k4arecorder.exe -l 5 D:\Knowledge\Graduation_Design\WorkSpace\Resources\output-2.mkv
Options:
-h, --help Prints this help
--list List the currently connected K4A devices
--device Specify the device index to use (default: 0)
-l, --record-length Limit the recording to N seconds (default: infinite)
-c, --color-mode Set the color sensor mode (default: 1080p), Available options:
3072p, 2160p, 1536p, 1440p, 1080p, 720p, 720p_NV12, 720p_YUY2, OFF
-d, --depth-mode Set the depth sensor mode (default: NFOV_UNBINNED), Available options:
NFOV_2X2BINNED, NFOV_UNBINNED, WFOV_2X2BINNED, WFOV_UNBINNED, PASSIVE_IR, OFF
--depth-delay Set the time offset between color and depth frames in microseconds (default: 0)
A negative value means depth frames will arrive before color frames.
The delay must be less than 1 frame period.
-r, --rate Set the camera frame rate in Frames per Second
Default is the maximum rate supported by the camera modes.
Available options: 30, 15, 5
--imu Set the IMU recording mode (ON, OFF, default: ON)
--external-sync Set the external sync mode (Master, Subordinate, Standalone default: Standalone)
--sync-delay Set the external sync delay off the master camera in microseconds (default: 0)
This setting is only valid if the camera is in Subordinate mode.
-e, --exposure-control Set manual exposure value (-11 to 1) for the RGB camera (default: auto exposure)
You can create aruco and apriltag board image at aruco board
This extrinsic calibration is a Python implementation based on the article Extrinsic Calibration of Multiple Azure Kinect Cameras. To perform the calibration, run the following command:
python calib/calib.pyThe implementation of Kinect Fusion is referred the repository by JinWenWang. Please refer to the repository for detailed prerequisites.
To obtain the open-source dataset, you can download it from TUM dataset. After downloading the raw sequences, you will need to run the pre-processing script under dataset/. For example:
python dataset/preprocess.py --config configs/fr1_desk.yaml
python dataset/preprocess.py --config configs/fr1_metallic_sphere.yamlAfter obtaining the processed sequence,you can simply run kinfu.py,which will perform the tracking and mapping headlessly and save the results.
For example:
python kinfu.py --config configs/fr1_desk.yaml --save_dir reconstruct/fr1_desk
python kinfu.py --config configs/fr1_metallic_sphere.yaml --save_dir reconstruct/fr1_metallic_sphereOr if you want to visualize the tracking and reconstruction process on-the-fly. run the following command:
python kinfu_gui.py --config configs/fr1_desk.yamlmain package used:
pip install opencv-python pykinect_azure open3d pupil_apriltags imageio trimesh scikit-image
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