| ---
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| license: mit
|
| ---
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|
|
| # EdgeSAM - Efficient Segment Anything Model
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|
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| EdgeSAM is an accelerated variant of the Segment Anything Model (SAM) optimized for edge devices using ONNX Runtime.
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|
|
| ## Model Files
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|
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| - `edge_sam_3x_encoder.onnx` - Image encoder (1024x1024 input)
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| - `edge_sam_3x_decoder.onnx` - Mask decoder with prompt support
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|
|
| ## Usage
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|
|
| ### API Request Format
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|
|
| ```python
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| import requests
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| import base64
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|
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| # Encode your image
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| with open("image.jpg", "rb") as f:
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| image_b64 = base64.b64encode(f.read()).decode()
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|
|
| # Make request
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| response = requests.post(
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| "https://YOUR-ENDPOINT-URL",
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| json={
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| "inputs": image_b64,
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| "parameters": {
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| "point_coords": [[512, 512]], # Click point in 1024x1024 space
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| "point_labels": [1], # 1 = foreground, 0 = background
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| "return_mask_image": True
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| }
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| }
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| )
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|
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| result = response.json()
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| ```
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|
|
| ### Response Format
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|
|
| ```json
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| [
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| {
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| "mask_shape": [1024, 1024],
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| "has_object": true,
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| "mask": "<base64_encoded_png>"
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| }
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| ]
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| ```
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|
|
| ### Parameters
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|
|
| - **point_coords**: Array of `[x, y]` coordinates in 1024x1024 space (optional)
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| - **point_labels**: Array of labels (1=foreground, 0=background) corresponding to points (optional)
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| - **box_coords**: Bounding box `[x1, y1, x2, y2]` (optional, not yet implemented)
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| - **return_mask_image**: Return base64-encoded PNG mask (default: `true`)
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|
|
| ### Coordinate System
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|
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| All coordinates should be in **1024x1024** space, regardless of original image size. The handler automatically resizes input images to 1024x1024 before processing.
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|
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| Example: For a click at the center of any image, use `[512, 512]`.
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|
|
| ## Local Testing
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|
|
| ```bash
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| # Install dependencies
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| pip install -r requirements.txt
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|
|
| # Run test script
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| python test_handler.py
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| ```
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|
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| This will create:
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| - `test_input.png` - Test image with red circle
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| - `test_output_mask.png` - Generated segmentation mask
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| - `test_output_overlay.png` - Overlay visualization
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|
|
| ## Technical Details
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|
|
| - **Input**: RGB images (auto-resized to 1024x1024)
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| - **Preprocessing**: Normalized to [0, 1] range (`/ 255.0`)
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| - **Hardware**: Supports CUDA GPU with automatic CPU fallback
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| - **Framework**: ONNX Runtime Web compatible
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| |
