Let's now compute the HOG descriptors using the scikit-image feature module's hog() function and visualize them:
from skimage.feature import hogfrom skimage import exposureimage = rgb2gray(imread('../images/cameraman.jpg'))fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualize=True) print(image.shape, len(fd))# ((256L, 256L), 2048)fig, (axes1, axes2) = pylab.subplots(1, 2, figsize=(15, 10), sharex=True, sharey=True)axes1.axis('off'), axes1.imshow(image, cmap=pylab.cm.gray), axes1.set_title('Input image')
Let's now rescale the histogram for a better display:
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))axes2.axis('off'), ...
