[{'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:test_images_path='test_images/*'\nexample_images=glob.glob(test_images_path)\nimages = []\ntitles = []\ny_start_stop = [400, 656]  # Min and max in y to search in slide_window(). Trees tops caused false positives\noverlap = 0.5  # 50% overlap\nfor img_src in example_images:\nt1 = time.time()\nimg = mpimg.imread(img_src)\ndraw_img = np.copy(img)\nimg = img.astype(np.float32) / 255  # IMPORTANT : reading *.jpeg's (scaled 0-255), but trained on *.png's (scaled 0-1)\nprint(np.min(img), np.max(img))\nwindows = slide_window(img, x_start_stop=[None, None], y_start_stop=y_start_stop,\nxy_window=(96, 96), xy_overlap=(overlap, overlap))\nhot_windows = search_windows(img, windows, svc, X_scaler, 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, hist_feat=hist_feat,\nhog_feat=hog_feat)\nwindow_img = draw_boxes(draw_img, hot_windows, color=(0, 0, 255), thick=6)\nimages.append(window_img)\ntitles.append('')\nprint(time.time() - t1, 'seconds to process one image searching', len(windows), 'windows')\nfig = plt.figure(figsize=(12, 18), dpi=300)\nvisualise(5, 2, images, titles)"}, {'role': 'assistant', 'content': "['data_exploration', 'result_visualization']"}]