Learn Computer Vision with OpenCV and Python

Learn Computer Vision with OpenCV and Python

Note: You will find real world examples (not only using implemented functions in OpenCV) and i'll add more by the time. It means that course content will expand with new special examples!.

***New Chapter***: "How to Prepare dataset and Train Your Deep Learning Model" was added to the course. You will learn how to prepare a simple dataset, label the objects and train your own deep learning model.

***New Special App***: "Search team logos" was added to the course. You will learn how you can compare images and find similar image/object in your dataset.

***New Chapter***: "Special Apps - Missing and Abandoned Object Detection" was added to the course. You will learn how to do an application for missing object detection and abandoned object detection

***New Chapter***: Facial Landmarks and Special Applications (real time sleep and smile detection) videos was added to the course!

***Different Special Applications Chapter***:  new videos in different topics will be shared under this chapter. You can look at "Soccer players detection" and "deep learning based API for object detection" examples. 

In this course, you are going to learn computer vision & image processing from scratch. You will reach all resources, have many examples and explanations of these examples.

The explanations are easy to understand and also you can ask the points you need.

I have shared key concepts with you without the heavily mathematical theory, so we can focus the implementation.

Maybe you can find some other resources, videos or blogs to learn about some of these topics explained in my course, but the advantage of this course is that, you will learn computer vision from scratch by following an order, so that you will not loss yourself between many different sources.

You will also find many special examples beside the fundamental topics.

I preferred to use OpenCV which is an open source computer vision library used and supported by many people!. I have used OpenCV with Python, because Python allows us to focus on the problem easily without spending time for programming syntax/complex codes.

I wish this course to be useful for you to learn computer vision, and Actively we can use 'questions and answers' area to share information...

You will learn the topics:

• The key concepts of computer Vision & OpenCV

• Basic operations: histogram equalization,thresholding, convolution, edge detection, sharpening ,morphological operations, image pyramids.

• Keypoints and keypoint matching

• Special App : mini game by using key points

• Image segmentation: segmentation and contours, contour properties, line detection, circle detection, blob detection, watershed segmentation.

• Special App: People counter 

• Object tracking:Tracking APIs, Filtering by Color.

• Special App: Tracking of moving object

• Object detection: haarcascade face and eye detection, HOG pedestrian detection

• Object detection with Deep Learning

• Extra Chapter: How to Prepare dataset and Train Your Deep Learning Model

• Extra Chapter: Special Apps - Missing and Abandoned Object Detection

• Extra Chapter: Facial Landmarks and Special Applications (real time sleep and smile detection)

• Extra Chapter: Different Special Applications ( will be updated with special examples in different topics )

Image processing basics, Object detection and tracking, Deep Learning, Facial landmarks and many special applications

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What you will learn

• Understanding the fundamentals of computer vision & image processing
• Build computer vision applications using OpenCV
• Improve programming skills in Python

Rating: 4.35

Level: All Levels

Duration: 8.5 hours

Instructor: Ibrahim Delibasoglu