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ultralytics 8.3.12 SAM and SAM2 multi-point prompts (#16643)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Mohammed Yasin 2024-10-13 23:20:40 +08:00 • committed by GitHub
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24
docs/en/models/mobile-sam.md
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@ -90,8 +90,17 @@ You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blo
# Load the model
model = SAM("mobile_sam.pt")
# Predict a segment based on a point prompt
# Predict a segment based on a single point prompt
model.predict("ultralytics/assets/zidane.jpg", points=[900, 370], labels=[1])
# Predict multiple segments based on multiple points prompt
model.predict("ultralytics/assets/zidane.jpg", points=[[400, 370], [900, 370]], labels=[1, 1])
# Predict a segment based on multiple points prompt per object
model.predict("ultralytics/assets/zidane.jpg", points=[[[400, 370], [900, 370]]], labels=[[1, 1]])
# Predict a segment using both positive and negative prompts.
model.predict("ultralytics/assets/zidane.jpg", points=[[[400, 370], [900, 370]]], labels=[[1, 0]])
```
### Box Prompt
@ -106,8 +115,17 @@ You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blo
# Load the model
model = SAM("mobile_sam.pt")
# Predict a segment based on a box prompt
model.predict("ultralytics/assets/zidane.jpg", bboxes=[439, 437, 524, 709])
# Predict a segment based on a single point prompt
model.predict("ultralytics/assets/zidane.jpg", points=[900, 370], labels=[1])
# Predict mutiple segments based on multiple points prompt
model.predict("ultralytics/assets/zidane.jpg", points=[[400, 370], [900, 370]], labels=[1, 1])
# Predict a segment based on multiple points prompt per object
model.predict("ultralytics/assets/zidane.jpg", points=[[[400, 370], [900, 370]]], labels=[[1, 1]])
# Predict a segment using both positive and negative prompts.
model.predict("ultralytics/assets/zidane.jpg", points=[[[400, 370], [900, 370]]], labels=[[1, 0]])
```
We have implemented `MobileSAM` and `SAM` using the same API. For more usage information, please see the [SAM page](sam.md).

30
docs/en/models/sam.md
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@ -58,8 +58,17 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
# Run inference with bboxes prompt
results = model("ultralytics/assets/zidane.jpg", bboxes=[439, 437, 524, 709])
# Run inference with points prompt
results = model("ultralytics/assets/zidane.jpg", points=[900, 370], labels=[1])
# Run inference with single point
results = predictor(points=[900, 370], labels=[1])
# Run inference with multiple points
results = predictor(points=[[400, 370], [900, 370]], labels=[1, 1])
# Run inference with multiple points prompt per object
results = predictor(points=[[[400, 370], [900, 370]]], labels=[[1, 1]])
# Run inference with negative points prompt
results = predictor(points=[[[400, 370], [900, 370]]], labels=[[1, 0]])
```
!!! example "Segment everything"
@ -107,8 +116,16 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
predictor.set_image("ultralytics/assets/zidane.jpg") # set with image file
predictor.set_image(cv2.imread("ultralytics/assets/zidane.jpg")) # set with np.ndarray
results = predictor(bboxes=[439, 437, 524, 709])
# Run inference with single point prompt
results = predictor(points=[900, 370], labels=[1])
# Run inference with multiple points prompt
results = predictor(points=[[400, 370], [900, 370]], labels=[[1, 1]])
# Run inference with negative points prompt
results = predictor(points=[[[400, 370], [900, 370]]], labels=[[1, 0]])
# Reset image
predictor.reset_image()
```
@ -245,6 +262,15 @@ model("ultralytics/assets/zidane.jpg", bboxes=[439, 437, 524, 709])
# Segment with points prompt
model("ultralytics/assets/zidane.jpg", points=[900, 370], labels=[1])
# Segment with multiple points prompt
model("ultralytics/assets/zidane.jpg", points=[[400, 370], [900, 370]], labels=[[1, 1]])
# Segment with multiple points prompt per object
model("ultralytics/assets/zidane.jpg", points=[[[400, 370], [900, 370]]], labels=[[1, 1]])
# Segment with negative points prompt.
model("ultralytics/assets/zidane.jpg", points=[[[400, 370], [900, 370]]], labels=[[1, 0]])
```
Alternatively, you can run inference with SAM in the command line interface (CLI):