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docs/src/analytics/analytics_stamp.rst

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+.. _analytics_stamp:
+
+Analyze time stamps
+====================
+
+SDK provides a script for timestamp analysis ``stamp_analytics.py`` . Tool details are visible in `tools/README.md <https://github.com/slightech/MYNT-EYE-S-SDK/tree/master/tools>`_ .
+
+Reference run commands and results on Linux:
+
+.. code-block:: bash
+
+  $ 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:
+
+.. image:: ../../images/stamp_analytics.png
+
+In addition, the script specific options can be executed ``-h`` to understand:
+
+.. code-block:: bash
+
+  $ 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.