Merge branch 'api' into 'main' (6fd6b2b6) · Commits · aflab / astrogeology / Autocnet

autocnet/matcher/ciratefi.py

deleted100644 → 0

+0 −758

File deleted.

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autocnet/matcher/mutual_information.py

+17 −103

Original line number	Diff line number	Diff line
		from math import floor
		import logging

		from autocnet.transformation.roi import Roi
		import numpy as np

		from scipy.ndimage.measurements import center_of_mass
		import skimage.transform as tf

		import logging
		# setup logging file
		log = logging.getLogger(__name__)

		def mutual_information(t1, t2, **kwargs):

		def mutual_information(reference_arr, moving_arr, **kwargs):
		"""
		Computes the correlation coefficient between two images using a histogram
		comparison (Mutual information for joint histograms). The corr_map coefficient
		@@ -17,12 +19,13 @@ def mutual_information(t1, t2, **kwargs):
		Parameters
		----------

		t1 : ndarray
		reference_arr : ndarray
		First image to use in the histogram comparison

		t2 : ndarray
		moving_arr : ndarray
		Second image to use in the histogram comparison


		Returns
		-------

		@@ -35,15 +38,15 @@ def mutual_information(t1, t2, **kwargs):
		numpy.histogram2d : for the kwargs that can be passed to the comparison
		"""

		if np.isnan(t1).any() or np.isnan(t2).any():
		log.warning('Unable to process due to NaN values in the input data')
		if np.isnan(reference_arr).any() or np.isnan(moving_arr).any():
		log.warning('Unable compute MI. Image sizes are not identical.')
		return

		if t1.shape != t2.shape:
		if reference_arr.shape != moving_arr.shape:
		log.warning('Unable compute MI. Image sizes are not identical.')
		return

		hgram, x_edges, y_edges = np.histogram2d(t1.ravel(),t2.ravel(), **kwargs)
		hgram, x_edges, y_edges = np.histogram2d(reference_arr.ravel(),moving_arr.ravel(), **kwargs)

		# Convert bins counts to probability values
		pxy = hgram / float(np.sum(hgram))
		@@ -54,92 +57,3 @@ def mutual_information(t1, t2, **kwargs):
		nzs = pxy > 0 # Only non-zero pxy values contribute to the sum
		return np.sum(pxy[nzs] * np.log(pxy[nzs] / px_py[nzs]))

		def mutual_information_match(d_template, s_image, subpixel_size=3,
		func=None, **kwargs):
		"""
		Applys the mutual information matcher function over a search image using a
		defined template


		Parameters
		----------
		d_template : ndarray
		The input search template used to 'query' the destination
		image

		s_image : ndarray
		The image or sub-image to be searched

		subpixel_size : int
		Subpixel area size to search for the center of mass
		calculation

		func : function
		Function object to be used to compute the histogram comparison

		Returns
		-------
		x : float
		The x offset

		y : float
		The y offset

		max_corr : float
		The strength of the correlation in the range [0, 4].

		corr_map : ndarray
		Map of corrilation coefficients when comparing the template to
		locations within the search area
		"""

		if func == None:
		func = mutual_information

		image_size = s_image.shape
		template_size = d_template.shape

		y_diff = image_size[0] - template_size[0]
		x_diff = image_size[1] - template_size[1]

		max_corr = -np.inf
		corr_map = np.zeros((y_diff+1, x_diff+1))
		max_i = -1 # y
		max_j = -1 # x
		for i in range(y_diff+1):
		for j in range(x_diff+1):
		sub_image = s_image[i:i+template_size[1], # y
		j:j+template_size[0]] # x
		corr = func(sub_image, d_template, **kwargs)
		if corr > max_corr:
		max_corr = corr
		max_i = i
		max_j = j
		corr_map[i, j] = corr

		y, x = np.unravel_index(np.argmax(corr_map, axis=None), corr_map.shape)

		upper = int(2 + floor(subpixel_size / 2))
		lower = upper - 1
		# x, y are the location of the upper left hand corner of the template in the image
		area = corr_map[y-lower:y+upper,
		x-lower:x+upper]

