{"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 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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\nimport numpy as np\nimport gzip\nimport os\nfrom os.path import basename\nimport glob\nimport time\nimport cv2\nimport pandas as pd\nimport random\nfrom PIL import Image\n#import scipy.ndimage\nfrom scipy import ndimage\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nfrom scipy.misc import imresize\nfrom skimage.transform import resize\n\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import Model\nfrom keras.layers import Input, merge, Conv2D, MaxPooling2D, UpSampling2D, Concatenate\n#from keras.layers import Dense, Conv2D, Input, MaxPool2D, UpSampling2D, Concatenate, Conv2DTranspose\nfrom keras.optimizers import Adam\nfrom keras.optimizers import SGD\nfrom keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping\nfrom keras.preprocessing.image import array_to_img, img_to_array, load_img, ImageDataGenerator\nfrom keras import backend as K\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"K.set_image_dim_ordering('th') # Theano dimension ordering in this code\nINPUT_PATH = '/kaggle/input'\ndims = [128, 128] \nimg_rows = dims[0]\nimg_cols = dims[1]\ntrain = sorted(glob.glob(INPUT_PATH + 'train/*.jpg'))\nmasks = sorted(glob.glob(INPUT_PATH + 'train_masks/*.gif'))\ntest  = sorted(glob.glob(INPUT_PATH + 'test/*.jpg'))\nprint('Number of training images: ', len(train), ' Number of corresponding masks: ', len(masks), ' Number of test images: ', len(test))\n\nmeta = pd.read_csv(INPUT_PATH + 'metadata.csv')\nmask_df = pd.read_csv(INPUT_PATH + 'train_masks.csv')\nids_train = mask_df['img'].map(lambda s: s.split('_')[0]).unique()\nprint('Length of ids_train ', len(ids_train))\n","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}