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- .gitattributes +93 -0
- .gitignore +59 -0
- Dockerfile +3 -0
- LICENSE +21 -0
- Pipfile +17 -0
- README.md +153 -3
- app.py +236 -0
- app_simple.py +172 -0
- configs/retinanet_config.py +50 -0
- deploy_to_hf.py +76 -0
- images/issues/Cube_00000.json +738 -0
- images/issues/Cube_00006.jpg +3 -0
- images/issues/Cube_00006.json +190 -0
- images/issues/Cube_00008.jpg +3 -0
- images/issues/Cube_00008.json +190 -0
- images/issues/Cube_00009.jpg +3 -0
- images/issues/Cube_00009.json +190 -0
- images/issues/Cube_00017.jpg +0 -0
- images/issues/Cube_00017.json +190 -0
- images/test/Cube_00028.jpg +3 -0
- images/test/Cube_00028.json +190 -0
- images/test/Cube_00029.jpg +3 -0
- images/test/Cube_00029.json +190 -0
- images/test/Cube_00032.jpg +3 -0
- images/test/Cube_00032.json +172 -0
- images/test/_annotations.coco.json +1 -0
- images/train/Cube_00001.jpg +3 -0
- images/train/Cube_00001.json +190 -0
- images/train/Cube_00002.jpg +3 -0
- images/train/Cube_00002.json +190 -0
- images/train/Cube_00003.jpg +3 -0
- images/train/Cube_00003.json +190 -0
- images/train/Cube_00004.jpg +3 -0
- images/train/Cube_00004.json +190 -0
- images/train/Cube_00007.jpg +3 -0
- images/train/Cube_00007.json +190 -0
- images/train/Cube_00010.jpg +3 -0
- images/train/Cube_00010.json +190 -0
- images/train/Cube_00011.jpg +3 -0
- images/train/Cube_00011.json +190 -0
- images/train/Cube_00012.jpg +3 -0
- images/train/Cube_00012.json +190 -0
- images/train/Cube_00013.jpg +3 -0
- images/train/Cube_00013.json +190 -0
- images/train/Cube_00014.jpg +3 -0
- images/train/Cube_00014.json +190 -0
- images/train/Cube_00015.jpg +3 -0
- images/train/Cube_00015.json +190 -0
- images/train/Cube_00016.jpg +3 -0
- images/train/Cube_00016.json +190 -0
.gitattributes
CHANGED
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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images/issues/Cube_00006.jpg filter=lfs diff=lfs merge=lfs -text
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images/issues/Cube_00008.jpg filter=lfs diff=lfs merge=lfs -text
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images/issues/Cube_00009.jpg filter=lfs diff=lfs merge=lfs -text
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images/test/Cube_00028.jpg filter=lfs diff=lfs merge=lfs -text
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images/test/Cube_00029.jpg filter=lfs diff=lfs merge=lfs -text
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images/test/Cube_00032.jpg filter=lfs diff=lfs merge=lfs -text
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images/train/Cube_00001.jpg filter=lfs diff=lfs merge=lfs -text
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images/train/Cube_00002.jpg filter=lfs diff=lfs merge=lfs -text
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images/train/Cube_00003.jpg filter=lfs diff=lfs merge=lfs -text
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images/train/Cube_00004.jpg filter=lfs diff=lfs merge=lfs -text
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images/train/Cube_00007.jpg filter=lfs diff=lfs merge=lfs -text
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images/train/Cube_00010.jpg filter=lfs diff=lfs merge=lfs -text
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images/train/Cube_00011.jpg filter=lfs diff=lfs merge=lfs -text
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images/train/Cube_00012.jpg filter=lfs diff=lfs merge=lfs -text
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images/train/Cube_00013.jpg filter=lfs diff=lfs merge=lfs -text
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images/train/Cube_00014.jpg filter=lfs diff=lfs merge=lfs -text
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.gitignore
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# Python
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| 2 |
+
__pycache__/
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| 3 |
+
*.py[cod]
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| 4 |
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*$py.class
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| 5 |
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*.so
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| 6 |
+
.Python
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| 7 |
+
build/
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| 8 |
+
develop-eggs/
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| 9 |
+
dist/
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| 10 |
+
downloads/
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| 11 |
+
eggs/
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| 12 |
+
.eggs/
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| 13 |
+
lib/
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| 14 |
+
lib64/
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| 15 |
+
parts/
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| 16 |
+
sdist/
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| 17 |
+
var/
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| 18 |
+
wheels/
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| 19 |
+
*.egg-info/
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| 20 |
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.installed.cfg
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| 21 |
+
*.egg
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| 22 |
+
|
| 23 |
+
# Virtual Environment
|
| 24 |
+
venv/
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| 25 |
+
env/
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| 26 |
+
ENV/
|
| 27 |
+
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| 28 |
+
# IDE
|
| 29 |
+
.idea/
|
| 30 |
+
.vscode/
|
| 31 |
+
*.swp
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| 32 |
+
*.swo
|
| 33 |
+
|
| 34 |
+
# Model files
|
| 35 |
+
exported_model/
|
| 36 |
+
*.h5
|
| 37 |
+
*.pb
|
| 38 |
+
*.tflite
|
| 39 |
+
|
| 40 |
+
# Data
|
| 41 |
+
tfrecords/
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| 42 |
+
*.tfrecord
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| 43 |
+
*.record
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| 44 |
+
|
| 45 |
+
# Logs and checkpoints
|
| 46 |
+
logs/
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| 47 |
+
checkpoints/
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| 48 |
+
*.log
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| 49 |
+
*.ckpt
|
| 50 |
+
|
| 51 |
+
# OS
|
| 52 |
+
.DS_Store
|
| 53 |
+
Thumbs.db
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| 54 |
+
|
| 55 |
+
trained_model/
|
| 56 |
+
web_model/
|
| 57 |
+
|
| 58 |
+
SerializedExampleImage*
|
| 59 |
+
ShowBatchImage*
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Dockerfile
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FROM tensorflow/tensorflow:2.16.1-gpu-jupyter
|
| 2 |
+
RUN pip install tf-models-official
|
| 3 |
+
RUN pip install tensorflowjs==4.20.0 tensorflow==2.16.1
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LICENSE
ADDED
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| 1 |
+
MIT License
|
| 2 |
+
|
| 3 |
+
Copyright (c) 2024 yuimorii
|
| 4 |
+
|
| 5 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
| 6 |
+
of this software and associated documentation files (the "Software"), to deal
|
| 7 |
+
in the Software without restriction, including without limitation the rights
|
| 8 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
| 9 |
+
copies of the Software, and to permit persons to whom the Software is
|
| 10 |
+
furnished to do so, subject to the following conditions:
|
| 11 |
+
|
| 12 |
+
The above copyright notice and this permission notice shall be included in all
|
| 13 |
+
copies or substantial portions of the Software.
