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pep8 + addressing some of Stefan's comments

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Brian Holt
2011-09-17 18:35:03 +01:00
committed by Stefan van der Walt
parent d0bbb525a8
commit 945ede743d
4 changed files with 129 additions and 131 deletions
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CONTRIBUTORS.txt
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@@ -68,3 +68,5 @@
- Andreas Mueller
Example data set loader.
- Brian Holt
Histograms of Oriented Gradients
scikits/image/feature/__init__.py
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@@ -1,2 +1 @@
from hog import histogram_of_oriented_gradients
from hog import hog
scikits/image/feature/hog.py
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"""Extract Histogram of Oriented Gradients feature from image."""
# Authors: Brian Holt
#
# License: BSD
"""
:author: Brian Holt, 2011
:license: modified BSD
"""
import numpy as np
from scipy import sqrt, pi, arctan, cos, sin
def histogram_of_oriented_gradients(image, n_orientations=9, ppc=(8,8),
cpb=(3,3), visualise=False,
apply_normalisation=False):
""" Histogram of oriented gradients (HOG) for a given image.
def hog(image, n_orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(3, 3), visualise=False, normalise=False):
""" Extract Histogram of Oriented Gradients (HOG) for a given image.
Compute a Histogram of Oriented Gradients (HOG) by
1) (optional) global image normalisation
2) computing the gradient image in x and y
3) computing gradient histograms
3) normalise across blocks
4) flatten into a feature vector
4) flatten into a feature vector
Parameters
----------
@@ -28,86 +26,91 @@ def histogram_of_oriented_gradients(image, n_orientations=9, ppc=(8,8),
n_orientations : int
number of orientation bins
ppc : 2 tuple (int,int)
pixels_per_cell : 2 tuple (int,int)
pixels per cell, size in pixels of a cell
cpb : 2 tuple (int,int)
cells_per_block : 2 tuple (int,int)
cells per block, number of cells in each block
visualise : bool
visualise : bool, optional
return an image of the HOG
apply_normalisation : bool
apply the initial optional global normalisation
normalise : bool, optional
apply power law compression to normalise the image before
processing
Returns
-------
newarr : ndarray
newarr : ndarray
HOG for the image as a 1D (flattened) array.
hog_image : PIL Image (if visualise=True)
A visualisation of the HOG image
References
----------
* http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
* Dalal, N and Triggs, B, Histograms of Oriented Gradients for Human Detection,
IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005
San Diego, CA, USA
* http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
* Dalal, N and Triggs, B, Histograms of Oriented Gradients for
Human Detection, IEEE Computer Society Conference on Computer
Vision and Pattern Recognition 2005 San Diego, CA, USA
"""
image = np.atleast_2d(image)
image = np.atleast_2d(image)
"""
The first stage applies an optional global image normalisation
equalisation that is designed to reduce the influence of illumination
effects. In practice we use gamma (power law) compression, either
The first stage applies an optional global image normalisation
equalisation that is designed to reduce the influence of illumination
effects. In practice we use gamma (power law) compression, either
computing the square root or the log of each colour channel.
Image texture strength is typically proportional to the local surface
illumination so this compression helps to reduce the effects of local
Image texture strength is typically proportional to the local surface
illumination so this compression helps to reduce the effects of local
shadowing and illumination variations.
"""
if apply_normalisation:
image = sqrt(image)
"""
The second stage computes first order image gradients. These capture
contour, silhouette and some texture information, while providing
further resistance to illumination variations. The locally dominant
colour channel is used, which provides colour invariance to a large extent.
Variant methods may also include second order image derivatives, which
act as primitive bar detectors - a useful feature for capturing,
e.g. bar like structures in bicycles and limbs in humans
"""
if image.ndim == 3:
# replace RGB with locally dominant colour channel
pass # TODO
pass # TODO
if normalise:
image = sqrt(image)
"""
The second stage computes first order image gradients. These capture
contour, silhouette and some texture information, while providing
further resistance to illumination variations. The locally dominant
colour channel is used, which provides colour invariance to a large
extent. Variant methods may also include second order image derivatives,
which act as primitive bar detectors - a useful feature for capturing,
e.g. bar like structures in bicycles and limbs in humans.
