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Migrating the EfficientSAM model to the OpenCV model zoo (#258)
* a * add efficientsam model and basic demo * update license * remove example images * update readme * update readme * update demo * update demo * update readme * update SAM and __init__ * update demo and sam * update label * add present gif * update readme * add efficientSAM gif to readme of opencvzoo * cv version 4.10.0， remove camera branch
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‎README.md

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![messi](./models/human_segmentation_pphumanseg/example_outputs/messi.jpg)
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### Image Segmentation with [EfficientSAM](./models/image_segmentation_efficientsam/)
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![sam_present](./models/image_segmentation_efficientsam/example_outputs/sam_present.gif)
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### License Plate Detection with [LPD_YuNet](./models/license_plate_detection_yunet/)
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![license plate detection](./models/license_plate_detection_yunet/example_outputs/lpd_yunet_demo.gif)

‎models/__init__.py

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from .facial_expression_recognition.facial_fer_model import FacialExpressionRecog
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from .object_tracking_vittrack.vittrack import VitTrack
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from .text_detection_ppocr.ppocr_det import PPOCRDet
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from .image_segmentation_efficientsam.efficientSAM import EfficientSAM
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class ModuleRegistery:
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def __init__(self, name):
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MODELS.register(FacialExpressionRecog)
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MODELS.register(VitTrack)
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MODELS.register(PPOCRDet)
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MODELS.register(EfficientSAM)

‎models/image_segmentation_efficientsam/LICENSE

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‎models/image_segmentation_efficientsam/README.md

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# image_segmentation_efficientsam
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EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
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Notes:
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- The current implementation of the EfficientSAM demo uses the EfficientSAM-Ti model, which is specifically tailored for scenarios requiring higher speed and lightweight.
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- MD5 value of "efficient_sam_vitt.pt" is 7A804DA508F30EFC59EC06711C8DCD62
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- SHA-256 value of "efficient_sam_vitt.pt" is DFF858B19600A46461CBB7DE98F796B23A7A888D9F5E34C0B033F7D6EB9E4E6A
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## Demo
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### Python
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Run the following command to try the demo:
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```shell
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python demo.py --input /path/to/image
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```
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Click only **once** on the object you wish to segment in the displayed image. After the click, the segmentation result will be shown in a new window.
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## Result
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Here are some of the sample results that were observed using the model:
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![test1_res.jpg](./example_outputs/example1.png)
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![test2_res.jpg](./example_outputs/example2.png)
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Video inference result:
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![sam_present.gif](./example_outputs/sam_present.gif)
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## Model metrics:
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## License
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All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
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#### Contributor Details
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## Reference
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- https://arxiv.org/abs/2312.00863
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- https://github.com/yformer/EfficientSAM

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