{"cells":[{"metadata":{"trusted":true,"_uuid":"9b505d8b5b77ecae0d7f66e60bf0fc862ecf92e8"},"cell_type":"code","source":"import cv2                 \nimport numpy as np         \nimport os                  \nfrom random import shuffle \nfrom tqdm import tqdm      \n\n\n#TRAIN_DIR = 'Cat and Dog/training_set/train/train'\n#TEST_DIR = 'Cat and Dog/test_set/test/test'\nIMG_SIZE = 50\nLR = 1e-3\n\nMODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR, '2conv-basic')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"c218caba8656df6e928fec8ac5378d30e011cfda"},"cell_type":"code","source":"def label_img(img):\n    word_label = img.split('.')[-3]\n    if word_label == 'cat': return [1,0]\n    elif word_label == 'dog': return [0,1]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"7b5a6307618e8f4e22b6901263055621511bd199"},"cell_type":"code","source":"def create_train_data():\n    training_data = []\n    for img in tqdm(os.listdir(TRAIN_DIR)):\n        label = label_img(img)\n        path = os.path.join(TRAIN_DIR,img)\n        img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)\n        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))\n        training_data.append([np.array(img),np.array(label)])\n    shuffle(training_data)\n    np.save('train_data.npy', training_data)\n    return training_data","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"89ad05f23f6d1de7073344d77a6aef1589445cfc"},"cell_type":"code","source":"def process_test_data():\n    testing_data = []\n    for img in tqdm(os.listdir(TEST_DIR)):\n        path = os.path.join(TEST_DIR,img)\n        img_num = img.split('.')[0]\n        img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)\n        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))\n        testing_data.append([np.array(img), img_num])\n        \n    shuffle(testing_data)\n    np.save('test_data.npy', testing_data)\n    return testing_data","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"682f06d9f0db81c44221fb48d712759de0169842"},"cell_type":"code","source":"#train_data = create_train_data()\n#train_data = np.load('train_data.npy')\n","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}