		# Compute the y, x shift (subpixel) using scipys center_of_mass function
		cmass = center_of_mass(area)

		if area.shape != (subpixel_size+2, subpixel_size+2):
		log.warning("Max correlation is too close to the boundary.")
		return None, None, 0, None

		subpixel_y_shift = subpixel_size - 1 - cmass[0]
		subpixel_x_shift = subpixel_size - 1 - cmass[1]

		y += subpixel_y_shift
		x += subpixel_x_shift

		# Compute the idealized shift (image center)
		y -= (s_image.shape[0] / 2) - (d_template.shape[0] / 2)
		x -= (s_image.shape[1] / 2) - (d_template.shape[1] / 2)

		return float(x), float(y), float(max_corr), corr_map

autocnet/matcher/subpixel.py

+89 −1

Original line number	Diff line number	Diff line
		@@ -10,6 +10,7 @@ import logging

		import numpy as np

		from scipy.ndimage.measurements import center_of_mass
		from skimage import transform as tf
		from skimage import registration
		from skimage import filters
		@@ -28,7 +29,7 @@ import PIL
		from PIL import Image

		from autocnet.matcher.naive_template import pattern_match
		from autocnet.matcher.mutual_information import mutual_information_match
		from autocnet.matcher.mutual_information import mutual_information
		from autocnet.spatial import isis
		from autocnet.io.db.model import Measures, Points, Images, JsonEncoder
		from autocnet.graph.node import NetworkNode
		@@ -1400,3 +1401,90 @@ def smart_register_point(pointid, parameters=[], shared_kwargs={}, valid_reproje
		log.info(f'Ignoring measures: {measures_to_set_false}')

		return measures_to_update, measures_to_set_false


		def mutual_information_match(moving_roi,
		reference_roi,
		affine=tf.AffineTransform(),
		subpixel_size=3,
		func=None, **kwargs):
		"""
		Applies the mutual information matcher function over a search image using a
		defined template


		Parameters
		----------
		moving_roi : roi
		The input search template used to 'query' the destination
		image

		reference_roi : roi
		The image or sub-image to be searched

		subpixel_size : int
		Subpixel area size to search for the center of mass
		calculation

		func : function
		Function object to be used to compute the histogram comparison

		Returns
		-------
		new_affine :AffineTransform
		The affine transformation

		max_corr : float
		The strength of the correlation in the range [0, 4].

		corr_map : ndarray
		Map of corrilation coefficients when comparing the template to
		locations within the search area
		"""
		reference_roi.clip()
		moving_roi.clip(affine=affine)

		moving_image = moving_roi.clipped_array
		reference_template = reference_roi.clipped_array

		if func == None:
		func = mutual_information

		image_size = moving_image.shape
		template_size = reference_template.shape

		y_diff = image_size[0] - template_size[0]
		x_diff = image_size[1] - template_size[1]

		max_corr = -np.inf
		corr_map = np.zeros((y_diff+1, x_diff+1))
		for i in range(y_diff+1):
		for j in range(x_diff+1):
		sub_image = moving_image[i:i+template_size[1], # y
		j:j+template_size[0]] # x
		corr = func(sub_image, reference_template, **kwargs)
		if corr > max_corr:
		max_corr = corr
		corr_map[i, j] = corr

		y, x = np.unravel_index(np.argmax(corr_map, axis=None), corr_map.shape)

		upper = int(2 + floor(subpixel_size / 2))
		lower = upper - 1
		area = corr_map[y-lower:y+upper,
		x-lower:x+upper]

		# Compute the y, x shift (subpixel) using scipys center_of_mass function
		cmass = center_of_mass(area)
		if area.shape != (subpixel_size + 2, subpixel_size + 2):
		return None, 0, None


		subpixel_y_shift = subpixel_size - 1 - cmass[0]
		subpixel_x_shift = subpixel_size - 1 - cmass[1]
		y = abs(y - (corr_map.shape[1])/2)
		x = abs(x - (corr_map.shape[0])/2)
		y += subpixel_y_shift
		x += subpixel_x_shift
		new_affine = tf.AffineTransform(translation=(-x, -y))
		return new_affine, np.max(max_corr), corr_map
		No newline at end of file

autocnet/matcher/tests/test_ciratefi.py

deleted100644 → 0

+0 −182

Original line number	Diff line number	Diff line
		import math
		import unittest
		import logging
		import re

		import numpy as np
		from imageio import imread
		from scipy.ndimage.interpolation import rotate

		from autocnet.examples import get_path
		from autocnet.transformation import roi
		from .. import ciratefi

		import pytest

		# Can be parameterized for more exhaustive tests
		upsampling = 10
		alpha = math.pi/2
		cifi_thresh = 90
		rafi_thresh = 90
		tefi_thresh = 100
		use_percentile = True
		radii = list(range(1, 3))