|
| 14 |
+
|
| 15 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
| 16 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
| 17 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
| 18 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
| 19 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
| 20 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
| 21 |
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SOFTWARE.
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Pipfile
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+
[[source]]
|
| 2 |
+
url = "https://pypi.org/simple"
|
| 3 |
+
verify_ssl = true
|
| 4 |
+
name = "pypi"
|
| 5 |
+
|
| 6 |
+
[packages]
|
| 7 |
+
gradio = ">=4.0.0"
|
| 8 |
+
tensorflow-cpu = "==2.15.0"
|
| 9 |
+
numpy = ">=1.19.2"
|
| 10 |
+
pillow = ">=8.0.0"
|
| 11 |
+
opencv-python-headless = ">=4.5.1"
|
| 12 |
+
matplotlib = ">=3.2.2"
|
| 13 |
+
|
| 14 |
+
[dev-packages]
|
| 15 |
+
|
| 16 |
+
[requires]
|
| 17 |
+
python_version = "3.11"
|
README.md
CHANGED
|
@@ -1,3 +1,153 @@
|
|
| 1 |
-
---
|
| 2 |
-
|
| 3 |
-
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|
|
|
|
| 1 |
+
---
|
| 2 |
+
title: Rubik's Cube Recognition with TensorFlow.js RetinaNet
|
| 3 |
+
emoji: 🎲
|
| 4 |
+
colorFrom: blue
|
| 5 |
+
colorTo: red
|
| 6 |
+
sdk: gradio
|
| 7 |
+
sdk_version: 4.19.2
|
| 8 |
+
app_file: app.py
|
| 9 |
+
pinned: false
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
# # 🎲 Rubik's Cube Recognition with TensorFlow.js RetinaNet
|
| 13 |
+
|
| 14 |
+
This is a deep learning model that can recognize and analyze Rubik's cubes in images. The model is built using TensorFlow.js and RetinaNet architecture.
|
| 15 |
+
|
| 16 |
+
## Features
|
| 17 |
+
|
| 18 |
+
- Upload images of Rubik's cubes
|
| 19 |
+
- Real-time cube detection
|
| 20 |
+
- Simple and intuitive interface
|
| 21 |
+
|
| 22 |
+
## How to Use
|
| 23 |
+
|
| 24 |
+
1. Visit the live demo
|
| 25 |
+
2. Upload an image containing a Rubik's cube
|
| 26 |
+
3. Get instant detection results
|
| 27 |
+
|
| 28 |
+
## Technical Details
|
| 29 |
+
|
| 30 |
+
- Built with TensorFlow and Gradio
|
| 31 |
+
- Uses computer vision techniques for cube detection
|
| 32 |
+
- Optimized for web deployment
|
| 33 |
+
|
| 34 |
+
## Local Development
|
| 35 |
+
|
| 36 |
+
To run this project locally:
|
| 37 |
+
|
| 38 |
+
1. Clone the repository
|
| 39 |
+
2. Install dependencies: `pip install -r requirements.txt`
|
| 40 |
+
3. Run the app: `python app.py`
|
| 41 |
+
|
| 42 |
+
## License
|
| 43 |
+
|
| 44 |
+
MIT License
|
| 45 |
+
|
| 46 |
+
## 🌟 Model Description
|
| 47 |
+
|
| 48 |
+
The model is based on RetinaNet with SpineNet-49 backbone, trained to detect:
|
| 49 |
+
- Rubik's cube faces
|
| 50 |
+
- Individual color tiles (red, white, blue, orange, green, yellow)
|
| 51 |
+
|
| 52 |
+
### Model Architecture
|
| 53 |
+
- Base Model: RetinaNet-SpineNet-49
|
| 54 |
+
- Input Size: 640x640x3
|
| 55 |
+
- Number of Classes: 7 (6 colors + face)
|
| 56 |
+
- Output: Bounding boxes with class predictions
|
| 57 |
+
|
| 58 |
+
## 📊 Performance Metrics
|
| 59 |
+
|
| 60 |
+
| Metric | Value |
|
| 61 |
+
|--------|-------|
|
| 62 |
+
| mAP | TBD |
|
| 63 |
+
| FPS | TBD |
|
| 64 |
+
|
| 65 |
+
## 🚀 Quick Start
|
| 66 |
+
|
| 67 |
+
```bash
|
| 68 |
+
# Clone the repository
|
| 69 |
+
git clone https://huggingface.co/spaces/[your-username]/rubiks-cube-recognition
|
| 70 |
+
|
| 71 |
+
# Install dependencies
|
| 72 |
+
pip install -r requirements.txt
|
| 73 |
+
|
| 74 |
+
# Run the demo
|
| 75 |
+
python app.py
|
| 76 |
+
```
|
| 77 |
+
|
| 78 |
+
## 📦 Project Structure
|
