{"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","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"from keras.applications.mobilenet_v2 import MobileNetV2\nfrom keras.preprocessing import image\nfrom keras.applications.mobilenet_v2 import preprocess_input\nfrom keras.models import Model\nfrom keras.layers import Dense, GlobalAveragePooling2D\nfrom keras import backend as K\n\n# create the base pre-trained model\nbase_model = MobileNetV2(weights='imagenet')\nmodel = Model(inputs=[base_model.input], outputs=[base_model.get_layer('Logits').output])\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from glob import glob\nimagePatches = glob('/kaggle/input/covidchestxraydataset-1/images/*.png', recursive=True)\n#imagenumbers = []\n#imagenumbers.append(imagePatches)\n#imagePatches[:100]\n\nfeatures = []\nimagenumber = []\nfor img in imagePatches:\n        imagenumber.append(img)\n        img_data = image.load_img(img, target_size=(224, 224))\n        img_data = image.img_to_array(img_data)\n        img_data = np.expand_dims(img_data, axis=0)\n        img_data = preprocess_input(img_data)\n        feats = model.predict(img_data)\n        features.append(feats.flatten())\nmobilenet_feature = np.array(features)\nfeatures_mobilenet_v2 = pd.DataFrame(mobilenet_feature)\nnmbr = np.array(imagenumber)\nimg_no = pd.DataFrame(nmbr)\nimg_no.to_csv('img_no_normal_moblenetv2_logits_png.csv',index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mobilenet_feature","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"features_mobilenet_v2","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler\ndata = mobilenet_feature\nscaler = MinMaxScaler()\nscaler.fit(data)\nfeatures_norm_covid_data = scaler.transform(data)\nnormalized_norm_feature_covid_data = pd.DataFrame(features_norm_covid_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"normalized_norm_feature_covid_data","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"normalized_norm_feature_covid_data.to_csv('normalized_normal_mobilenet_v2_logits_png.csv',index=False) ","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}