Python for Computer Vision with OpenCV and Deep Learning

Learn the latest techniques in computer vision with Python , OpenCV , and Deep Learning!

4.54 (12164 reviews)

Udemy

platform

English

language

Programming Languages

What you will learn

Understand basics of NumPy

Manipulate and open Images with NumPy

Use OpenCV to work with image files

Use Python and OpenCV to draw shapes on images and videos

Perform image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphological operations.

Create Color Histograms with OpenCV

Open and Stream video with Python and OpenCV

Detect Objects, including corner, edge, and grid detection techniques with OpenCV and Python

Create Face Detection Software

Segment Images with the Watershed Algorithm

Track Objects in Video

Use Python and Deep Learning to build image classifiers

Work with Tensorflow, Keras, and Python to train on your own custom images.

Course Gallery

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Rating & Reviews

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Comidoc Review

Our Verdict

This comprehensive course on Python for Computer Vision with OpenCV and Deep Learning offers a thorough introduction to the field, backed by practical exercises that help reinforce key concepts. However, for those seeking a more up-to-date and in-depth exploration of specific topics, additional resources may be required.

What We Liked

Covers a wide range of topics in computer vision, from image processing and manipulation to machine learning and neural networks
Provides clear explanations and examples for each topic, allowing learners to grasp complex concepts easily
Includes practical hands-on exercises that help students apply their knowledge in real-world scenarios
Structured in a way that gradually builds on previous topics, providing a solid foundation for understanding computer vision

Potential Drawbacks

Some content is outdated, which may cause confusion when working with current libraries and versions
Lacks detailed explanations of some theoretical concepts, leaving students wanting more in-depth information
Limited guidance on creating custom datasets for deep learning projects
Installation instructions could be improved to better match current system configurations