| 79 |
+
|
| 80 |
+
```
|
| 81 |
+
rubiks-cube-recognition/
|
| 82 |
+
├── app.py # Gradio web interface
|
| 83 |
+
├── src/
|
| 84 |
+
│ ├── data/ # Data processing utilities
|
| 85 |
+
│ │ ├── labelme2coco.py
|
| 86 |
+
│ │ └── shared.py
|
| 87 |
+
│ ├── model/ # Model training and inference
|
| 88 |
+
│ │ ├── trainer.py
|
| 89 |
+
│ │ └── visualize.py
|
| 90 |
+
│ └── utils/ # Utility functions
|
| 91 |
+
├── configs/ # Model configurations
|
| 92 |
+
├── examples/ # Example images and results
|
| 93 |
+
└── requirements.txt
|
| 94 |
+
```
|
| 95 |
+
|
| 96 |
+
## 🔧 Usage
|
| 97 |
+
|
| 98 |
+
### Training
|
| 99 |
+
|
| 100 |
+
```python
|
| 101 |
+
python src/model/trainer.py --config configs/retinanet_config.py
|
| 102 |
+
```
|
| 103 |
+
|
| 104 |
+
### Inference
|
| 105 |
+
|
| 106 |
+
```python
|
| 107 |
+
python src/model/visualize.py --image path/to/image.jpg
|
| 108 |
+
```
|
| 109 |
+
|
| 110 |
+
## 📝 Dataset
|
| 111 |
+
|
| 112 |
+
The model is trained on a custom dataset of Rubik's cube images, annotated with:
|
| 113 |
+
- Face detection
|
| 114 |
+
- Color tile detection
|
| 115 |
+
|
| 116 |
+
### Data Format
|
| 117 |
+
- Annotations: COCO format
|
| 118 |
+
- Image size: 640x640
|
| 119 |
+
- Classes: 7 (face, red_tile, white_tile, blue_tile, orange_tile, green_tile, yellow_tile)
|
| 120 |
+
|
| 121 |
+
## 🤝 Contributing
|
| 122 |
+
|
| 123 |
+
Contributions are welcome! Please feel free to submit a Pull Request.
|
| 124 |
+
|
| 125 |
+
## 📄 License
|
| 126 |
+
|
| 127 |
+
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
|
| 128 |
+
|
| 129 |
+
## 🙏 Acknowledgements
|
| 130 |
+
|
| 131 |
+
- TensorFlow Model Garden
|
| 132 |
+
- RetinaNet Implementation
|
| 133 |
+
- SpineNet Architecture
|
| 134 |
+
|
| 135 |
+
## 📧 Contact
|
| 136 |
+
|
| 137 |
+
- GitHub: https://github.com/itsiiromiuy
|
| 138 |
+
- Hugging Face: https://huggingface.co/itsyuimorii
|
| 139 |
+
|
| 140 |
+
## References
|
| 141 |
+
|
| 142 |
+
- [How to Train Custom Object Detection Models using RetinaNet](https://medium.com/@van.evanfebrianto/how-to-train-custom-object-detection-models-using-retinanet-aeed72f5d701)
|
| 143 |
+
- labelme2coco: https://github.com/fcakyon/labelme2coco
|
| 144 |
+
|
| 145 |
+
- **Keras RetinaNet**: https://github.com/fizyr/keras-retinanet
|
| 146 |
+
- **TensorFlow 2.x RetinaNet**: https://github.com/srihari-humbarwadi/retinanet-tensorflow2.x
|
| 147 |
+
- **SpineNet-PyTorch**: https://github.com/yan-roo/SpineNet-Pytorch
|
| 148 |
+
|
| 149 |
+
- **LabelMe to COCO Converter**: https://github.com/wkentaro/labelme
|
| 150 |
+
- **labelme-json-to-coco-json**: https://roboflow.com/convert/labelme-json-to-coco-json
|
| 151 |
+
|
| 152 |
+
|
| 153 |
+
|
app.py
ADDED
|
@@ -0,0 +1,236 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
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|
|
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|
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|
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|
|
|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import gradio as gr
|
| 2 |
+
import tensorflow as tf
|
| 3 |
+
import numpy as np
|
| 4 |
+
from PIL import Image
|
| 5 |
+
import cv2
|
| 6 |
+
import os
|
| 7 |
+
|
| 8 |
+
# Simplified category index
|
| 9 |
+
CATEGORY_INDEX = {
|
| 10 |
+
1: {'id': 1, 'name': 'face'},
|
| 11 |
+
2: {'id': 2, 'name': 'red_tile'},
|
| 12 |
+
3: {'id': 3, 'name': 'white_tile'},
|
| 13 |
+
4: {'id': 4, 'name': 'blue_tile'},
|
| 14 |
+
5: {'id': 5, 'name': 'orange_tile'},
|
| 15 |
+
6: {'id': 6, 'name': 'green_tile'},
|
| 16 |
+
7: {'id': 7, 'name': 'yellow_tile'}
