[{'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 get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):\n"""\nReturn HOG features and visualization (optionally)\n"""\nif vis == True:\nfeatures, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),\ncells_per_block=(cell_per_block, cell_per_block), transform_sqrt=False,\nvisualise=True, feature_vector=False)\nreturn features, hog_image\nelse:\nfeatures = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),\ncells_per_block=(cell_per_block, cell_per_block), transform_sqrt=False,\nvisualise=False, feature_vector=feature_vec)\nreturn features\ndef extract_features(imgs, cspace=\'RGB\', orient=8,\npix_per_cell=8, cell_per_block=4, hog_channel=0):\nfeatures = []\nfor file in imgs:\nimage = mpimg.imread(file)\nif cspace != \'RGB\':\nif cspace == \'HSV\':\nfeature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\nelif cspace == \'LUV\':\nfeature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)\nelif cspace == \'HLS\':\nfeature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\nelif cspace == \'YUV\':\nfeature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)\nelif cspace == \'YCrCb\':\nfeature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)\nelse: feature_image = np.copy(image)\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)\nfeatures.append(hog_features)\nreturn features'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]