mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 19:48:43 +08:00
Himanshu Mishra
197 lines
7.4 KiB
Python
197 lines
7.4 KiB
Python
import os
|
|
import numpy as np
|
|
from scipy import ndimage as ndi
|
|
import skimage as si
|
|
from skimage import color
|
|
from skimage import data
|
|
from skimage import feature
|
|
from skimage import img_as_float
|
|
from skimage import draw
|
|
from numpy.testing import (assert_raises,
|
|
assert_almost_equal,
|
|
)
|
|
|
|
|
|
def test_histogram_of_oriented_gradients_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))
|
|
|
|
assert len(fd) == 9 * (256 // 8) * (512 // 8)
|
|
|
|
|
|
def test_histogram_of_oriented_gradients_output_correctness():
|
|
img = color.rgb2gray(data.astronaut())
|
|
correct_output = np.load(os.path.join(si.data_dir, 'astronaut_GRAY_hog.npy'))
|
|
|
|
output = feature.hog(img, orientations=9, pixels_per_cell=(8, 8),
|
|
cells_per_block=(3, 3), feature_vector=True,
|
|
transform_sqrt=False, visualise=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))
|
|
assert len(fd) == 9 * (150 // 8) * (200 // 8)
|
|
|
|
|
|
def test_hog_color_image_unsupported_error():
|
|
image = np.zeros((20, 20, 3))
|
|
assert_raises(ValueError, feature.hog, image)
|
|
|
|
|
|
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), visualise=True, transform_sqrt=False)
|
|
(hog_uint8, hog_img_uint8) = feature.hog(
|
|
image_uint8, orientations=4, pixels_per_cell=(8, 8),
|
|
cells_per_block=(1, 1), visualise=True, transform_sqrt=False)
|
|
(hog_float_norm, hog_img_float_norm) = feature.hog(
|
|
image_float, orientations=4, pixels_per_cell=(8, 8),
|
|
cells_per_block=(1, 1), visualise=True, transform_sqrt=True)
|
|
(hog_uint8_norm, hog_img_uint8_norm) = feature.hog(
|
|
image_uint8, orientations=4, pixels_per_cell=(8, 8),
|
|
cells_per_block=(1, 1), visualise=True, transform_sqrt=True)
|
|
|
|
# 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.circle(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), visualise=True,
|
|
transform_sqrt=False)
|
|
|
|
# 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_normalise_none_error_raised():
|
|
img = np.array([1, 2, 3])
|
|
assert_raises(ValueError, feature.hog, img, normalise=True)
|
|
|
|
if __name__ == '__main__':
|
|
np.testing.run_module_suite()
|