Suitability study for real-time depth map generation using stereo matchers in OpenCV and Python

Stereo imaging provides an easy and cost-effective method to measure 3D surfaces, especially due to the availability of extensive free program libraries like OpenCV. An extension of the application to the field of forestry was aimed at here in the context of a project to capture the elevation profile of forest roads by means of stereo imaging. For this purpose, an analysis of the methods contained in OpenCV for the successful generation of depth maps was carried out. The program sections comprised the reading of the image stream, the image correction on the basis of calibrations carried out in advance as well as the generation of the disparity maps by the stereo matchers. These are then converted back into depth maps and stored in suitable memory formats. A data set of the image size 1280x864 pixels consisting of 30 stereo image pairs was used. The aim was to design an evaluation program which allows the processing of the described steps within one second for 30 image pairs. With a sequential processing of all steps under the used test system and the usage of a local stereo matcher a processing time of 4.37 s was determined. Steps to reduce the processing time included parallelizing the image preparation of the two frames of the image pair. Further reduction in total processing time was achieved by processing multiple image pairs simultaneously and using storage formats without compression. A total processing time of 0.8 s could be achieved by outsourcing the stereo matching to the graphics card. However, the tested method did not achieve the desired resolutions in depth as well as in the image plane. This was made possible by using semi-global matchers, which are up to 10 times slower but significantly more accurate, and which were therefore used for further investigations of the forest path profile.