[{'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:out_images = []\nout_maps = []\nout_titles = []\nout_boxes = []\nystart = 400\nystop = 656\nscale = 1.5\nfor img_src in example_images:\nimg_boxes = []\nt = time.time()\ncount = 0\nimg = mpimg.imread(img_src)\ndraw_img = np.copy(img)\nheatmap = np.zeros_like(img[:, :, 0])\nimg = img.astype(np.float32) / 255\nimg_tosearch = img[ystart:ystop, :, :]\nctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')\nif scale != 1:  # resize whole image instead of separate windows\nimshape = ctrans_tosearch.shape\nctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1] / scale), np.int(imshape[0] / scale)))\nch1 = ctrans_tosearch[:, :, 0]\nch2 = ctrans_tosearch[:, :, 1]\nch3 = ctrans_tosearch[:, :, 2]\nnxblocks = (ch1.shape[1] // pix_per_cell) - 1  # Note : '//' causes integers to be result, instead of floats\nnyblocks = (ch1.shape[0] // pix_per_cell) - 1\nnfeat_per_block = orient * cell_per_block ** 2\nwindow = 64\nnblocks_per_window = (window // pix_per_cell) - 1\ncells_per_step = 2  # Instead of overlap, define how many cells to step\nnxsteps = (nxblocks - nblocks_per_window) // cells_per_step\nnysteps = (nyblocks - nblocks_per_window) // cells_per_step\nhog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)\nhog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)\nhog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)\nfor xb in range(nxsteps):\nfor yb in range(nysteps):\ncount += 1\nypos = yb * cells_per_step\nxpos = xb * cells_per_step\nhog_feat1 = hog1[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\nhog_feat2 = hog2[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\nhog_feat3 = hog3[ypos:ypos + nblocks_per_window, xpos:xpos + nblocks_per_window].ravel()\nhog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))\nxleft = xpos * pix_per_cell\nytop = ypos * pix_per_cell\nsubimg = cv2.resize(ctrans_tosearch[ytop:ytop + window, xleft:xleft + window], (64, 64))\nspatial_features = bin_spatial(subimg, size=spatial_size)\nhist_features = color_hist(subimg, nbins=hist_bins)\ntest_features = X_scaler.transform(\nnp.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))\ntest_prediction = svc.predict((test_features))\nif test_prediction == 1:\nxbox_left = np.int(xleft * scale)\nytop_draw = np.int(ytop * scale)\nwin_draw = np.int(window * scale)\ncv2.rectangle(draw_img, (xbox_left, ytop_draw + ystart),\n(xbox_left + win_draw, ytop_draw + win_draw + ystart), (0, 0, 255))\nimg_boxes.append(\n((xbox_left, ytop_draw + ystart), (xbox_left + win_draw, ytop_draw + win_draw + ystart)))\nheatmap[ytop_draw + ystart:ytop_draw + win_draw + ystart, xbox_left:xbox_left + win_draw] += 1\nprint(time.time() - t, 'seconds to run, total windows = ', count)\nout_images.append(draw_img)\nout_titles.append(img_src[-12:])\nout_titles.append(img_src[-12:])\nout_images.append(heatmap)\nout_maps.append(heatmap)\nout_boxes.append(img_boxes)\nfig = plt.figure(figsize=(12, 24))\nvisualise(6, 2, out_images, out_titles)"}, {'role': 'assistant', 'content': "['data_exploration', 'modelling']"}]