Quick Start with OpenCV introduces image and video processing through 14 hands-on activities in Python. Five guided beginner labs establish the core workflow, then nine intermediate challenges ask you to implement image-analysis and transformation techniques with OpenCV, NumPy, and supporting visualization tools.
You will work with supplied media files rather than abstract examples, inspecting pixels and channels, producing processed images and video output, and checking your code against concrete requirements.
What You Will Learn
- Load, display, and save images, and read video frames into files or a new video
- Inspect and modify pixels, image dimensions, regions of interest, and color channels
- Combine images with addition, blending, subtraction, and bitwise operations
- Convert color spaces and isolate objects with color-based masks
- Calculate histograms and cumulative distributions and detect edges with gradients and OpenCV algorithms
- Draw bounding boxes and class labels to visualize object-detection annotations
- Locate templates and detect lines and circles in images
- Apply morphological operations and build reusable rotation, flipping, scaling, and color augmentations
Who This Course Is For
This course is for Python learners beginning computer vision and for developers who want practical OpenCV exercises beyond basic image loading. The guided labs provide an accessible start, while the challenges suit learners ready to complete partially prepared programs and tune image-processing results.
Prerequisites: Basic Python skills are recommended, including functions, loops, arrays, and reading files. Familiarity with NumPy arrays is helpful for the intermediate challenges; no prior computer-vision project is required.
Learning environment: The course uses Ubuntu 22.04 environments with OpenCV-Python and supplied image or video assets. Twelve activities run in a WebIDE and two use a VNC desktop for visual output. You edit Python files, run them from a terminal, and inspect saved or displayed results.
Frequently Asked Questions
Do I need a webcam or my own image dataset?
No. The activities use supplied local images and a local video file. The video lab reads frames from video.mp4; it does not capture live camera input.
Can I view the generated video inside the lab?
Not directly. The video lab writes an output.avi file, but its instructions note that video playback is not supported in the current environment. You can download the output and view it with a local video player.
Does this course train object-detection or deep-learning models?
No. It visualizes existing bounding-box annotations and creates image augmentations, but it does not train neural networks or detection models. The focus is classical image processing and OpenCV operations.
Is every activity beginner level?
No. The course contains five Beginner guided labs and nine Intermediate challenges. Complete the early image, video, arithmetic, and color-space labs before attempting gradients, template matching, morphology, shape detection, or augmentation if OpenCV is new to you.
