How to get the lane ID from output?

[lucky-xu-1994]

Hi, thank you for your great work! It did well on my own datasets!
but I have trouble with getting the lane ID directly from seg_out. It seems to need a label.png to get the lane ID like in CUlane.
So, how can I directly get the lane ID？
please give me some advise, thank you very much!

[arangesh]

The ground truth segmentation labels are not necessary during inference but we do use them to assign consistent lane IDs to the lanes obtained from our approach (see this line). Doing so does not affect our scores on CULane but makes the visualization more consistent by assigning the same color to lanes across different images.
I will try to push a change soon that makes this optional so that you can run inference on CULane without needing the label files.

[lucky-xu-1994]

The ground truth segmentation labels are not necessary during inference but we do use them to assign consistent lane IDs to the lanes obtained from our approach (see this line). Doing so does not affect our scores on CULane but makes the visualization more consistent by assigning the same color to lanes across different images.
I will try to push a change soon that makes this optional so that you can run inference on CULane without needing the label files.

thanks a lot!

[arangesh]

I have pushed the necessary changes. The infer_*.py scripts can now run without the label.png files.

[lucky-xu-1994]

I have pushed the necessary changes. The infer_*.py scripts can now run without the label.png files.

thank you for modifying the scripts that can now run without the 'label.png' files.
but it still can't get the lane ID like in culane.
as shown below, It only presents 3 lanes as a result, but dosen't show which lane ID it is(used to get the lane ID by match_multi_class). In culane the ID is 1,2,3.

the following ID in culane is 0,1,2,3

[arangesh]

Unfortunately, we don't differentiate between different lanes in our approach. We detect all the lanes and assign each one a unique ID. However, the IDs are not consistent and can change between images. For example, the left most lane can be assigned ID 1 in one image, and be assigned ID 2 in the next.

You can use some simple logic to convert the IDs to match CULane. For example, you can use the average horizontal location of each lane to figure out which lanes are to the left and which ones are to the right.

[lucky-xu-1994]

Unfortunately, we don't differentiate between different lanes in our approach. We detect all the lanes and assign each one a unique ID. However, the IDs are not consistent and can change between images. For example, the left most lane can be assigned ID 1 in one image, and be assigned ID 2 in the next.

You can use some simple logic to convert the IDs to match CULane. For example, you can use the average horizontal location of each lane to figure out which lanes are to the left and which ones are to the right.

ok, thank you for your advice!