LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models

Overview

LaneDet

Introduction

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models. Developers can reproduce these SOTA methods and build their own methods.

demo image

Table of Contents

Benchmark and model zoo

Supported backbones:

  • ResNet
  • ERFNet
  • VGG
  • DLA (comming soon)

Supported detectors:

Installation

Clone this repository

git clone https://github.com/turoad/lanedet.git

We call this directory as $LANEDET_ROOT

Create a conda virtual environment and activate it (conda is optional)

conda create -n lanedet python=3.8 -y
conda activate lanedet

Install dependencies

# Install pytorch firstly, the cudatoolkit version should be same in your system. (you can also use pip to install pytorch and torchvision)
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch

# Or you can install via pip
pip install torch torchvision

# Install python packages
python setup.py build develop

Data preparation

CULane

Download CULane. Then extract them to $CULANEROOT. Create link to data directory.

cd $RESA_ROOT
mkdir -p data
ln -s $CULANEROOT data/CULane

For CULane, you should have structure like this:

$CULANEROOT/driver_xx_xxframe    # data folders x6
$CULANEROOT/laneseg_label_w16    # lane segmentation labels
$CULANEROOT/list                 # data lists

Tusimple

Download Tusimple. Then extract them to $TUSIMPLEROOT. Create link to data directory.

cd $RESA_ROOT
mkdir -p data
ln -s $TUSIMPLEROOT data/tusimple

For Tusimple, you should have structure like this:

$TUSIMPLEROOT/clips # data folders
$TUSIMPLEROOT/lable_data_xxxx.json # label json file x4
$TUSIMPLEROOT/test_tasks_0627.json # test tasks json file
$TUSIMPLEROOT/test_label.json # test label json file

For Tusimple, the segmentation annotation is not provided, hence we need to generate segmentation from the json annotation.

python tools/generate_seg_tusimple.py --root $TUSIMPLEROOT
# this will generate seg_label directory

Getting Started

Training

For training, run

python main.py [configs/path_to_your_config] --gpus [gpu_ids]

For example, run

python main.py configs/resa/resa50_culane.py --gpus 0 1 2 3

Testing

For testing, run

python main.py [configs/path_to_your_config] --validate --load_from [path_to_your_model] [gpu_num]

For example, run

python main.py configs/resa/resa50_culane.py --validate --load_from culane_resnet50.pth --gpus 0 1 2 3

Currently, this code can output the visualization result when testing, just add --view. We will get the visualization result in work_dirs/xxx/xxx/visualization.

For example, run

python main.py configs/resa/resa50_culane.py --validate --load_from culane_resnet50.pth --gpus 0 --view

Contributing

We appreciate all contributions to improve LaneDet. Any pull requests or issues are welcomed.

Licenses

This project is released under the Apache 2.0 license.

Acknowledgement

Comments
  • How can I properly change the input image size on CondLane?

    How can I properly change the input image size on CondLane?

    Currently I'm detecting lanes using tools/detect.py.

    For Condlane inference, I changed this

    batch_size=1 # from 8 (for condlane inference)
    

    And tried these configs for FHD input image

    img_height = 1080 # from 320
    img_width = 1920 # from 800
    
    ori_img_h = 1080 # from 590
    ori_img_w = 1920 # from 1640
    
    crop_bbox = [0,540,1920,1080] # from [0, 270, 1640, 590]
    

    Changing img_scale = (800,320) results

    The size of tensor a must match the size of tensor b at non-singleton dimension 3
    

    How can I properly change the input image size (ex. FHD) on CondLane config file?

    opened by parkjbdev 20
  • curvature estimation

    curvature estimation

    Hello, I would like to know if there is any way to get real-time lane detection and curvature detection using deep learning. I have seen traditional computer vision algorithms but I am looking for a Deep Learning model that could help me out with this. Any suggestions will be very helpful. Thanks in advance.

    opened by k-nayak 9
  • Really bad inference results

    Really bad inference results

    The inference outputs from the model are really bad even for very easy images.

