{"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":"traincsv=pd.read_csv('/kaggle/input/aptos2019-blindness-detection/train.csv')\ntrainImgsPath='/kaggle/input/aptos2019-blindness-detection/train_images'\ntestImgsPath='/kaggle/input/aptos2019-blindness-detection/test_images'\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import cv2 as cv2\nfrom tqdm import tqdm \nimport matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def getNImageFromFolder(folderName,N):\n    imgs=[]\n    folder=os.listdir(folderName)\n    for img in tqdm(folder):\n        if(len(imgs)<N):\n            imgPath=os.path.join(folderName,img)\n            tempImg=cv2.imread(imgPath)\n            tempImg=cv2.cvtColor(tempImg,cv2.COLOR_BGR2RGB)\n            if tempImg is not None:\n                imgs.append(tempImg)\n        else :\n            break\n    return imgs","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def plotNImages(imgs,r,c):\n    row,col=r,c\n    fig=plt.figure(figsize=(row*col,row+col))\n    for i in range(0,len(imgs)):\n        fig.add_subplot(row,col,i+1)\n        plt.imshow(imgs[i])\n    plt.show()   \n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"trainImgs=getNImageFromFolder(trainImgsPath,20)\nplotNImages(trainImgs,4,5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Concept:Unsharp Masking...first blur an image and subtract the blur in the original to get sharpened version\ndef preprocessImgs(imgs,size,sigmaX):\n    ppImgs=[]\n    imgSize=size\n    for i in range(0,len(imgs)):\n        tempImg=imgs[i]\n        #tempImg=cv2.cvtColor(tempImg,cv2.COLOR_RGB2GRAY)        \n        tempImg=cv2.resize(tempImg,(imgSize,imgSize))       \n        blurredImg=cv2.GaussianBlur(tempImg,(0,0),sigmaX)\n        tempImg=cv2.addWeighted(tempImg,4,blurredImg,-4 ,128)\n        ppImgs.append(tempImg)\n    return ppImgs","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ppImgs=preprocessImgs(trainImgs,size=500,sigmaX=38)\nplotNImages(ppImgs,r=4,c=5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def cropImagesFromCircleCenter():\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}