Week 1
Image basics and OpenCV
Read, display, transform and manipulate images using OpenCV.
OpenCVPixels
Detailed learning path
Computer Vision Engineer
A 12-week hands-on computer vision path using real traffic workflows, OpenCV, CNNs, YOLO and deployment-ready projects.
Weekly roadmap
What you will learn week by week
Inspired by portfolio-first learning platforms: each module has a clear outcome, practice activity and project artifact.
Week 2
Filtering, edges and contours
Detect edges, contours and shapes in images and video frames.
CannyContours
Week 3
Video processing workflows
Load video streams, process frames and save annotated outputs.
VideoFrames
Week 4
Traffic scene analysis
Use actual traffic examples to understand vehicles, lanes and motion.
TrafficVehicles
Week 5
Classical object detection
Template matching, background subtraction and motion-based detection.
MotionDetection
Week 6
CNN foundations
Understand convolution, pooling and feature extraction.
CNNsFeature maps
Week 7
Transfer learning
Train image classifiers using pretrained models.
Transfer learningTensorFlow
Week 8
YOLO object detection
Detect vehicles, people and objects using YOLO-based workflows.
YOLOObject detection
Week 9
Traffic counting project
Count vehicles, estimate density and prepare traffic insights.
CountingDensity
Week 10
OCR and document vision
Extract text from images and documents.
OCRTesseract
Week 11
Deployment basics
Deploy a simple CV app with Streamlit or Flask.
StreamlitFlask
Week 12
Portfolio presentation
Package the project with README, demo video and result screenshots.
GitHubPortfolio
Capstone project
Traffic Monitoring System
Build a computer vision system that detects vehicles, counts traffic and produces a simple dashboard/report from real traffic video.
PythonOpenCVYOLOStreamlit