    1. Using Laneatt_Res18_Culane straight-lines2-laneatt-res18

    2. Using SCNN_Res50_Culane straight-lines2-scnn-res50

    Any idea why this is happening? I've just done normal inference without any changes.

    opened by sowmen 9
  • ImportError: connot import name 'nms_impl' form partially initialized module 'lanedet.ops' (most likely due to a circular improt)o)

    ImportError: connot import name 'nms_impl' form partially initialized module 'lanedet.ops' (most likely due to a circular improt)o)

    When I run: python tools/detect.py configs/resa/resa34_culane.py --img images --load_from resa_r34_culane.pth --savedir ./vis Traceback (most recent call last): File "D:/XXX/XXX/XXX/lanedet-main/tools/detect.py", line 8, in from lanedet.datasets.process import Process File "D:\XXX\XXX\XXX\lanedet-main\lanedet_init_.py", line 1, in from .ops import * File "D:\XXX\XXX\XXX\lanedet-main\lanedet\ops_init_.py", line 1, in from .nms import nms File "D:\XXX\XXX\XXX\lanedet-main\lanedet\ops\nms.py", line 29, in from . import nms_impl ImportError: cannot import name 'nms_impl' from partially initialized module 'lanedet.ops' (most likely due to a circular import) (D:\XXX\XXX\XXX\lanedet-main\lanedet\ops_init_.py)

    opened by readerrubic 8
  • custom image size for resa !

    custom image size for resa !

    Hello,

    I have tried testing with the CULane dataset with rsea and it is working well with the example video_example/05081544_0305/
    With the following image configuration: img_height = 288 img_width = 800 cut_height = 240 ori_img_h = 590 ori_img_w = 1640

    05081544_0305-000073

    But with custom image of configurations: img_height = 288 img_width = 800 cut_height = 240 ori_img_h = 1208 // 590 ori_img_w = 1920 //1640

    With above parameters: custom image 05081544_0305-000001

    With defaut parameters: custom image 05081544_0305-000001

    Could you please assist me which params needs to be tuned.

    Appreciate any response.

    Regards, Ajay

    opened by ajay1606 7
  • Can't convert the model to onnx

    Can't convert the model to onnx

    `sample_input = torch.rand((32, 3, 3, 3))

    torch.onnx.export( net1.module, # PyTorch Model sample_input, # Input tensor '/content/drive/MyDrive/MobileNetV2-model-onnx.onnx', # Output file (eg. 'output_model.onnx') opset_version = 12, # Operator support version input_names = ['input'], # Input tensor name (arbitary) output_names = ['output'] # Output tensor name (arbitary) )`

    Got this Error:

    TypeError Traceback (most recent call last) in () 5 opset_version=12, # Operator support version 6 input_names=['input'], # Input tensor name (arbitary) ----> 7 output_names=['output'] # Output tensor name (arbitary) 8 )

     21     def forward(self, batch):
     22         output = {}
    

    ---> 23 fea = self.backbone(batch['img']) 24 25 if self.aggregator:

    TypeError: new(): invalid data type 'str'

    enhancement 
    opened by AbdulFMS 6
  • HELP! A circular import error message appears in nms.py

    HELP! A circular import error message appears in nms.py

    from . import nms_impl ImportError: cannot import name 'nms_impl' from partially initialized module 'la nedet.ops' (most likely due to a circular import) (D:\lanedet-main\lanedet\ops_ init_.py)

    opened by 13xyz7 6
  • Unable to find model file

    Unable to find model file

    Hello, Thank you so much for sharing a very much useful repository.

    I have followed the step by step instructions given, and have downloaded all the datasets as mentioned in the below image

    image

    Training: python main.py configs/resa/resa50_culane.py --gpus 0

    After running the above command, i was able to see following window: image

    But i couldn't find any model file such as culane_resnet50.pth ,resa_r34_culane.pth !! As it mentioned in the example run case.

    Alternatively, is it possible to share the pre-trained model file?

    As I am a beginner, I greatly appreciate your understanding and kind response.

