36 lines
1.4 KiB
Markdown
36 lines
1.4 KiB
Markdown
# Ultra-Fast-Lane-Detection(UFLD)
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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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git clone https://github.com/cfzd/Ultra-Fast-Lane-Detection.git
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// download its weights 'tusimple_18.pth'
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// copy tensorrtx/ufld/gen_wts.py into Ultra-Fast-Lane-Detection/
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// ensure the file name is tusimple_18.pth and lane.wts in gen_wts.py
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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 ( 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/ufld and run
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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 PATH_TO_YOUR_IMAGE_FOLDER // deserialize plan file and run inference, the images 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 result using opencv rather than saving the result in postprocess.
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2. If there are some bugs where you inference with multi batch_size, just modify the code in preprocess or postprocess, it's not complicated.
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3. Some results are stored in resluts folder.
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