Course Information

  • Sessions 1 day
  • Duration 7.5 hrs
  • Level Intermediate
  • Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

Download Course Brochure

Certification

  • Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.

Additional Information

Duration

2 months (Full Time)

Assessment

3 hours online assessment after each module

Class (No of teacher : student): 1:20

Intake

  • 3 Nov 2025 to 29 Sep 2026
  • 4 May 2026 to 26 June 2026
  • 2 Jan 2026 to 2 Mar 2026
  • 2 Mar 2026 to 27 Apr 2026

Enrolment Requirement

  • Age: 21 years old and above
  • Language Proficiency: At least C6 for GCE "O" Level English
  • Academic: At least C6 for GCE "O" Level in any 3 subjects

Graduation Requirement

  • Attendance: 75%
  • Assessment: Passed

Python OpenCV Computer Vision Training

Course Code: C592

What's This Course About

Discover the vast potential of computer vision with our Python OpenCV training at Tertiary Courses. This comprehensive course ensures participants not only set up their Raspberry Pi effectively but truly master the capabilities of OpenCV. You'll gain hands-on experience working with images, videos, webcams, and the Pi camera. Venture into the captivating world of timelapse videos, blending images, and artistic image transitioning techniques, ensuring a deep-rooted understanding of the intricate facets of computer vision.

The course doesn't stop at foundational knowledge. Dive into advanced realms as you transform images, manipulate colorspaces, and track objects with precision. Learn the nuances of employing high- and low-pass filters to refine images, detect prominent contours, edges, lines, and circles with ease, and master the cutting-edge domain of face recognition. Backed by expert guidance and immersive practical sessions, this training is your definitive pathway to conquering Python-driven OpenCV Computer Vision expertise.

WSQ Funding

Full Fee $350.00 Before GST
GST $31.50 9% of fee
Baseline Nett $206.50 SG/PR age 21+ · 50% funded
MCES / SME Nett $136.50 SG age 40+ · 70% funded
Funding and Grant Applications

No funding is available for this course.

For WSQ funding, please checkout the details at WSQ - Image and Video Processing with OpenCV

Course Fee

$350.00 (GST-exclusive)
$381.50 (GST-inclusive)

Course Date

* Required Fields

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Course Details

What You'll Learn

Topic 1 Overview of Computer Vision

Overview of Computer Vision

Computer Vision Industrial Applications

Overview of Raspberry Pi

Setup Raspberry Pi

Introduction to OpenCV

Install OpenCV on Raspberry Pi

Topic 2 Image Processing

Basic Image and Video Operations

Drawing Shapes

Color Space

Image Addition and Blending

Geometric Transformation

Image Filtering

Morphological Transformation

Topic 3 Feature Extraction and Description

Understanding Features

Corner Detection

Thresholding

Edge Detection

Template Matching

Topic 4 Machine Learning Based Computer Vision

Haar Cascade Object Detection

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 21-65 years old

Minimum Software/Hardware Requirement

Software:

Download and Install the following software

Sign up free Google Colab account

Hardware: Window or Mac Laptops

Job Roles

Job Roles

  • Computer Vision Engineer
  • Machine Learning Engineer
  • Robotics Developer
  • Augmented Reality Developer
  • Video Analytics Specialist
  • Multimedia Developer
  • Image Processing Engineer
  • Visual Effects Artist
  • Software Developer (with a focus on visual applications)
  • Drone Technology Developer
  • Biometrics Systems Engineer
  • Autonomous Vehicle Systems Developer
  • Medical Imaging Specialist
  • CCTV and Surveillance Systems Designer
  • Game Developer.

Trainers

Trainers

Richard Wan

Richard Wan is an ACLP-certified lecturer and software consultant with over 40 years of experience in software and hardware development, spanning AI, computer vision, and machine learning. He began his programming career with 8-bit computing in the late 1970s and went on to earn his M.Sc. in Electrical Engineering (Computer Vision) from the University of Wisconsin–Madison. His professional contributions include co-founding multiple high-tech companies, pioneering digital publishing technologies, and leading AI-driven software development in healthcare, defense, and manufacturing.

Richard has taught a wide range of technical courses, including machine learning with Scikit-Learn, deep learning with TensorFlow and PyTorch, and computer vision with OpenCV. In predictive analytics, he emphasizes the use of PyTorch for building deep learning models that can forecast trends, detect anomalies, and classify outcomes. His teaching approach blends decades of hands-on development with structured, beginner-friendly instruction, equipping learners with practical skills to transform data into prediction.

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