feature_extract.py

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

def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):
    """ Define a function to return HOG features and visualization """
    # Call with two outputs if vis==True
    if vis == True:
        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=True,
                                  visualise=vis, feature_vector=feature_vec)
        return features, hog_image
    # Otherwise call with one output
    else:
        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=True,
                       visualise=vis, feature_vector=feature_vec)
        return features

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

def color_hist(img, nbins=32, bins_range=(0, 256)):
    """ Define a function to compute color histogram features """

    # Compute the histogram of the color channels separately
    channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)
    channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)
    channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)
    # 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

def extract_features(imgs, cspace='RGB', spatial_size=(32, 32), hist_bins=32, orient=9, pix_per_cell=8,
                     cell_per_block=2, hog_channel=0, spatial_feat=True, hist_feat=True, hog_feat=True):
    """ Define a function to extract features from a list of images """
    # Create a list to append feature vectors to
    features = []
    # Iterate through the list of images
    for file in imgs:

        # apply color conversion if other than 'RGB'
        feature = single_img_features(file, color_space=cspace, spatial_size=spatial_size,
                                           hist_bins=hist_bins, orient=orient,
                                           pix_per_cell=pix_per_cell, cell_per_block=cell_per_block,
                                           hog_channel=hog_channel,
                                           spatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat)
        # Append the new feature vector to the features list
        features.append(feature)
    # Return list of feature vectors
    return features

# Define a function to extract features from a single image window
# This function is very similar to extract_features()
# just for a single image rather than list of images
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
                        hist_bins=32, orient=9,
                        pix_per_cell=8, cell_per_block=2, hog_channel=0,
                        spatial_feat=True, hist_feat=True, hog_feat=True):
    #1) Define an empty list to receive features
    img_features = []
    #2) Apply color conversion if other than 'RGB'
    if color_space != 'RGB':
        if color_space == 'HSV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
        elif color_space == 'LUV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
        elif color_space == 'HLS':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
        elif color_space == 'YUV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
        elif color_space == 'YCrCb':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    else: feature_image = np.copy(img)
    #3) Compute spatial features if flag is set
    if spatial_feat == True:
        spatial_features = bin_spatial(feature_image, size=spatial_size)
        #4) Append features to list
        img_features.append(spatial_features)
    #5) Compute histogram features if flag is set
    if hist_feat == True:
        hist_features = color_hist(feature_image, nbins=hist_bins)
        #6) Append features to list
        img_features.append(hist_features)
    #7) Compute HOG features if flag is set
    if hog_feat == True:
        if hog_channel == 'ALL':
            hog_features = []
            for channel in range(feature_image.shape[2]):
                hog_features.extend(get_hog_features(feature_image[:,:,channel],
                                    orient, pix_per_cell, cell_per_block,
                                    vis=False, feature_vec=True))
        else:
            hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
                        pix_per_cell, cell_per_block, vis=False, feature_vec=True)
        #8) Append features to list
        img_features.append(hog_features)

    #9) Return concatenated array of features
    return np.concatenate(img_features)