[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:def extract_features(imgs, color_space=\'RGB\', spatial_size=(32, 32),\nhist_bins=32, orient=9,\npix_per_cell=8, cell_per_block=2, hog_channel=0,\nspatial_feat=True, hist_feat=True, hog_feat=True):\n"""\nFunction to extract features from a list of images\n"""\nfeatures = []\nfor file in imgs:\nfile_features = []\nimage = mpimg.imread(file)\nif color_space != \'RGB\':\nif color_space == \'HSV\':\nfeature_image = cv2.cvtColor((image, cv2.COLOR_RGB2HSV))\nelif color_space == \'LUV\':\nfeature_image = cv2.cvtColor((image, cv2.COLOR_RGB2LUV))\nelif color_space == \'HLS\':\nfeature_image = cv2.cvtColor((image, cv2.COLOR_RGB2HLS))\nelif color_space == \'YUV\':\nfeature_image = cv2.cvtColor((image, cv2.COLOR_RGB2YUV))\nelif color_space == \'YCrCb\':\nfeature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)\nelse:\nfeature_image = np.copy(image)\nif spatial_feat == True:\nspatial_features = bin_spatial(feature_image, size=spatial_size)\nfile_features.append(spatial_features)\nif hist_feat == True:\nhist_features = color_hist(feature_image, nbins=hist_bins)\nfile_features.append(hist_features)\nif hog_feat == True:\nif hog_channel == \'ALL\':\nhog_features = []\nfor channel in range(feature_image.shape[2]):\nhog_features.append(get_hog_features(feature_image[:, :, channel],\norient, pix_per_cell, cell_per_block,\nvis=False, feature_vec=True))\nhog_features = np.ravel(hog_features)\nelse:\nhog_features = get_hog_features(feature_image[:, :, hog_channel], orient,\npix_per_cell, cell_per_block, vis=False, feature_vec=True)\nfile_features.append(hog_features)\nfeatures.append(np.concatenate(file_features))\nreturn features'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]