Add mini web demo

.gitignore

@@ -35,3 +35,8 @@ model_ts*.txt
 .idea
 .vscode
 _darcs
+
+# demo 
+**/node_modules
+yarn.lock
+package-lock.json

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

demo/configs/webpack/common.js

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+const { resolve } = require("path");
+const HtmlWebpackPlugin = require("html-webpack-plugin");
+const FriendlyErrorsWebpackPlugin = require("friendly-errors-webpack-plugin");
+const CopyPlugin = require("copy-webpack-plugin");
+const webpack = require("webpack");
+
+module.exports = {
+  entry: "./src/index.tsx",
+  resolve: {
+    extensions: [".js", ".jsx", ".ts", ".tsx"],
+  },
+  output: {
+    path: resolve(__dirname, "dist"),
+  },
+  module: {
+    rules: [
+      {
+        test: /\.mjs$/,
+        include: /node_modules/,
+        type: "javascript/auto",
+        resolve: {
+          fullySpecified: false,
+        },
+      },
+      {
+        test: [/\.jsx?$/, /\.tsx?$/],
+        use: ["ts-loader"],
+        exclude: /node_modules/,
+      },
+      {
+        test: /\.css$/,
+        use: ["style-loader", "css-loader"],
+      },
+      {
+        test: /\.(scss|sass)$/,
+        use: ["style-loader", "css-loader", "postcss-loader"],
+      },
+      {
+        test: /\.(jpe?g|png|gif|svg)$/i,
+        use: [
+          "file-loader?hash=sha512&digest=hex&name=img/[contenthash].[ext]",
+          "image-webpack-loader?bypassOnDebug&optipng.optimizationLevel=7&gifsicle.interlaced=false",
+        ],
+      },
+      {
+        test: /\.(woff|woff2|ttf)$/,
+        use: {
+          loader: "url-loader",
+        },
+      },
+    ],
+  },
+  plugins: [
+    new CopyPlugin({
+      patterns: [
+        {
+          from: "node_modules/onnxruntime-web/dist/*.wasm",
+          to: "[name][ext]",
+        },
+        {
+          from: "model",
+          to: "model",
+        },
+        {
+          from: "src/assets",
+          to: "assets",
+        },
+      ],
+    }),
+    new HtmlWebpackPlugin({
+      template: "./src/assets/index.html",
+    }),
+    new FriendlyErrorsWebpackPlugin(),
+    new webpack.ProvidePlugin({
+      process: "process/browser",
+    }),
+  ],
+};

demo/configs/webpack/dev.js

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+// development config
+const { merge } = require("webpack-merge");
+const commonConfig = require("./common");
+
+module.exports = merge(commonConfig, {
+  mode: "development",
+  devServer: {
+    hot: true, // enable HMR on the server
+    open: true,
+    // These headers enable the cross origin isolation state
+    // needed to enable use of SharedArrayBuffer for ONNX 
+    // multithreading. 
+    headers: {
+      "Cross-Origin-Opener-Policy": "same-origin",
+      "Cross-Origin-Embedder-Policy": "credentialless",
+    },
+  },
+  devtool: "cheap-module-source-map",
+});

demo/configs/webpack/prod.js

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+// production config
+const { merge } = require("webpack-merge");
+const { resolve } = require("path");
+const Dotenv = require("dotenv-webpack");
+const commonConfig = require("./common");
+
+module.exports = merge(commonConfig, {
+  mode: "production",
+  output: {
+    filename: "js/bundle.[contenthash].min.js",
+    path: resolve(__dirname, "../../dist"),
+    publicPath: "/",
+  },
+  devtool: "source-map",
+  plugins: [new Dotenv()],
+});

