jason-li-831202/Object-Segmentation-Web

This project used Yolov8/AnimeGAN and Flask to accomplish the task of background segmentation , background remove and background replacement. Additionally, you can change different display styles.

Object Background Removal Web using YoloV8 with Flask

Web application for real-time object segmentation using Flask , YOLOv8, AnimeGANv2/v3 model in ONNX weights.

After obtaining images through the camera, it is possible to separate the target and background in the scene, and composite the target with a Web URL/Local Path video background. Additionally, you can change different display styles and save a screenshot.

➤ Contents

1. Requirements

2. ONNX-Model

3. Examples

4. Docker

5. Demo

6. Updates

7. License

➤ Requirements

• OpenCV, Flask, gevent, onnxruntime and youtube-dl.

• Install :

  The requirements.txt file should list all Python libraries that your notebooks depend on, and they will be installed using:

  pip install -r requirements.txt
  

• Note :

  If you use a YouTube URL as the link to replace your background, please make sure to modify the following.

  1. YouTube Unable to extract uploader id, so you need to revise [Your Path]\site-packages\youtube_dl\extractor\youtube.py :

  • 'uploader_id': self._search_regex(r'/(?:channel|user)/([^/?&#]+)', owner_profile_url, 'uploader id') if owner_profile_url else None,

    $\Downarrow$

  • 'uploader_id': self._search_regex(r'/(?:channel/|user/|@)([^/?&#]+)', owner_profile_url, 'uploader id', default=None),

  2. Youtube does no longer have a like/dislike count, so you need to comment out [Your Path]\site-packages\pafy\backend_youtube_dl.py :

  • self._likes = self._ydl_info['like_count']

  • self._dislikes = self._ydl_info['dislike_count']

➤ ONNX-model

You can convert the YOLOv8-seg model to ONNX using the following Google Colab notebook :

• The License of the models is GPL-3.0 license: License

• Quantize ONNX models :

  Converting a model to use float16 instead of float32 can decrease the model size.

  python onnxQuantization.py -i <path-of-your-onnx-model>
  

➤ Examples

• Setting Config :

  model_config = {
      "model_path": 'models/yolov8n-seg-coco.onnx', # model path
      "classes_path" : 'models/coco_label.txt', # classes path
      "box_score" : 0.4,
      "box_nms_iou" : 0.45,
      "box_aspect_ratio" : None,
      "box_stretch" : None,
  }
  
  cam_config = {
      "cam_id" : 0,
      'exposure': -2, # init cam exposure
      'contrast': 50 # init cam contrast
  }

  After running, the config information will appear above the menu :

  

• Run :

  python Application.py
  ... 
  The server will be accessible at [ http://localhost:8080 ].
  

  Display Mode	Describe
  DisplayType.NONE	Just show your webcam image.
  DisplayType.BASIC_MODE	Show detect and segmentation target results on image.
  DisplayType.DETECT_MODE	Separate the target box and background on image.
  DisplayType.SEMANTIC_MODE	Separate the target segmentation and background on image.

➤ Docker

From the root directory, run :

docker build -t <name-of-your-image> -f ./docker/Dockerfile .
docker run --rm --privileged --device="/dev/video0:/dev/video0" -t -p 8080:8080 <name-of-your-image>

➤ Demo

• Demo Youtube Video

• Display Mode Switch

  

• Display Style Switch

  

• Display Background Removal

  

➤ Updates

• 2023/05/05 - Added images with downloadable transparent backgrounds.
• 2023/11/22 - Fixed youtube_dl bug, and remove pafy package.
• 2023/11/26 - Fixed custom diff model size.

➤ License

WiFi Analyzer is licensed under the GNU General Public License v3.0 (GPLv3).

GPLv3 License key requirements :

• Disclose Source
• License and Copyright Notice
• Same License
• State Changes