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import numpy as np
import cv2
from skimage.feature import hog
class FeatureExtractor(object):
"""
Helps extracting features from an image in regions.
"""
def __init__(self, image, orient=10, pix_per_cell=8, cell_per_block=2):
"""
Initialises an instance.
Parameters
----------
image : Image to extract features from.
orient : HoG orientations.
pix_per_cell : HoG pixels per cell.
cell_per_block : HoG cells per block.
"""
self.image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
(self.h, self.w, self.d) = self.image.shape
self.hog_features = []
self.pix_per_cell = pix_per_cell
for channel in range(self.d):
self.hog_features.append(
hog(self.image[:, :, channel], orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=True,
visualise=False, feature_vector=False)
)
self.hog_features = np.asarray(self.hog_features)
def hog(self, x, y, k):
"""
Gets HoG features for specified region of the image.
Parameters
----------
x : Image X coordinate.
y : Image Y coordinate.
k : Region size (single value, side of a square region).
Returns
-------
HoG vector for the specified region.
"""
hog_k = (k // self.pix_per_cell) - 1
hog_x = max((x // self.pix_per_cell) - 1, 0)
hog_x = self.hog_features.shape[2] - hog_k if hog_x + hog_k > self.hog_features.shape[2] else hog_x
hog_y = max((y // self.pix_per_cell) - 1, 0)
hog_y = self.hog_features.shape[1] - hog_k if hog_y + hog_k > self.hog_features.shape[1] else hog_y
return np.ravel(self.hog_features[:, hog_y:hog_y+hog_k, hog_x:hog_x+hog_k, :, :, :])
def bin_spatial(self, image, size=(16, 16)):
"""
Computes spatial vector.
Parameters
----------
image : Image to get spatial vector for.
size : Kernel size.
Returns
-------
Spatial vector.
"""
return cv2.resize(image, size).ravel()
# Define a function to compute color histogram features
def color_hist(self, image, nbins=16, bins_range=(0, 256)):
"""
Computes feature vector based on color channel histogram.
Parameters
----------
image : Image to get spatial vector for.
nbins : Number of histogram bins.
bins_range : Range for bins.
Returns
-------
Color histogram feature vector.
"""
# Compute the histogram of the color channels separately
channel1_hist = np.histogram(image[:, :, 0], bins=nbins, range=bins_range)
channel2_hist = np.histogram(image[:, :, 1], bins=nbins, range=bins_range)
channel3_hist = np.histogram(image[:, :, 2], bins=nbins, range=bins_range)
# Concatenate the histograms into a single feature vector
return np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
def feature_vector(self, x=0, y=0, k=64):
"""
Calculates combined feature vector based on spatial, color histogram and Hog features for specified region.
Region defaults to entire image.
Parameters
----------
x : Image X coordinate.
y : Image Y coordinate.
k : Region size (single value, side of a square region).
Returns
-------
Combined concatenated vector.
"""
features = []
spatial_features = self.bin_spatial(self.image[y:y + k, x:x + k, :])
features.append(spatial_features)
hist_features = self.color_hist(self.image[y:y + k, x:x + k, :])
features.append(hist_features)
hog_features = self.hog(x, y, k)
features.append(hog_features)
return np.concatenate(features)