demo/package.json

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+{
+  "name": "se-demo",
+  "version": "0.1.0",
+  "license": "MIT",
+  "scripts": {
+    "build": "yarn run clean-dist && webpack --config=configs/webpack/prod.js && mv dist/*.wasm dist/js && cp -R dataset dist",
+    "clean-dist": "rimraf dist/*",
+    "lint": "eslint './src/**/*.{js,ts,tsx}' --quiet",
+    "start": "yarn run start-dev",
+    "test": "yarn run start-model-test",
+    "start-dev": "webpack serve --config=configs/webpack/dev.js"
+  },
+  "devDependencies": {
+    "@babel/core": "^7.18.13",
+    "@babel/preset-env": "^7.18.10",
+    "@babel/preset-react": "^7.18.6",
+    "@babel/preset-typescript": "^7.18.6",
+    "@pmmmwh/react-refresh-webpack-plugin": "^0.5.7",
+    "@testing-library/react": "^13.3.0",
+    "@types/node": "^18.7.13",
+    "@types/react": "^18.0.17",
+    "@types/react-dom": "^18.0.6",
+    "@types/underscore": "^1.11.4",
+    "@typescript-eslint/eslint-plugin": "^5.35.1",
+    "@typescript-eslint/parser": "^5.35.1",
+    "babel-loader": "^8.2.5",
+    "copy-webpack-plugin": "^11.0.0",
+    "css-loader": "^6.7.1",
+    "dotenv": "^16.0.2",
+    "dotenv-webpack": "^8.0.1",
+    "eslint": "^8.22.0",
+    "eslint-plugin-react": "^7.31.0",
+    "file-loader": "^6.2.0",
+    "fork-ts-checker-webpack-plugin": "^7.2.13",
+    "friendly-errors-webpack-plugin": "^1.7.0",
+    "html-webpack-plugin": "^5.5.0",
+    "image-webpack-loader": "^8.1.0",
+    "postcss-loader": "^7.0.1",
+    "postcss-preset-env": "^7.8.0",
+    "process": "^0.11.10",
+    "rimraf": "^3.0.2",
+    "sass": "^1.54.5",
+    "sass-loader": "^13.0.2",
+    "style-loader": "^3.3.1",
+    "tailwindcss": "^3.1.8",
+    "ts-loader": "^9.3.1",
+    "typescript": "^4.8.2",
+    "webpack": "^5.74.0",
+    "webpack-cli": "^4.10.0",
+    "webpack-dev-server": "^4.10.0",
+    "webpack-dotenv-plugin": "^2.1.0",
+    "webpack-merge": "^5.8.0"
+  },
+  "dependencies": {
+    "konva": "^8.3.12",
+    "npyjs": "^0.4.0",
+    "onnxruntime-web": "^1.14.0",
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0",
+    "react-konva": "^18.2.1",
+    "underscore": "^1.13.6",
+    "react-refresh": "^0.14.0"
+  }
+}

demo/postcss.config.js

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+const tailwindcss = require("tailwindcss");
+module.exports = {
+  plugins: ["postcss-preset-env", 'tailwindcss/nesting', tailwindcss],
+};

