open-azure-kinect 0.1.0

Cross-platform playback library for Microsoft Azure Kinect MKV files.

Open Azure Kinect

Cross-platform Python playback library for Azure Kinect MKV files.

Calibration Example

It is possible to playback Azure Kinect videos files (mkv) without using the official SDK. This allows the software to be used on systems where the depth engine is not implemented, such as MacOS. The library currently only supports the playback of mkv files and does not provide direct access to the Azure Kinect device.

The following functions are currently supported:

• Reading colour, infrared and depth stream from mkv
• Reading and parsing calibration data from mkv
• Image alignment and point transformation (⚠️ maybe not as accurate as the Azure Kinect SDK)

Installation

pip install open-azure-kinect

Usage

In order to load an MKV file, it is necessary to create a new instance of the OpenK4APlayback class. Note that if the is_looping flag is set, the stream will not stop playing at the EOF of the stream. It will automatically close and reopen the file.

from openk4a.playback import OpenK4APlayback

azure = OpenK4APlayback("my-file.mkv")
azure.is_looping = True # set loop option if necessary
azure.open()

After that, it is possible to read the available stream information.

for stream in azure.streams:
    print(stream)

# print clip duration
print(azure.duration_ms)

And read the actual capture information (image data).

while capture := azure.read():
    # read color frame as numpy array
    color_image = capture.color

    # print current timestamp in ms (of the video timeline)
    print(azure.timestamp_ms)

Seek

With seek(timestamp_ms: int) it is possible to jump to a specific position in the video. The current implementation is not very efficient as the library just skips frames until the timestamp is reached. In the future, this should be replaced with a ffmpeg controlled seek.

# jump +1 second into the future
azure.seek(azure.timestamp_ms + 1000)

Calibration Data

To access the calibration data of the two cameras (Color, Depth), use the parsed information property.

color_calib = azure.color_calibration
depth_calib = azure.depth_calibration

Image and Point Transformations

The class CameraTransform handles the transformation task between the different cameras.

⚠️ Be aware that this part of the framework is still under development! Please open a PR if you like to improve it.

import numpy as np

from openk4a.transform import CameraTransform

transform = CameraTransform(azure.color_calibration, azure.depth_calibration)

# transform points from color to depth image (using epipolar search)
depth_points = transform.transform_2d_color_to_depth(np.array([[300, 400], [200, 200]]))

# create 3d pointcloud from depthmap
points_3d = transform.create_pointcloud(depth_map)

# transform color image into depth image
transformed_color = transform.align_image_color_to_depth(color, depth_map)

Examples

Please find more templates on how to use this library in the examples folder.

Development and Examples

To run the examples or develop the library please install the dev-requirements.txt and requirements.txt.

pip install -r dev-requirements.txt
pip install -r requirements.txt

There is already an example script demo.py which provides insights on how to use the library.

About

Thanks to tikuma-lsuhsc for creating python-ffmpegio and helping me extract the Azure Kinect data.