yolosegment2labelme

LICENSE

@@ -0,0 +1,19 @@
+Copyright (c) 2018 The Python Packaging Authority
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1 +1,74 @@
-# yolosegment_to_labelmejson
+# yolosegment2labelme
+
+**yolosegment2labelme** is a Python package that allows you to convert YOLO segmentation prediction results to LabelMe JSON format. This tool facilitates the annotation process by generating JSON files that are compatible with LabelMe and other labeling annotation tools.
+
+## Features
+
+- Convert YOLO segmentation prediction results to LabelMe JSON format.
+- Compatible with various YOLO models.
+- Easy-to-use command-line interface.
+- Supports batch processing of images.
+- Customizable confidence threshold for predictions.
+- Highly customizable and extensible for specific use cases.
+
+## Installation
+
+You can install **yolosegment2labelme** via pip:
+
+```bash
+pip install yolosegment2labelme
+```
+
+## Usage
+
+After installation, you can use the `yolosegment2labelme` command-line interface to convert YOLO segmentation prediction results to LabelMe JSON format. Here's a basic example:
+
+```bash
+yolosegment2labelme --images /path/to/images
+```
+
+or with custom yolo segmentation model
+
+```bash
+yolosegment2labelme --model yolov8n-seg.pt --images /path/to/images --conf 0.3
+```
+
+This command will process the images located in the specified directory (`/path/to/images`), using the YOLO model weights file `yolov8n-seg.pt`, and generate LabelMe JSON files with a confidence threshold of 0.3.
+
+For more options and advanced usage, refer to the [documentation](https://github.com/Abonia1/yolosegment2labelme).
+
+## Sample Images
+The table below displays sample images along with their corresponding annotations generated using yolosegment2labelme:
+
+## Sample Images
+
+| Sample Image 1                                      | Sample Image 2                                      |
+|-----------------------------------------------------|-----------------------------------------------------|
+| ![Sample Image 1](images/labelme_test/sample1.png)      | ![Sample Image 2](images/labelme_test/sample2.png)      |
+| Sample Annotation for Image 1                      | Sample Annotation for Image 2                      |
+
+| Sample Image 3                                      | Sample Image 4                                      |
+|-----------------------------------------------------|-----------------------------------------------------|
+| ![Sample Image 3](images/labelme_test/sample3.png)      | ![Sample Image 4](images/labelme_test/sample4.png)      |
+| Sample Annotation for Image 3                      | Sample Annotation for Image 4                      |
+
+
+## Documentation
+
+The documentation for **yolosegment2labelme** can be found on GitHub: [yolosegment2labelme Documentation](https://github.com/Abonia1/yolosegment2labelme)
+
+## Contributing
+
+Contributions are welcome! If you'd like to contribute to **yolosegment2labelme**, please check out the [Contribution Guidelines](CONTRIBUTING.md).
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Authors
+
+- Abonia Sojasingarayar - [GitHub](https://github.com/Abonia1)
+
+## Support
+
+If you encounter any issues or have questions about **yolosegment2labelme**, please feel free to open an issue on GitHub: [yolosegment2labelme Issues](https://github.com/Abonia1/yolosegment2labelme/issues)

build/lib/yolosegment2labelme/polygon_saver.py

@@ -0,0 +1,86 @@
+import json
+import cv2
+import os
+
+class PolygonSaver:
+    """
+    Class to save image information along with polygon shapes and xy coordinates to a JSON file.
+    """
+    def __init__(self):
+        pass
+
+    def save_image_info_with_polygons(self, image_path, xy_coordinates, label_name, output_dir):
+        """
+        Save image information along with polygon shapes and xy coordinates to a JSON file.
+        If the JSON file already exists, append the new mask information to it.
+        
+        Args:
+            image_path (str): Path to the image file.
+            xy_coordinates (list): List of xy coordinates.
+            label_name (str): Label name for the polygon shapes.
+            output_dir (str): Directory to save the JSON file.
+        """
+        image = cv2.imread(image_path)
+        height, width = image.shape[:2]
+
+        json_data = {
+            "version": "0.3.3",
+            "flags": {},
+            "shapes": [],
+            "imagePath": os.path.basename(image_path),
+            "imageData": None,
+            "imageHeight": height,
+            "imageWidth": width,
+            "text": ""
+        }
+
+        xy_coordinate_as_lists = [xy_coordinate.tolist() for xy_coordinate in xy_coordinates]
+
+        json_data["shapes"].append({
+            "label": label_name,
+            "text": "",
+            "points": xy_coordinate_as_lists,
+            "group_id": None,
+            "shape_type": "polygon",
+            "flags": {}
+        })
+
+        for shape in json_data['shapes']:
+            for point in shape['points']:
+                shape['points'] = point
+
+        json_file_name = os.path.join(output_dir, os.path.basename(image_path).replace(os.path.splitext(image_path)[1], ".json"))
+
+        if os.path.exists(json_file_name):
+            with open(json_file_name, 'r') as json_file:
+                existing_data = json.load(json_file)
+
+            existing_data["shapes"].extend(json_data["shapes"])
+
+            with open(json_file_name, 'w') as json_file:
+                json.dump(existing_data, json_file, indent=2)
+        else:
+            with open(json_file_name, 'w') as json_file:
+                json.dump(json_data, json_file, indent=2)
+
+        print(f"Polygon information saved for {image_path} with label {label_name}")
+
+    def generate_json_with_results(self, results, output_dir):
+        """
+        Generate JSON files with results obtained from YOLO model predictions.
+        
+        Args:
+            results (list): List of results obtained from YOLO model predictions.
+            output_dir (str): Directory to save the JSON files.
+        """
+        for result in results:
+            if result.masks is None:
+                continue
+            class_labels = result.boxes.cls.cpu().numpy()
+            label_names = {k: v for k, v in result.names.items()}
+
+            for mask, class_label in zip(result.masks, class_labels):
+                xy_coordinates = mask.xy
+                image_path = result.path
+                label_name = label_names[int(class_label)]
+                self.save_image_info_with_polygons(image_path, xy_coordinates, label_name, output_dir)

build/lib/yolosegment2labelme/yolosegment2labelme.py

@@ -0,0 +1,28 @@
+import argparse
+from polygon_saver import PolygonSaver
+from ultralytics import YOLO
+
+def main():
+    parser = argparse.ArgumentParser(description='Convert YOLO results to JSON files.')
+    parser.add_argument('--model', default='yolov8n-seg.pt', help='Path to YOLO model weights file (default is yolov8n)')
+    parser.add_argument('--images', required=True, help='Path to folder containing images')
+    parser.add_argument('--conf', type=float, default=0.2, help='Confidence threshold')
+    args = parser.parse_args()
+
+    # Use the input images directory as the output directory for JSON files
+    output_dir = args.images
+
+    # Instantiate the PolygonSaver class
+    polygon_saver = PolygonSaver()
+
+    # Load the YOLO model
+    yolo_model = YOLO(args.model)
+
+    # Get results from YOLO model predictions
+    results = yolo_model.predict(args.images, save=True, conf=args.conf)
+
+    # Generate JSON files with results
+    polygon_saver.generate_json_with_results(results, output_dir)
+
+if __name__ == "__main__":
+    main()