Easy Installation and Demonstration of Opencv AI Kit (OAK-D) on Windows

Table of Contents

1. Introduction
2. Installing Depth AI on Windows
3. Running Object Detection
4. Running Face Detection
5. Exploring Depth Tracking
6. Practical Applications of Depth Tracking
7. Conclusion

Introduction

In this article, we will explore the concept of depth tracking in simple terms. Depth tracking involves detecting the distance of objects. We will dive into the world of visual recognition and its application in various projects. Specifically, we will focus on the OpenCV AI Kit Version D, a fascinating device that enables easy and efficient visual recognition. This article will guide You through the process of installing the kit on a Windows system and provide an overview of its characteristics and features. We will explore three key aspects: object detection, face detection, and depth tracking. So, let's get started!

Installing Depth AI on Windows

To begin with, we need to install the Depth AI module on our Windows system. We can accomplish this in two simple steps. First, let's visit the GitHub repository and download the Depth AI module. Once downloaded, extract the files and navigate to the "install_requirements.py" file. We can run this file using the Python prompt command to complete the installation process. It's important to ensure that Python is already installed on your system. Once the installation is complete, we can proceed to explore the capabilities of the OpenCV AI Kit.

Running Object Detection

Object detection is the Core functionality of the OpenCV AI Kit. It allows us to detect various objects in real-time using different neural network models. To run object detection, we need to navigate to the Depth AI main folder and execute the "depthai_demo.py" file. There are several pre-trained models available for object detection, such as MobileNet SSD, Tiny YOLO, and YOLO v3. We can choose the desired model by specifying its name in the command line argument. Each model has its own set of categories it can detect, including vehicles, animals, and more. By running the object detection demo, we can witness the kit in action and observe the accuracy of the detection.

Running Face Detection

Another useful feature provided by the OpenCV AI Kit is face detection. By utilizing the power of neural networks, the kit can accurately detect faces in real-time. To run face detection, we follow a similar process to object detection. We navigate to the Depth AI main folder and execute the "depthai_demo.py" file. However, this time, we choose the face detection model, such as "adas_0001". The kit will then start detecting faces, drawing bounding boxes around them. It's important to note that face detection may have a slight lag due to the processing time of the neural network. Nevertheless, the kit provides smooth video output at 30 frames per Second, ensuring a seamless experience.

Exploring Depth Tracking

One of the most intriguing features of the OpenCV AI Kit is depth tracking. Depth tracking allows us to determine the distance of objects from the camera and Visualize it using a heat map. Closer objects appear yellow, while farther objects appear red or even black. This feature provides valuable information for various applications. By simply running the "depthai_demo.py" file with the appropriate command line argument, we can experience depth tracking firsthand. The kit displays the distance of objects in real-time, creating a fascinating perceptual experience.

Practical Applications of Depth Tracking

The applications of depth tracking are vast and diverse. One practical use case is removing oneself from the background without the need for a green screen. By leveraging the depth information provided by the kit, it becomes possible to segment the foreground accurately. This opens up new possibilities for video editing and content creation. Another application is in robotic arm control, where knowing the distance between the arm and objects is crucial to avoid collisions. Depth tracking can also enhance emotion tracking by providing more precise depth information for facial expressions. This can lead to more accurate emotion recognition systems.

Conclusion

In this article, we explored the concept of depth tracking and its application in the OpenCV AI Kit Version D. We learned how to install the kit on a Windows system and ran demonstrations of object detection, face detection, and depth tracking. The OpenCV AI Kit offers a wide range of possibilities for visual recognition projects, enabling developers, companies, students, and freelancers to incorporate advanced computer vision techniques effortlessly. The applications of depth tracking are diverse and hold immense potential for various industries. With further developments and improvements, the OpenCV AI Kit will Continue to revolutionize the field of computer vision.

Highlights:

• Understanding the concept of depth tracking and its significance in computer vision
• Installing the OpenCV AI Kit Version D on a Windows system
• Exploring object detection capabilities using pre-trained models
• Running real-time face detection with accurate bounding boxes
• Visualizing depth information and distance using a heat map
• Practical applications of depth tracking in video editing, robotics, and emotion recognition

FAQ

Q: Can the OpenCV AI Kit be used for real-time video editing? A: Yes, the OpenCV AI Kit can be used for real-time video editing. By leveraging its depth tracking feature, you can easily segment the foreground and remove the background without the need for a green screen.

Q: How accurate is the face detection feature of the OpenCV AI Kit? A: The face detection feature of the OpenCV AI Kit is quite accurate. However, due to the processing time of the neural network, there may be a slight lag in the detection.

Q: What are some practical applications of depth tracking? A: Depth tracking has various practical applications. It can be used in robotics for precise control of robotic arms to avoid collisions with objects. It can also enhance emotion tracking by providing more accurate depth information for facial expressions.

Q: Are there any limitations to object detection using the OpenCV AI Kit? A: The accuracy and performance of object detection using the OpenCV AI Kit depend on the chosen neural network model and the quality of the training data. Some models may perform better than others in specific scenarios.

Q: Can the OpenCV AI Kit be used for real-time emotion recognition? A: Yes, the OpenCV AI Kit can be used for real-time emotion recognition. By leveraging its depth tracking and facial detection capabilities, it becomes possible to analyze facial expressions accurately.