"""
gx = np.zeros(image.shape)
gy = np.zeros(image.shape)
gy = np.zeros(image.shape)
gx[:-1, :-1] = image[:-1,:-1]-image[:-1,1:]
gy[:-1, :-1] = image[:-1,:-1]-image[1:,:-1]
#gx[:-1, :-1] = np.diff(image, n=1, axis=0)
#gy[:-1, :-1] = np.diff(image, n=1, axis=1)
#import Image
#Image.fromarray(gx).show()
#Image.fromarray(gy).show()
#Image.fromarray(gy).show()
"""
The third stage aims to produce an encoding that is sensitive to
local image content while remaining resistant to small changes in pose
or appearance. The adopted method pools gradient orientation information
locally in the same way as the SIFT [Lowe 2004] feature. The image window
The third stage aims to produce an encoding that is sensitive to
local image content while remaining resistant to small changes in pose
or appearance. The adopted method pools gradient orientation information
locally in the same way as the SIFT [Lowe 2004] feature. The image window
is divided into small spatial regions, called "cells". For each cell we
accumulate a local 1-D histogram of gradient or edge orientations over
all the pixels in the cell. This combined cell-level 1-D histogram
forms the basic "orientation histogram" representation. Each orientation
histogram divides the gradient angle range into a fixed number of
predetermined bins. The gradient magnitudes of the pixels in the cell
are used to vote into the orientation histogram.
accumulate a local 1-D histogram of gradient or edge orientations over
all the pixels in the cell. This combined cell-level 1-D histogram
forms the basic "orientation histogram" representation. Each orientation
histogram divides the gradient angle range into a fixed number of
predetermined bins. The gradient magnitudes of the pixels in the cell
are used to vote into the orientation histogram.
"""
magnitude = sqrt(gx**2+gy**2)
orientation = arctan(gy/(gx+1e-15))*(180/pi)+90
magnitude = sqrt(gx ** 2 + gy ** 2)
orientation = arctan(gy / (gx + 1e-15)) * (180 / pi) + 90
# compute n_orientations integral images
integral_images = []
@@ -115,85 +118,89 @@ def histogram_of_oriented_gradients(image, n_orientations=9, ppc=(8,8),
#create new integral image for this orientation
# isolate orientations in this range
temp_ori = np.where(orientation < 180/n_orientations*(i+1),
temp_ori = np.where(orientation < 180 / n_orientations * (i + 1),
orientation, 0)
temp_ori = np.where(orientation >= 180/n_orientations*i,
temp_ori = np.where(orientation >= 180 / n_orientations * i,
temp_ori, 0)
# select magnitudes for those orientations
cond2 = temp_ori > 0
temp_mag = np.where(cond2, magnitude, 0)
temp_mag = np.where(cond2, magnitude, 0)
#compute integral image
integral = np.cumsum(np.cumsum(temp_mag, axis=0, dtype=float),
axis=1, dtype=float)
axis=1, dtype=float)
integral_images.append(integral)
sx,sy = image.shape
cx, cy = ppc
bx, by = cpb
n_cellsx = int(np.floor(sx//cx)) # number of cells in x
n_cellsy = int(np.floor(sy//cy)) # number of cells in y
sx, sy = image.shape
cx, cy = pixels_per_cell
bx, by = cells_per_block
n_cellsx = int(np.floor(sx // cx)) # number of cells in x
n_cellsy = int(np.floor(sy // cy)) # number of cells in y
# now for each cell, compute the histogram
orientation_histogram = np.zeros((n_cellsx, n_cellsy, n_orientations))
radius = min(cx, cy) // 2 - 1
hog_image = None
if visualise:
import Image, ImageDraw
hog_image = Image.new("F", (sy,sx))
import Image
import ImageDraw
hog_image = Image.new("F", (sy, sx))
draw = ImageDraw.Draw(hog_image)
for x in range(0, n_cellsx):
for y in range(0, n_cellsy):
for o in range(0, n_orientations):
# compute the histogram from integral image
#print x, y, o
A = integral_images[o][x*cx, y*cy]
B = integral_images[o][(x+1)*cx-1, y*cy]
C = integral_images[o][(x+1)*cx-1, (y+1)*cy-1]
D = integral_images[o][x*cx, (y+1)*cy-1]
A = integral_images[o][x * cx, y * cy]
B = integral_images[o][(x + 1) * cx - 1, y * cy]
C = integral_images[o][(x + 1) * cx - 1, (y + 1) * cy - 1]
D = integral_images[o][x * cx, (y + 1) * cy - 1]
