{"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":{},"cell_type":"markdown","source":"# What is Pneumonia?\n## Infection that inflames air sacs in one or both lungs, which may fill with fluid. With pneumonia, the air sacs may fill with fluid or pus. The infection can be life-threatening to anyone, but particularly to infants, children and people over 65. Symptoms include a cough with phlegm or pus, fever, chills and difficulty breathing.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport PIL\nfrom PIL import Image","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Below is the X-Ray image of a person suffering from Pneumonia.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"\nimage=\"../input/chest-xray-pneumonia/chest_xray/train/PNEUMONIA/person1011_bacteria_2942.jpeg\"\nPIL.Image.open(image)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Below is the X-ray image of normal lungs.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"image=\"../input/chest-xray-pneumonia/chest_xray/train/NORMAL/IM-0151-0001.jpeg\"\nPIL.Image.open(image)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import tensorflow as tf","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.preprocessing.image import ImageDataGenerator","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Using ImageDataGenerator to load the training and validation images","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"training_dir=\"../input/chest-xray-pneumonia/chest_xray/train/\"\ntraining_generator=ImageDataGenerator(rescale=1/255,featurewise_center=False,  # set input mean to 0 over the dataset\n        samplewise_center=False,  # set each sample mean to 0\n        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n        samplewise_std_normalization=False,  # divide each input by its std\n        zca_whitening=False,  # apply ZCA whitening\n        rotation_range = 30,  # randomly rotate images in the range (degrees, 0 to 180)\n        zoom_range = 0.2, # Randomly zoom image \n        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n        horizontal_flip = False,  # randomly flip images\n        vertical_flip=False)\ntrain_generator=training_generator.flow_from_directory(training_dir,target_size=(200,200),batch_size=4,class_mode='binary')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"validation_dir=\"../input/chest-xray-pneumonia/chest_xray/val/\"\nvalidation_generator=ImageDataGenerator(rescale=1/255)\nval_generator=validation_generator.flow_from_directory(validation_dir,target_size=(200,200),batch_size=4,class_mode='binary')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_dir=\"../input/chest-xray-pneumonia/chest_xray/test/\"\ntest_generator=ImageDataGenerator(rescale=1/255)\ntest_generator=test_generator.flow_from_directory(test_dir,target_size=(200,200),batch_size=16,class_mode='binary')","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}