    Regards, Ajay

    opened by ajay1606 5
  • TypeError: expected string or bytes-like object

    TypeError: expected string or bytes-like object

    python setup.py build develop

    File "/home/zzj/anaconda3/envs/Lanedet/lib/python3.8/site-packages/pkg_resources/_vendor/packaging/version.py", line 275, in init match = self._regex.search(version) TypeError: expected string or bytes-like object

    ubuntu20.04 what can i do?

    opened by hzzzzjzyq 5
  • Error

    Error

    if don't modify (from .nms import nms) from lanedet/ops/init.py to (from . import *) there will be an error. and if don't modify (from . import nms_impl) from lanedet/ops/nms.py to (from . import *) there will be an error. And when run inference, there is no lanedet directory in the tools directory, resulting in module error from lanedet/tools/detect.py line 8~12. Is there any other way to remove the error?

    opened by gui-hoon 5
  • Mobilenetv2 for condlane got error.

    Mobilenetv2 for condlane got error.

    Hey @Turoad, thanks for your work, it's very useful. I recently customized to train condlane with mobilenetv2 backbone but got this error!!

    Traceback (most recent call last):
      File "main.py", line 65, in <module>
        main()
      File "main.py", line 35, in main
        runner.train()
      File "/mnt/09a762a6-3f6e-469b-8d6d-e9fa625e24b9/USER/LuanDD/lanedet/lanedet/engine/runner.py", line 94, in train
        self.train_epoch(epoch, train_loader)
      File "/mnt/09a762a6-3f6e-469b-8d6d-e9fa625e24b9/USER/LuanDD/lanedet/lanedet/engine/runner.py", line 67, in train_epoch
        output = self.net(data)
      File "/mnt/09a762a6-3f6e-469b-8d6d-e9fa625e24b9/USER/LuanDD/pyenv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
        result = self.forward(*input, **kwargs)
      File "/mnt/09a762a6-3f6e-469b-8d6d-e9fa625e24b9/USER/LuanDD/pyenv/lib/python3.6/site-packages/mmcv/parallel/data_parallel.py", line 42, in forward
        return super().forward(*inputs, **kwargs)
      File "/mnt/09a762a6-3f6e-469b-8d6d-e9fa625e24b9/USER/LuanDD/pyenv/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 165, in forward
        return self.module(*inputs[0], **kwargs[0])
      File "/mnt/09a762a6-3f6e-469b-8d6d-e9fa625e24b9/USER/LuanDD/pyenv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
        result = self.forward(*input, **kwargs)
      File "/mnt/09a762a6-3f6e-469b-8d6d-e9fa625e24b9/USER/LuanDD/lanedet/lanedet/models/nets/detector.py", line 29, in forward
        fea = self.neck(fea)
      File "/mnt/09a762a6-3f6e-469b-8d6d-e9fa625e24b9/USER/LuanDD/pyenv/lib/python3.6/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
        result = self.forward(*input, **kwargs)
      File "/mnt/09a762a6-3f6e-469b-8d6d-e9fa625e24b9/USER/LuanDD/lanedet/lanedet/models/necks/fpn.py", line 113, in forward
        assert len(inputs) >= len(self.in_channels)
    AssertionError
    

    Can you help me clarify it? This is my config

    net = dict(
        type='Detector',
    )
    
    backbone = dict(
        type='MobileNet',
        net='MobileNetV2',
        pretrained=True,
        # replace_stride_with_dilation=[False, False, False],
        out_conv=False,
        # in_channels=[64, 128, 256, 512]
    )
    
    featuremap_out_channel = 1280
    featuremap_out_stride = 32 
    
    sample_y = range(590, 270, -8)
    
    batch_size = 8
    aggregator = dict(
        type='TransConvEncoderModule',
        in_dim=1280,
        attn_in_dims=[1280, 64],
        attn_out_dims=[64, 64],
        strides=[1, 1],
        ratios=[4, 4],
        pos_shape=(batch_size, 10, 25),
    )
    
    neck=dict(
        type='FPN',
        in_channels=[64, 128, 256, 64],
        out_channels=64,
        num_outs=4,
        #trans_idx=-1,
    )
    
