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You can download it from https://www.python.org/.\n\n2. **Install Necessary Libraries**: Open your terminal or command prompt and run:\n\n   pip install opencv-python numpy matplotlib\n\n#### Step 2: Prepare Your Image Dataset\n\n1. **Organize Your Images**: Create a directory and place all your JPG images in this directory. You can use the Standford cars dataset : https://www.kaggle.com/datasets/jessicali9530/stanford-cars-dataset\n\n#### Step 3: Generate ORB Descriptors\n\n1. **Open Terminal or Command Prompt**: Navigate to the directory containing the `descriptorsGenerator.py` script.\n\n2. **Run the Descriptors Generator**: Execute the following command to generate ORB descriptors:\n\n   python descriptorsGenerator.py --dataset path_to_image_dataset --output path_to_output_directory\n\n   - Replace `path_to_image_dataset` with the path to your image directory.\n   - Replace `path_to_output_directory` with the path where you want to save the descriptor files.\n\n#### Step 4: Generate the Output Image with Matches\n\n1. **Run the CBIR Script**: Use the `CBIR.py` script to generate an output image with matches. You will need to specify the query image and the dataset directory.\n\n2. **Command to Run**:\n\n   python CBIR.py --query path_to_query_image --dataset path_to_image_dataset\n\n   - Replace `path_to_query_image` with the path to your query image.\n   - Replace `path_to_image_dataset` with the path to your image dataset directory.\n\nBy following these steps, you can generate an output image that shows the matches for your query image using the CBIR system.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoseruiz01%2Fnontextualdataextraction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjoseruiz01%2Fnontextualdataextraction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoseruiz01%2Fnontextualdataextraction/lists"}