Fixed database typo and removed unnecessary class identifier.
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venv/Lib/site-packages/skimage/feature/tests/test_hog.py
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venv/Lib/site-packages/skimage/feature/tests/test_hog.py
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import os
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import numpy as np
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from scipy import ndimage as ndi
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from skimage import color
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from skimage import data
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from skimage import feature
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from skimage import img_as_float
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from skimage import draw
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from skimage._shared.testing import assert_almost_equal, fetch
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from skimage._shared import testing
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def test_hog_output_size():
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img = img_as_float(data.astronaut()[:256, :].mean(axis=2))
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fd = feature.hog(img, orientations=9, pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), block_norm='L1')
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assert len(fd) == 9 * (256 // 8) * (512 // 8)
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def test_hog_output_correctness_l1_norm():
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img = color.rgb2gray(data.astronaut())
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correct_output = np.load(fetch('data/astronaut_GRAY_hog_L1.npy'))
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output = feature.hog(img, orientations=9, pixels_per_cell=(8, 8),
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cells_per_block=(3, 3), block_norm='L1',
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feature_vector=True, transform_sqrt=False,
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visualize=False)
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assert_almost_equal(output, correct_output)
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def test_hog_output_correctness_l2hys_norm():
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img = color.rgb2gray(data.astronaut())
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correct_output = np.load(fetch('data/astronaut_GRAY_hog_L2-Hys.npy'))
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output = feature.hog(img, orientations=9, pixels_per_cell=(8, 8),
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cells_per_block=(3, 3), block_norm='L2-Hys',
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feature_vector=True, transform_sqrt=False,
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visualize=False)
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assert_almost_equal(output, correct_output)
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def test_hog_image_size_cell_size_mismatch():
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image = data.camera()[:150, :200]
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fd = feature.hog(image, orientations=9, pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), block_norm='L1')
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assert len(fd) == 9 * (150 // 8) * (200 // 8)
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def test_hog_basic_orientations_and_data_types():
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# scenario:
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# 1) create image (with float values) where upper half is filled by
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# zeros, bottom half by 100
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# 2) create unsigned integer version of this image
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# 3) calculate feature.hog() for both images, both with 'transform_sqrt'
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# option enabled and disabled
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# 4) verify that all results are equal where expected
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# 5) verify that computed feature vector is as expected
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# 6) repeat the scenario for 90, 180 and 270 degrees rotated images
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# size of testing image
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width = height = 35
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image0 = np.zeros((height, width), dtype='float')
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image0[height // 2:] = 100
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for rot in range(4):
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# rotate by 0, 90, 180 and 270 degrees
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image_float = np.rot90(image0, rot)
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# create uint8 image from image_float
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image_uint8 = image_float.astype('uint8')
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(hog_float, hog_img_float) = feature.hog(
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image_float, orientations=4, pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), visualize=True, transform_sqrt=False,
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block_norm='L1')
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(hog_uint8, hog_img_uint8) = feature.hog(
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image_uint8, orientations=4, pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), visualize=True, transform_sqrt=False,
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block_norm='L1')
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(hog_float_norm, hog_img_float_norm) = feature.hog(
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image_float, orientations=4, pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), visualize=True, transform_sqrt=True,
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block_norm='L1')
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(hog_uint8_norm, hog_img_uint8_norm) = feature.hog(
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image_uint8, orientations=4, pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), visualize=True, transform_sqrt=True,
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block_norm='L1')
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# set to True to enable manual debugging with graphical output,
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# must be False for automatic testing
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if False:
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import matplotlib.pyplot as plt
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plt.figure()
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plt.subplot(2, 3, 1)
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plt.imshow(image_float)
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plt.colorbar()
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plt.title('image')
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plt.subplot(2, 3, 2)
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plt.imshow(hog_img_float)
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plt.colorbar()
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plt.title('HOG result visualisation (float img)')
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plt.subplot(2, 3, 5)
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plt.imshow(hog_img_uint8)
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plt.colorbar()
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plt.title('HOG result visualisation (uint8 img)')
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plt.subplot(2, 3, 3)
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plt.imshow(hog_img_float_norm)
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plt.colorbar()
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plt.title('HOG result (transform_sqrt) visualisation (float img)')
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plt.subplot(2, 3, 6)
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plt.imshow(hog_img_uint8_norm)
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plt.colorbar()
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plt.title('HOG result (transform_sqrt) visualisation (uint8 img)')
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plt.show()
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# results (features and visualisation) for float and uint8 images must
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# be almost equal
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assert_almost_equal(hog_float, hog_uint8)
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assert_almost_equal(hog_img_float, hog_img_uint8)
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# resulting features should be almost equal
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# when 'transform_sqrt' is enabled
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# or disabled (for current simple testing image)
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assert_almost_equal(hog_float, hog_float_norm, decimal=4)
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assert_almost_equal(hog_float, hog_uint8_norm, decimal=4)
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# reshape resulting feature vector to matrix with 4 columns (each
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# corresponding to one of 4 directions); only one direction should
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# contain nonzero values (this is manually determined for testing
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# image)
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actual = np.max(hog_float.reshape(-1, 4), axis=0)
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if rot in [0, 2]:
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# image is rotated by 0 and 180 degrees
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desired = [0, 0, 1, 0]
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elif rot in [1, 3]:
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# image is rotated by 90 and 270 degrees
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desired = [1, 0, 0, 0]
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else:
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raise Exception('Result is not determined for this rotation.')
