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Merge pull request #1899 from OrkoHunter/hog_normalise

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FIX: Replace hog normalise kwarg with transform_sqrt
This commit is contained in:
Josh Warner
2016-01-30 23:11:31 -06:00
parent 2e162478c9 4997fda94f
commit a08d75a786
2 changed files with 41 additions and 19 deletions
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skimage/feature/_hog.py
+24 -6
View File
@@ -2,11 +2,12 @@ from __future__ import division
import numpy as np
from .._shared.utils import assert_nD
from . import _hoghistogram
import warnings
def hog(image, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(3, 3), visualise=False, normalise=False,
feature_vector=True):
cells_per_block=(3, 3), visualise=False, transform_sqrt=False,
feature_vector=True, normalise=None):
"""Extract Histogram of Oriented Gradients (HOG) for a given image.
Compute a Histogram of Oriented Gradients (HOG) by
@@ -29,12 +30,16 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
Number of cells in each block.
visualise : bool, optional
Also return an image of the HOG.
normalise : bool, optional
transform_sqrt : bool, optional
Apply power law compression to normalise the image before
processing.
processing. DO NOT use this if the image contains negative
values. Also see `notes` section below.
feature_vector : bool, optional
Return the data as a feature vector by calling .ravel() on the result
just before returning.
normalise : bool, deprecated
The parameter is deprecated. Use `transform_sqrt` for power law
compression. `normalise` has been deprecated.
Returns
-------
@@ -51,6 +56,13 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
Human Detection, IEEE Computer Society Conference on Computer
Vision and Pattern Recognition 2005 San Diego, CA, USA
Notes
-----
Power law compression, also known as Gamma correction, is used to reduce
the effects of shadowing and illumination variations. The compression makes
the dark regions lighter. When the kwarg `transform_sqrt` is set to
``True``, the function computes the square root of each color channel
and then applies the hog algorithm to the image.
"""
image = np.atleast_2d(image)
@@ -66,7 +78,13 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
assert_nD(image, 2)
if normalise:
if normalise is not None:
raise ValueError("The normalise parameter was removed due to incorrect "
"behavior; it only applied a square root instead of a "
"true normalization. If you wish to duplicate the old "
"behavior, set ``transform_sqrt=True``.")
if transform_sqrt:
image = np.sqrt(image)
"""
@@ -173,7 +191,7 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
overlapping grid of blocks covering the detection window into a combined
feature vector for use in the window classifier.
"""
if feature_vector:
normalised_blocks = normalised_blocks.ravel()
skimage/feature/tests/test_hog.py
+17 -13
View File
@@ -24,11 +24,11 @@ def test_histogram_of_oriented_gradients_output_size():
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),
output = feature.hog(img, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(3, 3), feature_vector=True,
normalise=False, visualise=False)
transform_sqrt=False, visualise=False)
assert_almost_equal(output, correct_output)
@@ -49,7 +49,7 @@ def test_hog_basic_orientations_and_data_types():
# 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 'normalise'
# 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
@@ -70,16 +70,16 @@ def test_hog_basic_orientations_and_data_types():
(hog_float, hog_img_float) = feature.hog(
image_float, orientations=4, pixels_per_cell=(8, 8),
cells_per_block=(1, 1), visualise=True, normalise=False)
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, normalise=False)
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, normalise=True)
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, normalise=True)
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
@@ -101,11 +101,11 @@ def test_hog_basic_orientations_and_data_types():
plt.subplot(2, 3, 3)
plt.imshow(hog_img_float_norm)
plt.colorbar()
plt.title('HOG result (normalise) visualisation (float img)')
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 (normalise) visualisation (uint8 img)')
plt.title('HOG result (transform_sqrt) visualisation (uint8 img)')
plt.show()
# results (features and visualisation) for float and uint8 images must
@@ -113,7 +113,7 @@ def test_hog_basic_orientations_and_data_types():
assert_almost_equal(hog_float, hog_uint8)
assert_almost_equal(hog_img_float, hog_img_uint8)
# resulting features should be almost equal when 'normalise' is enabled
# 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)
@@ -157,7 +157,7 @@ def test_hog_orientations_circle():
(hog, hog_img) = feature.hog(image, orientations=orientations,
pixels_per_cell=(8, 8),
cells_per_block=(1, 1), visualise=True,
normalise=False)
transform_sqrt=False)
# set to True to enable manual debugging with graphical output,
# must be False for automatic testing
@@ -188,5 +188,9 @@ def test_hog_orientations_circle():
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()