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--- |
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license: apple-ascl |
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base_model: |
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- apple/DepthPro |
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library_name: transformers.js |
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pipeline_tag: depth-estimation |
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--- |
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https://huggingface.co/apple/DepthPro with ONNX weights to be compatible with Transformers.js. |
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## Usage (Transformers.js) |
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If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using: |
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```bash |
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npm i @huggingface/transformers |
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``` |
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```js |
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import { AutoProcessor, AutoModelForDepthEstimation, RawImage } from "@huggingface/transformers"; |
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// Load model and processor |
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const depth = await AutoModelForDepthEstimation.from_pretrained("onnx-community/DepthPro-ONNX", { dtype: "q4" }); |
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const processor = await AutoProcessor.from_pretrained("onnx-community/DepthPro-ONNX"); |
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// Read and prepare image |
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const image = await RawImage.read("https://raw.githubusercontent.com/huggingface/transformers.js-examples/main/depth-pro-node/assets/image.jpg"); |
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const inputs = await processor(image); |
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// Run depth estimation model |
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const { predicted_depth, focallength_px } = await depth(inputs); |
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// Normalize the depth map to [0, 1] |
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const depth_map_data = predicted_depth.data; |
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let minDepth = Infinity; |
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let maxDepth = -Infinity; |
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for (let i = 0; i < depth_map_data.length; ++i) { |
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minDepth = Math.min(minDepth, depth_map_data[i]); |
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maxDepth = Math.max(maxDepth, depth_map_data[i]); |
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} |
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const depth_tensor = predicted_depth |
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.sub_(minDepth) |
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.div_(-(maxDepth - minDepth)) // Flip for visualization purposes |
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.add_(1) |
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.clamp_(0, 1) |
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.mul_(255) |
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.round_() |
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.to("uint8"); |
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// Save the depth map |
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const depth_image = RawImage.fromTensor(depth_tensor); |
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depth_image.save("depth.png"); |
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``` |
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The following images illustrate the input image and its corresponding depth map generated by the model: |
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| Input Image | Depth Map | |
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| ---------------------------------- | -------------------------------- | |
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|  |  | |
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--- |
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Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |
