HOG feature Descriptor

hog.py

#creating hog features 
fd, hog_image = hog(resized_img, orientations=9, pixels_per_cell=(8, 8), 
                    cells_per_block=(2, 2), visualize=True, multichannel=True)

hog_shape.py

fd.shape

reading_image.py

#importing required libraries
from skimage.io import imread, imshow
from skimage.transform import resize
from skimage.feature import hog
from skimage import exposure
import matplotlib.pyplot as plt
%matplotlib inline


#reading the image
img = imread('puppy.jpeg')
imshow(img)
print(img.shape)

resize_image.py

#resizing image 
resized_img = resize(img, (128,64)) 
imshow(resized_img) 
print(resized_img.shape)

subplot.py

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8), sharex=True, sharey=True) 

ax1.imshow(resized_img, cmap=plt.cm.gray) 
ax1.set_title('Input image') 

# Rescale histogram for better display 
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10)) 

ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray) 
ax2.set_title('Histogram of Oriented Gradients')

plt.show()