[{'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 slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],\nxy_window=(64, 64), xy_overlap=(0.5, 0.5)):\nif x_start_stop[0] == None:\nx_start_stop[0] = 0\nif x_start_stop[1] == None:\nx_start_stop[1] = img.shape[1]\nif y_start_stop[0] == None:\ny_start_stop[0] = 0\nif y_start_stop[1] == None:\ny_start_stop[1] = img.shape[0]\nxspan = x_start_stop[1] - x_start_stop[0]\nyspan = y_start_stop[1] - y_start_stop[0]\nnx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))\nny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))\nnx_buffer = np.int(xy_window[0]*(xy_overlap[0]))\nny_buffer = np.int(xy_window[1]*(xy_overlap[1]))\nnx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step)\nny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step)\nwindow_list = []\nfor ys in range(ny_windows):\nfor xs in range(nx_windows):\nstartx = xs*nx_pix_per_step + x_start_stop[0]\nendx = startx + xy_window[0]\nstarty = ys*ny_pix_per_step + y_start_stop[0]\nendy = starty + xy_window[1]\nwindow_list.append(((startx, starty), (endx, endy)))\nreturn window_list\ndef single_img_features(img, 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):\nimg_features = []\nif color_space != 'RGB':\nif color_space == 'HSV':\nfeature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)\nelif color_space == 'LUV':\nfeature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)\nelif color_space == 'HLS':\nfeature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)\nelif color_space == 'YUV':\nfeature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)\nelif color_space == 'YCrCb':\nfeature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)\nelse: feature_image = np.copy(img)\nif spatial_feat == True:\nspatial_features = bin_spatial(feature_image, size=spatial_size)\nimg_features.append(spatial_features)\nif hist_feat == True:\nhist_features = color_hist(feature_image, nbins=hist_bins)\nimg_features.append(hist_features)\nif hog_feat == True:\nif hog_channel == 'ALL':\nhog_features = []\nfor channel in range(feature_image.shape[2]):\nhog_features.extend(get_hog_features(feature_image[:,:,channel],\norient, pix_per_cell, cell_per_block,\nvis=False, feature_vec=True))\nelse:\nhog_features = get_hog_features(feature_image[:,:,hog_channel], orient,\npix_per_cell, cell_per_block, vis=False, feature_vec=True)\nimg_features.append(hog_features)\nreturn np.concatenate(img_features)\ndef search_windows(img, windows, clf, scaler, color_space='RGB',\nspatial_size=(32, 32), hist_bins=32,\nhist_range=(0, 256), orient=9,\npix_per_cell=8, cell_per_block=2,\nhog_channel=0, spatial_feat=True,\nhist_feat=True, hog_feat=True):\non_windows = []\nfor window in windows:\ntest_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))\nfeatures = single_img_features(test_img, color_space=color_space,\nspatial_size=spatial_size, hist_bins=hist_bins,\norient=orient, pix_per_cell=pix_per_cell,\ncell_per_block=cell_per_block,\nhog_channel=hog_channel, spatial_feat=spatial_feat,\nhist_feat=hist_feat, hog_feat=hog_feat)\ntest_features = scaler.transform(np.array(features).reshape(1, -1))\nprediction = clf.predict(test_features)\nif prediction == 1:\non_windows.append(window)\nreturn on_windows"}, {'role': 'assistant', 'content': "['data_preprocessing', 'prediction']"}]