Metadata-Version: 2.1
Name: tf-siammask
Version: 1.0.1
Summary: SiamMask implementation by Tensorflow 2
Home-page: https://github.com/Licht-T/tf-siammask
License: MIT
Description: # tf-siammask
        [SiamMask](https://github.com/foolwood/SiamMask) implementation with Tensorflow 2.
        
        ## Example
        ```python
        import numpy as np
        import PIL.Image
        import siammask
        
        sm = siammask.SiamMask()
        
        # Weight files are automatically retrieved from GitHub Releases
        sm.load_weights()
        
        # Adjust this parameter for the better mask prediction
        sm.box_offset_ratio = 1.5
        
        img_prev = np.array(PIL.Image.open('data/cat1.jpg'))[..., ::-1]
        box_prev = np.array([[227, 184], [381, 274]])
        img_next = np.array(PIL.Image.open('data/cat2.jpg'))[..., ::-1]
        
        # Predicted box and mask images is created if `debug=True`
        box, mask = sm.predict(img_prev, box_prev, img_next, debug=True)
        ```
        
        ### Test data
        
        | |  Previous frame  |  Next frame |
        | ---- | ---- | ---- |
        | File name | `./data/cat1_with_box.jpg` | `./data/cat2.jpg` |
        | Image |  ![cat](https://raw.githubusercontent.com/Licht-T/tf-siammask/master/data/cat1_with_box.jpg)  |  ![cat](https://raw.githubusercontent.com/Licht-T/tf-siammask/master/data/cat2.jpg)  |
        
        ### Predicted mask for `./data/cat2.jpg`
        ![mask](https://raw.githubusercontent.com/Licht-T/tf-siammask/master/data/predicted_mask.png)
        
        ## TODO
        * [x] Bounding-box regression
        * [x] Mask refinement network
        * [x] Pre-trained model for Tensorflow 2.0
        * [ ] Training code
        * [ ] Object tracking code
        
        ## Reference
        ```
        @inproceedings{wang2019fast,
            title={Fast online object tracking and segmentation: A unifying approach},
            author={Wang, Qiang and Zhang, Li and Bertinetto, Luca and Hu, Weiming and Torr, Philip HS},
            booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
            year={2019}
        }
        ```
Keywords: siammask,tensorflow
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
