{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os\nimport matplotlib.pyplot as plt\nimport cv2\nimport math","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv('../input/vinbigdata-1024-jpg-dataset/train.csv')\ndf_class_id =  pd.unique(df['image_id'])\nprint(df_class_id.shape)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"save_dir_train_diseases = '/kaggle/tmp/diseases/'\nsave_dir_train_normal = '/kaggle/tmp/normal/'\nsave_dir_test = '/kaggle/tmp/test/'\n\nos.makedirs(save_dir_train_diseases, exist_ok=True)\nos.makedirs(save_dir_train_normal, exist_ok=True)\nos.makedirs(save_dir_test, exist_ok=True)\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"border_color_list = [(255,0,0), (0,255,0), (0,0,255), (255,255,0), (0,255,255), (255,0,255), \n                     (125,0,0),(0,125,0), (0,0,125), (125,125,0), (0,125,125), (189,252,201)\n                    , (180,77,250), (221,160,221)]\nfor i in range(15000):\n    image_id = df_class_id[i]\n    img = cv2.imread(f'../input/vinbigdata-1024-jpg-dataset/train/{image_id}.jpg')\n    df_img = df[(df.loc[:,'image_id'] == image_id)].reset_index(drop=True)\n\n    for j in range(df_img.shape[0]):\n\n        if df_img .loc[j,'class_id'] == 14:\n            continue\n\n        for k in range(14):        \n            if df_img .loc[j,'class_id'] == k:\n                border_color = border_color_list[k]\n\n        a = df_img.loc[j,'x_min']\n        b = df_img.loc[j,'y_min']\n        c = df_img.loc[j,'x_max']\n        d = df_img.loc[j,'y_max']\n        cv2.rectangle(img, (int(a), int(b)), (int(c), int(d)), border_color, 2)\n    for j in range(df_img.shape[0]):\n        if df_img .loc[j,'class_id'] != 14:\n            cv2.imwrite(save_dir_train_diseases +image_id+'.jpg',img)\n            break\n        if j == df_img.shape[0] - 1:\n            if df_img .loc[df_img.shape[0]-1,'class_id'] == 14:\n                cv2.imwrite(save_dir_train_normal +image_id+'.jpg',img)\n\n!tar -zcf diseases.tar.gz -C \"/kaggle/tmp/diseases/\" .\n!tar -zcf normal.tar.gz -C \"/kaggle/tmp/normal/\" .\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv('../input/vinbigdata-chest-xray-abnormalities-detection/sample_submission.csv')\ndf_class_id =  pd.unique(df['image_id'])\nfor i in range(3000):\n    image_id = df_class_id[i]\n    img = cv2.imread(f'../input/vinbigdata-1024-jpg-dataset/test/{image_id}.jpg')\n    cv2.imwrite(save_dir_test +image_id+'.jpg',img)\n    \n!tar -zcf test.tar.gz -C \"/kaggle/tmp/test/\" .","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv('../input/vinbigdata-1024-jpg-dataset/train.csv')\ndf.to_csv('train.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nlist_diseases = [\n    '0 - Aortic enlargement',\n    '1 - Atelectasis',\n    '2 - Calcification',\n    '3 - Cardiomegaly',\n    '4 - Consolidation',\n    '5 - ILD',\n    '6 - Infiltration',\n    '7 - Lung Opacity',\n    '8 - Nodule/Mass',\n    '9 - Other lesion',\n    '10 - Pleural effusion',\n    '11 - Pleural thickening',\n    '12 - Pneumothorax',\n    '13 - Pulmonary fibrosis]']\n\nfor i in range(14):\n    globals()[f'img{i}'] = np.full((150, 150, 3) , 255, np.uint8)\n\n    cv2.rectangle(eval(f'img{i}') ,(25,25),(125,125), (border_color_list[i][2], border_color_list[i][1], border_color_list[i][0]),2)\n    plt.figure(figsize=(25, 25))\n    plt.subplot(2, 7, i +1) \n    plt.title(f'{list_diseases[i]}')\n    plt.imshow(eval(f'img{i}'))\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":4}