fangchangma / pykitti

Python tools for working with KITTI data.

pykitti

This package provides a minimal set of tools for working with the KITTI dataset [1] in Python. So far only the raw datasets and odometry benchmark datasets are supported, but we're working on adding support for the others. We welcome contributions from the community.

Installation

Using pip

You can install pykitti via pip using

pip install pykitti

From source

To install the package from source, simply clone or download the repository to your machine

git clone https://github.com/utiasSTARS/pykitti.git

and run the provided setup tool

cd pykitti
python setup.py install

Assumptions

This package assumes that you have also downloaded the calibration data associated with the sequences you want to work on (these are separate files from the sequences themselves), and that the directory structure is unchanged from the original structure laid out in the KITTI zip files.

Notation

Homogeneous coordinate transformations are provided as 4x4 numpy.array objects and are denoted as T_destinationFrame_originFrame.

Pinhole camera intrinsics for camera N are provided as 3x3 numpy.array objects and are denoted as K_camN. Stereo pair baselines are given in meters as b_gray for the monochrome stereo pair (cam0 and cam1), and b_rgb for the color stereo pair (cam2 and cam3).

Example

More detailed examples can be found in the demos directory, but the general idea is to specify what dataset you want to load, then access the parts you need and do something with them.

import pykitti

basedir = '/your/dataset/dir'
date = '2011_09_26'
drive = '0019'

# The 'frames' argument is optional - default: None, which loads the whole dataset.
# Calibration and timestamp data are read automatically. 
# Other sensor data (cameras, IMU, Velodyne) are available via properties 
# that create generators when accessed.
data = pykitti.raw(basedir, date, drive, frames=range(0, 50, 5))

# dataset.calib:      Calibration data are accessible as a named tuple
# dataset.timestamps: Timestamps are parsed into a list of datetime objects
# dataset.oxts:       Returns a generator that loads OXTS packets as named tuples
# dataset.camN:       Returns a generator that loads individual images from camera N
# dataset.gray:       Returns a generator that loads monochrome stereo pairs (cam0, cam1)
# dataset.rgb:        Returns a generator that loads RGB stereo pairs (cam2, cam3)
# dataset.velo:       Returns a generator that loads velodyne scans as [x,y,z,reflectance]

point_velo = np.array([0,0,0,1])
point_cam0 = data.calib.T_cam0_velo.dot(point_velo)

point_imu = np.array([0,0,0,1])
point_w = [o.T_w_imu.dot(point_imu) for o in data.oxts]

for cam0_image in data.cam0:
    pass

rgb_iterator = data.rgb # Assign the generator so it doesn't 
cam2_image, cam3_image = next(rgb_iterator)

OpenCV

Image data can be automatically converted to an OpenCV-friendly format (i.e., uint8 with BGR color channel ordering) simply by specifying an additional parameter in the constructor:

data = pykitti.raw(basedir, date, drive, frames=range(0, 50, 5), imformat='cv2')

Note: This package does not actually require that OpenCV be installed on your system, except to run demo_raw_cv2.py.

References

[1] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, "Vision meets robotics: The KITTI dataset," Int. J. Robot. Research (IJRR), vol. 32, no. 11, pp. 1231–1237, Sep. 2013. http://www.cvlibs.net/datasets/kitti/ `