kl3m3n's blog
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lizhuo_lin
on:
Stereo vision (OpenCV and Labview comparison)
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张斌
on:
Point cloud registration tool
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GohanTYO
on:
Qt+PCL+OpenCV (Kinect 3D face tracking)
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Klemen
on:
Qt GUI for PCL (OpenNI) Kinect stream
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efreet
on:
OpenCV and Qt based GUI (Hough circle detection example)
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Spalabuser
on:
Homography mapping calculation (Labview code)
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rameshr
on:
Optical water level measurements with automatic water refill in Labview
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xuexue0224
on:
Kalman filter (OpenCV) and MeanShift (Labview) tracking
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Aaatif
on:
Serial data send with CRC (cyclic redundancy check) - Labview and Arduino/ARM
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hsaid
on:
Color image segmentation based on K-means clustering using LabVIEW Machine Learning Toolkit
Re: Stereo vision (OpenCV and Labview comparison)
Hello,
the purpose of this post is to evaluate and compare (only visually) the performance of the stereo vision libraries from OpenCV and Labview. I have used a pair of horizontally positioned webcameras (Logitech c210) mounted on a stand with a baseline distance of approximately 100 mm. The OpenCV stereo functions were built as a .dll and called in Labview (FULL CALIBRATION AND MEASUREMENT EXAMPLE IS A PART OF Labview PCLOpenCV Toolkit THAT CAN BE DOWNLOADED FROM ONE OF MY PREVIOUS POSTS). One of the advantages of the OpenCV calibration procedure is that the rectification transformation for both cameras can be accessed, while this is (to my knowledge) impossible in Labview. All parameters regarding the calibration are saved in a user-specified file and are read during the measurement procedure.
The calibration based on Labview's stereo library can be found here:
For the purpose of testing the matching algorithms (to obtain the disparity image), I have also used a DLP projector to project a random pattern on the measured scene. This helps to make the correspondence problem a little easier for the regions with less texture. The results are shown in Figures 1-4 below. The rectified images from both cameras, the disparity map (semi global block matching) and the reconstructed 3D surface are shown for OpenCV and Labview, where the measured scene was not illuminated in one case and illuminated with the projector in the other case.
Figure 1. OpenCV without projection pattern
Figure 2. Labview without projection pattern
Figure 3. OpenCV with projection pattern
Figure 4. Labview with projection pattern
Also, I have achieved 5-10 frames per second performance with OpenCV. I have not yet tested the speed performance of the Labview stereo library. I have also noted some discrepancies in the calibration parameters between both calibration procedures.
I am wondering for some time now if the Labview stereo library is partially (fully?) based on the one form OpenCV? There is definitely a lack in documentation in Labview regarding this, and to my experience the NI support could also be a bit better.
Any comments and experiences regarding this are appreciated.
Best regards,
K
