{"cells":[{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import os\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport pandas as pd","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"base_path = '/kaggle/input/siim-isic-melanoma-classification'\ntrain = pd.read_csv(os.path.join(base_path, 'train.csv'))\nlen(train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"newpath = r'/kaggle/working/grayscale' \nif not os.path.exists(newpath):\n    os.makedirs(newpath)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"save_path = '/kaggle/working/grayscale'\nfor i in range(0,len(train)):\n    image = cv2.imread(base_path + '/jpeg/train/' + train['image_name'][i] + '.jpg')\n    image_rieze = cv2.resize(image, (200,200))\n    final_image = cv2.cvtColor(image_rieze, cv2.COLOR_RGB2GRAY)\n    completeName = os.path.join(save_path, train['image_name'][i]+'.jpg') \n    cv2.imwrite(completeName, final_image)","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}