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Merge pull request wang-xinyu#189 from hwh-hit/hwh
update readme
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lane_det/README.md

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The Pytorch implementation is [Ultra-Fast-Lane-Detection](https://github.com/cfzd/Ultra-Fast-Lane-Detection).
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## How to Run
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```
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1. generate lane.wts and lane.onnx from pytorch with tusimple_18.pth
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git clone https://github.com/wang-xinyu/tensorrtx.git
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// go to Ultra-Fast-Lane-Detection
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python gen_wts.py
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// a file 'lane.wts' will be generated.
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// then
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// then ( not necessary )
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python pth2onnx.py
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//a file 'lane.onnx' will be generated.
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2. build tensorrtx/lane_det and run
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```
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mkdir build
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cd build
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cmake ..
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make
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sudo ./lane_det -s // serialize model to plan file i.e. 'DBNet.engine'
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sudo ./lane_det -d ../data // deserialize plan file and run inference, the images in data will be processed.
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```
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mkdir build
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cd build
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cmake ..
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make
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sudo ./lane_det -s // serialize model to plan file i.e. 'lane.engine'
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sudo ./lane_det -d ../data // deserialize plan file and run inference, the images in data will be processed.
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```
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## More Information
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1. Changed the preprocess and postprocess in tensorrtx, give a different way to convert NHWC to NCHW in preprocess and just show the reslut using opencv rather than saving the result in postprocess.

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