Computer Vision with TensorFlow and OpenCV

Course provided by Model Institute of Engineering & Technology

5 modules

Explore the fundamentals of Artificial Intelligence & Machine Learning

5.5 Level NCrF

National Credit Framework

90 Hours

Flexible Learning

Beginner Level

No prior experience required

Micro Credit Course

03 Credits

Course Overview

This course delivers full-stack skill training in computer vision pipelines, guiding learners through image classification and object detection workflows using real-world datasets. Participants will gain hands-on experience applying TensorFlow and OpenCV to ingest, process, and analyze real-time video and image data. By the end, students will be capable of designing, training, and deploying end-to-end vision systems—from preprocessing to inference—for applications across automation, smart systems, healthcare and beyond.

Key Learning Highlights

Gain hands-on skills in designing end-to-end workflows for computer vision projects.
Train deep learning models to categorize and label images accurately.
Implement models that can identify and localize multiple objects in images and videos.
Work with live image and video streams using TensorFlow and OpenCV.
Learn to integrate CV models into production environments for real-world applications.

🛠️ Tools and Platforms

Python (3.10+)

OpenCV

TensorFlow 2.x

Google Colab

Flask

Streamlit

Learning Outcome

By the end of this course, students will be able to:

Understand and apply core image processing techniques using OpenCV.
Build and train deep learning models for image classification with TensorFlow.
Implement real-time image and video analysis including object detection and tracking.
Integrate feature detection, segmentation, and inference into practical computer vision pipelines.
Design, test, and deploy a complete real-world computer vision application as a capstone project.

Master the course with just 5 Modules

This course is designed across five modules, starting with the basics of computer vision and image processing using OpenCV. You’ll then progress to building deep learning models with TensorFlow, followed by real-time object detection and tracking. The program concludes with a capstone project, where you design and deploy a complete computer vision application.

Fundamentals of Image Processing with OpenCV

Introduction to OpenCV and its role in computer vision.
Image reading, resizing, and writing operations.
Color space conversions (RGB, Grayscale, HSV).
Image transformations and geometric modifications.
Image smoothing and filtering techniques (Gaussian, Median, Bilateral).
Edge detection and morphological operations.

Deep Learning for Image Classification using TensorFlow

Introduction to TensorFlow and Keras APIs.
Building a Convolutional Neural Network (CNN) from scratch.
Training and validating models on image datasets (e.g., CIFAR-10, Fashion-MNIST).
Model optimization using data augmentation and dropout.
Evaluation metrics and visualization of results.

Feature Extraction and Segmentation Techniques

Introduction to keypoint detectors: Harris, SIFT, ORB.
Feature matching and drawing correspondence.
Contour detection and shape analysis.
Image thresholding and segmentation using clustering.
Region of interest (ROI) isolation and mask application.

Object Detection and Real-Time Applications

Overview of object detection techniques: Haar cascades, HOG+SVM.
Using TensorFlow Object Detection API with pre-trained models (e.g., SSD, YOLOv5).
Object tracking methods: centroid tracking, optical flow, Kalman filtering.
Real-time object detection with webcam/video stream integration.
Custom dataset preparation and annotation tools (LabelImg, Roboflow).

Capstone Project

Helmet detection system for industrial compliance.
Smart garbage sorting using object detection.
Face recognition-based attendance system.
Traffic density monitoring with real-time tracking.

Roles

Computer Vision Developer
Robotics Engineer