|
| 17 |
+
}
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
def preprocess_image(image):
|
| 21 |
+
"""
|
| 22 |
+
Preprocess input image
|
| 23 |
+
"""
|
| 24 |
+
if image is None:
|
| 25 |
+
return None
|
| 26 |
+
|
| 27 |
+
# Convert to PIL image
|
| 28 |
+
if isinstance(image, np.ndarray):
|
| 29 |
+
image = Image.fromarray(image)
|
| 30 |
+
|
| 31 |
+
# Resize to model expected size
|
| 32 |
+
image = image.resize((640, 640))
|
| 33 |
+
|
| 34 |
+
# Convert to numpy array and normalize
|
| 35 |
+
image_array = np.array(image) / 255.0
|
| 36 |
+
|
| 37 |
+
# Add batch dimension
|
| 38 |
+
image_array = np.expand_dims(image_array, axis=0)
|
| 39 |
+
|
| 40 |
+
return image_array.astype(np.float32)
|
| 41 |
+
|
| 42 |
+
|
| 43 |
+
def load_model():
|
| 44 |
+
"""
|
| 45 |
+
Load pretrained model
|
| 46 |
+
"""
|
| 47 |
+
try:
|
| 48 |
+
# Try to load saved model
|
| 49 |
+
if os.path.exists('exported_model'):
|
| 50 |
+
model = tf.saved_model.load('exported_model')
|
| 51 |
+
return model
|
| 52 |
+
else:
|
| 53 |
+
# If no model file exists, return None
|
| 54 |
+
return None
|
| 55 |
+
except Exception as e:
|
| 56 |
+
print(f"Model loading failed: {e}")
|
| 57 |
+
return None
|
| 58 |
+
|
| 59 |
+
|
| 60 |
+
def predict_image(image):
|
| 61 |
+
"""
|
| 62 |
+
Make predictions on input image
|
| 63 |
+
"""
|
| 64 |
+
if image is None:
|
| 65 |
+
return "Please upload an image", None
|
| 66 |
+
|
| 67 |
+
try:
|
| 68 |
+
# Preprocess image
|
| 69 |
+
processed_image = preprocess_image(image)
|
| 70 |
+
|
| 71 |
+
if processed_image is None:
|
| 72 |
+
return "Image preprocessing failed", None
|
| 73 |
+
|
| 74 |
+
# Load model
|
| 75 |
+
model = load_model()
|
| 76 |
+
|
| 77 |
+
if model is None:
|
| 78 |
+
return "Model not found. This is a demo version, actual model needs to be trained first.\n\nDetected a Rubik's cube image!", image
|
| 79 |
+
|
| 80 |
+
# Make prediction
|
| 81 |
+
model_fn = model.signatures['serving_default']
|
| 82 |
+
|
| 83 |
+
# Convert input format
|
| 84 |
+
input_tensor = tf.convert_to_tensor(processed_image)
|
| 85 |
+
|
| 86 |
+
# Execute inference
|
| 87 |
+
predictions = model_fn(input_tensor)
|
| 88 |
+
|
| 89 |
+
# Parse results
|
| 90 |
+
detection_boxes = predictions['detection_boxes'][0].numpy()
|
| 91 |
+
detection_classes = predictions['detection_classes'][0].numpy().astype(
|
| 92 |
+
int)
|
| 93 |
+
detection_scores = predictions['detection_scores'][0].numpy()
|
| 94 |
+
|
| 95 |
+
# Filter low confidence detections
|
| 96 |
+
valid_detections = detection_scores > 0.5
|
| 97 |
+
valid_boxes = detection_boxes[valid_detections]
|
| 98 |
+
valid_classes = detection_classes[valid_detections]
|
| 99 |
+
valid_scores = detection_scores[valid_detections]
|
| 100 |
+
|
| 101 |
+
# Generate result description
|
| 102 |
+
if len(valid_boxes) > 0:
|
| 103 |
+
result_text = f"Detected {len(valid_boxes)} objects:\n"
|
| 104 |
+
for i, (cls, score) in enumerate(zip(valid_classes, valid_scores)):
|
| 105 |
+
class_name = CATEGORY_INDEX.get(
|
| 106 |
+
cls, {}).get('name', f'class_{cls}')
|
| 107 |
+
result_text += f"- {class_name}: {score:.2f}\n"
|
| 108 |
+
else:
|
| 109 |
+
result_text = "No Rubik's cube related objects detected"
|
| 110 |
+
|
| 111 |
+
# Draw detection boxes on image (simplified version)
|
| 112 |
+
output_image = draw_boxes_on_image(
|
| 113 |
+
image, valid_boxes, valid_classes, valid_scores)
|
| 114 |
+
|
| 115 |
+
return result_text, output_image
|
| 116 |
+
|
| 117 |
+
except Exception as e:
|
| 118 |
+
error_msg = f"Error occurred during prediction: {str(e)}\n\nThis is a demo version."
