[{'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:import numpy as np\nimport cv2\nimport glob\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport pickle\n%matplotlib inline\nclass images():\ndef __init__(self):\nself.images=glob.glob('camera_cal/calibration*.jpg')\nself.objpints,self.imgpoints=calibrate_cam(self.images)\ndef calibrate_cam(self,images):\nobjp = np.zeros((6*9,3), np.float32)\nobjp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2)\nobjpoints = [] # 3d points in real world space\nimgpoints = [] # 2d points in image plane.\nfor idx, fname in enumerate(images):\nimg = cv2.imread(fname)\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\nret, corners = cv2.findChessboardCorners(gray, (9,6), None)\nif ret == True:\nobjpoints.append(objp)\nimgpoints.append(corners)\nreturn objpoints,imgpoints\ndef undistort_img(img,objpoints,imgpoints):\nimg_size = (img.shape[1], img.shape[0])\nret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None,None)\ndst_img = cv2.undistort(img, mtx, dist, None, mtx)\nreturn dst_img,mtx,dist\ndef pipeline(img, s_thresh=(170, 255), sx_thresh=(40, 100)):\nimg = np.copy(img)\nhsv = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)\nl_channel = hsv[:,:,1]\ns_channel = hsv[:,:,2]\nsobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0)\nabs_sobelx = np.absolute(sobelx)\nscaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))\nsxbinary = np.zeros_like(scaled_sobel)\nsxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1\ns_binary = np.zeros_like(s_channel)\ns_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1\ncolor_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, s_binary))\ncombined_binary = np.zeros_like(sxbinary)\ncombined_binary[(s_binary == 1) | (sxbinary == 1)] = 1\nreturn combined_binary\ndef corners_unwarp(img, src, dst):\nM = None\nwarped = np.copy(img)\nimg_size = (img.shape[1], img.shape[0])\nM = cv2.getPerspectiveTransform(src, dst)\nwarped = cv2.warpPerspective(img, M, img_size)\nreturn warped, M"}, {'role': 'assistant', 'content': "['helper_functions', 'data_preprocessing']"}]