		@pytest.fixture
		def img():
		return imread(get_path('AS15-M-0298_SML.png'), as_gray=True)

		@pytest.fixture
		def img_coord():
		return 482.09783936, 652.40679932

		@pytest.fixture
		def template(img, img_coord):
		coord_x, coord_y = img_coord
		template= roi.Roi(img, coord_x, coord_y, 5, 5).clip()
		template = rotate(template, 90)
		return template

		@pytest.fixture
		def search():
		coord_x, coord_y = (482.09783936, 652.40679932)
		img = imread(get_path('AS15-M-0298_SML.png'), as_gray=True)
		search = roi.Roi(img, coord_x, coord_y, 21, 21).clip()
		return search

		@pytest.fixture
		def offset_template(img, img_coord):
		coord_x, coord_y = img_coord
		coord_x += 1
		coord_y += 1
		offset_template = roi.Roi(img, coord_x, coord_y, 5, 5).clip()
		return offset_template

		def test_cifi_radii_too_large(template, search, caplog):
		# check all logs
		ciratefi.cifi(template, search, 1.0, radii=[100], use_percentile=False)
		num_pattern = '\d+'
		captured_log = caplog.records[0].getMessage()
		match = re.findall(num_pattern, captured_log)
		assert (f'Max Radii is larger than original template, '\
		f'this may produce sub-par results.Max radii: {match[0]} max template dimension: {match[1]}'\
		== caplog.records[0].getMessage())


		def test_cifi_bounds_error(template, search):
		with pytest.raises(ValueError), pytest.warns(UserWarning):
		ciratefi.cifi(template, search, -1.1, use_percentile=False)

		def test_cifi_radii_none_error(template, search):
		with pytest.raises(ValueError):
		ciratefi.cifi(template, search, 90, radii=None)

		def test_cifi_scales_none_error(template, search):
		with pytest.raises(ValueError):
		ciratefi.cifi(template, search, 90, scales=None)

		def test_cifi_template_too_large_error(template, search):
		with pytest.raises(ValueError):
		ciratefi.cifi(search,template, 90, scales=None)

		@pytest.mark.parametrize('cifi_thresh, radii', [(90,list(range(1, 3)))])
		@pytest.mark.filterwarnings('ignore::UserWarning') # skimage deprecation warnings to move to new defaults
		def test_cifi(template, search, cifi_thresh, radii):
		pixels, scales = ciratefi.cifi(template, search, thresh=cifi_thresh,
		radii=radii, use_percentile=True)

		assert search.shape == scales.shape
		assert (np.floor(search.shape[0]/2), np.floor(search.shape[1]/2)) in pixels
		assert pixels.size in range(0,search.size)

		@pytest.mark.filterwarnings('ignore::UserWarning') # skimage deprecation warnings to move to new defaults
		def test_rafi_warning(template, search,caplog):
		rafi_pixels = [(10, 10)]
		rafi_scales = np.ones(search.shape, dtype=float)
		ciratefi.rafi(template, search, rafi_pixels,
		rafi_scales, thresh=1, radii=[100],
		use_percentile=False)

		def test_rafi_bounds_error(template, search):
		rafi_pixels = [(10, 10)]
		rafi_scales = np.ones(search.shape, dtype=float)
		with pytest.raises(ValueError) as f, pytest.warns(UserWarning) as g:
		ciratefi.rafi(template, search, rafi_pixels, rafi_scales, -1.1, use_percentile=False)

		def test_rafi_radii_list_none_error(template, search):
		rafi_pixels = [(10, 10)]
		with pytest.raises(ValueError):
		ciratefi.rafi(search, template, rafi_pixels, -1.1, radii=None)

		def test_rafi_pixel_list_error(template, search):
		rafi_pixels = []
		rafi_scales = np.ones(search.shape, dtype=float)
		with pytest.raises(ValueError):
		ciratefi.rafi(template, search, rafi_pixels, rafi_scales)

		def test_rafi_scales_list_error(template, search):
		rafi_pixels = [(10, 10)]
		with pytest.raises(ValueError):
		ciratefi.rafi(template, search, rafi_pixels, None)

		def test_rafi_template_bigger_error(template, search):
		rafi_pixels = [(10, 10)]
		rafi_scales = np.ones(search.shape, dtype=float)
		with pytest.raises(ValueError):
		ciratefi.rafi(search, template, rafi_pixels,rafi_scales)

		def test_rafi_shape_mismatch(template, search):
		rafi_pixels = [(10, 10)]
		rafi_scales = np.ones(search.shape, dtype=float)[:10]
		with pytest.raises(ValueError):
		ciratefi.rafi(template, search, rafi_pixels, rafi_scales)

