features_functions.py

import numpy as np
import cv2
from skimage.feature import hog


# 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), 
                          transform_sqrt=False, 
                          visualise=True, feature_vector=False)
        return features, hog_image
    else:      
        # Use skimage.hog() to get features only
        features = hog(img, orientations=orient,
                          pixels_per_cell=(pix_per_cell, pix_per_cell), 
                          cells_per_block=(cell_per_block, cell_per_block), 
                          transform_sqrt=False, 
                          visualise=False, feature_vector=True)
        features = [] # Remove this line
        return features

# Define a function to compute binned color features  
def bin_spatial(img, size=(32, 32)):
    # Use cv2.resize().ravel() to create the feature vector
    features = cv2.resize(img, size).ravel() 
    # Return the feature vector
    return features


# Define a function to compute color histogram features  
def color_hist(img, nbins=32):
    # Compute the histogram of the color channels separately
    channel1_hist = np.histogram(img[:,:,0], bins=nbins)
    channel2_hist = np.histogram(img[:,:,1], bins=nbins)
    channel3_hist = np.histogram(img[:,:,2], bins=nbins)
    # Concatenate the histograms into a single feature vector
    hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
    # Return the individual histograms, bin_centers and feature vector
    return hist_features