The Real-Time Object Detection project using OpenCV and Python was developed as a simple experimental tool to detect common objects (COCO dataset) easily with your built-in webcam.
It uses OpenCV’s readNet method along with the external YOLOv3-tiny model (which can be upgraded to the full-sized YOLOv3 model).
OpenCV’s readNet method runs only on CPU (not GPU), making it very resource-intensive and therefore not optimal for large-scale AI projects.
This Object Detection implementation in Python applies an image and video classifier using pretrained YOLOv3 models.
The YOLOv3 models are based on the official YOLOv3 paper released in 2018, and the implementation originates from the Darknet framework.
Furthermore, this project includes an option to perform real-time classification using the webcam.
If you are new to Python programming and unsure which IDE to use, here is a list of the Best Python IDE for Windows, Linux, and Mac OS that may suit your needs.
You can also check out How to Download and Install the Latest Version of Python on Windows.
To start executing this project, make sure that you have installed Python 3.9 and PyCharm on your computer.
How to run the Real-Time Object Detection using OpenCV Python: A step-by-step Guide with Source Code
These are the steps on how to run Real-Time Object Detection OpenCV Python With Source Code.
- Download the given source code below.
Download the given source code and unzip the source code.
- Import the project to your PyCharm IDE.
Next, import the source code that you’ve downloaded to your PyCharm IDE.
- Run the project.
Lastly, run the project with the command "py main.py"
import cv2
import numpy as np
import time