orientation_histogram[x, y, o] = A + C - D - B
if visualise:
centre = tuple([y*cy + cy//2 , x*cx + cx//2])
dx = radius*cos(1.0*o/n_orientations*np.pi)
dy = radius*sin(1.0*o/n_orientations*np.pi)
draw.line([(centre[0]-dx, centre[1]-dy),
(centre[0]+dx, centre[1]+dy)],
if visualise:
centre = tuple([y * cy + cy // 2, x * cx + cx // 2])
dx = radius * cos(float(o) / n_orientations * np.pi)
dy = radius * sin(float(o) / n_orientations * np.pi)
draw.line([(centre[0] - dx, centre[1] - dy),
(centre[0] + dx, centre[1] + dy)],
fill=orientation_histogram[x, y, o])
"""
The fourth stage computes normalisation, which takes local groups of
cells and contrast normalises their overall responses before passing
to next stage. Normalisation introduces better invariance to illumination,
shadowing, and edge contrast. It is performed by accumulating a measure
of local histogram "energy" over local groups of cells that we call
"blocks". The result is used to normalise each cell in the block.
Typically each individual cell is shared between several blocks, but
its normalisations are block dependent and thus different. The cell
thus appears several times in the final output vector with different
normalisations. This may seem redundant but it improves the performance.
We refer to the normalised block descriptors as Histogram of Oriented
The fourth stage computes normalisation, which takes local groups of
cells and contrast normalises their overall responses before passing
to next stage. Normalisation introduces better invariance to illumination,
shadowing, and edge contrast. It is performed by accumulating a measure
of local histogram "energy" over local groups of cells that we call
"blocks". The result is used to normalise each cell in the block.
Typically each individual cell is shared between several blocks, but
its normalisations are block dependent and thus different. The cell
thus appears several times in the final output vector with different
normalisations. This may seem redundant but it improves the performance.
We refer to the normalised block descriptors as Histogram of Oriented
Gradient (HOG) descriptors.
"""
"""
n_blocksx = (n_cellsx - bx) + 1
n_blocksy = (n_cellsy - by) + 1
normalised_blocks = np.zeros((n_blocksx, n_blocksy,
n_blocksy = (n_cellsy - by) + 1
normalised_blocks = np.zeros((n_blocksx, n_blocksy,
bx, by, n_orientations))
for x in range(0, n_blocksx):
for y in range(0, n_blocksy):
block = orientation_histogram[x:x+bx, y:y+by, :]
block = orientation_histogram[x:x + bx, y:y + by, :]
eps = 1e-5
normalised_blocks[x, y, :] = block / sqrt(block.sum()**2 + eps)
normalised_blocks[x, y, :] = block / sqrt(block.sum() ** 2 + eps)
"""
The final step collects the HOG descriptors from all blocks of a dense
overlapping grid of blocks covering the detection window into a combined
The final step collects the HOG descriptors from all blocks of a dense
overlapping grid of blocks covering the detection window into a combined
feature vector for use in the window classifier
"""
return normalised_blocks.ravel(), hog_image
if visualise:
return normalised_blocks.ravel(), hog_image
else:
return normalised_blocks.ravel()
scikits/image/feature/tests/test_hog.py
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# Authors: Brian Holt
#
# License: BSD
import numpy as np
import scipy as sp
from scipy import ndimage
import scipy
from numpy.testing import assert_raises
from scikits.image.feature import histogram_of_oriented_gradients
from scikits.image.feature import hog
def test_histogram_of_oriented_gradients():
img = sp.lena().astype(np.int8)
img = scipy.lena().astype(np.int8)
fd, hog_image = histogram_of_oriented_gradients(img,
n_orientations=9,
ppc=(8,8),
cpb=(1,1),
visualise=False)
fd = hog(img, n_orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(1, 1))
assert len(fd) == 9 * (512//8) ** 2
if __name__ == '__main__':
import nose
nose.runmodule()
from numpy.testing import run_module_suite
run_module_suite()