lesson_functions.py 文件源码

def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
                        hist_bins=32, hist_range=(0, 256), orient=9, 
                        pix_per_cell=8, cell_per_block=2, hog_channel=0,
                        spatial_feat=True, hist_feat=True, hog_feat=True):    
    img_features = []
    # 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)      

    if spatial_feat == True:
        spatial_features = bin_spatial(feature_image, size=spatial_size)
        img_features.append(spatial_features)
    if hist_feat == True:
        # Apply color_hist()
        hist_features = color_hist(feature_image, nbins=hist_bins, 
                                    bins_range=hist_range)
        img_features.append(hist_features)
    if hog_feat == True:
    # Call get_hog_features() with vis=False, feature_vec=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)
        # Append the new feature vector to the features list
        img_features.append(hog_features)

    # Return list of feature vectors
    return np.concatenate(img_features)

# Convert windows to heatmap numpy array.