{"cells":[{"metadata":{},"cell_type":"markdown","source":""},{"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\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 read-only \"../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# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Importing the essential libraries."},{"metadata":{},"cell_type":"markdown","source":"LOADING TIFF IMAGES INTO NUMPY ARRAY."},{"metadata":{},"cell_type":"markdown","source":"###  **Importing libraries is the most important part.Firstly go to your environment base directory from command prompt for e.g. (C:\\Users\\Admin\\anaconda3>) then install the library by pip install (name of library)tifffile and you are good to go.**"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nimport tifffile as tiff\nimport os\nimport pandas as pd","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"img = tiff.imread('../input/hubmap-kidney-segmentation/train/0486052bb.tiff')\nimg_array = np.array(img)\nplt.imshow(img)             # To show the image","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(img_array)                   #print the array","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"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":4}