Histogram of Oriented Gradients

sk_hog.py

import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
from skimage.feature import hog

# Read in our vehicles and non-vehicles
images = glob.glob('*.jpeg')
cars = []
notcars = []

for image in images:
    if 'image' in image or 'extra' in image:
        notcars.append(image)
    else:
        cars.append(image)
        
# Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):
    if vis == True:
        # Use skimage.hog() to get both features and a visualization
        features, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
                    cells_per_block=(cell_per_block, cell_per_block), visualise=vis, feature_vector=False)
        return features, hog_image
    else:      
        # Use skimage.hog() to get features only
        features = [] # Remove this line
        features = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
                    cells_per_block=(cell_per_block, cell_per_block), visualise=vis, feature_vector=feature_vec)
        return features

# Generate a random index to look at a car image
ind = np.random.randint(0, len(cars))
# Read in the image
image = mpimg.imread(cars[ind])
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Define HOG parameters
orient = 9
pix_per_cell = 8
cell_per_block = 2
# Call our function with vis=True to see an image output
features, hog_image = get_hog_features(gray, orient, 
                        pix_per_cell, cell_per_block, 
                        vis=True, feature_vec=False)


# Plot the examples
fig = plt.figure()
plt.subplot(121)
plt.imshow(image, cmap='gray')
plt.title('Example Car Image')
plt.subplot(122)
plt.imshow(hog_image, cmap='gray')
plt.title('HOG Visualization')