{"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_minor":4,"nbformat":4,"cells":[{"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\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 read-only \"../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# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cd /kaggle/working","metadata":{"execution":{"iopub.status.busy":"2023-04-24T01:24:18.586871Z","iopub.execute_input":"2023-04-24T01:24:18.587607Z","iopub.status.idle":"2023-04-24T01:24:18.595671Z","shell.execute_reply.started":"2023-04-24T01:24:18.587564Z","shell.execute_reply":"2023-04-24T01:24:18.594143Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mkdir train_images_denoise_speckle_0.1","metadata":{"execution":{"iopub.status.busy":"2023-04-24T01:24:19.563373Z","iopub.execute_input":"2023-04-24T01:24:19.564111Z","iopub.status.idle":"2023-04-24T01:24:20.680641Z","shell.execute_reply.started":"2023-04-24T01:24:19.564073Z","shell.execute_reply":"2023-04-24T01:24:20.678728Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import cv2\nimport os\n\n# Define the input and output directories\ninput_dir = \"/kaggle/input/speckle-noise-0-1/train_images_speckle_0.1\"\noutput_dir = \"/kaggle/working/train_images_denoise_speckle_0.1\"\ni=0\n\n# Create the output directory if it doesn't exist\nif not os.path.exists(output_dir):\n    os.makedirs(output_dir)\n\n# Iterate over all the images in the input directory\nfor filename in os.listdir(input_dir):\n    if filename.endswith(\".jpg\") or filename.endswith(\".png\"):\n        # Read the image\n        image_path = os.path.join(input_dir, filename)\n        print(i)\n        i=i+1\n        img = cv2.imread(image_path)\n        \n\n        # Apply speckle noise reduction using a bilateral filter\n        filtered = cv2.bilateralFilter(img, 0, 50, 5)\n\n        # Convert the image to grayscale\n        gray = cv2.cvtColor(filtered, cv2.COLOR_BGR2GRAY)\n\n        # Apply contrast enhancement using CLAHE\n        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))\n        cl = clahe.apply(gray)\n\n        # Apply threshold to create a binary image\n        thresh = cv2.threshold(cl, 200, 255, cv2.THRESH_BINARY)[1]\n\n        # Find the contours in the binary image\n        contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n        # Draw the contours on the original image\n        cv2.drawContours(img, contours, -1, (0, 255, 0), 2)\n\n        # Save the output image in the output directory\n        output_path = os.path.join(output_dir, filename)\n        cv2.imwrite(output_path, img)\n","metadata":{"execution":{"iopub.status.busy":"2023-04-24T01:24:23.342136Z","iopub.execute_input":"2023-04-24T01:24:23.342565Z","iopub.status.idle":"2023-04-24T03:40:53.108112Z","shell.execute_reply.started":"2023-04-24T01:24:23.342521Z","shell.execute_reply":"2023-04-24T03:40:53.103233Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\ndf = pd.read_csv(\"../input/understanding_cloud_organization/train.csv\")\ndf.to_csv('train.csv')","metadata":{"execution":{"iopub.status.busy":"2023-04-24T01:24:00.442368Z","iopub.execute_input":"2023-04-24T01:24:00.443103Z","iopub.status.idle":"2023-04-24T01:24:09.819301Z","shell.execute_reply.started":"2023-04-24T01:24:00.443061Z","shell.execute_reply":"2023-04-24T01:24:09.818104Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}