fitst commit

demo/src/components/helpers/Interfaces.tsx

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+// Copyright (c) Meta Platforms, Inc. and affiliates.
+// All rights reserved.
+
+// This source code is licensed under the license found in the
+// LICENSE file in the root directory of this source tree.
+
+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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+// Copyright (c) Meta Platforms, Inc. and affiliates.
+// All rights reserved.
+
+// This source code is licensed under the license found in the
+// LICENSE file in the root directory of this source tree.
+
+// 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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+// Copyright (c) Meta Platforms, Inc. and affiliates.
+// All rights reserved.
+
+// This source code is licensed under the license found in the
+// LICENSE file in the root directory of this source tree.
+
+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

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+// Copyright (c) Meta Platforms, Inc. and affiliates.
+// All rights reserved.
+
+// This source code is licensed under the license found in the
+// LICENSE file in the root directory of this source tree.
+
+
+// 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 };