demo/src/App.tsx

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+import { InferenceSession, Tensor } from "onnxruntime-web";
+import React, { useContext, useEffect, useState } from "react";
+import "./assets/scss/App.scss";
+import { handleImageScale } from "./components/helpers/scaleHelper";
+import { modelScaleProps } from "./components/helpers/Interfaces";
+import { onnxMaskToImage } from "./components/helpers/maskUtils";
+import { modelData } from "./components/helpers/onnxModelAPI";
+import Stage from "./components/Stage";
+import AppContext from "./components/hooks/createContext";
+const ort = require("onnxruntime-web");
+/* @ts-ignore */
+import npyjs from "npyjs";
+
+// Define image, embedding and model paths
+const IMAGE_PATH = "/assets/data/dogs.jpg";
+const IMAGE_EMBEDDING = "/assets/data/dogs_embedding.npy";
+const MODEL_DIR = "/model/sam_onnx_quantized_example.onnx";
+
+const App = () => {
+  const {
+    clicks: [clicks],
+    image: [, setImage],
+    maskImg: [, setMaskImg],
+  } = useContext(AppContext)!;
+  const [model, setModel] = useState<InferenceSession | null>(null); // ONNX model
+  const [tensor, setTensor] = useState<Tensor | null>(null); // Image embedding tensor
+
+  // The ONNX model expects the input to be rescaled to 1024. 
+  // The modelScale state variable keeps track of the scale values.
+  const [modelScale, setModelScale] = useState<modelScaleProps | null>(null);
+
+  // Initialize the ONNX model. load the image, and load the SAM
+  // pre-computed image embedding
+  useEffect(() => {
+    // Initialize the ONNX model
+    const initModel = async () => {
+      try {
+        if (MODEL_DIR === undefined) return;
+        const URL: string = MODEL_DIR;
+        const model = await InferenceSession.create(URL);
+        setModel(model);
+      } catch (e) {
+        console.log(e);
+      }
+    };
+    initModel();
+
+    // Load the image
+    const url = new URL(IMAGE_PATH, location.origin);
+    loadImage(url);
+
+    // Load the Segment Anything pre-computed embedding
+    Promise.resolve(loadNpyTensor(IMAGE_EMBEDDING, "float32")).then(
+      (embedding) => setTensor(embedding)
+    );
+  }, []);
+
+  const loadImage = async (url: URL) => {
+    try {
+      const img = new Image();
+      img.src = url.href;
+      img.onload = () => {
+        const { height, width, samScale } = handleImageScale(img);
+        setModelScale({
+          height: height,  // original image height
+          width: width,  // original image width
+          samScale: samScale, // scaling factor for image which has been resized to longest side 1024
+        });
+        img.width = width; 
+        img.height = height; 
+        setImage(img);
+      };
+    } catch (error) {
+      console.log(error);
+    }
+  };
+
+  // Decode a Numpy file into a tensor. 
+  const loadNpyTensor = async (tensorFile: string, dType: string) => {
+    let npLoader = new npyjs();
+    const npArray = await npLoader.load(tensorFile);
+    const tensor = new ort.Tensor(dType, npArray.data, npArray.shape);
+    return tensor;
+  };
+
+  // Run the ONNX model every time clicks has changed
+  useEffect(() => {
+    runONNX();
+  }, [clicks]);
+
+  const runONNX = async () => {
+    try {
+      if (
+        model === null ||
+        clicks === null ||
+        tensor === null ||
+        modelScale === null
+      )
+        return;
+      else {
+        // Preapre the model input in the correct format for SAM. 
+        // The modelData function is from onnxModelAPI.tsx.
+        const feeds = modelData({
+          clicks,
+          tensor,
+          modelScale,
+        });
+        if (feeds === undefined) return;
+        // Run the SAM ONNX model with the feeds returned from modelData()
+        const results = await model.run(feeds);
+        const output = results[model.outputNames[0]];
+        // The predicted mask returned from the ONNX model is an array which is 
+        // rendered as an HTML image using onnxMaskToImage() from maskUtils.tsx.
+        setMaskImg(onnxMaskToImage(output.data, output.dims[2], output.dims[3]));
+      }
+    } catch (e) {
+      console.log(e);
+    }
+  };
+
+  return <Stage />;
+};
+
+export default App;

demo/src/assets/index.html

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+<!DOCTYPE html>
+<html lang="en" dir="ltr" prefix="og: https://ogp.me/ns#" class="w-full h-full">
+  <head>
+    <meta charset="utf-8" />
+    <meta
+      name="viewport"
+      content="width=device-width, initial-scale=1, shrink-to-fit=no"
+    />
+    <title>Segment Anything Demo</title>
+
+    <!--  Meta Tags -->
+    <meta property="og:type" content="website" />
+    <meta property="og:title" content="Segment Anything Demo" />
+  </head>
+  <body class="w-full h-full">
+    <div id="root" class="w-full h-full"></div>
+  </body>
+</html>

demo/src/assets/scss/App.scss

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+@tailwind base;
+@tailwind components;
+@tailwind utilities;

