Add mini web demo

demo/README.md

@@ -0,0 +1,95 @@
+## Segment Anything Simple Web demo
+
+This **front-end only** demo shows how to load a fixed image and `.npy` file of the SAM image embedding, and run the SAM ONNX model in the browser using Web Assembly with mulithreading enabled by `SharedArrayBuffer`, Web Worker, and SIMD128.
+
+<img src="https://github.com/facebookresearch/segment-anything/raw/main/assets/minidemo.gif" width="500"/>
+
+## Run the app
+
+```
+yarn && yarn start
+```
+
+Navigate to [`http://localhost:8081/`](http://localhost:8081/)
+
+Move your cursor around to see the mask prediction update in real time.
+
+## Change the image, embedding and ONNX model
+
+In the [ONNX Model Example notebook](https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb) upload the image of your choice and generate and save corresponding embedding.
+
+Initialize the predictor
+
+```python
+checkpoint = "sam_vit_h_4b8939.pth"
+model_type = "vit_h"
+sam = sam_model_registry[model_type](checkpoint=checkpoint)
+sam.to(device='cuda')
+predictor = SamPredictor(sam)
+```
+
+Set the new image and export the embedding
+
+```
+image = cv2.imread('src/assets/dogs.jpg')
+predictor.set_image(image)
+image_embedding = predictor.get_image_embedding().cpu().numpy()
+np.save("dogs_embedding.npy", image_embedding)
+```
+
+Save the new image and embedding in `/assets/data`and update the following paths to the files at the top of`App.tsx`:
+
+```py
+const IMAGE_PATH = "/assets/data/dogs.jpg";
+const IMAGE_EMBEDDING = "/assets/data/dogs_embedding.npy";
+const MODEL_DIR = "/model/sam_onnx_quantized_example.onnx";
+```
+
+Optionally you can also export the ONNX model. Currently the example ONNX model from the notebook is saved at `/model/sam_onnx_quantized_example.onnx`.
+
+**NOTE: if you change the ONNX model by using a new checkpoint you need to also re-export the embedding.**
+
+## ONNX multithreading with SharedArrayBuffer
+
+To use multithreading, the appropriate headers need to be set to create a cross origin isolation state which will enable use of `SharedArrayBuffer` (see this [blog post](https://cloudblogs.microsoft.com/opensource/2021/09/02/onnx-runtime-web-running-your-machine-learning-model-in-browser/) for more details)
+
+The headers below are set in `configs/webpack/dev.js`:
+
+```js
+headers: {
+    "Cross-Origin-Opener-Policy": "same-origin",
+    "Cross-Origin-Embedder-Policy": "credentialless",
+}
+```
+
+## Structure of the app
+
+**`App.tsx`**
+
+- Initializes ONNX model
+- Loads image embedding and image
+- Runs the ONNX model based on input prompts
+
+**`Stage.tsx`**
+
+- Handles mouse move interaction to update the ONNX model prompt
+
+**`Tool.tsx`**
+
+- Renders the image and the mask prediction
+
+**`helpers/maskUtils.tsx`**
+
+- Conversion of ONNX model output from array to an HTMLImageElement
+
+**`helpers/onnxModelAPI.tsx`**
+
+- Formats the inputs for the ONNX model
+
+**`helpers/scaleHelper.tsx`**
+
+- Handles image scaling logic for SAM (longest size 1024)
+
+**`hooks/`**
+
+- Handle shared state for the app