Data Science & Analytics · PPL

OpenCV Computer Vision Certification

A specialist program designed to develop practical computer vision skills using OpenCV for image processing, object detection, video analysis, and vision applications.

  • 3 DaysDuration
  • PPLAccredited
  • 3 LanguagesArabic · English · Hindi
  • ₹13,999.00 Per delegate

This course is accredited by PPL

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— The journey

Course Outline

What the programme covers, module by module.

Module 1: Introduction to Computer Vision & OpenCV

  • Computer vision fundamentals
  • OpenCV overview
  • Computer vision applications
  • Image processing concepts
  • OpenCV ecosystem
  • Vision workflow

Module 2: OpenCV Environment Setup

  • Python environment
  • OpenCV installation
  • NumPy integration
  • Development tools
  • Loading OpenCV
  • Project structure

Module 3: Digital Image Fundamentals

  • Pixels
  • Image dimensions
  • Resolution
  • Colour channels
  • Image coordinates
  • Image representation

Module 4: Reading & Manipulating Images

  • Reading images
  • Displaying images
  • Saving images
  • Image properties
  • Pixel manipulation
  • Region selection

Module 5: Colour Spaces

  • BGR
  • RGB
  • Grayscale
  • HSV
  • Colour conversion
  • Colour-based analysis

Module 6: Image Transformations

  • Resizing
  • Cropping
  • Rotation
  • Translation
  • Flipping
  • Affine transformations

Module 7: Drawing & Image Annotation

  • Lines
  • Rectangles
  • Circles
  • Polygons
  • Text overlays
  • Image annotations

Module 8: Image Filtering & Smoothing

  • Image kernels
  • Blurring
  • Gaussian filtering
  • Median filtering
  • Bilateral filtering
  • Noise reduction

Module 9: Thresholding & Binary Images

  • Binary images
  • Simple thresholding
  • Adaptive thresholding
  • Otsu's method
  • Mask creation
  • Threshold applications

Module 10: Edge Detection

  • Image gradients
  • Sobel operators
  • Laplacian
  • Canny edge detection
  • Edge maps
  • Parameter tuning

Module 11: Morphological Operations

  • Erosion
  • Dilation
  • Opening
  • Closing
  • Morphological gradient
  • Image cleanup

Module 12: Contours & Shape Analysis

  • Contour detection
  • Contour properties
  • Area and perimeter
  • Bounding rectangles
  • Shape approximation
  • Object measurements

Module 13: Histograms & Image Enhancement

  • Image histograms
  • Histogram analysis
  • Histogram equalization
  • Contrast enhancement
  • CLAHE concepts
  • Image normalization

Module 14: Feature Detection & Description

  • Corners
  • Keypoints
  • ORB
  • Feature descriptors
  • Feature extraction
  • Local image features

Module 15: Feature Matching

  • Descriptor matching
  • Brute-force matching
  • FLANN concepts
  • Match filtering
  • Homography concepts
  • Image matching

Module 16: Image Segmentation

  • Segmentation fundamentals
  • Colour segmentation
  • Threshold segmentation
  • Watershed concepts
  • Masking
  • Region extraction

Module 17: Face Detection & Analysis

  • Face detection concepts
  • Haar cascades
  • Face localization
  • Eye detection
  • Detection pipelines
  • Real-time processing

Module 18: Video Processing with OpenCV

  • Video capture
  • Video files
  • Webcam streams
  • Frame processing
  • Video writing
  • Real-time pipelines

Module 19: Motion Detection & Object Tracking

  • Frame differencing
  • Background subtraction
  • Motion detection
  • Object tracking
  • Tracking algorithms
  • Video analytics

Module 20: Object Detection & Deep Learning

  • Object detection fundamentals
  • OpenCV DNN module
  • Pretrained models
  • Neural network inference
  • Bounding boxes
  • Detection pipelines

Module 21: Computer Vision Optimization

  • Processing performance
  • Frame-rate optimization
  • Image resizing strategies
  • Efficient operations
  • Memory considerations
  • Real-time performance

Module 22: Practical Computer Vision Project

  • Image/video acquisition
  • Preprocessing
  • Feature extraction
  • Object detection
  • Real-time processing
  • Application development
— 01.2 · Is it right for you?

Who it's for & what's included

Pick a delivery method to see exactly who it suits and everything you receive.

Who it's for

Classroom

Best for learners who want face-to-face tuition and to network with peers in person.

What's included

Everything you get

  • Live instructor on-site
  • Printed workbook & materials
  • Group exercises & case studies
Who it's for

Online Instructor-Led

Best for learners who want a live instructor and a fixed schedule, without the travel.

What's included

Everything you get

  • Live instructor via video call
  • Digital workbook & resources
  • Session recordings
Who it's for

Self-Paced

Best for self-motivated learners who need maximum flexibility around work and life.

What's included

Everything you get

  • On-demand video lessons
  • Interactive quizzes
  • 24/7 access on any device
— What you will master

Course Objectives

01

Understand fundamental computer vision and digital image processing concepts.

02

Use Python and OpenCV to read, manipulate, transform, and enhance images.

03

Apply filtering, thresholding, edge detection, and morphological operations.

04

Detect contours, shapes, keypoints, and visual features within images.

05

Perform image segmentation, feature matching, and object analysis.

06

Build video processing, motion detection, and object tracking workflows.

07

Integrate pretrained deep learning models with OpenCV for object detection.

08

Develop optimized real-time computer vision applications.

— Questions answered

Frequently Asked Questions

What is OpenCV?
OpenCV is an open-source computer vision and image processing library used to develop applications involving images, videos, object detection, tracking, and visual analysis.
Who should attend this course?
The course is suitable for computer vision engineers, machine learning engineers, data scientists, AI developers, Python developers, and technical working professionals.
Do I need previous programming experience?
Basic Python knowledge is recommended because practical exercises use Python, NumPy, and OpenCV for image and video processing.
Which topics are covered?
The course covers image manipulation, colour spaces, transformations, filtering, thresholding, edges, contours, feature detection, segmentation, face detection, video processing, tracking, and deep learning integration.
What practical skills will I develop?
You will develop skills in image processing, feature extraction, segmentation, object detection, video analysis, motion tracking, deep learning inference, and real-time computer vision application development.
— Trusted by learners

What our delegates say

★★★★★

"The structure, the practice exams, the instructor — all top tier. Passed first try."

AS
Aarti SharmaSenior Project Manager · TCS
★★★★★

"Best training I have attended. The content is exactly what modern projects need."

JD
James DonovanProgramme Director · Capgemini
★★★★★

"24/7 support actually means 24/7 — got help on my mock exam at 2am. Worth every dollar."

MO
Maya OkaforPMO Lead · Standard Bank

★ 4.8 / 5 from 12,000+ verified learner reviews on Trustpilot & Google.

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