Learning OpenCV — 90 Days Robotics Challenge

A progressive learning workspace for Computer Vision, image processing, and human body tracking.

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Table of Contents

1. Overview
2. Architecture
3. Development Phases
4. Capstone Highlight
5. Getting Started
6. Usage
7. Engineering Notes
8. Roadmap
9. Contributing
10. License

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Overview

This workspace tracks hands-on progress through a structured 90-day robotics curriculum, covering fundamental image processing, real-time object detection, and AI-driven body landmark tracking. The codebase follows a deliberate progression — from raw pixel manipulation and NumPy array math to geometric facial meshes and full-body pose estimation using MediaPipe's ML pipeline.

Each script is a self-contained unit targeting a single concept, making the repository useful both as a personal reference and as a structured guide for engineers entering the Computer Vision domain.

Key Features

• Zero-config webcam scripts
• MediaPipe Face Mesh integration
• Real-time object isolation
• Contour-based coin counting
• Interactive HSV color picking

Built with: Python, OpenCV, MediaPipe, NumPy, Matplotlib, Pillow.

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Latest Updates

Date	Update	Details
May 10, 2026	Assignment #2 Completed	Interactive image drawing utility with shape support (line, circle, rectangle, text), color selection, and save functionality
May 9, 2026	Assignment #1 Completed	Grayscale image converter — load image, convert BGR to grayscale, save or display with user-selected file type

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Architecture

📁 Repository structure

learning_opencv/
├── AI_Face_Detector.py      # Haar Cascade face tracking
├── face_track.py            # MediaPipe Face Mesh
├── pose_track.py            # MediaPipe body landmark tracking
├── cv2_mpe.py               # Hand tracking tasks API
├── objact_isolate.py        # Color-based background masking
├── color_picker.py          # Interactive BGR to HSV utility
├── coin_counter.py          # Static image contour counting
├── webcam_counter.py        # Live webcam object counting
├── video_capture.py         # Boilerplate webcam setup & FPS
├── lec_2.py                 # Drawing matrices and shapes
├── learaning_pixal.py       # PIL/NumPy pixel manipulation
├── haarcascade_frontalface_default.xml
├── hand_landmarker.task
└── Assignments/
    ├── Assignment_no_1.py           # First assignment task
    └── assignment_no_2.py           # Image drawing utility (line, circle, rectangle, text)

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Development Phases

Phase	Goal	Status	Outcome
Phase 1: Basics	Pixel math & shape drawing	✅ Complete	Mastered array conversions
Phase 2: Object Detection	Color masking & contours	✅ Complete	Built live coin counter
Phase 3: AI Tracking	Face, Pose, and Hands	🔄 In Progress	MediaPipe meshes integrated

Note: Status indicators follow the convention: ✅ Complete · 🔄 In Progress · 🗓 Planned.

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Capstone Highlight

• Real-time Face Mesh generation
• Accurate HSV color isolation
• Live webcam object counting

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Getting Started

Prerequisites

• Python ≥ 3.9
• Webcam (for live tracking)

Installation

git clone https://github.com/relvixx/learning_opencv.git
cd learning_opencv
pip install opencv-python mediapipe numpy matplotlib pillow

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Usage

# Run traditional Haar Cascade Face Detection
python AI_Face_Detector.py

# Run advanced MediaPipe Pose Tracking
python pose_track.py

# Launch interactive color picker for HSV masking
# Adjust trackbar sliders to isolate a target hue range
python color_picker.py

Tip

Start with color_picker.py before running objact_isolate.py. The picker outputs precise HSV lower/upper bounds that you can paste directly into the isolation script's masking parameters — no guesswork required.

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Engineering Notes

Note

The project deliberately separates classical CV (AI_Face_Detector.py using Haar Cascades) from ML-based inference (face_track.py using MediaPipe). This dual approach makes the performance and accuracy trade-offs between the two paradigms immediately observable — a useful reference point when choosing a detection strategy for constrained hardware.

Important

The hand_landmarker.task model file must be present in the repository root at runtime. MediaPipe's HandLandmarker API resolves this path relatively — if you restructure directories, update the model_asset_path argument in cv2_mpe.py accordingly, or the process will exit silently.

Warning

Several static scripts (e.g., coin_counter.py) contain hardcoded absolute file paths pointing to local directories. Running them unmodified on any machine other than the original development environment will raise FileNotFoundError. Always audit and update image paths before executing static-image scripts.

Known Limitations

• Hardcoded local file paths in static scripts require manual update before use on a new machine.
• Code comments include Hinglish phrasing, which may reduce readability for international contributors.

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Roadmap

• Refactor local file paths to relative paths
• Add ROS2 integration for physical robotics
• Build a hand-gesture volume controller

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Contributing

Open for feedback and educational PRs. Ensure you update file paths to relative directories before submitting.

Important

There is no automated test suite at this stage. Before opening a PR, manually verify that each modified script executes without errors against a live webcam feed or the expected static image input. Document any environment-specific dependencies in your PR description.

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License

Distributed under the MIT License. See LICENSE for full terms.

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_{Built with ♥ by relvixx  ·  Learning OpenCV — 90 Days Robotics Challenge  ·  2026}