demo/src/components/Stage.tsx

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+import React, { useContext } from "react";
+import * as _ from "underscore";
+import Tool from "./Tool";
+import { modelInputProps } from "./helpers/Interfaces";
+import AppContext from "./hooks/createContext";
+
+const Stage = () => {
+  const {
+    clicks: [, setClicks],
+    image: [image],
+  } = useContext(AppContext)!;
+
+  const getClick = (x: number, y: number): modelInputProps => {
+    const clickType = 1;
+    return { x, y, clickType };
+  };
+
+  // Get mouse position and scale the (x, y) coordinates back to the natural
+  // scale of the image. Update the state of clicks with setClicks to trigger
+  // the ONNX model to run and generate a new mask via a useEffect in App.tsx
+  const handleMouseMove = _.throttle((e: any) => {
+    let el = e.nativeEvent.target;
+    const rect = el.getBoundingClientRect();
+    let x = e.clientX - rect.left;
+    let y = e.clientY - rect.top;
+    const imageScale = image ? image.width / el.offsetWidth : 1;
+    x *= imageScale;
+    y *= imageScale;
+    const click = getClick(x, y);
+    if (click) setClicks([click]);
+  }, 15);
+
+  const flexCenterClasses = "flex items-center justify-center";
+  return (
+    <div className={`${flexCenterClasses} w-full h-full`}>
+      <div className={`${flexCenterClasses} relative w-[90%] h-[90%]`}>
+        <Tool handleMouseMove={handleMouseMove} />
+      </div>
+    </div>
+  );
+};
+
+export default Stage;

demo/src/components/Tool.tsx

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+import React, { useContext, useEffect, useState } from "react";
+import AppContext from "./hooks/createContext";
+import { ToolProps } from "./helpers/Interfaces";
+import * as _ from "underscore";
+
+const Tool = ({ handleMouseMove }: ToolProps) => {
+  const {
+    image: [image],
+    maskImg: [maskImg, setMaskImg],
+  } = useContext(AppContext)!;
+
+  // Determine if we should shrink or grow the images to match the
+  // width or the height of the page and setup a ResizeObserver to
+  // monitor changes in the size of the page
+  const [shouldFitToWidth, setShouldFitToWidth] = useState(true);
+  const bodyEl = document.body;
+  const fitToPage = () => {
+    if (!image) return;
+    const imageAspectRatio = image.width / image.height;
+    const screenAspectRatio = window.innerWidth / window.innerHeight;
+    setShouldFitToWidth(imageAspectRatio > screenAspectRatio);
+  };
+  const resizeObserver = new ResizeObserver((entries) => {
+    for (const entry of entries) {
+      if (entry.target === bodyEl) {
+        fitToPage();
+      }
+    }
+  });
+  useEffect(() => {
+    fitToPage();
+    resizeObserver.observe(bodyEl);
+    return () => {
+      resizeObserver.unobserve(bodyEl);
+    };
+  }, [image]);
+
+  const imageClasses = "";
+  const maskImageClasses = `absolute opacity-40 pointer-events-none`;
+
+  // Render the image and the predicted mask image on top
+  return (
+    <>
+      {image && (
+        <img
+          onMouseMove={handleMouseMove}
+          onMouseOut={() => _.defer(() => setMaskImg(null))}
+          onTouchStart={handleMouseMove}
+          src={image.src}
+          className={`${
+            shouldFitToWidth ? "w-full" : "h-full"
+          } ${imageClasses}`}
+        ></img>
+      )}
+      {maskImg && (
+        <img
+          src={maskImg.src}
+          className={`${
+            shouldFitToWidth ? "w-full" : "h-full"
+          } ${maskImageClasses}`}
+        ></img>
+      )}
+    </>
+  );
+};
+
+export default Tool;

demo/src/components/helpers/Interfaces.tsx

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+import { Tensor } from "onnxruntime-web";
+
+export interface modelScaleProps {
+  samScale: number;
+  height: number;
+  width: number;
+}
+
+export interface modelInputProps {
+  x: number;
+  y: number;
+  clickType: number;
+}
+
+export interface modeDataProps {
+  clicks?: Array<modelInputProps>;
+  tensor: Tensor;
+  modelScale: modelScaleProps;
+}
+
+export interface ToolProps {
+  handleMouseMove: (e: any) => void;
+}

