AI and machine learning
Computer Vision with OpenCV
Learn classical image processing in OpenCV on plant pictures: thresholds, edges, contours, measuring, tracking and counting.
Computer Vision with OpenCV is a free, self-paced online course from EDWartens for engineers, developers and students applying AI to real work. It has 12 modules and takes about 19.4 hours, with written notes, a practice task per module, a practical project and a final assessment of 15 questions (pass mark 60%). Learning costs nothing and needs only a free account. The optional EDWartens Certificate of Completion, verifiable by code, is a one-off US$23.99 for you; it is not a vendor credential. Updated 1 November 2026.
- Free
- Beginner
- 19.4 hours
- Self-paced
- Certificate optional
- Lab practice in your browser
No card, no trial. The course is free for good. Certificate US$23.99, optional.
- Who it is for
- Engineers, developers and students applying AI to real work
- Jobs
- Roles that use this skill: AI engineer, machine learning engineer, data analyst and automation developer. No course guarantees a job.
- Tools
- Google Colab (free); the first lesson shows how to check which OpenCV version you have. A phone camera for the projects. Hardware: A phone camera, a tray and a ruler for the projects. No industrial camera needed.
- Updated
After this course you can
By the end you will be able to:
- Read, convert, crop and measure images in OpenCV
- Threshold and clean binary images
- Find contours and turn them into counts and millimetre measurements
- Subtract background and track objects across a counting line
- Choose cameras, lenses and lighting
Who it is for, and what you need first
Who it is for
Engineers, developers and students applying AI to real work.
Level: Beginner. Beginner in vision. Needs basic Python and NumPy — the Python for AI course, or equivalent.
What you need
Software: Google Colab (free); the first lesson shows how to check which OpenCV version you have. A phone camera for the projects..
Hardware: A phone camera, a tray and a ruler for the projects. No industrial camera needed.. What to download, and how.
How this course works
Watch the lessons
Short video lessons, one module at a time, at your own pace.
Read the notes
Open the Reading tab for the written notes and diagrams of the module.
Practise in the 3D labOptional
Try the module in the free 3D lab, in your browser.
Check yourself
Answer the questions to see what stuck before you move on.
Final assessment
Pass the final once you have been through the modules.
Certificate, if you want oneOptional
Optional, with a page anyone can use to verify it.
What you learn
12 modules in order, at your own pace. 19.4 hours of video lessons, with written notes and a practice task in each module.
- 1An image is an array24m
- 2Drawing, arithmetic, masks and HSV colour1h 25m
- 3Thresholding and morphology, with lab practice: 45m
- 4Smoothing, gradients and Canny edges40m
- 5Contours, shapes and measurement, with lab practice: 30m
- 6Video, motion and background subtraction49m
- 7Tracking and counting across a line27m
- 8Histograms, template matching and Hough1h 11m
Show the other 4 modules
- 9Haar cascades, corners and the limits of classical vision, with lab practice: 31m
- 10Cameras, lenses, lighting and calibration1h 33m
- 11The project: a conveyor counter, evaluated6h 51m
- 12Final assessment
Software you need
What to download, where from, what it costs and how to install it. Every link goes to the maker's own site, never a mirror.
OpenCV is already installed in Colab. A phone camera is enough for the projects.
Show what to download (2 tools)
Required
- 01Free, GPU time not guaranteed
Google Colab
Google, in the browser
- Runs on
- Any modern web browser
- Account
- A free Google account
Colab is free to use. In the free version GPUs and TPUs are heavily restricted and not guaranteed, sessions can run for at most 12 hours, and idle sessions are stopped. Paid plans give more reliable access.
Steps
- 1.Open colab.research.google.com and sign in with your Google account.
- 2.Click New notebook (or open the notebook your course links to).
- 3.For a GPU, choose Runtime, then Change runtime type, then pick a GPU if one is offered.
- 4.Type code in a cell and press Shift and Enter to run it.
- Save a copy to your Google Drive so your changes are kept.
- Files on the runtime are deleted when the session ends. Save outputs to Drive.
Open Google Colabcolab.research.google.com
Optional
Useful, not needed to finish the course.
