as_float32(dst_roi)

		mi_metric = mutual_information(base_roi, dst_roi)

		mi_metric = mutual_information(base_roi, dst_roi)"""
		mi_metric = 0.0
		if mi_metric is None:
		print('MI Metric Failure. Returning.')
		m = {'id': measure.id,
		'status': False}
		else:
		metric = maxcorr
		new_x, new_y = affine([x, y])[0]
		dist = np.linalg.norm([source.apriorisample-x, source.aprioriline-y])
		new_x, new_y = updated_affine([measure.sample, measure.line])[0]
		dist = np.linalg.norm([measure.sample-new_x,
		measure.line-new_y])
		cost = cost_func(dist, metric)
		print(new_x, new_y, dist)

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