Hair Color Detection with Python, ONNX, and MediaPipe

Analyzing facial features and hair characteristics is a fundamental task in modern computer vision, powering applications from virtual try-ons to digital avatars. This text-based course guides you through the process of segmenting hair and identifying its color using accessible, high-performance tools.\n\nBy reading and practicing the concepts in this guide, you will transition from a beginner to confidently building an automated hair color detection pipeline. You will understand how to leverage pre-trained machine learning models and combine them into a cohesive, functional application.\n\nWhat you'll learn:\n- Understand the foundational concepts of image segmentation, color spaces, and face detection\n- Configure a modern Python environment using virtual environments and clean dependency management\n- Implement MediaPipe to locate facial regions and isolate the head area accurately\n- Load and run pre-trained ONNX models for precise hair segmentation masks\n- Apply color analysis techniques to extract and classify the dominant hair color from segmented regions\n- Write clean, structured Python code utilizing type hints for better readability and maintenance\n\nThe course starts with essential terminology and the theory of image segmentation before guiding you step-by-step through environment setup, face detection, model inference with ONNX, and color classification. You will learn by reading detailed explanations and analyzing structured code snippets.\n\nThis course is designed for beginners interested in computer vision and Python programming. No prior experience with machine learning or image processing is required to get started.\n\nStart reading today to build your own computer vision application from scratch.