{"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)\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 the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import cv2\nimport numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport os\nimport random\nimport gc","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"a3bb3a099f9f66aacec6fd4b701d5c63066c41c5"},"cell_type":"code","source":"train_dir = '../input/train'\ntest_dir = '../input/test'\n\ntrain_dogs = ['../input/train/{}'.format(i) for i in os.listdir(train_dir) if 'dog' in i]\ntrain_cats = ['../input/train/{}'.format(i) for i in os.listdir(train_dir) if 'cat' in i]\n\ntest_imgs = ['../input/test/{}'.format(i) for i in os.listdir(test_dir)]\n\ntrain_imgs = train_dogs[:2000] + train_cats[:2000]\nrandom.shuffle (train_imgs)\n\ndel train_dogs\ndel train_cats\ngc.collect()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"9b1ef380b602605175b2970b73145b923156e110"},"cell_type":"code","source":"import matplotlib.image as mpimg\nfor ima in train_imgs[0:3]:\n    img = mpimg.imread(ima)\n    imgplot = plt.imshow(img)\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"c1a1e4103456d8080ff23115a3513e8287e50326"},"cell_type":"code","source":"nrows = 150\nncolumns = 150\nchannels = 3","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2c2707708a7d748a0972004e0e97415e8ce2ed6e"},"cell_type":"code","source":"def read_and_process_image(list_of_imgs):\n \n   \n    X = [] \n    Y = [] \n\n    for image in list_of_imgs:\n        X.append(cv2.resize(cv2.imread(image, cv2.IMREAD_COLOR), (nrows,ncolumns), interpolation=cv2.INTER_CUBIC))\n    \n        if 'dog' in image:\n            Y.append(1)\n        elif 'cat' in image:\n            Y.append(0)\n        \n    return X,Y     \n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"787a7ca8abc21b0cd26dc386b882ed205f7d700a"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3aad2844c1b184d5930c12bb05450140b76cae57"},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}