|
| 119 |
+
return error_msg, image
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
def draw_boxes_on_image(image, boxes, classes, scores):
|
| 123 |
+
"""
|
| 124 |
+
Draw detection boxes on image (simplified version)
|
| 125 |
+
"""
|
| 126 |
+
try:
|
| 127 |
+
# Convert to OpenCV format
|
| 128 |
+
if isinstance(image, Image.Image):
|
| 129 |
+
cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
|
| 130 |
+
else:
|
| 131 |
+
cv_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
|
| 132 |
+
|
| 133 |
+
h, w = cv_image.shape[:2]
|
| 134 |
+
|
| 135 |
+
# Draw detection boxes
|
| 136 |
+
for box, cls, score in zip(boxes, classes, scores):
|
| 137 |
+
if score > 0.5:
|
| 138 |
+
# Convert coordinates (assuming normalized coordinates)
|
| 139 |
+
y1, x1, y2, x2 = box
|
| 140 |
+
x1, x2 = int(x1 * w), int(x2 * w)
|
| 141 |
+
y1, y2 = int(y1 * h), int(y2 * h)
|
| 142 |
+
|
| 143 |
+
# Draw rectangle
|
| 144 |
+
cv2.rectangle(cv_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
|
| 145 |
+
|
| 146 |
+
# Add label
|
| 147 |
+
class_name = CATEGORY_INDEX.get(
|
| 148 |
+
cls, {}).get('name', f'class_{cls}')
|
| 149 |
+
label = f"{class_name}: {score:.2f}"
|
| 150 |
+
cv2.putText(cv_image, label, (x1, y1-10),
|
| 151 |
+
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
|
| 152 |
+
|
| 153 |
+
# Convert back to RGB
|
| 154 |
+
result_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
|
| 155 |
+
return Image.fromarray(result_image)
|
| 156 |
+
|
| 157 |
+
except Exception as e:
|
| 158 |
+
print(f"Error drawing detection boxes: {e}")
|
| 159 |
+
return image
|
| 160 |
+
|
| 161 |
+
# Create Gradio interface
|
| 162 |
+
|
| 163 |
+
|
| 164 |
+
def create_demo():
|
| 165 |
+
with gr.Blocks(title="Rubik's Cube Recognition System") as demo:
|
| 166 |
+
gr.Markdown("""
|
| 167 |
+
# 🎲 Rubik's Cube Recognition System
|
| 168 |
+
|
| 169 |
+
This is a deep learning-based Rubik's cube recognition system using RetinaNet architecture for object detection.
|
| 170 |
+
|
| 171 |
+
**Features:**
|
| 172 |
+
- Detect cube faces and color tiles
|
| 173 |
+
- Support 6 color recognition: Red, White, Blue, Orange, Green, Yellow
|
| 174 |
+
- Real-time detection and visualization
|
| 175 |
+
|
| 176 |
+
**How to use:**
|
| 177 |
+
1. Upload an image containing a Rubik's cube
|
| 178 |
+
2. Click the "Analyze Image" button
|
| 179 |
+
3. View detection results and visualization
|
| 180 |
+
""")
|
| 181 |
+
|
| 182 |
+
with gr.Row():
|
| 183 |
+
with gr.Column():
|
| 184 |
+
input_image = gr.Image(
|
| 185 |
+
label="Upload Rubik's Cube Image",
|
| 186 |
+
type="pil"
|
| 187 |
+
)
|
| 188 |
+
|
| 189 |
+
analyze_btn = gr.Button(
|
| 190 |
+
"🔍 Analyze Image",
|
| 191 |
+
variant="primary",
|
| 192 |
+
size="lg"
|
| 193 |
+
)
|
| 194 |
+
|
| 195 |
+
with gr.Column():
|
| 196 |
+
result_text = gr.Textbox(
|
| 197 |
+
label="Detection Results",
|
| 198 |
+
lines=10,
|
| 199 |
+
max_lines=15
|
| 200 |
+
)
|
| 201 |
+
|
| 202 |
+
output_image = gr.Image(
|
| 203 |
+
label="Detection Visualization",
|
| 204 |
+
type="pil"
|
| 205 |
+
)
|
| 206 |
+
|
| 207 |
+
# Example images section
|
| 208 |
+
gr.Markdown("### 📋 Usage Examples")
|
| 209 |
+
gr.Markdown(
|
| 210 |
+
"Upload Rubik's cube images similar to the following for testing:")
|
| 211 |
+
|
| 212 |
+
# Bind events
|
| 213 |
+
analyze_btn.click(
|
| 214 |
+
fn=predict_image,
|
| 215 |
+
inputs=[input_image],
|
| 216 |
+
outputs=[result_text, output_image]
|
| 217 |
+
)
|
| 218 |
+
|
| 219 |
+
# Auto-analyze when image is uploaded
|
| 220 |
+
input_image.change(
|
| 221 |
+
fn=predict_image,
|
| 222 |
+
inputs=[input_image],
|
| 223 |
+
outputs=[result_text, output_image]
|
| 224 |
+
)
|
| 225 |
+
|
| 226 |
+
return demo
|
| 227 |
+
|
| 228 |
+
|
| 229 |
+
# Launch application
|
| 230 |
+
if __name__ == "__main__":
|
| 231 |
+
demo = create_demo()
|
| 232 |
+
demo.launch(
|
| 233 |
+
server_name="0.0.0.0",
|
| 234 |
+
server_port=7860,
|
| 235 |
+
show_error=True
|
| 236 |
+
)
|
app_simple.py
ADDED
|
@@ -0,0 +1,172 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import gradio as gr
|
| 2 |
+
import numpy as np
|
| 3 |
+
from PIL import Image
|
| 4 |
+
import os
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
def analyze_rubiks_cube(image):
|
| 8 |
+
"""
|
| 9 |
+
Simplified function to analyze Rubik's Cube images
|
| 10 |
+
"""
|
| 11 |
+
if image is None:
|
| 12 |
+
return "Please upload an image", None
|
| 13 |
+
|
| 14 |
+
try:
|
| 15 |
+
# Basic image processing
|
| 16 |
+
if isinstance(image, np.ndarray):
|
| 17 |
+
image = Image.fromarray(image)
|
| 18 |
+
|
| 19 |
+
# Get image information
|
| 20 |
+
width, height = image.size
|
| 21 |
+
|
| 22 |
+
# Simple color analysis
|
| 23 |
+
image_array = np.array(image)
|
| 24 |
+
|
| 25 |
+
# Calculate main colors
|
| 26 |
+
colors = {
|
| 27 |
+
'red': 0, 'green': 0, 'blue': 0,
|
| 28 |
+
'yellow': 0, 'orange': 0, 'white': 0
|
| 29 |
+
}
|
| 30 |
+
|
| 31 |
+
# Simplified color detection logic
|
| 32 |
+
result_text = f"""
|
| 33 |
+
🎲 Rubik's Cube Image Analysis Results
|
| 34 |
+
|
| 35 |
+
📊 Image Information:
|
| 36 |
+
- Size: {width} x {height} pixels
|
| 37 |
+
- Format: {image.format if hasattr(image, 'format') else 'PIL Image'}
|
| 38 |
+
|
| 39 |
+
🔍 Detection Status:
|
| 40 |
+
✅ Image uploaded successfully
|
| 41 |
+
✅ Image format is correct
|
| 42 |
+
⚠️ Complete AI model is still in training
|
| 43 |
+
|
| 44 |
+
📝 Note:
|
| 45 |
+
This is a demo version of the Rubik's cube recognition system.
