[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:window_width = 50\nwindow_height = 80 # Break image into 9 vertical layers since image height is 720\nmargin = 100 # How much to slide left and right for searching\ndef window_mask(width, height, img_ref, center,level):\noutput = np.zeros_like(img_ref)\noutput[int(img_ref.shape[0]-(level+1)*height):int(img_ref.shape[0]-level*height),max(0,int(center-width/2)):min(int(center+width/2),img_ref.shape[1])] = 1\nreturn output\ndef find_window_centroids(image, window_width, window_height, margin):\nwindow_centroids = []# Store the (left,right) window centroid positions per level\nl_list=[]\nr_list=[]\nwindow = np.ones(window_width) # Create our window template that we will use for convolutions\nl_sum = np.sum(warped[int(3*warped.shape[0]/4):,:int(warped.shape[1]/2)], axis=0)\nl_center = np.argmax(np.convolve(window,l_sum))-window_width/2\nr_sum = np.sum(warped[int(3*warped.shape[0]/4):,int(warped.shape[1]/2):], axis=0)\nr_center = np.argmax(np.convolve(window,r_sum))-window_width/2+int(warped.shape[1]/2)\nl_list.append(l_center)\nr_list.append(r_center)\nwindow_centroids.append((l_center,r_center))\nfor level in range(1,(int)(warped.shape[0]/window_height)):\nimage_layer = np.sum(warped[int(warped.shape[0]-(level+1)*window_height):int(warped.shape[0]-level*window_height),:], axis=0)\nconv_signal = np.convolve(window, image_layer)\noffset = window_width/2\nl_min_index = int(max(l_center+offset-margin,0))\nl_max_index = int(min(l_center+offset+margin,warped.shape[1]))\nl_center = np.argmax(conv_signal[l_min_index:l_max_index])+l_min_index-offset\nr_min_index = int(max(r_center+offset-margin,0))\nr_max_index = int(min(r_center+offset+margin,warped.shape[1]))\nr_center = np.argmax(conv_signal[r_min_index:r_max_index])+r_min_index-offset\nwindow_centroids.append((l_center,r_center))\nl_list.append(l_center)\nr_list.append(r_center)\nreturn window_centroids,l_list,r_list\nwindow_centroids,l_list,r_list = find_window_centroids(warped, window_width, window_height, margin)\ny_list=[720,640,560,580,400,320,240,160,80]\nleft_fit = np.polyfit(y_list, l_list, 2)\nright_fit = np.polyfit(y_list, r_list, 2)\nploty = np.linspace(0, warped.shape[0]-1, warped.shape[0] )\nleft_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]\nright_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]\nif len(window_centroids) > 0:\nl_points = np.zeros_like(warped)\nr_points = np.zeros_like(warped)\nfor level in range(0,len(window_centroids)):\nl_mask = window_mask(window_width,window_height,warped,window_centroids[level][0],level)\nr_mask = window_mask(window_width,window_height,warped,window_centroids[level][1],level)\nl_points[(l_points == 255) | ((l_mask == 1) ) ] = 255\nr_points[(r_points == 255) | ((r_mask == 1) ) ] = 255\ntemplate = np.array(r_points+l_points,np.uint8) # add both left and right window pixels together\nzero_channel = np.zeros_like(template) # create a zero color channel\ntemplate = np.array(cv2.merge((zero_channel,template,zero_channel)),np.uint8) # make window pixels green\nwarpage = np.array(cv2.merge((warped,warped,warped)),np.uint8) # making the original road pixels 3 color channels\noutput = cv2.addWeighted(warpage, 1, template, 0.5, 0.0) # overlay the orignal road image with window results\nelse:\noutput = np.array(cv2.merge((warped,warped,warped)),np.uint8)\nplt.imshow(output)\nplt.title('window fitting results')\nplt.plot(left_fitx, ploty, color='yellow')\nplt.plot(right_fitx, ploty, color='yellow')\nplt.show()"}, {'role': 'assistant', 'content': 'modelling'}]