Analyze time stamps¶
SDK provides a script for timestamp analysis
stamp_analytics.py . Tool details are visible in tools/README.md .
Reference run commands and results on Linux:
$ python tools/analytics/stamp_analytics.py -i dataset -c tools/config/mynteye/mynteye_config.yaml stamp analytics ... input: dataset outdir: dataset open dataset ... save to binary files ... binimg: dataset/stamp_analytics_img.bin binimu: dataset/stamp_analytics_imu.bin img: 1007, imu: 20040 rate (Hz) img: 25, imu: 500 sample period (s) img: 0.04, imu: 0.002 diff count imgs: 1007, imus: 20040 imgs_t_diff: 1006, imus_t_diff: 20039 diff where (factor=0.1) imgs where diff > 0.04*1.1 (0) imgs where diff < 0.04*0.9 (0) imus where diff > 0.002*1.1 (0) imus where diff < 0.002*0.9 (0) image timestamp duplicates: 0 save figure to: dataset/stamp_analytics.png stamp analytics done
The analysis result graph will be saved in the dataset directory, as follows:
In addition, the script specific options can be executed
-h to understand:
$ python tools/analytics/stamp_analytics.py -h
Suggestions when recording data sets
record.cc annotation display image inside
dataset.cc annotation display image inside
cv::imwrite() . Because these operations are time-consuming, they can cause images to be discarded. In other words, consumption can’t keep up with production, so some images are discarded.
GetStreamDatas() used in
record.cc only caches the latest 4 images.