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Python scripts for the Segment Anythin 2 (SAM2) model in ONNX
| Date | Stars |
|---|---|
| 2026-07-24 | 288 |
| 2026-07-25 | 288 |
| 2026-07-28 | 288 |
| 2026-07-30 | 288 |
| 2026-08-06 | 288 |
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# ONNX-SAM2-Segment-Anything  # Important - Still in development, use it at your own risk. - For now only the image prediction is available, the video prediction will be available soon. - Other limitations: Only default resolution (1024x1024) # Installation ```shell git clone https://github.com/ibaiGorordo/ONNX-SAM2-Segment-Anything.git cd ONNX-SAM2-Segment-Anything pip install -r requirements.txt ``` ### ONNX Runtime For Nvidia GPU computers: `pip install onnxruntime-gpu` Otherwise: `pip install onnxruntime` # ONNX model - Use this Google Colab notebook to convert the encoder and decoder models: [](https://colab.research.google.com/drive/1tqdYbjmFq4PK3Di7sLONd0RkKS0hBgId?usp=sharing) - Place the models in the `models` folder # Original Semgent Anything Model 2 (SAM2) The original SAM2 model can be found in this repository: [SAM2 Repository](https://github.com/facebookresearch/segment-anything-2) - The License of the models is Apache 2.0: [License](https://github.com/facebookresearch/segment-anything-2/blob/main/LICENSE) # Examples  ## **Image inference**: Runs the image segmentation model on an image given some points defined in the script. ```shell python image_segmentation.py ``` Usage: - `point_coords`: This is a list of 2D numpy arrays, where each element in the list correspond to a different label. For example, for 3 different labels, the list will contain 3 numpy arrays. Each numpy array contains Nx2 points, where N is the number of points and the second axis contains the X,Y coordinates (of the original image) - `point_labels`: This is a list of 1D numpy arrays, where each element in the list correspond to a different label. For example, for 3 different labels, the list will contain 3 numpy arrays. Each numpy array contains N points, where N is the number of points. The value can be 0 or 1, where 0 represents a negative value and 1 a positive value, i.e. the objects is present at that pixel location.  ## **SAM2 Annotation App**: A minimal GUI to annotate images with the SAM2 model. ```shell python webcam_depth_estimation.py ``` Annotation Controls (Video: https://youtu.be/9lW3_g1fjnA?si=X49Vz1ow45NMMYVn) - **Left click**: Adds a positive point, but if another point is close enough, it will delete it - **Right click**: Adds a negative point - **Left click and drag**: Draws a rectangle - **Add label button**: Adds a new label for annotation - **Delete label button**: Deletes the last label # References: * SAM2 Repository: [https://github.com/facebookresearch/segment-anything-2](https://github.com/facebookresearch/segment-anything-2)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8370fae4042e02bc, topic:onnx, topic:onnxruntime
matched fp:8370fae4042e02bc, topic:computer-vision, readme:image segmentation, readme:depth estimation