Add multi-points input, foreground/background points input and box input to EfficientSAM model

README.md

@@ -73,6 +73,10 @@ Some examples are listed below. You can find more in the directory of each model
 
 ![messi](./models/human_segmentation_pphumanseg/example_outputs/messi.jpg)
 
+### Image Segmentation with [EfficientSAM](./models/image_segmentation_efficientsam/)
+
+![sam_present](./models/image_segmentation_efficientsam/example_outputs/sam_present.gif)
+
 ### License Plate Detection with [LPD_YuNet](./models/license_plate_detection_yunet/)
 
 ![license plate detection](./models/license_plate_detection_yunet/example_outputs/lpd_yunet_demo.gif)

models/__init__.py

@@ -20,6 +20,7 @@
 from .facial_expression_recognition.facial_fer_model import FacialExpressionRecog
 from .object_tracking_vittrack.vittrack import VitTrack
 from .text_detection_ppocr.ppocr_det import PPOCRDet
+from .image_segmentation_efficientsam.efficientSAM import EfficientSAM
 
 class ModuleRegistery:
     def __init__(self, name):
@@ -94,3 +95,4 @@ def register(self, item):
 MODELS.register(FacialExpressionRecog)
 MODELS.register(VitTrack)
 MODELS.register(PPOCRDet)
+MODELS.register(EfficientSAM)

models/image_segmentation_efficientsam/LICENSE

@@ -0,0 +1,201 @@
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models/image_segmentation_efficientsam/README.md

@@ -0,0 +1,49 @@
+# image_segmentation_efficientsam
+
+EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
+
+Notes:
+- The current implementation of the EfficientSAM demo uses the EfficientSAM-Ti model, which is specifically tailored for scenarios requiring higher speed and lightweight. 
+- image_segmentation_efficientsam_ti_2024may.onnx(supports single point infering)
+  - MD5 value: 117d6a6cac60039a20b399cc133c2a60
+  - SHA-256 value: e3957d2cd1422855f350aa7b044f47f5b3eafada64b5904ed330b696229e2943
+- image_segmentation_efficientsam_ti_2025april.onnx
+  - MD5 value: f23cecbb344547c960c933ff454536a3
+  - SHA-256 value: 4eb496e0a7259d435b49b66faf1754aa45a5c382a34558ddda9a8c6fe5915d77
+
+
+## Demo
+
+### Python
+Run the following command to try the demo:
+
+```shell
+python demo.py --input /path/to/image
+```
+
+**Click** to select foreground points, **drag** to use box to select and **long press** to select background points on the object you wish to segment in the displayed image. After clicking the **Enter**, the segmentation result will be shown in a new window. Clicking the **Backspace** to clear all the prompts.
+
+## Result
+
+Here are some of the sample results that were observed using the model:
+
+![test1_res.jpg](./example_outputs/example1.png)
+![test2_res.jpg](./example_outputs/example2.png)
+
+Video inference result:
+
+![sam_present.gif](./example_outputs/sam_present.gif)
+
+## Model metrics:
+
+## License
+
+All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
+
+#### Contributor Details
+
+## Reference
+
+- https://arxiv.org/abs/2312.00863
+- https://github.com/yformer/EfficientSAM
+- https://github.com/facebookresearch/segment-anything