Détectez les objets en temps réel avec YOLO et Deep Stream

Table of Contents

1. Introduction

• Introduction to Python examples in Deep Stream

2. Running Python Examples with YOLO

• Step 1: Setting up the environment
• Step 2: Modifying the Deep Stream test Python script
• Step 3: Running the YOLO application
• Step 4: Displaying the detected objects

3. Adding Metadata Extraction

• Step 1: Modifying the Deep Stream test Python script
• Step 2: Adding metadata extraction code
• Step 3: Configuring the display preferences
• Step 4: Final modifications to run YOLO with metadata extraction

4. Github Repository and Installation

• Step 1: Accessing the Github repository
• Step 2: Cloning the repository
• Step 3: Copying the modified script
• Step 4: Running the Deep Stream application with YOLO

5. Running YOLO with Xavier NX

• Step 1: Running YOLO on Xavier NX
• Step 2: Adjusting the frame rate for optimal performance
• Step 3: Running YOLO on Xavier NX without VNC

6. Running YOLO with MP4 Files

• Step 1: Finding a suitable MP4 file
• Step 2: Running the Deep Stream application with YOLO and MP4 files

7. Conclusion

Running Python Examples with YOLO

Python examples in Deep Stream can be enhanced by integrating the YOLO (You Only Look Once) object detection model. This allows for real-time object detection and metadata extraction. In this guide, we will walk you through the steps to run Python examples in Deep Stream with YOLO.

Step 1: Setting up the environment

Before getting started, ensure that you have the necessary environment set up to run Python examples with YOLO. This includes having Deep Stream and the required dependencies installed.

Step 2: Modifying the Deep Stream test Python script

To incorporate YOLO into the Deep Stream Python script, you will need to make some modifications. These modifications involve adding code snippets to extract metadata and configure the preferences for displayed objects.

Step 3: Running the YOLO application

Once the Deep Stream Python script has been modified, you can proceed to run the YOLO application. This application utilizes the YOLO object detection model to detect and classify objects in real-time.

Step 4: Displaying the detected objects

To Visualize the detected objects, the modified Deep Stream script will display bounding boxes around the objects. You can customize the preferences to display specific classes of objects, such as birds and persons.

By following these steps, you can successfully run Python examples in Deep Stream with YOLO, allowing for real-time object detection and metadata extraction.

Pros:

• Real-time object detection
• Metadata extraction for deeper analysis
• Customizable display preferences for detected objects

Cons:

• Requires modification of the Deep Stream Python script
• Initial setup and dependencies installation may be time-consuming

To access the complete Tutorial and the Github repository for the modified Deep Stream script, please visit here.

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