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