		@pytest.mark.parametrize("rafi_thresh, radii, alpha", [(90, list(range(1, 3)),math.pi/2)])
		@pytest.mark.filterwarnings('ignore::UserWarning') # skimage deprecation warnings to move to new defaults
		def test_rafi(template, search, rafi_thresh, radii, alpha):
		rafi_pixels = [(10, 10)]
		rafi_scales = np.ones(search.shape, dtype=float)
		pixels, scales = ciratefi.rafi(template, search, rafi_pixels, rafi_scales,
		thresh=rafi_thresh, radii=radii, use_percentile=True,
		alpha=alpha)

		assert (np.floor(search.shape[0]/4), np.floor(search.shape[1]/4)) in pixels
		assert pixels.size in range(0, search.size)

		# Alternate approach to the more verbose tests above - this tests all combinations
		@pytest.mark.parametrize("tefi_pixels", [[(10,10)], None])
		@pytest.mark.parametrize("tefi_scales", [np.ones((42,42), dtype=float),
		None,
		np.ones((42,42), dtype=float)[:10]])
		@pytest.mark.parametrize("tefi_angles", [[3.14159265], None])
		@pytest.mark.parametrize("thresh", [-1.1, 90])
		@pytest.mark.parametrize("reverse", [False, True])
		def test_tefi_errors(template, search, tefi_pixels, tefi_scales, tefi_angles, thresh, reverse):
		with pytest.raises(ValueError):
		if reverse:
		template, search = search, template
		ciratefi.tefi(template, search, tefi_pixels, tefi_scales,
		tefi_angles, thresh=-1.1, use_percentile=False, alpha=math.pi/2)

		@pytest.mark.filterwarnings('ignore::UserWarning') # skimage deprecation warnings to move to new defaults
		def test_tefi(template, search):
		tefi_pixels = [(10, 10)]
		tefi_scales = np.ones(search.shape, dtype=float)
		tefi_angles = [3.14159265]

		pixel = ciratefi.tefi(template, search, tefi_pixels, tefi_scales, tefi_angles,
		thresh=tefi_thresh, use_percentile=True, alpha=math.pi/2,
		upsampling=10)

		assert np.equal((11.5, 11.5), (pixel[1], pixel[0])).all()

		@pytest.mark.filterwarnings('ignore::UserWarning') # skimage deprecation warnings to move to new defaults
		@pytest.mark.parametrize("cifi_thresh, rafi_thresh, tefi_thresh, alpha, radii",[
		(90,90,100,math.pi/2,list(range(1, 3)))])
		def test_ciratefi(template, search, cifi_thresh, rafi_thresh, tefi_thresh, alpha, radii):
		results = ciratefi.ciratefi(template, search, upsampling=10, cifi_thresh=cifi_thresh,
		rafi_thresh=rafi_thresh, tefi_thresh=tefi_thresh,
		use_percentile=True, alpha=alpha, radii=radii)

		assert len(results) == 3
		assert (np.array(results[1], results[0]) < 1).all()

autocnet/matcher/tests/test_mutual_information.py

+0 −12

Original line number	Diff line number	Diff line
		@@ -20,15 +20,3 @@ def test_bad_mi():
		corrilation = mutual_information.mutual_information(test_image1, test_image2)
		assert corrilation == pytest.approx(0)

		def test_mutual_information():
		d_template = np.array([[i for i in range(50, 100)] for j in range(50)])
		s_image = np.ones((100, 100))

		s_image[25:75, 25:75] = d_template

		x_offset, y_offset, max_corr, corr_map = mutual_information.mutual_information_match(d_template, s_image, bins=20)
		assert x_offset == 0.01711861257171421
		assert y_offset == 0.0
		assert max_corr == 2.9755967600033015
		assert corr_map.shape == (51, 51)
		assert np.min(corr_map) >= 0.0