demo/src/components/helpers/maskUtils.tsx

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+// Functions for handling mask output from the ONNX model
+
+// Convert the onnx model mask prediction to ImageData
+function arrayToImageData(input: any, width: number, height: number) {
+  const [r, g, b, a] = [0, 114, 189, 255]; // the masks's blue color
+  const arr = new Uint8ClampedArray(4 * width * height).fill(0);
+  for (let i = 0; i < input.length; i++) {
+
+    // Threshold the onnx model mask prediction at 0.0
+    // This is equivalent to thresholding the mask using predictor.model.mask_threshold
+    // in python
+    if (input[i] > 0.0) {
+      arr[4 * i + 0] = r;
+      arr[4 * i + 1] = g;
+      arr[4 * i + 2] = b;
+      arr[4 * i + 3] = a;
+    }
+  }
+  return new ImageData(arr, height, width);
+}
+
+// Use a Canvas element to produce an image from ImageData
+function imageDataToImage(imageData: ImageData) {
+  const canvas = imageDataToCanvas(imageData);
+  const image = new Image();
+  image.src = canvas.toDataURL();
+  return image;
+}
+
+// Canvas elements can be created from ImageData
+function imageDataToCanvas(imageData: ImageData) {
+  const canvas = document.createElement("canvas");
+  const ctx = canvas.getContext("2d");
+  canvas.width = imageData.width;
+  canvas.height = imageData.height;
+  ctx?.putImageData(imageData, 0, 0);
+  return canvas;
+}
+
+// Convert the onnx model mask output to an HTMLImageElement
+export function onnxMaskToImage(input: any, width: number, height: number) {
+  return imageDataToImage(arrayToImageData(input, width, height));
+}

demo/src/components/helpers/onnxModelAPI.tsx

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+import { Tensor } from "onnxruntime-web";
+import { modeDataProps } from "./Interfaces";
+
+const modelData = ({ clicks, tensor, modelScale }: modeDataProps) => {
+  const imageEmbedding = tensor;
+  let pointCoords;
+  let pointLabels;
+  let pointCoordsTensor;
+  let pointLabelsTensor;
+
+  // Check there are input click prompts
+  if (clicks) {
+    let n = clicks.length;
+
+    // If there is no box input, a single padding point with 
+    // label -1 and coordinates (0.0, 0.0) should be concatenated
+    // so initialize the array to support (n + 1) points.
+    pointCoords = new Float32Array(2 * (n + 1));
+    pointLabels = new Float32Array(n + 1);
+
+    // Add clicks and scale to what SAM expects
+    for (let i = 0; i < n; i++) {
+      pointCoords[2 * i] = clicks[i].x * modelScale.samScale;
+      pointCoords[2 * i + 1] = clicks[i].y * modelScale.samScale;
+      pointLabels[i] = clicks[i].clickType;
+    }
+
+    // Add in the extra point/label when only clicks and no box
+    // The extra point is at (0, 0) with label -1
+    pointCoords[2 * n] = 0.0;
+    pointCoords[2 * n + 1] = 0.0;
+    pointLabels[n] = -1.0;
+
+    // Create the tensor
+    pointCoordsTensor = new Tensor("float32", pointCoords, [1, n + 1, 2]);
+    pointLabelsTensor = new Tensor("float32", pointLabels, [1, n + 1]);
+  }
+  const imageSizeTensor = new Tensor("float32", [
+    modelScale.height,
+    modelScale.width,
+  ]);
+
+  if (pointCoordsTensor === undefined || pointLabelsTensor === undefined)
+    return;
+
+  // There is no previous mask, so default to an empty tensor
+  const maskInput = new Tensor(
+    "float32",
+    new Float32Array(256 * 256),
+    [1, 1, 256, 256]
+  );
+  // There is no previous mask, so default to 0
+  const hasMaskInput = new Tensor("float32", [0]);
+
+  return {
+    image_embeddings: imageEmbedding,
+    point_coords: pointCoordsTensor,
+    point_labels: pointLabelsTensor,
+    orig_im_size: imageSizeTensor,
+    mask_input: maskInput,
+    has_mask_input: hasMaskInput,
+  };
+};
+
+export { modelData };