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assert_almost_equal(actual, desired, decimal=2)
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def test_hog_orientations_circle():
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# scenario:
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# 1) create image with blurred circle in the middle
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# 2) calculate feature.hog()
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# 3) verify that the resulting feature vector contains uniformly
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# distributed values for all orientations, i.e. no orientation is
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# lost or emphasized
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# 4) repeat the scenario for other 'orientations' option
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# size of testing image
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width = height = 100
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image = np.zeros((height, width))
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rr, cc = draw.disk((int(height / 2), int(width / 2)), int(width / 3))
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image[rr, cc] = 100
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image = ndi.gaussian_filter(image, 2)
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for orientations in range(2, 15):
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(hog, hog_img) = feature.hog(image, orientations=orientations,
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pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), visualize=True,
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transform_sqrt=False,
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block_norm='L1')
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# set to True to enable manual debugging with graphical output,
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# must be False for automatic testing
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if False:
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import matplotlib.pyplot as plt
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plt.figure()
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plt.subplot(1, 2, 1)
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plt.imshow(image)
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plt.colorbar()
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plt.title('image_float')
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plt.subplot(1, 2, 2)
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plt.imshow(hog_img)
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plt.colorbar()
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plt.title('HOG result visualisation, '
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'orientations=%d' % (orientations))
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plt.show()
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# reshape resulting feature vector to matrix with N columns (each
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# column corresponds to one direction),
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hog_matrix = hog.reshape(-1, orientations)
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# compute mean values in the resulting feature vector for each
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# direction, these values should be almost equal to the global mean
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# value (since the image contains a circle), i.e., all directions have
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# same contribution to the result
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actual = np.mean(hog_matrix, axis=0)
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desired = np.mean(hog_matrix)
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assert_almost_equal(actual, desired, decimal=1)
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def test_hog_visualization_orientation():
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"""Test that the visualization produces a line with correct orientation
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The hog visualization is expected to draw line segments perpendicular to
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the midpoints of orientation bins. This example verifies that when
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orientations=3 and the gradient is entirely in the middle bin (bisected
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by the y-axis), the line segment drawn by the visualization is horizontal.
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"""
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width = height = 11
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image = np.zeros((height, width), dtype='float')
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image[height // 2:] = 1
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_, hog_image = feature.hog(
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image,
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orientations=3,
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pixels_per_cell=(width, height),
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cells_per_block=(1, 1),
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visualize=True,
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block_norm='L1'
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)
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middle_index = height // 2
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indices_excluding_middle = [x for x in range(height) if x != middle_index]
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assert (hog_image[indices_excluding_middle, :] == 0).all()
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assert (hog_image[middle_index, 1:-1] > 0).all()
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def test_hog_block_normalization_incorrect_error():
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img = np.eye(4)
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with testing.raises(ValueError):
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feature.hog(img, block_norm='Linf')
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@testing.parametrize("shape,multichannel", [
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((3, 3, 3), False),
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((3, 3), True),
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((3, 3, 3, 3), True),
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])
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def test_hog_incorrect_dimensions(shape, multichannel):
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img = np.zeros(shape)
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with testing.raises(ValueError):
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feature.hog(img, multichannel=multichannel, block_norm='L1')
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def test_hog_output_equivariance_multichannel():
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img = data.astronaut()
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img[:, :, (1, 2)] = 0
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hog_ref = feature.hog(img, multichannel=True, block_norm='L1')
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for n in (1, 2):
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hog_fact = feature.hog(np.roll(img, n, axis=2), multichannel=True,
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block_norm='L1')
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assert_almost_equal(hog_ref, hog_fact)
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