- 02Free
OpenCV (opencv-python)
OpenCV, a Python or npm package
- Runs on
- Windows, macOS and Linux with Python 3
- Account
- None needed
Free, open source under the Apache 2.0 licence.
Steps
- 1.Install Python 3 and create a virtual environment.
- 2.Run: pip install opencv-python
- 3.Test it: python -c "import cv2; print(cv2.__version__)"
- Install only one OpenCV package per environment: opencv-python, opencv-contrib-python, or their headless versions. They all use the same cv2 name and clash.
- Use opencv-contrib-python if your course needs the extra modules.
- On servers or Docker without a screen, use opencv-python-headless.
Official download pagepypi.org
Checked against each maker's own page on 27 September 2026. Trial lengths and editions change; the maker's page is the final word.
Practise in the 3D lab
Tune the same ideas you code in OpenCV, a region, a threshold and a blob size, on a camera inspecting parts in the AI Workbench, and measure the false rejects.
- Vision inspection on the conveyorAI Workbench, about 25 minOpen in the lab (opens in a new tab): Vision inspection on the conveyor
Optional. Skip it whenever you like: it never blocks your progress or your certificate.
Course project, about 7 hours
Count parts on a conveyor: a synthetic belt with known truth, then your own video
Build a part counter from detection, morphology, contours, a centroid tracker and a counting line. Test it on a synthetic belt video with a known count. Then run it on your own phone video and report misses and double counts separately.
- 8documents in the pack
- 7steps to follow
- 6things to hand in
- 7 hof work, at your pace
- PlanProject plan: camera count check on PET line 3
- FDSFunctional design: part counter pipeline and parameters
- DatasheetTest video datasheet: bench and site
- DatasheetCamera and lighting specification for line 3 (stage 3 input)
- ProcedureTest procedure: counting against ground truth
- Test reportTest record: bench and site trials of the part counter
The project is optional practice. The certificate needs only the modules and the final assessment.
After the course, your next step
Show what you can do, then keep going in the same field.
A certificate you can verify
Finish the modules and pass the final assessment (15 questions, pass mark 60%) and you can add a certificate with a public verification page. One-off, US$23.99 for you. It is not a vendor credential.
Want a free certificate? Do the matching Free internship: 4 weeks, nothing to buy, with a free certificate of completion.
Computer Vision
Eight short weeks of how computers see: image processing, detection and quality inspection, up to a model on a small device: notes, diagrams, a few videos and worked exercises. Finish weeks 1 to 4 and the final, then claim a free Internship Completion Certificate with your real dates and study hours, and a QR code anyone can scan to verify it. Training, not employment.
See the internshipPart of a learning path
Courses in a planned order, so each one builds on the last.
More free courses
Roles that use this skill
- AI engineer
- Machine learning engineer
- Data analyst
- Automation developer
These are the kinds of roles where the skill is used. We make no promise of a job or an interview.
Plain notes
- Free: the whole course, the notes, the practice tasks, the final assessment and support from practising engineers.
- Paid, and optional: the course certificate, and the Engineer-verified internship (the Free internship costs nothing).
- No job promise: the course teaches a skill. It does not place you in a job.
- Lessons by: ProgrammingKnowledge, Murtaza's Workshop - Robotics and AI, freeCodeCamp.org, SmartVisionLights, Nicolai Nielsen. Independent creators; the lessons stream from their own channels. EDWartens wrote the study plan, notes, practice tasks and assessments.
ProgrammingKnowledge · Murtaza's Workshop - Robotics and AI · freeCodeCamp.org · SmartVisionLights · Nicolai Nielsen. If you are one of these creators and want a lesson removed or credited differently, write to info@edwartens.com.
Practise what you learn in the free 3D lab
Technical work is judged on what you can do: wiring, commissioning, finding a fault. Doing a task yourself teaches more than watching it a second time, and a lab lets you try it safely before you meet real equipment. It does not replace working on real hardware.
- Practise safely, so a mistake costs nothing.
- The lab checks your work like a site test, so you know when it is right.