    loss_weights=dict(
            hm_weight=1,
            kps_weight=0.4,
            row_weight=1.,
            range_weight=1.,
        )
    
    num_lane_classes=1
    heads=dict(
        type='CondLaneHead',
        heads=dict(hm=num_lane_classes),
        in_channels=(64, ),
        num_classes=num_lane_classes,
        head_channels=64,
        head_layers=1,
        disable_coords=False,
        branch_in_channels=64,
        branch_channels=64,
        branch_out_channels=64,
        reg_branch_channels=64,
        branch_num_conv=1,
        hm_idx=2,
        mask_idx=0,
        compute_locations_pre=True,
        location_configs=dict(size=(batch_size, 1, 80, 200), device='cuda:0')
    )
    
    optimizer = dict(type='AdamW', lr=3e-4, betas=(0.9, 0.999), eps=1e-8)
    optimizer = dict(type='SGD', lr=3e-3)
    
    epochs = 40
    total_iter = (88880 // batch_size) * epochs
    total_iter = (3688 // batch_size) * epochs
    
    import math
    scheduler = dict(
        type = 'MultiStepLR',
        milestones=[15, 25, 35],
        gamma=0.1
    )
    
    seg_loss_weight = 1.0
    eval_ep = 1
    save_ep = 1 
    
    img_norm = dict(
        mean=[75.3, 76.6, 77.6],
        std=[50.5, 53.8, 54.3]
    )
    
    img_height = 320 
    img_width = 800
    cut_height = 0 
    ori_img_h = 590
    ori_img_w = 1640
    
    mask_down_scale = 4
    hm_down_scale = 16
    num_lane_classes = 1
    line_width = 3
    radius = 6
    nms_thr = 4
    img_scale = (800, 320)
    crop_bbox = [0, 270, 1640, 590]
    mask_size = (1, 80, 200)
    
    train_process = [
        dict(type='Alaug',
        transforms=[dict(type='Compose', params=dict(bboxes=False, keypoints=True, masks=False)),
        dict(
            type='Crop',
            x_min=crop_bbox[0],
            x_max=crop_bbox[2],
            y_min=crop_bbox[1],
            y_max=crop_bbox[3],
            p=1),
        dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1),
        dict(
            type='OneOf',
            transforms=[
                dict(
                    type='RGBShift',
                    r_shift_limit=10,
                    g_shift_limit=10,
                    b_shift_limit=10,
                    p=1.0),
                dict(
                    type='HueSaturationValue',
                    hue_shift_limit=(-10, 10),
                    sat_shift_limit=(-15, 15),
                    val_shift_limit=(-10, 10),
                    p=1.0),
            ],
            p=0.7),
        dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2),
        dict(
            type='OneOf',
            transforms=[
                dict(type='Blur', blur_limit=3, p=1.0),
                dict(type='MedianBlur', blur_limit=3, p=1.0)
            ],
            p=0.2),
        dict(type='RandomBrightness', limit=0.2, p=0.6),
        dict(
            type='ShiftScaleRotate',
            shift_limit=0.1,
            scale_limit=(-0.2, 0.2),
            rotate_limit=10,
            border_mode=0,
            p=0.6),
        dict(
            type='RandomResizedCrop',
            height=img_scale[1],
            width=img_scale[0],
            scale=(0.8, 1.2),
            ratio=(1.7, 2.7),
            p=0.6),
        dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1),]
        ),
        dict(type='CollectLane',
            down_scale=mask_down_scale,
            hm_down_scale=hm_down_scale,
            max_mask_sample=5,
            line_width=line_width,
            radius=radius,
            keys=['img', 'gt_hm'],
            meta_keys=[
                'gt_masks', 'mask_shape', 'hm_shape',
                'down_scale', 'hm_down_scale', 'gt_points'
            ]
        ),
        #dict(type='Resize', size=(img_width, img_height)),
        dict(type='Normalize', img_norm=img_norm),
        dict(type='ToTensor', keys=['img', 'gt_hm'], collect_keys=['img_metas']),
    ]
    
    
    val_process = [
        dict(type='Alaug',
            transforms=[dict(type='Compose', params=dict(bboxes=False, keypoints=True, masks=False)),
                dict(type='Crop',
                x_min=crop_bbox[0],
                x_max=crop_bbox[2],
                y_min=crop_bbox[1],
                y_max=crop_bbox[3],
                p=1),
            dict(type='Resize', height=img_scale[1], width=img_scale[0], p=1)]
        ),
        #dict(type='Resize', size=(img_width, img_height)),
        dict(type='Normalize', img_norm=img_norm),
        dict(type='ToTensor', keys=['img']),
    ]
    
