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Do not overwrite folders with the same name, as the content of the training and testing sets is different

Tusimple

Prepare the dataset

  1. The TuSimple dataset can be downloaded at their github repo. However, you'll also need segmentation labels, list6_train.txt, list6_val.txt and list_test.txt provided by @cardwing, thanks for their efforts.
  2. Change the TUSIMPLE_ROOT in configs/lane_detection/common/datasets/_utils.py to your dataset's location.
  3. Pre-processing:

First put the data lists you downloaded before in TUSIMPLE.BASE_DIR/lists. Then:

  python tools/tusimple_list_convertor.py
  1. Prepare official evaluation scripts:
cd tools/tusimple_evaluation
mkdir output

Then change data_dir to your TuSimple base directory in autotest_tusimple.sh. Mind that you need extra ../../ if relative path is used.

  1. If you use Bézier curve methods, download Bézier curve GT from here and unzip them in TUSIMPLE_ROOT/bezier_labels/. More info on curves are in CURVE.md.

Description

Directory Structure

Note that the structure of Tusimple dataset downloaded from kaggle is different from the original structure. It contains train_set folder, test_set folder. You need to move the clips in the test_set folder to the train_set folder. Do not overwrite folders with the same name, as the content of the training and testing sets is different.

    <TUSIMPLE.BASE_DIR>
      ├─ clips
      ├─ lists
      ├─ segGT6
      ├─ label_data_0313.json
      ├─ label_data_0531.json
      ├─ label_data_0601.json
      ├─ bezier_labels
      │  ├─ train_3.json
      │  └─ ... 
      └─ test_label.json

Label Data Format

{
   'lanes': list. A list of lanes. For each list of one lane, the elements are width values on image.
   'h_samples': list. A list of height values corresponding to the 'lanes', which means len(h_samples) == len(lanes[i])
   'raw_file': str. 20th frame file path in a clip.
}

There will be at most 5 lane markings in lanes. The polylines are organized by the same distance gap (h_sample in each label data) from the recording car. It means you can pair each element in one lane and h_samples to get position of lane marking in images.

For example,

{
  "lanes": [
        [-2, -2, -2, -2, 632, 625, 617, 609, 601, 594, 586, 578, 570, 563, 555, 547, 539, 532, 524, 516, 508, 501, 493, 485, 477, 469, 462, 454, 446, 438, 431, 423, 415, 407, 400, 392, 384, 376, 369, 361, 353, 345, 338, 330, 322, 314, 307, 299],
        [-2, -2, -2, -2, 719, 734, 748, 762, 777, 791, 805, 820, 834, 848, 863, 877, 891, 906, 920, 934, 949, 963, 978, 992, 1006, 1021, 1035, 1049, 1064, 1078, 1092, 1107, 1121, 1135, 1150, 1164, 1178, 1193, 1207, 1221, 1236, 1250, 1265, -2, -2, -2, -2, -2],
        [-2, -2, -2, -2, -2, 532, 503, 474, 445, 416, 387, 358, 329, 300, 271, 241, 212, 183, 154, 125, 96, 67, 38, 9, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2],
        [-2, -2, -2, 781, 822, 862, 903, 944, 984, 1025, 1066, 1107, 1147, 1188, 1229, 1269, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2]
       ],
  "h_samples": [240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710],
  "raw_file": "path_to_clip"
}

-2 in lanes means on some h_sample, there is no exsiting lane marking. The first existing point in the first lane is (632, 280).

Segmentation Label Generation [Advanced]

There is no precise corresponding generation script for the provided segmentation labels, although they are in good quality. If you plan to generate segmentation labels yourself, or simply can't download from Google Drive, refer to #40 for links to Baidu Drive, C++ scripts and Python scripts.