diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..b144c11 Binary files /dev/null and b/.DS_Store differ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..335ea9d --- /dev/null +++ b/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. \ No newline at end of file diff --git a/README.md b/README.md index fbc1d77..19ef8a0 100644 --- a/README.md +++ b/README.md @@ -1 +1,74 @@ -# yolosegment_to_labelmejson \ No newline at end of file +# 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) \ No newline at end of file diff --git a/build/lib/yolosegment2labelme/__init__.py b/build/lib/yolosegment2labelme/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build/lib/yolosegment2labelme/polygon_saver.py b/build/lib/yolosegment2labelme/polygon_saver.py new file mode 100644 index 0000000..281f3bf --- /dev/null +++ b/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) diff --git a/build/lib/yolosegment2labelme/yolosegment2labelme.py b/build/lib/yolosegment2labelme/yolosegment2labelme.py new file mode 100644 index 0000000..499238c --- /dev/null +++ b/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() diff --git a/dist/yolosegment2labelme-0.0.1-py3-none-any.whl b/dist/yolosegment2labelme-0.0.1-py3-none-any.whl new file mode 100644 index 0000000..55f655b Binary files /dev/null and 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b/pyproject.toml @@ -0,0 +1,18 @@ +[project] +name = "yolosegment2labelme" +version = "0.0.1" +authors = [ + { name="Abonia Sojasingarayar", email="aboniaa@gmail.com" }, +] +description = "A small example package" +readme = "README.md" +requires-python = ">=3.8" +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", +] + +[project.urls] +Homepage = "https://github.com/Abonia1/yolosegment2labelme" +Issues = "https://github.com/Abonia1/yolosegment2labelme/issues" \ No newline at end of file diff --git a/runs/segment/predict/1.jpg b/runs/segment/predict/1.jpg new file mode 100644 index 0000000..496ffdb Binary files /dev/null and b/runs/segment/predict/1.jpg differ diff --git a/runs/segment/predict/10.jpg b/runs/segment/predict/10.jpg new file mode 100644 index 0000000..0eabe4f Binary files /dev/null and b/runs/segment/predict/10.jpg differ diff --git a/runs/segment/predict/2.jpg 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b/runs/segment/predict/7.jpg differ diff --git a/runs/segment/predict/8.jpg b/runs/segment/predict/8.jpg new file mode 100644 index 0000000..54c1824 Binary files /dev/null and b/runs/segment/predict/8.jpg differ diff --git a/runs/segment/predict/9.jpg b/runs/segment/predict/9.jpg new file mode 100644 index 0000000..c61d4a3 Binary files /dev/null and b/runs/segment/predict/9.jpg differ diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..9d5f797 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,3 @@ +# Inside of setup.cfg +[metadata] +description-file = README.md \ No newline at end of file diff --git a/yolosegment2labelme.egg-info/PKG-INFO b/yolosegment2labelme.egg-info/PKG-INFO new file mode 100644 index 0000000..e19bb71 --- /dev/null +++ b/yolosegment2labelme.egg-info/PKG-INFO @@ -0,0 +1,94 @@ +Metadata-Version: 2.1 +Name: yolosegment2labelme +Version: 0.0.1 +Summary: A small example package +Author: Abonia Sojasingarayar +Author-email: Abonia Sojasingarayar +Project-URL: Homepage, https://github.com/Abonia1/yolosegment2labelme +Project-URL: Issues, https://github.com/Abonia1/yolosegment2labelme/issues +Keywords: python,yolo,segmentation,json,labelme,anylabeling +Classifier: Programming Language :: Python :: 3 +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: OS Independent +Requires-Python: >=3.8 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: numpy +Requires-Dist: opencv-python +Requires-Dist: pillow +Requires-Dist: ultralytics + +# 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) diff --git a/yolosegment2labelme.egg-info/SOURCES.txt b/yolosegment2labelme.egg-info/SOURCES.txt new file mode 100644 index 0000000..176c96d --- /dev/null +++ b/yolosegment2labelme.egg-info/SOURCES.txt @@ -0,0 +1,13 @@ +LICENSE +README.md +pyproject.toml +setup.py +yolosegment2labelme/__init__.py +yolosegment2labelme/polygon_saver.py +yolosegment2labelme/yolosegment2labelme.py +yolosegment2labelme.egg-info/PKG-INFO +yolosegment2labelme.egg-info/SOURCES.txt +yolosegment2labelme.egg-info/dependency_links.txt +yolosegment2labelme.egg-info/entry_points.txt +yolosegment2labelme.egg-info/requires.txt +yolosegment2labelme.egg-info/top_level.txt \ No newline at end of file diff --git a/yolosegment2labelme.egg-info/dependency_links.txt b/yolosegment2labelme.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/yolosegment2labelme.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/yolosegment2labelme.egg-info/entry_points.txt b/yolosegment2labelme.egg-info/entry_points.txt new file mode 100644 index 0000000..c9ff28d --- /dev/null +++ b/yolosegment2labelme.egg-info/entry_points.txt @@ -0,0 +1,2 @@ +[console_scripts] +yolosegment2labelme = yolosegment2labelme.yolosegment2labelme:main diff --git a/yolosegment2labelme.egg-info/requires.txt b/yolosegment2labelme.egg-info/requires.txt new file mode 100644 index 0000000..d5d5f4a --- /dev/null +++ b/yolosegment2labelme.egg-info/requires.txt @@ -0,0 +1,4 @@ +numpy +opencv-python +pillow +ultralytics diff --git a/yolosegment2labelme.egg-info/top_level.txt b/yolosegment2labelme.egg-info/top_level.txt new file mode 100644 index 0000000..38289ee --- /dev/null +++ b/yolosegment2labelme.egg-info/top_level.txt @@ -0,0 +1 @@ +yolosegment2labelme diff --git a/yolosegment2labelme/.DS_Store b/yolosegment2labelme/.DS_Store new file mode 100644 index 