Path Det Dataset curation - TuSimple

[m-zain-khawaja]

Description:

Parse the TuSimple lane detection dataset to create a dataset comprising input images in PNG format and a single drivable path as the ground truth, derived as the mid-line between the left/right ego lanes.

• Please create a script called process_tusimple.py in this folder (https://github.com/autowarefoundation/autoware.privately-owned-vehicles/tree/main/PathDet/create_path/TuSimple) which is responsible for creating the ground truth and saving the data.
• The ground truth drivable path information should be stored as a list keypoints defining the path in a JSON format.
• Please ensure that keypoints are stored in relative coordinates, where the top left-most corner of the image is 0,0 and the bottom right-most corner of the image is 1,1, and all other coordinates are floating point values in the range (0,1) for x,y directions
• Please ensure ground truth images are stored in PNG format
• Please also save a semantic drivable path mask which is drawn upon the input RGB image for data auditing purposes.

Data Summary:

• RGB image in PNG Format
• Drivable path keypoints in JSON Format
• Semantic Drivable Path Mask draw on top of RGB image in PNG format (not used during training, only for data auditing purposes)