mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 01:43:45 +08:00
Emmanuelle Gouillart
135 lines
6.1 KiB
Python
135 lines
6.1 KiB
Python
import numpy as np
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from scipy import ndimage
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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 numpy.testing import *
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def test_histogram_of_oriented_gradients():
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img = img_as_float(data.lena()[: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))
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assert len(fd) == 9 * (256 // 8) * (512 // 8)
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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))
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assert len(fd) == 9 * (150 // 8) * (200 // 8)
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def test_hog_color_image_unsupported_error():
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image = np.zeros((20, 20, 3))
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assert_raises(ValueError, feature.hog, image)
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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 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 'normalise' 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(image_float, orientations=4, pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), visualise=True, normalise=False)
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(hog_uint8, hog_img_uint8) = feature.hog(image_uint8, orientations=4, pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), visualise=True, normalise=False)
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(hog_float_norm, hog_img_float_norm) = feature.hog(image_float, orientations=4, pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), visualise=True, normalise=True)
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(hog_uint8_norm, hog_img_uint8_norm) = feature.hog(image_uint8, orientations=4, pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), visualise=True, normalise=True)
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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); plt.imshow(image_float); plt.colorbar(); plt.title('image')
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plt.subplot(2, 3, 2); plt.imshow(hog_img_float); plt.colorbar(); plt.title('HOG result visualisation (float img)')
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plt.subplot(2, 3, 5); plt.imshow(hog_img_uint8); plt.colorbar(); plt.title('HOG result visualisation (uint8 img)')
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plt.subplot(2, 3, 3); plt.imshow(hog_img_float_norm); plt.colorbar(); plt.title('HOG result (normalise) visualisation (float img)')
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plt.subplot(2, 3, 6); plt.imshow(hog_img_uint8_norm); plt.colorbar(); plt.title('HOG result (normalise) visualisation (uint8 img)')
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plt.show()
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# results (features and visualisation) for float and uint8 images must 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 when 'normalise' is enabled 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 corresponding to one of 4 directions),
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# only one direction should contain nonzero values (this is manually determined for testing 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 distributed values for all orientations,
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# i.e. no orientation is 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.circle(height/2, width/2, width/3)
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image[rr, cc] = 100
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image = ndimage.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, pixels_per_cell=(8, 8),
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cells_per_block=(1, 1), visualise=True, normalise=False)
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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); plt.imshow(image); plt.colorbar(); plt.title('image_float')
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plt.subplot(1, 2, 2); plt.imshow(hog_img); plt.colorbar(); plt.title('HOG result visualisation, orientations=%d' % (orientations))
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plt.show()
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# reshape resulting feature vector to matrix with N columns (each 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 direction,
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# these values should be almost equal to the global mean value (since the image contains a circle),
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# i.e. all directions have 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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if __name__ == '__main__':
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from numpy.testing import run_module_suite
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run_module_suite()
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