|
| 46 |
+
The complete RetinaNet model needs to be trained before accurate cube detection can be performed.
|
| 47 |
+
|
| 48 |
+
🚀 Feature Preview:
|
| 49 |
+
- Cube face detection
|
| 50 |
+
- Color tile recognition (Red, White, Blue, Orange, Green, Yellow)
|
| 51 |
+
- 3D position analysis
|
| 52 |
+
- Cube state evaluation
|
| 53 |
+
"""
|
| 54 |
+
|
| 55 |
+
return result_text, image
|
| 56 |
+
|
| 57 |
+
except Exception as e:
|
| 58 |
+
error_msg = f"Error processing image: {str(e)}"
|
| 59 |
+
return error_msg, image
|
| 60 |
+
|
| 61 |
+
|
| 62 |
+
def create_interface():
|
| 63 |
+
"""
|
| 64 |
+
Create Gradio interface
|
| 65 |
+
"""
|
| 66 |
+
with gr.Blocks(
|
| 67 |
+
title="🎲 Rubik's Cube Recognition System",
|
| 68 |
+
theme=gr.themes.Soft(),
|
| 69 |
+
css="""
|
| 70 |
+
.gradio-container {
|
| 71 |
+
max-width: 1200px;
|
| 72 |
+
margin: auto;
|
| 73 |
+
}
|
| 74 |
+
"""
|
| 75 |
+
) as demo:
|
| 76 |
+
|
| 77 |
+
gr.HTML("""
|
| 78 |
+
<div style="text-align: center; padding: 20px;">
|
| 79 |
+
<h1>🎲 Rubik's Cube Recognition System</h1>
|
| 80 |
+
<p style="font-size: 18px; color: #666;">
|
| 81 |
+
Intelligent Rubik's Cube Detection and Analysis Platform Based on Deep Learning
|
| 82 |
+
</p>
|
| 83 |
+
</div>
|
| 84 |
+
""")
|
| 85 |
+
|
| 86 |
+
with gr.Row():
|
| 87 |
+
with gr.Column(scale=1):
|
| 88 |
+
gr.Markdown("### 📤 Upload Image")
|
| 89 |
+
|
| 90 |
+
input_image = gr.Image(
|
| 91 |
+
label="Select Rubik's Cube Image",
|
| 92 |
+
type="pil",
|
| 93 |
+
height=400
|
| 94 |
+
)
|
| 95 |
+
|
| 96 |
+
analyze_btn = gr.Button(
|
| 97 |
+
"🔍 Start Analysis",
|
| 98 |
+
variant="primary",
|
| 99 |
+
size="lg",
|
| 100 |
+
scale=1
|
| 101 |
+
)
|
| 102 |
+
|
| 103 |
+
gr.Markdown("""
|
| 104 |
+
### 💡 Usage Tips
|
| 105 |
+
- Supports JPG, PNG formats
|
| 106 |
+
- Clear images with good lighting recommended
|
| 107 |
+
- Better results when cube takes up larger portion of image
|
| 108 |
+
""")
|
| 109 |
+
|
| 110 |
+
with gr.Column(scale=1):
|
| 111 |
+
gr.Markdown("### 📊 Analysis Results")
|
| 112 |
+
|
| 113 |
+
result_output = gr.Textbox(
|
| 114 |
+
label="Detection Report",
|
| 115 |
+
lines=15,
|
| 116 |
+
max_lines=20,
|
| 117 |
+
show_copy_button=True
|
| 118 |
+
)
|
| 119 |
+
|
| 120 |
+
output_image = gr.Image(
|
| 121 |
+
label="Processed Image",
|
| 122 |
+
type="pil",
|
| 123 |
+
height=400
|
| 124 |
+
)
|
| 125 |
+
|
| 126 |
+
gr.HTML("""
|
| 127 |
+
<div style="text-align: center; padding: 20px; border-top: 1px solid #eee; margin-top: 20px;">
|
| 128 |
+
<h3>🔬 Technical Features</h3>
|
| 129 |
+
<div style="display: flex; justify-content: space-around; flex-wrap: wrap;">
|
| 130 |
+
<div style="margin: 10px;">
|
| 131 |
+
<strong>🧠 AI Model</strong><br>
|
| 132 |
+
RetinaNet + SpineNet-49
|
| 133 |
+
</div>
|
| 134 |
+
<div style="margin: 10px;">
|
| 135 |
+
<strong>🎯 Detection Accuracy</strong><br>
|
| 136 |
+
7-class Object Detection
|
| 137 |
+
</div>
|
| 138 |
+
<div style="margin: 10px;">
|
| 139 |
+
<strong>⚡ Processing Speed</strong><br>
|
| 140 |
+
Real-time Inference
|
| 141 |
+
</div>
|
| 142 |
+
<div style="margin: 10px;">
|
| 143 |
+
<strong>🌐 Deployment Platform</strong><br>
|
| 144 |
+
Hugging Face Spaces
|
| 145 |
+
</div>
|
| 146 |
+
</div>
|
| 147 |
+
</div>
|
| 148 |
+
""")
|
| 149 |
+
|
| 150 |
+
# Bind events
|
| 151 |
+
analyze_btn.click(
|
| 152 |
+
fn=analyze_rubiks_cube,
|
| 153 |
+
inputs=[input_image],
|
| 154 |
+
outputs=[result_output, output_image]
|
| 155 |
+
)
|
| 156 |
+
|
| 157 |
+
input_image.change(
|
| 158 |
+
fn=analyze_rubiks_cube,
|
| 159 |
+
inputs=[input_image],
|
| 160 |
+
outputs=[result_output, output_image]
|
| 161 |
+
)
|
| 162 |
+
|
| 163 |
+
return demo
|
| 164 |
+
|
| 165 |
+
|
| 166 |
+
if __name__ == "__main__":
|
| 167 |
+
demo = create_interface()
|
| 168 |
+
demo.launch(
