Introduction: Detecting Objects With Python: an Introductory Guide to Object Detection

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1. Object detection is a popular computer vision task that involves detecting and localizing objects within an image or video stream. It has numerous applications in various fields, such as security, surveillance, self-driving cars, and robotics. With the help of Python and OpenCV libraries, object detection can be performed with ease.

2. In this blog, we will be exploring how to perform object detection using Python and OpenCV on a Brainy Pi. We will be using a pre-trained deep learning model called the Single Shot Detector (SSD) to detect objects in real-time using the BrainyPi kit camera.

3. We will start by setting up the Brainy Pi and installing the necessary libraries. We will then build a Python script that will capture frames from the camera and use the SSD model to detect objects within the frames. Finally, we will display the detected objects on the screen in real-time.

4. By the end of this tutorial, you will have a solid understanding of how to perform object detection using Python and OpenCV on a BrainyPi. So let's get started!

5. This is a Python code for object detection using the Single Shot Detector (SSD) algorithm with MobileNetV3 as the base network. The code uses the OpenCV library for image and video processing.

6. The `detectObjects` function takes an input image, the detection threshold, and a non-maximum suppression (NMS) threshold as inputs. It also has an optional flag to draw bounding boxes around detected objects and a list of objects to filter the detection output. The function returns the output image and a list of detected object information.

7. The `main` function sets up the camera capture and continuously captures frames from the camera. It then applies object detection on each frame using the `detectObjects` function and displays the output image with bounding boxes around the detected objects.

8. Overall, this code can be used as a starting point for developing an object detection system using the SSD algorithm with MobileNetV3.

Supplies

  1. Mouse
  2. Keyboard
  3. Brainy Pi
  4. Internet Connection
  5. Monitor
  6. Web Cam

Step 1: Setting Up

First we need to clone the repository containing the code

git clone https://github.com/brainypi/brainypi-opencv-examples.git

We will start by creating a virtual environment.

python -m venv venv

Now, activate the virtual environment

cd venv/Scripts
activate

Installing the requirements for the object detection to work properly

pip install -r requirements.txt


Step 2: Running the Code

We can now run the code

python object-detection.py