- Free, in your browser, with nothing to install.
Why learn with EDWartens
What you get with this course, and what comes after it.
- Free from the first lesson to the final assessment.
- Practice in a free 3D lab in your browser, with nothing to install.
- Video lessons with written notes and a project.
- An optional certificate that anyone can verify online.
- Free internships to keep practising afterwards.
Common questions
Do I need an industrial camera?
No. Every exercise runs on images and videos you make with a phone, plus a synthetic belt video the notebook generates with a known part count. The module on cameras and lighting tells you what to buy when the time comes.
Is this deep learning?
No, and deliberately. This course is the classical toolbox that solves most fixed-camera inspection jobs at a fraction of the cost. The Deep Learning and Machine Vision courses build on it. It also teaches you to measure when classical methods fail. Then a neural network is a decision, not a reflex.
What are the projects in the Computer Vision with OpenCV course?
There are two projects. The first is a conveyor part counter with detection, tracking and a counting line. You debug it on a synthetic belt video with a known count, then run it on your own phone video. Misses and double counts are reported separately. The second is a colour-sorting and size-check station on photos of bottle caps.
Is the Computer Vision with OpenCV course really free?
Yes. Every module, practice task, project and assessment. You create an account so your progress is saved and the assessments can be marked. The certificate is the only paid item, and only if you want it.
What certificate does the Computer Vision with OpenCV course give?
An EDWartens Certificate of Completion, issued when you have finished the modules and passed the final assessment (the project is optional practice), with a verification code anyone can check. It is not a vendor credential and is never described as one.
Who made the video lessons in the Computer Vision with OpenCV course?
The creators named in the Credits block at the foot of this page, on their own YouTube channels. EDWartens did not make the videos and the creators are not affiliated with EDWartens. What EDWartens wrote is the study plan, the notes, the practice tasks, the projects and the assessments.
How long does the Computer Vision with OpenCV course take?
Plan on about 19 hours in all, including the video lessons, notes and practice tasks. It is self-paced, so you work through the twelve modules at your own speed.
Do I have to use the lab?
No. The lab is optional practice. You can skip it whenever you like, and it never blocks your progress, the final assessment or your certificate.
Is the lab free?
Yes. Every lab level for this course opens in your browser, free, with nothing to install.
What topics does the Computer Vision with OpenCV course cover, module by module?
The Computer Vision with OpenCV course has 12 modules. It opens with "An image is an array", goes through "Drawing, arithmetic, masks and HSV colour", "Contours, shapes and measurement", "Histograms, template matching and Hough" and "The project: a conveyor counter, evaluated" and ends with "Final assessment". The full list with minutes is under "What you learn" on this page.
What can I do after the Computer Vision with OpenCV course?
After the Computer Vision with OpenCV course: Treat an image as an array and measure a part in millimetres from pixels; Detect parts by colour in HSV and by brightness with Otsu and adaptive thresholds; Clean binary images with morphology and separate touching parts. These are the practical outcomes the course is written around, and the final assessment of 15 questions checks them.
What software and hardware do I need for the Computer Vision with OpenCV course, and does it cost anything?
For the Computer Vision with OpenCV course, software: Google Colab (free); the first lesson shows how to check which OpenCV version you have. A phone camera for the projects. Hardware: A phone camera, a tray and a ruler for the projects. No industrial camera needed. The course itself is free.
Is the Computer Vision with OpenCV course suitable for me, and what level is it?
Computer Vision with OpenCV is rated beginner. Beginner in vision. Needs basic Python and NumPy — the Python for AI course, or equivalent. It is written for engineers, developers and students applying AI to real work, and takes about 19.4 hours at your own pace.
Which jobs use the skills from the Computer Vision with OpenCV course?
Skills from the Computer Vision with OpenCV course (for example: treat an image as an array and measure a part in millimetres from pixels) are used in roles such as AI engineer, machine learning engineer, data analyst and automation developer, and in machine learning engineer work. The course gives you practical evidence of the skill; it does not promise a job, and employers decide on experience and interview.
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Computer Vision with OpenCV, at your own pace. Add the certificate only if you want one.