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‎LICENSE.md

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‎README.md

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<h1 align="center"><span>NanoSAM C++</span></h1>
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This repo provides a C++ implementation of Nvidia's [NanoSAM](https://github.com/NVIDIA-AI-IOT/nanosam), a distilled segment-anything model, for real-time inference on GPU.
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<p align="center" margin: 0 auto;>
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<img src="assets/segment_with_single_click.gif" height="250px" width="360px" />
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<img src="assets/video.gif" height="250px" width="360px" />
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</p>
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## ⚙️ Usage
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1. There are two ways to load engines:
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- Option 1: Load engines built by trtexec for inference:
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```cpp
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#include "nanosam/nanosam.h"
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NanoSam nanosam(
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"resnet18_image_encoder.engine",
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"mobile_sam_mask_decoder.engine"
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);
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```
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- Option 2: Build engines directly from onnx files:
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```cpp
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NanoSam nanosam(
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"resnet18_image_encoder.onnx",
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"mobile_sam_mask_decoder.onnx"
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);
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```
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2. Using a prompt point, segment an object:
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```cpp
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Mat image = imread("assets/dog.jpg");
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// Foreground point
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vector<Point> points = { Point(1300, 900) };
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vector<float> labels = { 1 };
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Mat mask = nanosam.predict(image, points, labels);
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```
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<table style="margin-right:auto; text-align:center;">
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<tr>
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<td style="text-align: center;">Input</td>
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<td style="text-align: center;">Output</td>
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</tr>
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<tr>
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<td><img src="assets/dog.jpg" width=480px></td>
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<td><img src="assets/dog_mask.jpg" width=480px></td>
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</tr>
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</table>
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3. We can also create masks from bounding boxes:
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```cpp
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Mat image = imread("assets/dogs.jpg");
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// Bounding box top-left and bottom-right points
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vector<Point> points = { Point(100, 100), Point(750, 759) };
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vector<float> labels = { 2, 3 };
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Mat mask = nanosam.predict(image, points, labels);
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```
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<table style="margin-right:auto; text-align:center;">
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<tr>
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<td style="text-align: center;">Input</td>
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<td style="text-align: center;">Output</td>
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</tr>
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<tr>
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<td><img src="assets/dogs.jpg" width=480px></td>
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<td><img src="assets/dogs_mask.jpg" width=480px></td>
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</tr>
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</table>
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<details>
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<summary>Notes</summary>
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The point labels may be
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| Point Label | Description |
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|:--------------------:|-------------|
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| 0 | Background point |
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| 1 | Foreground point |
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| 2 | Bounding box top-left |
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| 3 | Bounding box bottom-right |
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</details>
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## Performance
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The inference time includes the pre-preprocessing time and the post-processing time:
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| Device | Image Shape(WxH) | Model Shape(WxH) | Inference Time(ms) |
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|----------------|------------|------------|------------|
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| RTX4090 |2048x1365 |1024x1024 |14 |
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## 🛠️ Installation
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1. Download the image encoder: [resnet18_image_encoder.onnx](https://drive.google.com/file/d/14-SsvoaTl-esC3JOzomHDnI9OGgdO2OR/view?usp=drive_link)
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2. Download the mask decoder: [mobile_sam_mask_decoder.onnx](https://drive.google.com/file/d/1jYNvnseTL49SNRx9PDcbkZ9DwsY8up7n/view?usp=drive_link)
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3. Download the [TensorRT](https://developer.nvidia.com/tensorrt) zip file that matches the Windows version you are using.
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4. Choose where you want to install TensorRT. The zip file will install everything into a subdirectory called `TensorRT-8.x.x.x`. This new subdirectory will be referred to as `<installpath>` in the steps below.
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5. Unzip the `TensorRT-8.x.x.x.Windows10.x86_64.cuda-x.x.zip` file to the location that you chose. Where:
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- `8.x.x.x` is your TensorRT version
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- `cuda-x.x` is CUDA version `11.8` or `12.0`
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6. Add the TensorRT library files to your system `PATH`. To do so, copy the DLL files from `<installpath>/lib` to your CUDA installation directory, for example, `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.Y\bin`, where `vX.Y` is your CUDA version. The CUDA installer should have already added the CUDA path to your system PATH.
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7. Ensure that the following is present in your Visual Studio Solution project properties:
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- `<installpath>/lib` has been added to your PATH variable and is present under **VC++ Directories > Executable Directories**.
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- `<installpath>/include` is present under **C/C++ > General > Additional Directories**.
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- nvinfer.lib and any other LIB files that your project requires are present under **Linker > Input > Additional Dependencies**.
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8. Download and install any recent [OpenCV](https://opencv.org/releases/) for Windows.
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## Acknowledgement
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Some of functions were borrowed from the following projects.
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- [NanoSAM](https://github.com/NVIDIA-AI-IOT/nanosam) - The distilled Segment Anything (SAM).
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- [TensorRTx](https://github.com/wang-xinyu/tensorrtx) - Implementation of popular deep learning networks with TensorRT network definition API.
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- [TensorRT](https://github.com/NVIDIA/TensorRT/tree/release/8.6/samples) - TensorRT samples.

‎assets/dog.jpg

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‎assets/dog_mask.jpg

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‎assets/dogs.jpg

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‎assets/dogs_mask.jpg

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‎assets/masked_dogs.jpg

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‎assets/segment_with_single_click.gif

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‎assets/video.gif

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