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Rendering RGB, Depth and Semantic Segmentation data using carla 0.9.6

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tedyhabtegebrial/carla_rendering

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CARLA Dataset generator

This is a script that generates RGB, Segmentation and depth ground-truth data from Carla. This repository works with Carla version 0.9.6.

Instructions

0. Clone this repository under carla/PythonAPI folder.

1. Run the CARLA simulator

./CarlaUE4.sh -p 2000 -quality-level Epic

2. In a separate terminal run the python client

python client_render.py --port 2000 --town Town05 --weather_id 2 --camera_group ForwardCameras

Configuration. Commandline arguments

camera_group: chooses which camera group to render. The choice should be within the keys in the 'cameras' dictionary defined in camera_configs.py file.
By default we have 3 camera groups. 'HorizontalCameras',
 'ForwardCameras', 'SideCameras'. There is the 'All',
 alternative that allows you to render with all camera groups at the same time.
town: choice of town to be rendered, you can choose 'Town01', 'Town02', 'Town03', 'Town04' or 'Town05'. default Town01
port: default=2000
weather_id: an integer to choose weather conditions, at the moment I am using 4 weather conditions numbered [0,1,2,3]
test_mode: this generates with a different random seed. Useful when you want to generate a sequence that is 'different' from other renderings. An example use case for turning on this flag is, when you want to generate a training and a test sequence by running two different simulations on the same city. In this case, you can run one simulation for training(without test_mode flag) and another for test(with test_mode flag).

Example usage for town 05 weather is 2 in test_mode:

python client_render.py --port 2000 --town Town05 --weather_id 2 --camera_group All --test_mode

TODO: save camera pose at each frame.

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Rendering RGB, Depth and Semantic Segmentation data using carla 0.9.6

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