Real-Time Car Detection using OpenCV Python With Source Code

Real-Time Car Detection using OpenCV and Python With Source Code

The Real-Time Car Detection OpenCV Python was developed using Python OpenCV, Vehicle detection is one of the widely used features by companies and organizations these days.

This technology uses computer vision to detect different types of vehicles in a video or real-time via a camera.

A Car Detection OpenCV Python finds its applications in traffic control, car tracking, creating parking sensors, and many more.

In this, we will learn how to build a car tracking system in Python for both recorded and live cam streamed videos.

This Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.

By the way, if you are new to Python programming and don’t know what Python IDE is, I have here a list of the Best Python IDE for Windows, Linux, and Mac OS that will suit you.

I also have here How to Download and Install the Latest Version of Python on Windows.

To start executing Real-Time Car Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm on your computer.

Real-Time Car or Vehicle Detection using OpenCV and Python With Source Code: Steps on how to run the project

Installed Libraries

import numpy as np
import cv2

Complete Source Code

import numpy as np
import cv2

cascade_src = 'cars.xml'
# video = 'data/Cars_On_Highway.mp4'
video = 'data/video1.avi'
# video = 'data/video2.avi'


def detectCars(filename):
  rectangles = []
  cascade = cv2.CascadeClassifier(cascade_src)

  vc = cv2.VideoCapture(filename)

  if vc.isOpened():
      rval , frame = vc.read()
  else:
      rval = False


  while rval:
    rval, frame = vc.read()
    frameHeight, frameWidth, fdepth = frame.shape

    # Resize
    frame = cv2.resize(frame, ( 600,  400 ))
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # haar detection.
    cars = cascade.detectMultiScale(gray, 1.3, 3)


    for (x, y, w, h) in cars:
      cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)


    # show result
    cv2.imshow("Result",frame)

    if cv2.waitKey(33) == ord('q'):
      break

  vc.release()


detectCars(video)

Output:

Download the Source Code below

Summary

Car detection is one of the widely used features by companies and organizations these days. This technology uses computer vision to detect different types of vehicles in video or in real time via a camera.

It finds its applications in traffic control, car tracking, creating parking sensors, and many more.

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Inquiries

If you have any questions or suggestions about Real-Time Car Detection OpenCV Python With Source Code, please feel free to leave a comment below.

Technology stack and requirements

To run this Python project on your development machine, you need:

• Python 3.10 or higher. Download from python.org or install via Anaconda if you prefer bundled packages.
• pip package manager. Comes with Python. Used to install project dependencies from requirements.txt.
• Virtual environment. Use venv or conda to isolate project dependencies from your global Python install.
• VS Code or PyCharm. Free code editors with Python syntax highlighting, IntelliSense, and debugging.
• Git. For version control and cloning source code repositories.

Installing the source code

1. Download or clone the repository. Get the ZIP archive from the download link on this page and extract it.
2. Create a virtual environment. Open a terminal in the project folder and run: python -m venv venv, then activate it (venv\Scripts\activate on Windows or source venv/bin/activate on Mac/Linux).
3. Install dependencies. Run pip install -r requirements.txt to install all libraries the project needs.
4. Configure environment variables. If the project uses API keys (OpenAI, Anthropic, database), create a .env file and set the required keys.
5. Run the project. Follow the run command in the README (usually python main.py or streamlit run app.py).

Using this project for your BSIT capstone

• Chapter 1 (Introduction). Discuss the real-world problem this system solves. Cite Philippine or international use cases where the manual process could be automated.
• Chapter 2 (RRL). Compare your project against 5-10 similar published works. Cite ACM, IEEE, or arXiv papers for academic-standard sources.
• Chapter 3 (Methodology). Document the model architecture, training data, hyperparameters, and evaluation metrics used.
• Chapter 4 (Results). Report accuracy, precision, recall, F1-score, and confusion matrix. Screenshot the running app on real inputs.
• Chapter 5 (Conclusion). Identify features for Version 2: better model, larger dataset, mobile deployment, or REST API.

Modules typical of Real-Time Car Detection using OpenCV Python

• Image dataset preparation. Train/val/test split, augmentation (rotation, flip, brightness).
• Model architecture. Pre-trained CNN (ResNet, EfficientNet) fine-tuned on your dataset.
• Training loop. PyTorch or TensorFlow/Keras training with early stopping and checkpoints.
• Evaluation. Confusion matrix, precision, recall, and per-class F1 scores.
• Inference API. FastAPI endpoint accepting image uploads and returning predictions.
• Web or mobile demo. Streamlit or React front-end for user interaction.

Common enhancements for capstone review

• Grad-CAM visualization. Show which pixels the model focused on when making a prediction.
• Data augmentation. Increase training data variety with imgaug or Albumentations library.
• Transfer learning benchmark. Compare training-from-scratch vs fine-tuning pre-trained models.
• Model quantization. Convert to TensorFlow Lite for mobile deployment.

Angel Jude Suarez

Full-Stack Developer at PIES IT Solution

Focuses on Python development, machine learning, and AI integration. Has built production AI systems including OpenAI Whisper integration for medical transcription and GPT-4o-powered diagnosis assistance. Strong background in pandas, scikit-learn, and TensorFlow.

Expertise: Python · PHP · Java · VB.NET · ASP.NET · Machine Learning · AI Integration · OpenCV · Django · CodeIgniter
 · View all posts by Angel Jude Suarez →