Real-Time Object Detection OpenCV Python – The task of executing real-time object detection with quick inference while retaining a base level of accuracy is known as real-time object detection.
Importance of Real-Time Object Detection Python OpenCV
Object detection is closely related to other similar computer vision techniques like image recognition and image segmentation in that it aids in the comprehension and analysis of situations in photos and video.
Real-Time Object Detection In Python: Project Details and Technology
| Project Name: | Real-Time Object Detection Project In Python With Source Code |
| Abstract | Object Detection Python OpenCV – The computer vision technique of object detection is used to locate instances of things in photos or movies. |
| Language/s Used: | Python Deep Learning |
| Python version (Recommended): | 3.8/3.9 |
| Database: | None |
| Type: | Desktop Application |
| Developer: | Source Code Hero |
| Updates: | 0 |
Real-Time Object Detection OpenCV Python: About the project
This Real-Time Object Detection OpenCV Python script is a small experimental tool to detect common objects (COCO) simply with your built-in webcam using Python OpenCV.
It makes use of the readNet method in opencv as well as the external yolov3-tiny model (which can be upgraded to the full-sized model).
The readNet function in Opencv only operates on the CPU (not the GPU), is quite resource intensive, and thus is not suitable for large AI applications.
Using pretrained yolov3 models, the Object Detection OpenCV Python provides an image and video object detection classifier.
The yolov3 models are from the official yolov3 paper from 2018. Darknet provided the yolov3 implementation. Also included in this project is the ability to perform real-time classification using the webcam.
This Real-Time Object Detection In Python also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.
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To start executing a Real-Time Object Detection OpenCV Python With Source Code, make sure that you have installed Python on your computer.
Real-Time Object Detection OpenCV Python With Source Code: Steps on how to run the project
Time needed: 5 minutes
These are the steps on how to run Real-Time Object Detection OpenCV Python With Source Code
- Step 1: Download Source Code
First, download the provided source code below.
- Step 2: Extract File
Next, after the download is finished extract the zip file.
- Step 3: Import the project to your PyCharm IDE.
Next, import the source code you’ve downloaded to your PyCharm IDE.
- Step 4: Install all Libraries.
Next, install all libraries given below.
- Step 5: Open cmd.
Next, click the folder directory of the project and type cmd.
- Step 6: Run Project.
last, run the project with the command “py main.py”
Download the Source Code below
Summary
This project implements an image and video object detection classifier using pretrained yolov3 models. The yolov3 models are taken from the official yolov3 paper which was released in 2018.
The yolov3 implementation is from darknet. Also, this project implements an option to perform classification real-time using the webcam.
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Inquiries
If you have any questions or suggestions about Real-Time Object Detection OpenCV Python With Source Code, please feel free to leave a comment below.
why didn’t you show your output ? if you show us the final output it will be more interesting.
I have always getting the error in line 13 which is
output_layers = [layers_names[i[0] – 1] for i in net.getUnconnectOutLayers()]
Can you help me with that