demo/src/components/helpers/scaleHelper.tsx

@@ -0,0 +1,12 @@
+
+// Helper function for handling image scaling needed for SAM
+const handleImageScale = (image: HTMLImageElement) => {
+  // Input images to SAM must be resized so the longest side is 1024
+  const LONG_SIDE_LENGTH = 1024;
+  let w = image.naturalWidth;
+  let h = image.naturalHeight;
+  const samScale = LONG_SIDE_LENGTH / Math.max(h, w);
+  return { height: h, width: w, samScale };
+};
+
+export { handleImageScale };

demo/src/components/hooks/context.tsx

@@ -0,0 +1,25 @@
+import React, { useState } from "react";
+import { modelInputProps } from "../helpers/Interfaces";
+import AppContext from "./createContext";
+
+const AppContextProvider = (props: {
+  children: React.ReactElement<any, string | React.JSXElementConstructor<any>>;
+}) => {
+  const [clicks, setClicks] = useState<Array<modelInputProps> | null>(null);
+  const [image, setImage] = useState<HTMLImageElement | null>(null);
+  const [maskImg, setMaskImg] = useState<HTMLImageElement | null>(null);
+
+  return (
+    <AppContext.Provider
+      value={{
+        clicks: [clicks, setClicks],
+        image: [image, setImage],
+        maskImg: [maskImg, setMaskImg],
+      }}
+    >
+      {props.children}
+    </AppContext.Provider>
+  );
+};
+
+export default AppContextProvider;

demo/src/components/hooks/createContext.tsx

@@ -0,0 +1,21 @@
+import { createContext } from "react";
+import { modelInputProps } from "../helpers/Interfaces";
+
+interface contextProps {
+  clicks: [
+    clicks: modelInputProps[] | null,
+    setClicks: (e: modelInputProps[] | null) => void
+  ];
+  image: [
+    image: HTMLImageElement | null,
+    setImage: (e: HTMLImageElement | null) => void
+  ];
+  maskImg: [
+    maskImg: HTMLImageElement | null,
+    setMaskImg: (e: HTMLImageElement | null) => void
+  ];
+}
+
+const AppContext = createContext<contextProps | null>(null);
+
+export default AppContext;

demo/src/index.tsx

@@ -0,0 +1,11 @@
+import * as React from "react";
+import { createRoot } from "react-dom/client";
+import AppContextProvider from "./components/hooks/context";
+import App from "./App";
+const container = document.getElementById("root");
+const root = createRoot(container!);
+root.render(
+  <AppContextProvider>
+    <App/>
+  </AppContextProvider>
+);

demo/tailwind.config.js

@@ -0,0 +1,6 @@
+/** @type {import('tailwindcss').Config} */
+module.exports = {
+  content: ["./src/**/*.{html,js,tsx}"],
+  theme: {},
+  plugins: [],
+};

demo/tsconfig.json

@@ -0,0 +1,24 @@
+{
+  "compilerOptions": {
+    "lib": ["dom", "dom.iterable", "esnext"],
+    "allowJs": true,
+    "skipLibCheck": true,
+    "strict": true,
+    "forceConsistentCasingInFileNames": true,
+    "noEmit": false,
+    "esModuleInterop": true,
+    "module": "esnext",
+    "moduleResolution": "node",
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "jsx": "react",
+    "incremental": true,
+    "target": "ESNext",
+    "useDefineForClassFields": true,
+    "allowSyntheticDefaultImports": true,
+    "outDir": "./dist/",
+    "sourceMap": true
+  },
+  "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", "src"],
+  "exclude": ["node_modules"]
+}