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import cv2
import numpy as np
from skimage import feature
class Descriptor:
Class that combines feature descriptors into a single descriptor
to produce a feature vector for an input image.
class _skHOGDescriptor:
Wrapper subclass for skimage.feature.hog. Wrapping skimage.feature.hog
in a class in which we also define a compute() function allows us to
mirror the usage of OpenCV cv2.HOGDescriptor class method compute().
def __init__(self, hog_bins, pix_per_cell, cells_per_block,
block_norm, transform_sqrt):
"""@see Descriptor.#__init__(...)"""
self.hog_bins = hog_bins
self.pix_per_cell = pix_per_cell
self.cells_per_block = cells_per_block
self.block_norm = block_norm
self.transform_sqrt = transform_sqrt
def compute(self, image):
multichannel = len(image.shape) > 2
sk_hog_vector = feature.hog(image, orientations=self.hog_bins,
block_norm=self.block_norm, transform_sqrt=self.transform_sqrt,
multichannel=multichannel, feature_vector=True)
return np.expand_dims(sk_hog_vector, 1)
def __init__(self, hog_features=False, hist_features=False,
spatial_features=False, hog_lib="cv", size=(64,64),
hog_bins=9, pix_per_cell=(8,8), cells_per_block=(2,2),
block_stride=None, block_norm="L1", transform_sqrt=True,
signed_gradient=False, hist_bins=16, spatial_size=(16,16)):
Set feature parameters. For HOG features, either the OpenCV
implementation (cv2.HOGDescriptor) or scikit-image implementation
(skimage.feature.hog) may be selected via @param hog_lib. Some
parameters apply to only one implementation (indicated below).
@param hog_features (bool): Include HOG features in feature vector.
@param hist_features (bool): Include color channel histogram features
in feature vector.
@param spatial_features (bool): Include spatial features in feature vector.
@param size (int, int): Resize images to this (width, height) before
computing features.
@param hog_lib ["cv", "sk"]: Select the library to be used for HOG
implementation. "cv" selects OpenCV (@see cv2.HOGDescriptor).
"sk" selects scikit-image (@see skimage.feature.hog).
@param pix_per_cell (int, int): HOG pixels per cell.
@param cells_per_block (int, int): HOG cells per block.
@param block_stride (int, int): [OpenCV only] Number of pixels by which
to shift block during HOG block normalization. Defaults to half of
@param block_norm: [scikit-image only] Block normalization method for
HOG. OpenCV uses L2-Hys.
@param transform_sqrt (bool): [scikit-image only].
@see skimage.feature.hog
@param hog_bins (int): Number of HOG gradient histogram bins.
@param signed_gradient (bool): [OpenCV only] Use signed gradient (True)
or unsigned gradient (False) for HOG. Currently, scikit-image HOG
only supports unsigned gradients.
@param hist_bins (int): Number of color histogram bins per color channel.
@param spatial_size (int, int): Resize images to (width, height) for
spatial binning.
self.hog_features = hog_features
self.hist_features = hist_features
self.spatial_features = spatial_features
self.size = size
self.hog_lib = hog_lib
self.pix_per_cell = pix_per_cell
self.cells_per_block = cells_per_block
if hog_lib == "cv":
winSize = size
cellSize = pix_per_cell
blockSize = (cells_per_block[0] * cellSize[0],
cells_per_block[1] * cellSize[1])
if block_stride is not None:
blockStride = self.block_stride
blockStride = (int(blockSize[0] / 2), int(blockSize[1] / 2))
nbins = hog_bins
derivAperture = 1
winSigma = -1.
histogramNormType = 0 # L2Hys (currently the only available option)
L2HysThreshold = 0.2
gammaCorrection = 1
nlevels = 64
signedGradients = signed_gradient
self.HOGDescriptor = cv2.HOGDescriptor(winSize, blockSize,
blockStride, cellSize, nbins, derivAperture, winSigma,
histogramNormType, L2HysThreshold, gammaCorrection,
nlevels, signedGradients)
self.HOGDescriptor = self._skHOGDescriptor(hog_bins, pix_per_cell,
cells_per_block, block_norm, transform_sqrt)
self.hist_bins = hist_bins
self.spatial_size = spatial_size
def getFeatureVector(self, image):
"""Return the feature vector for an image."""
if image.shape[:2] != self.size:
image = cv2.resize(image, self.size, interpolation=cv2.INTER_AREA)
feature_vector = np.array([])
if self.hog_features:
feature_vector = np.hstack(
(feature_vector, self.HOGDescriptor.compute(image)[:,0]))
if self.hist_features:
# np.histogram() returns a tuple if given a 2D array and an array
# if given a 3D array. To maintain compatibility with other
# functions in the object detection pipeline, check that the input
# array has three dimensions. Add axis if necessary.
# Note that histogram bin range assumes uint8 array.
if len(image.shape) < 3:
image = image[:, :, np.newaxis]
hist_vector = np.array([])
for channel in range(image.shape[2]):
channel_hist = np.histogram(image[:, :, channel],
bins=self.hist_bins, range=(0, 255))[0]
hist_vector = np.hstack((hist_vector, channel_hist))
feature_vector = np.hstack((feature_vector, hist_vector))
if self.spatial_features:
spatial_image = cv2.resize(image, self.spatial_size,
spatial_vector = spatial_image.ravel()
feature_vector = np.hstack((feature_vector, spatial_vector))
return feature_vector
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