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  license: mit
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- language:
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- - en
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- pipeline_tag: image-to-text
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ tags:
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+ - computer-vision
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+ - opencv
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+ - numpy
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+ - matplotlib
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+ - educational
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+ - beginner
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  license: mit
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+ language: en
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+ ---
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+
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+ # Computer Vision Learning Notebooks
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+
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+ ## Summary
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+ This repository contains a series of **educational notebooks** that introduce the fundamentals of Computer Vision (CV) in a simple, visual, step-by-step way.
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+ It is designed for both **technical and non-technical learners**, focusing on building intuition about how computers see and manipulate images.
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+
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+ The notebooks progress from working with raw arrays (NumPy) to real images (PIL/OpenCV) and finally to drawing shapes, annotations, and simple overlays.
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+
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+ ---
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+
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+ ## Notebooks Overview
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+ - **Notebook 1** → Introduction to images as arrays (NumPy basics).
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+ - **Notebook 2** → Loading, resizing, and exploring color channels of real images.
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+ - **Notebook 3** → Image transformations and manipulations (PIL / Pillow).
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+ - **Notebook 4** → Drawing shapes, lines, and text on blank canvases and real images (OpenCV).
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+
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+ ---
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+
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+ ## Intended Use
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+ These notebooks are ideal for:
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+ - Students beginning their journey in **Computer Vision**.
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+ - Non-technical learners who want to **visualize how machines interpret images**.
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+ - Developers looking for a clear, hands-on foundation before moving to advanced CV or deep learning topics.
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+
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+ ---
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+
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+ ## Features
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+ - Beginner-friendly explanations paired with visual outputs.
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+ - Bite-sized, progressive learning: **arrays → real images → drawing & annotations**.
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+ - Uses standard libraries: **NumPy, Matplotlib, PIL/Pillow, OpenCV**.
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+ - Each notebook contains concise theory, short code cells with comments, and visuals.
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+
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+ ---
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+
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+ ## How to Use
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+ 1. Open the notebooks in order (1 → 4) for the intended learning progression.
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+ 2. Run each cell step-by-step and observe results.
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+ 3. Tweak parameters (e.g., rectangle coordinates, circle radius, colors) to experiment and learn.
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+ 4. Use copies of images (`.copy()`) when you want to preserve originals during edits.
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+
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+ ---
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+
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+ ## Requirements (suggested)
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+ Install the basic packages used across the notebooks:
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+
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+ ```bash
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+ pip install numpy matplotlib pillow opencv-python