Overview
This example demonstrates the use of the Deep Learning API to perform People Detection using TensorFlow Object Detection Model.
Description
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.
Hardware and Software Requirements
LabVIEW 2018 64-bit and later
Vision Development Module 2019
Windows 10 64-bit or Linux RT 64-bit target
Steps to Implement or Execute Code
- Extract the contents of both zip file. Place the ssd_mobilenet_v1_coco.pb file at the same level as the NIWeek_ssdmobileNet.vi.
- Open the Block Diagram “NIWeek_ssdmobileNet.vi” and configure the Vision Acquisition Express VI to use your camera.
- On the front panel of NIWeek_ssdmobileNet.vi, fill in the following controls:
- Path to People in Frame .csv File: Enter the path to which you want to save the .csv file that will contain the number of people detected in the frame over the course of the day. The format should be like the following: C:\Users\nitest\Desktop.
- Graph Start Date: Change it to today's date.
- Run the VI.
- Observe the overlays around the objects being detected. The graph shows the number of people that has been detected at the same time in the feed over the course of the day.
Additional Information or References
T. Le
Vision Product Support Engineer
National Instruments
