aa64535e1d
* add csrnet * Add update result jpg CSRNet Inference result * fix pr format * add density plot code and update README.md fix img src --------- Co-authored-by: liulf <liulf@nncsys.com>
59 lines
1.4 KiB
Markdown
59 lines
1.4 KiB
Markdown
# csrnet
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The Pytorch implementation is [leeyeehoo/CSRNet-pytorch](https://github.com/leeyeehoo/CSRNet-pytorch).
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This repo is a TensorRT implementation of CSRNet.
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paper : [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://arxiv.org/abs/1802.10062)
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Dev environment:
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- Ubuntu 22.04
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- TensorRT 8.6
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- OpenCV 4.5.4
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- CMake 3.24
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- GPU Driver 535.113.01
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- CUDA 12.2
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- RTX3080
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# how to run
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```bash
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1. generate csrnet engine
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git clone https://github.com/leeyeehoo/CSRNet-pytorch.git
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git clone https://github.com/wang-xinyu/tensorrtx.git
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// copy gen_wts.py to CSRNet-pytorch
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// generate wts file
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python gen_wts.py
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// csrnet wts will be generated in CSRNet-pytorch
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2. build csrnet.engine
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// mv CSRNet-pytorch/csrnet.engine to tensorrtx/csrnet
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mv CSRNet-pytorch/csrnet.wts tensorrtx/csrnet
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// build
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mkdir build
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cmake ..
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make
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sudo ./csrnet -s ./csrnet.wts
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Loading weights: ./csrnet.wts
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build engine successfully : ./csrnet.engine
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// download images https://github.com/wang-xinyu/tensorrtx/assets/46584679/46bc4def-e573-44ae-996d-5d68927c78ff and copy to images
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sudo ./csrnet -d ./images
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// output e.g
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// enqueueV2 time: 0.0323869s
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// detect time:44ms
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// people num :22.9101 write_path: ../images/data.jpg
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```
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# result
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inference people num: 22.9101
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<p align="center">
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<img src= https://raw.githubusercontent.com/wang-xinyu/tensorrtx/dbf857d25f77bf64113fc99a745ccf4973bdd44e/Density_Plot.jpg>
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</p>
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