|
| 169 |
+
server_name="0.0.0.0",
|
| 170 |
+
server_port=7860,
|
| 171 |
+
show_error=True
|
| 172 |
+
)
|
configs/retinanet_config.py
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
| 1 |
+
"""
|
| 2 |
+
RetinaNet configuration for Rubik's Cube detection.
|
| 3 |
+
"""
|
| 4 |
+
|
| 5 |
+
from official.vision.configs import retinanet
|
| 6 |
+
from official.vision.configs.common import backbone
|
| 7 |
+
|
| 8 |
+
HEIGHT = 640
|
| 9 |
+
WIDTH = 640
|
| 10 |
+
BATCH_SIZE = 8
|
| 11 |
+
NUM_CLASSES = 7 # 6 colors + face
|
| 12 |
+
|
| 13 |
+
def get_config():
|
| 14 |
+
"""Get the default configuration for RetinaNet training."""
|
| 15 |
+
config = retinanet.retinanet_spinenet_mobile_coco()
|
| 16 |
+
|
| 17 |
+
# Model config
|
| 18 |
+
config.model.input_size = [HEIGHT, WIDTH, 3]
|
| 19 |
+
config.model.num_classes = NUM_CLASSES + 1 # Add background class
|
| 20 |
+
config.model.backbone.spinenet_mobile.model_id = '49'
|
| 21 |
+
config.model.detection_generator.tflite_post_processing.max_classes_per_detection = NUM_CLASSES + 1
|
| 22 |
+
|
| 23 |
+
# Training data config
|
| 24 |
+
config.train_data.input_path = 'tfrecords/train-*.tfrecord'
|
| 25 |
+
config.train_data.dtype = 'float32'
|
| 26 |
+
config.train_data.global_batch_size = BATCH_SIZE
|
| 27 |
+
config.train_data.parser.aug_scale_max = 1.0
|
| 28 |
+
config.train_data.parser.aug_scale_min = 1.0
|
| 29 |
+
|
| 30 |
+
# Validation data config
|
| 31 |
+
config.validation_data.input_path = 'tfrecords/valid-*.tfrecord'
|
| 32 |
+
config.validation_data.dtype = 'float32'
|
| 33 |
+
config.validation_data.global_batch_size = BATCH_SIZE
|
| 34 |
+
|
| 35 |
+
# Training config
|
| 36 |
+
config.trainer.train_steps = 10000
|
| 37 |
+
config.trainer.validation_steps = 100
|
| 38 |
+
config.trainer.steps_per_loop = 100
|
| 39 |
+
config.trainer.summary_interval = 100
|
| 40 |
+
config.trainer.checkpoint_interval = 100
|
| 41 |
+
config.trainer.validation_interval = 100
|
| 42 |
+
|
| 43 |
+
# Optimizer config
|
| 44 |
+
config.trainer.optimizer_config.learning_rate.type = 'cosine'
|
| 45 |
+
config.trainer.optimizer_config.learning_rate.cosine.initial_learning_rate = 0.1
|
| 46 |
+
config.trainer.optimizer_config.learning_rate.cosine.decay_steps = 10000
|
| 47 |
+
config.trainer.optimizer_config.warmup.linear.warmup_learning_rate = 0.05
|
| 48 |
+
config.trainer.optimizer_config.warmup.linear.warmup_steps = 100
|
| 49 |
+
|
| 50 |
+
return config
|
deploy_to_hf.py
ADDED
|
@@ -0,0 +1,76 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
"""
|
| 3 |
+
Deploy Rubik's Cube Recognition to Hugging Face Spaces
|
| 4 |
+
"""
|
| 5 |
+
|
| 6 |
+
import os
|
| 7 |
+
from huggingface_hub import HfApi, Repository
|
| 8 |
+
|
| 9 |
+
|
| 10 |
+
def deploy_to_huggingface():
|
| 11 |
+
"""Deploy the project to Hugging Face Spaces"""
|
| 12 |
+
|
| 13 |
+
# Set your token
|
| 14 |
+
token = "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"
|
| 15 |
+
|
| 16 |
+
# Initialize HF API
|
| 17 |
+
api = HfApi()
|
| 18 |
+
|
| 19 |
+
# Repository details
|
| 20 |
+
repo_id = "itsyuimorii/rubiks-cube-recognition"
|
| 21 |
+
repo_type = "space"
|
| 22 |
+
|
| 23 |
+
print(f"Deploying to: https://huggingface.co/spaces/{repo_id}")
|
| 24 |
+
|
| 25 |
+
try:
|
| 26 |
+
# Create repository if it doesn't exist
|
| 27 |
+
api.create_repo(
|
| 28 |
+
repo_id=repo_id,
|
| 29 |
+
token=token,
|
| 30 |
+
repo_type=repo_type,
|
| 31 |
+
exist_ok=True
|
| 32 |
+
)
|
| 33 |
+
print("✅ Repository created/verified")
|
| 34 |
+
|
| 35 |
+
# Upload files
|
| 36 |
+
files_to_upload = [
|
| 37 |
+
"README.md",
|
| 38 |
+
"app.py",
|
| 39 |
+
"requirements.txt",
|
| 40 |
+
"app_simple.py"
|
| 41 |
+
]
|
| 42 |
+
|
| 43 |
+
for file_path in files_to_upload:
|
| 44 |
+
if os.path.exists(file_path):
|
| 45 |
+
print(f"📤 Uploading {file_path}...")
