README_PREPARE_DATASET.md

How to prepare your dataset?

TLDR;

• Aligned images in your_dataset/img/*.png and ground truth in your_dataset/label_cor/*.txt
• Image in 1024 x 512 resolution
• Ground truth format:

  x_0 y_ceiling_0
  x_0 y_floor_0
  x_1 y_ceiling_1
  x_1 y_floor_1
  ...
  

  which follows the order of layout.

---

The dataset should be organized as below format for train.py to use it:

your_dataset/
|--img/
|  |--AAAA.png
|  |--BBBB.png
|  |--...
|--label_cor/
|  |--AAAA.txt
|  |--BBBB.txt
|  |--...

Please also note that:

• Your imaages should be aligned. If not, run python preprocess.py --img_glob "your_dataset/rawimg/*png" --output_dir your_dataset/img --rgbonly.
• All prefix names between images and grond truth should match. I.e if there is a ASDF.png in your_dataset/img/ directory, there should be a ASDF.txt in your_dataset/label_cor/.
• All images have to be in the resolution of 1024 width x 512 height for current implementation.
• Check your dataset is proper and ready for training by python dataset.py --root_dir your_dataset/ --ith -1 --out_dir your_dataset/visualize. Please check the ground truth visualization in your_dataset/visualize to make sure all thing go right.

More notes about ground truth

• Each line of the txt is a corners in image coordinate (top-left is origin)
• x in range 0~1023
• y in range 0~511
• Format:

  x_0 y_ceiling_0
  x_0 y_floor_0
  x_1 y_ceiling_1
  x_1 y_floor_1
  ...
  

  • Odd lines are ceiling corners; even lines are floor corners.
  • Note that x values have NOT to be monotonically increasing but have to follow the order of layout (see below example).
• One example of image and ground truth pair:

  • 158 186
    158 329
    353 185
    353 330
    594 154
    594 363
    713 100
    713 415
    692 77
    692 438
    965 150
    965 367
    

  • Please note that 713 100 and it floor correspondent 713 415 are occluded.
  •