Live Sketch OpenCV Python With Source Code

Live Sketch OpenCV Python With Source Code

The Live Sketch OpenCV Python was developed using Python OpenCV, In this Project we are going to make a Real-time/ live Sketch making script using OpenCV in Python. OpenCV makes it very easy for us to work with images and videos on the computer.

A Live Sketch is an application which will show a live sketch of your webcam feed. In this project we’ll be using NumPy and OpenCV. We will also make use of Numpy and Matplotlib to make this live sketch app.

What is OpenCV?

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library.

OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products.

What is NumPy?

NumPy is the fundamental package for scientific computing with Python. It contains among other things:

1. A powerful N-dimensional array object
2. Sophisticated (broadcasting) functions
3. Tools for integrating C/C++ and Fortran code
4. Useful linear algebra, Fourier transform, and random number capabilities Besides its obvious scientific uses.

In 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 you don’t know what would be the the Python IDE to use, I have here a list of Best Python IDE for Windows, Linux, Mac OS that will suit for you. I also have here How to Download and Install Latest Version of Python on Windows.

To start executing Live Sketch OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm in your computer.

Live Sketch OpenCV Python With Source Code : Steps on how to run the project

Installed Libraries

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

Complete Source Code

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt


def sketch(image):
    # Convert image to gray scale
    img_gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)

    # Clean up image using Gaussian Blur
    img_gray_blur = cv.GaussianBlur(img_gray, (5, 5), 0)

    # Extract Edges
    canny_edges = cv.Canny(img_gray_blur, 30, 70)

    # Do an invert binarize the image
    ret, mask = cv.threshold(canny_edges, 120, 255, cv.THRESH_BINARY_INV)

    return mask


def liveSketch():
    cap = cv.VideoCapture(0)

    while True:
        ret, frame = cap.read()
        cv.imshow("Live Sketch", sketch(frame))
        if cv.waitKey(1) == 27:
            break

    cap.release()
    cv.destroyAllWindows()


if __name__ == "__main__":
    liveSketch()

Output

Download Source Code below

Anyway, if you want to level up your programming knowledge, especially Python OpenCV, try this new article I’ve made for you Best OpenCV Projects With Source Code For Beginners 2021.

Summary

This article aims to implement a Computer Vision model that generates a Live Video Sketch of the real time footage of a Webcam.

Implementing a Computer Vision model that creates a live video sketch of real time webcam video. Source code is written in python and model is based on OpenCV.

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Inquiries

If you have any questions or suggestions about Live Sketch 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 Live Sketch 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 →