resize_on_disk.ipynb

{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"!git clone https://github.com/DanieleParravicini/DL-CompetitionsDatasets","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)\nfrom PIL import Image\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\npath = '/kaggle/input/ann-and-dl-vqa/dataset_vqa/'\nwriting_path = '/kaggle/working'\nIMG_HEIGHT, IMG_WIDTH = 320, 480\nscale_ratio = 0.4\nIMG_HEIGHT, IMG_WIDTH = int(IMG_HEIGHT*scale_ratio), int(IMG_WIDTH * scale_ratio)\nimport os\nif not (os.path.exists(os.path.join(writing_path , 'resized_test'))):\n    os.mkdir(os.path.join(writing_path , 'resized_test'))\n\nimg_to_resize = os.listdir(os.path.join(path, 'test'))\n\ndef resize(filename):\n    if filename.endswith('.png'):\n        source_path = os.path.join(path , 'test',filename)\n        new_path = os.path.join(writing_path , 'resized_test',filename)\n        if(os.path.exists(new_path)):\n            return\n        img = Image.open(source_path)\n        img = img.resize((IMG_WIDTH,IMG_HEIGHT ))\n        img = np.array(img)[:,:,:3]\n        img = Image.fromarray(img, 'RGB')\n        img.save(new_path, 'JPEG', quality=80)\n        \nfrom multiprocessing.dummy import Pool as ThreadPool\npool = ThreadPool(10)\nresults = pool.map(resize, img_to_resize)\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(os.listdir(os.path.join(writing_path , 'resized_test')))/len( os.listdir(os.path.join(path, 'test')))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import shutil\nshutil.rmtree('/kaggle/working/resized_train/', ignore_errors=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!cd resized_train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!git init\n!git add *","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!git commit --author=\"Daniele Parravicini <daniparra@hotmail.it>\" -m 'clverDataset 128x192' ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!git config --global user.email \"daniparra@hotmail.itm\"\n!git config --global user.name \"daniele parravicini\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!zip * files.zip","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!tar -zcvf 'files_clver_train.tar.gz' 'resized_train'","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}