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

Tip

Suggestions when recording data sets record.cc annotation display image inside cv::imshow(), 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.