|
| 46 |
+
api.upload_file(
|
| 47 |
+
path_or_fileobj=file_path,
|
| 48 |
+
path_in_repo=file_path,
|
| 49 |
+
repo_id=repo_id,
|
| 50 |
+
repo_type=repo_type,
|
| 51 |
+
token=token
|
| 52 |
+
)
|
| 53 |
+
print(f"✅ {file_path} uploaded successfully")
|
| 54 |
+
else:
|
| 55 |
+
print(f"⚠️ {file_path} not found, skipping...")
|
| 56 |
+
|
| 57 |
+
print("\n🎉 Deployment completed successfully!")
|
| 58 |
+
print(f"🌐 Visit your Space: https://huggingface.co/spaces/{repo_id}")
|
| 59 |
+
|
| 60 |
+
except Exception as e:
|
| 61 |
+
print(f"❌ Error during deployment: {e}")
|
| 62 |
+
return False
|
| 63 |
+
|
| 64 |
+
return True
|
| 65 |
+
|
| 66 |
+
|
| 67 |
+
if __name__ == "__main__":
|
| 68 |
+
# Install required package if not already installed
|
| 69 |
+
try:
|
| 70 |
+
import huggingface_hub
|
| 71 |
+
except ImportError:
|
| 72 |
+
print("Installing huggingface_hub...")
|
| 73 |
+
os.system("pip install huggingface_hub")
|
| 74 |
+
import huggingface_hub
|
| 75 |
+
|
| 76 |
+
deploy_to_huggingface()
|
images/issues/Cube_00000.json
ADDED
|
@@ -0,0 +1,738 @@
|
|
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images/issues/Cube_00017.jpg
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images/issues/Cube_00017.json
ADDED
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@@ -0,0 +1,190 @@
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images/test/Cube_00028.jpg
ADDED
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Git LFS Details
|
images/test/Cube_00028.json
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|
@@ -0,0 +1,190 @@
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|
images/test/Cube_00029.jpg
ADDED
|
Git LFS Details
|
images/test/Cube_00029.json
ADDED
|
@@ -0,0 +1,190 @@
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images/test/_annotations.coco.json
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|
images/train/Cube_00001.jpg
ADDED
|
Git LFS Details
|
images/train/Cube_00001.json
ADDED
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@@ -0,0 +1,190 @@
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|
images/train/Cube_00002.jpg
ADDED
|
Git LFS Details
|
images/train/Cube_00002.json
ADDED
|
@@ -0,0 +1,190 @@
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images/train/Cube_00003.jpg
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Git LFS Details
|
images/train/Cube_00003.json
ADDED
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@@ -0,0 +1,190 @@
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images/train/Cube_00004.jpg
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Git LFS Details
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images/train/Cube_00004.json
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|
images/train/Cube_00007.jpg
ADDED
|
Git LFS Details
|
images/train/Cube_00007.json
ADDED
|
@@ -0,0 +1,190 @@
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|
images/train/Cube_00010.jpg
ADDED
|
Git LFS Details
|
images/train/Cube_00010.json
ADDED
|
@@ -0,0 +1,190 @@
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|
images/train/Cube_00011.jpg
ADDED
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Git LFS Details
|
images/train/Cube_00011.json
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@@ -0,0 +1,190 @@
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|
images/train/Cube_00012.jpg
ADDED
|
Git LFS Details
|
images/train/Cube_00012.json
ADDED
|
@@ -0,0 +1,190 @@
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images/train/Cube_00013.jpg
ADDED
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Git LFS Details
|
images/train/Cube_00013.json
ADDED
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@@ -0,0 +1,190 @@
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|
images/train/Cube_00014.jpg
ADDED
|
Git LFS Details
|
images/train/Cube_00014.json
ADDED
|
@@ -0,0 +1,190 @@
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|
images/train/Cube_00015.jpg
ADDED
|
Git LFS Details
|
images/train/Cube_00015.json
ADDED
|
@@ -0,0 +1,190 @@
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images/train/Cube_00016.jpg
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Git LFS Details
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images/train/Cube_00016.json
ADDED
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@@ -0,0 +1,190 @@
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