Edge Insights for Vision

Documentation

  • 2021.4.2
  • 01/25/2022
  • Public Content
Contents

Single and Multi-Object Detection on Intel® Vision Accelerator on Windows*

This Windows* tutorial uses the sample application called "Object Detection YOLO* V3 Python* Demo."
Object Detection YOLO V3 Python Demo uses the following components of
OpenVINO™
Toolkit:
  • OpenCV: to decode the input video and display a frame with detections that are rendered as bounding boxes and labels, if provided.
  • Inference Engine: to perform inference on a decoded frame using deep learning models provided.
By default, this sample application displays latency and FPS.
Instructions in this tutorial are provided for the Intel® Vision Accelerator.
For each configuration, the sample demonstrates two detection types:
  • Single detection
     uses a basic data set to perform one-by-one person detection.
  • Multi-detection
     uses an advanced data set to perform multi-object detection, such as a person and a car.
While running the sample applications, you will gain familiarity with the
Intel® Distribution of OpenVINO™ toolkit
.

Single and Multi-Object Detection with Hardware Acceleration on an Intel® Vision Accelerator

By running the application on the Intel® Vision Accelerator, you are offloading processing of inference to the Intel® Vision Accelerator and freeing up your CPU for other applications.
If you used the GPU instructions and left your command prompt window open, skip ahead to
Step 2
.
Run these steps on the
target system
.
Step 1: Initialize the
Intel® Distribution of OpenVINO™ toolkit
 Environment
  1. Open a command prompt window in Administrator mode.
  2. Go to the bin directory located in the
    Intel® Distribution of OpenVINO™ toolkit
     installation path: 
    cd C:\Program Files (x86)\Intel\openvino_2021\bin
  3. Run
    setupvars.bat
  4. Go to the sample application directory in which the Object Detection YOLO V3 Python demo is located: 
    cd C:\Users\<username>\Downloads\YOLOv3
Leave the command prompt window open for the next step.
Step 2: Run the Single Detection Application on an Intel® Vision Accelerator
  1. Run the Object Detection YOLO V3 Python Demo sample application: 
    python object_detection_demo.py -i Sample_videos\one-by-one-person-detection.mp4 -m tensorflow-yolo-v3\FP32\frozen_darknet_yolov3_model.xml -d HDDL -t 0.1 -at yolo
    Success is indicated by an image that shows a single individual in a bounding box. At the left side of the image you see the latency. You might not clearly see some bounding boxes and detections if scene components are the same color as the bounding box or text.
  2. Press the
    ESC
     key to exit the demo.
Leave the command prompt window open for the next step.
Step 3: Run the Multi-Detection Application on an Intel® Vision Accelerator
  1. Run the Object Detection YOLO V3 Python Demo sample application: 
    python object_detection_demo.py -i Sample_videos\person-bicycle-car-detection.mp4 -m tensorflow-yolo-v3\FP32\frozen_darknet_yolov3_model.xml -d HDDL -t 0.1 -at yolo
    Success is indicated by an image that shows one or more objects and/or people. At the left side of the image you see the latency. You might not clearly see some bounding boxes and detections if scene components are the same color as the bounding box or text.
  2. Press the
    ESC
     key to exit the demo.

Summary and Next Steps

In this tutorial, you learned to run inference applications on different processing units using the sample application "Object Detection YOLO V3 Python Demo." In the process, you gained familiarity with the
Intel® Distribution of OpenVINO™ toolkit
, which was installed with the
Edge Insights for Vision
.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.