Advanced modules samples

Advanced modules C++ samples
C++ samples of the advanced modules are showing some algorithms in action.
In the following table, for each sample, you will find:
its module
if the sample is using the SDK pipelines
the main algorithms or classes demonstrated in the sample
Name |
Description |
Module |
Using SDK pipelines |
UI framework used for display |
Main algorithms or classes demonstrated in the sample |
---|---|---|---|---|---|
Focuses an event-based camera using a blinking pattern |
Calibration |
No |
SDK UI |
||
Estimates intrinsics parameters of an event-based camera using a blinking pattern |
Calibration |
Yes |
OpenCV |
||
Filters events with a noise filter Activity, Trail or STC) and displays them on the screen |
CV |
No |
SDK UI |
||
Displays event rate from an event-based camera or from a recorded file |
CV |
No |
SDK UI |
||
Computes the sparse optical flow for moving objects |
CV |
No |
SDK UI |
||
Computes the optical flow for moving objects at each event along the edge’s normal |
CV |
No |
SDK UI |
||
Undistort and distort coordinates of events |
CV |
No |
OpenCV |
||
Displays events in a 3D space |
CV |
No |
ImGui |
||
Tracks Active Markers in 2D |
CV |
No |
SDK UI |
|
|
Tracks 2D edgelets |
CV3D |
No |
OpenCV |
||
Tracks known 3D objects |
CV3D |
No |
SDK UI |
||
Tracks Active Markers in 3D |
CV3D |
No |
Ogre |
||
Counts small objects moving vertically (e.g. bulk counting) |
Analytics |
No |
SDK UI |
||
Monitors jets being dispensed and sends alarms when the dispensing frequency isn’t correct |
Analytics |
No |
SDK UI |
||
Counts and estimates the sizes of objects moving vertically |
Analytics |
No |
SDK UI |
||
Tracks any moving object |
Analytics |
No |
SDK UI |
||
Tracks simple non-colliding objects |
Analytics |
No |
SDK UI |
||
Estimates vibration frequency per pixel and shows the dominant frequency (the most common frequency among all pixels) |
Analytics |
No |
SDK UI |
||
Inference Pipeline of Detection and Tracking |
ML |
No |
OpenCV |
Advanced modules Python samples
Python samples of the advanced modules are showing some algorithms in action.
In the following table, for each sample, you will find its modules and the main algorithm or classes demonstrated in the sample.
Name |
Description |
Module |
Main algorithms demonstrated in the sample |
---|---|---|---|
Visualize the 3D locations of a calibration pattern detected during intrinsics calibration |
Calibration |
N/A |
|
Computes the mapping between the camera’s reference system and the world’s reference system (camera is rigidly attached) |
Calibration |
||
Filters events with a noise filter (Activity, Trail or STC) and displays them on the screen |
CV |
|
|
Displays event rate from an event-based camera or from a RAW file |
CV |
||
Computes the sparse optical flow for moving objects |
CV |
||
Computes the dense normal optical flow for moving objects at each event along the edge’s normal |
CV |
||
Tracks known 3D objects |
CV3D |
||
Counts small objects moving vertically (e.g. bulk counting) |
Analytics |
||
Monitors jets being dispensed and sends alarms when the dispensing frequency isn’t correct |
Analytics |
||
Counts and estimates the sizes of objects moving vertically |
Analytics |
||
Tracks any moving object |
Analytics |
||
Tracks simple non-colliding objects |
Analytics |
||
Estimates vibration frequency per pixel and shows the dominant frequency (the most common frequency among all pixels) |
Analytics |
||
Inference Pipeline of Detection and Tracking |
ML |
|
|
Evaluates Detection and Tracking pipeline on coco KPIs |
ML |
||
Visualizes Ground Truth and ML detection results |
ML |
||
Trains Detection model with Pytorch Lightning |
ML |
||
Exports detection checkpoint to torch.jit model |
ML |
||
Performs classification inference |
ML |
||
Trains supervised classification model with Pytorch Lightning |
ML |
||
Exports classification checkpoint to torch.jit model |
ML |
||
Performs Optical Flow inference with a flow network |
ML |
||
Trains Optical Flow model |
ML |
||
Exports flow checkpoint to torch.jit model |
ML |
||
Writes HDF5 feature files from event files |
ML |
||
Visualizes a precomputed HDF5 dataset with or without bbox labels |
ML |
||
Visualizes the Moving MNIST |
ML |
||
Converts Bbox from text format to numpy format |
ML |