0000000..fb5a6b3 Binary files /dev/null and b/yolosegment2labelme/.DS_Store differ diff --git a/yolosegment2labelme/__init__.py b/yolosegment2labelme/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/yolosegment2labelme/__pycache__/polygon_saver.cpython-311.pyc b/yolosegment2labelme/__pycache__/polygon_saver.cpython-311.pyc new file mode 100644 index 0000000..7ca0c3e Binary files /dev/null and b/yolosegment2labelme/__pycache__/polygon_saver.cpython-311.pyc differ diff --git a/yolosegment2labelme/example.ipynb b/yolosegment2labelme/example.ipynb new file mode 100644 index 0000000..cfc3dd8 --- /dev/null +++ b/yolosegment2labelme/example.ipynb @@ -0,0 +1,200 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "image 1/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/1.jpg: 448x640 1 bear, 139.5ms\n", + "image 2/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/10.jpg: 448x640 1 cup, 1 laptop, 1 cell phone, 2 books, 114.3ms\n", + "image 3/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/2.jpg: 640x448 1 dog, 81.0ms\n", + "image 4/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/3.jpg: 480x640 4 chairs, 97.6ms\n", + "image 5/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/4.jpg: 544x640 2 persons, 2 sports balls, 99.3ms\n", + "image 6/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/5.jpg: 448x640 4 persons, 1 sports ball, 3 kites, 85.7ms\n", + "image 7/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/6.jpg: 448x640 1 bird, 76.8ms\n", + "image 8/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/7.jpg: 416x640 1 person, 1 dog, 67.1ms\n", + "image 9/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg: 448x640 11 persons, 5 cars, 4 traffic lights, 75.6ms\n", + "image 10/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/9.jpg: 448x640 1 person, 74.4ms\n", + "Speed: 1.0ms preprocess, 91.1ms inference, 72.5ms postprocess per image at shape (1, 3, 448, 640)\n", + "Results saved to \u001b[1mruns/segment/predict2\u001b[0m\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/1.jpg with label bear\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/10.jpg with label cup\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/10.jpg with label laptop\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/10.jpg with label book\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/10.jpg with label cell phone\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/10.jpg with label book\n", + "Polygon information saved for 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/Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/7.jpg with label dog\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/7.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label car\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for 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"stream", + "text": [ + "\n", + "image 1/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/1.jpg: 448x640 1 bear, 111.5ms\n", + "image 2/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/10.jpg: 448x640 1 cup, 1 laptop, 1 cell phone, 5 books, 149.6ms\n", + "image 3/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/2.jpg: 640x448 1 dog, 92.1ms\n", + "image 4/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/3.jpg: 480x640 4 chairs, 101.8ms\n", + "image 5/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/4.jpg: 544x640 2 persons, 2 sports balls, 119.7ms\n", + "image 6/10 /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/5.jpg: 448x640 6 persons, 1 sports ball, 3 kites, 91.8ms\n", + "image 7/10 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/Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label car\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label car\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label car\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label traffic light\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label traffic light\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label traffic light\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label car\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label car\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label traffic light\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label car\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label car\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/8.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/9.jpg with label person\n", + "Polygon information saved for /Users/aboniasojasingarayar/Documents/Github/yolosegment2labelme/yolosegment2labelme/../images/9.jpg with label tv\n" + ] + } + ], + "source": [ + "!python yolosegment2labelme.py --images ../images " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/yolosegment2labelme/polygon_saver.py b/yolosegment2labelme/polygon_saver.py new file mode 100644 index 0000000..281f3bf --- /dev/null +++ b/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) diff --git a/yolosegment2labelme/runs/segment/predict/1.jpg b/yolosegment2labelme/runs/segment/predict/1.jpg new file mode 100644 index 0000000..496ffdb Binary files /dev/null and b/yolosegment2labelme/runs/segment/predict/1.jpg differ diff --git a/yolosegment2labelme/runs/segment/predict/10.jpg 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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() diff --git a/yolosegment2labelme/yolov8n-seg.pt b/yolosegment2labelme/yolov8n-seg.pt new file mode 100644 index 0000000..6d8d8b5 Binary files /dev/null and b/yolosegment2labelme/yolov8n-seg.pt differ diff --git 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