{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport pandas as pd\nimport matplotlib.pylab as plt\nfrom keras.preprocessing.image import load_img\nimport os\nimport cv2\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n\nstringpath = r\"../input/pku-autonomous-driving/\"\n# Any results you write to the current directory are saved as output.\ntrain_data   = pd.read_csv('../input/pku-autonomous-driving/train.csv')\n\ntrain_imagesfolder = os.listdir(stringpath + r\"/train_images\") # dir is your directory path\ntrainimagesfilecount = len(train_imagesfolder)\n\ntrain_masksfolder = os.listdir(stringpath +  r\"train_masks\") # dir is your directory path\ntrainmasksfilecount = len(train_imagesfolder)\n\ntraindata   = pd.read_csv(stringpath + r\"train.csv\")\n\n\ndef CreateMaskImages(imageName):\n\n    trainimage = cv2.imread(stringpath  + \"/train_images/\" + imageName + '.jpg')\n    imagemask = cv2.imread(stringpath + \"/train_masks/\" + imageName + \".jpg\",0)\n    try:\n        imagemaskinv = cv2.bitwise_not(imagemask)\n        res = cv2.bitwise_and(trainimage,trainimage,mask = imagemaskinv)\n        plt.imshow(imagemask)\n        cv2.imwrite(\"MaskTrain/\" + imageName + \".jpg\", res)\n    except:\n        print(\"exception for image\" + imageName)\n        cv2.imwrite(\"MaskTrain/\" + imageName + \".jpg\", trainimage)\n        \n        \nfor i in range(len(traindata)):\n  ImageName = traindata.loc[i, \"ImageId\"]\n  print(ImageName)\n  #CreateMaskImages(ImageName)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}