This example demonstrates the use of the Deep Learning API to perform People Detection using TensorFlow Object Detection Model.
This example uses a pre-trained TensorFlow Object Detection model SSD_Mobilenet_v1_Coco model downloaded from TensorFlow’s Github. It detects people and objects from a live feed and overlays the class of the object detected. It also indicates the current number of people present in the feed and keeps track of how many people has been captured over time. The graph information is saved to a .csv file to a location of choice. This example demonstrates how to load a pre-trained model from the Model Zoo using the Model Importer API, supply LabVIEW image data as input and run the model.
LabVIEW 2018 64-bit and later
Vision Development Module 2019
Windows 10 64-bit or Linux RT 64-bit target
Example code from the Example Code Exchange in the NI Community is licensed with the MIT license.
Very impressive!
Thanks a lot for this example thuyanhl
Now I have an idea how to use other pretrained models with LabVIEW!
@thuyanhl
You implied in your statement under “Hardware and Software Requirements” that this could be run on a Linux RT 64-bit target? But according to the 2019 Real-Time Module ReadMe (http://www.ni.com/pdf/manuals/374714k.html) LV only supports 32-bit.
Can this example be deployed to a NI Linux RT Target, if so how?
@thuyanhl
One question: How did you identify the names for the outputs (num_detections, detection_boxes and so forth)? (see image).
Thank you for your help
Peter