    # dataset_path = './data/CULane'
    dataset_path = './data/Merge_data'
    # val_path = './data/CULane'
    dataset = dict(
        train=dict(
            type='CULane',
            data_root=dataset_path,
            split='train',
            processes=train_process,
        ),
        val=dict(
            type='CULane',
            data_root=dataset_path,
            split='test',
            processes=val_process,
        ),
        test=dict(
            type='CULane',
            data_root=dataset_path,
            split='test',
            processes=val_process,
        )
    )
    
    
    workers = 6
    log_interval = 100
    lr_update_by_epoch=True
    

    Thank you so much

    opened by luan1412167 4
  • CondLane如何修改检测的车道线数量?

    CondLane如何修改检测的车道线数量?

    使用测试kaist数据集测试[CondLane],最多只能检测出3条车道线,很明显的车道检测不出来,请问是限制了检测车道线数量了吗,在那里可以配置? https://github.com/Turoad/lanedet/issues/58#issuecomment-1131143127 按照此处的配置方法似乎不管用。 1559193232373910975

    opened by w-jinkui 0
  • PermissionError: [Errno 13] Permission denied: 'C:\\Users\\L00653~1\\AppData\\Local\\Temp\\tmphpklern8\\tmpkydalnxp.py'

    PermissionError: [Errno 13] Permission denied: 'C:\\Users\\L00653~1\\AppData\\Local\\Temp\\tmphpklern8\\tmpkydalnxp.py'

    Traceback (most recent call last): File "tools/detect.py", line 86, in process(args) File "tools/detect.py", line 68, in process cfg = Config.fromfile(args.config) File "d:\lanedet\lanedet\utils\config.py", line 180, in fromfile cfg_dict, cfg_text = Config._file2dict(filename) File "d:\lanedet\lanedet\utils\config.py", line 105, in _file2dict shutil.copyfile(filename, File "C:\Users\l00653465\Anaconda3\envs\lanedet\lib\shutil.py", line 264, in copyfile with open(src, 'rb') as fsrc, open(dst, 'wb') as fdst: PermissionError: [Errno 13] Permission denied: 'C:\Users\L00653~1\AppData\Local\Temp\tmphpklern8\tmpkydalnxp.py'

    在进行训练和测试的时候都会报这个错

    opened by Sober-xz 1
  • KeyError:  Unable to find

    KeyError: Unable to find "net" key in the trained model from detect.py

    Hi Guys,

    I am using this project on conda env with gpu configured. I was trying to just run the inference files first to try it out, but I get the following error:

    Traceback (most recent call last): File "c:\CULane\lanedet\tools\detect.py", line 86, in process(args) File "c:\CULane\lanedet\tools\detect.py", line 72, in process detect = Detect(cfg) File "c:\CULane\lanedet\tools\detect.py", line 24, in init load_network(self.net, self.cfg.load_from) File "c:\culane\lanedet\lanedet\utils\net_utils.py", line 48, in load_network net.load_state_dict(pretrained_model['net'], strict=True) KeyError: 'net'

    I have used following command: $ python detect.py' 'lanedet/configs/resa/resa34_culane.py' '--img' 'image\' '--load_from' 'C:\Users\blackbug\.cache\torch\hub\checkpoints\resnet34-333f7ec4.pth' '--savedir' './vis'

    I tried to look at the model loaded from the downloaded resnet model file; it looks valid with all the trained layers, just "net" isnt part of the dictionary. Any help is appreciated! Thank you!

    opened by kkarnatak 0
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