{"cells":[{"metadata":{},"cell_type":"markdown","source":"**Demo****"},{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport keras","execution_count":null,"outputs":[]},{"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\n\n      \n#Any results you write to the current directory are saved as output","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"idea\nUse data unmodified --> resize --> divide dataset into test(for train) and train --> sum all data len=? [type1=? 2=? 3=? noncancer=?]\n--> cancer or noncancer? --> type1,2 or 3? --> accuracy"},{"metadata":{"trusted":true},"cell_type":"code","source":"#import all the used function\nimport tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport cv2\n\n#pip install opencv-python\n#!pip install keras\n#!pip install --upgrade \"tensorflow==1.7.*\"\n#!pip install tensorflow\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#unzipping data before using\n#local_zip = '/kaggle/input/intel-mobileodt-cervical-cancer-screening/'\n#zip_ref = zipfile.ZipFile(local_zip, 'r')\n#zip_ref.extractall('/kaggle/input/intel-mobileodt-cervical-cancer-screening/')\n#zip_ref.close()\nprint(os.listdir(\"../input/intel-mobileodt-cervical-cancer-screening/train/train\"))","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"#/kaggle/input/intel-mobileodt-cervical-cancer-screening/additional/  as dataset\n#os.listdir(rock_dir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from subprocess import check_output\nprint(check_output([\"ls\", \"../input/intel-mobileodt-cervical-cancer-screening/\"]).decode(\"utf8\"))\nfrom glob import glob\n#separate data\nTRAIN_DATA = \"../input/intel-mobileodt-cervical-cancer-screening/train/train\"\ntype_1_files = glob(os.path.join(TRAIN_DATA, \"Type_1\", \"*.jpg\"))\ntype_1_ids = np.array([s[len(os.path.join(TRAIN_DATA, \"Type_1\"))+1:-4] for s in type_1_files])\ntype_2_files = glob(os.path.join(TRAIN_DATA, \"Type_2\", \"*.jpg\"))\ntype_2_ids = np.array([s[len(os.path.join(TRAIN_DATA, \"Type_2\"))+1:-4] for s in type_2_files])\ntype_3_files = glob(os.path.join(TRAIN_DATA, \"Type_3\", \"*.jpg\"))\ntype_3_ids = np.array([s[len(os.path.join(TRAIN_DATA, \"Type_3\"))+1:-4] for s in type_3_files])\n\nprint(len(type_1_files), len(type_2_files), len(type_3_files))\nprint(\"Type 1\", type_1_ids[:10])\nprint(\"Type 2\", type_2_ids[:10])\nprint(\"Type 3\", type_3_ids[:10])\n#test data set\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"TEST_DATA = \"../input/intel-mobileodt-cervical-cancer-screening/test/test\"\ntest_files = glob(os.path.join(TEST_DATA, \"*.jpg\"))\ntest_ids = np.array([s[len(TEST_DATA)+1:-4] for s in test_files])\nprint(len(test_ids))\nprint(test_ids[:10])\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":""},{"metadata":{"trusted":true},"cell_type":"code","source":"#additional data set\nADDITIONAL_DATA = \"../input/intel-mobileodt-cervical-cancer-screening/\"\nadditional_type_1_files = glob(os.path.join(ADDITIONAL_DATA, \"additional_Type_1_v2/Type_1\", \"*.jpg\"))\nadditional_type_1_ids = np.array([s[len(os.path.join(ADDITIONAL_DATA, \"additional_Type_1_v2/Type_1\"))+1:-4] for s in additional_type_1_files])\nadditional_type_2_files = glob(os.path.join(ADDITIONAL_DATA, \"additional_Type_2_v2/Type_2\", \"*.jpg\"))\nadditional_type_2_ids = np.array([s[len(os.path.join(ADDITIONAL_DATA, \"additional_Type_2_v2/Type_2\"))+1:-4] for s in additional_type_2_files])\nadditional_type_3_files = glob(os.path.join(ADDITIONAL_DATA, \"additional_Type_3_v2/Type_3\", \"*.jpg\"))\nadditional_type_3_ids = np.array([s[len(os.path.join(ADDITIONAL_DATA, \"additional_Type_3_v2/Type_3\"))+1:-4] for s in additional_type_3_files])\n\n\nprint(len(additional_type_1_files), len(additional_type_2_files), len(additional_type_3_files))\nprint(\"Type 1\", additional_type_1_ids[:10])\nprint(\"Type 2\", additional_type_2_ids[:10])\nprint(\"Type 3\", additional_type_3_ids[:10])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#try cv2\nimport cv2\n\nimg=cv2.imread(test_files[1])\ncv2.imshow(\"img\",img)\ncv2.waitKey(0)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#crop the cervix\n#using cv2 to detect circle and draw rectangle\n","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":{"trusted":true},"cell_type":"code","source":"","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":{},"cell_type":"markdown","source":""},{"metadata":{"trusted":true},"cell_type":"code","source":"","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":{},"cell_type":"markdown","source":""},{"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":1}