Learn Computer Vision with OpenCV and Python

Learn Computer Vision with OpenCV and Python, available at $54.99, has an average rating of 4.35, with 54 lectures, based on 140 reviews, and has 1014 subscribers.

You will learn about Understanding the fundamentals of computer vision & image processing Build computer vision applications using OpenCV Improve programming skills in Python Object detection and tracking examples Deep Learning for Computer Vision Beside learning some OpenCV functions, Also you will have many special examples with own algorithm This course is ideal for individuals who are Passion to learn computer vision from scratch or For students looking for computer vision applications It is particularly useful for Passion to learn computer vision from scratch or For students looking for computer vision applications.

Enroll now: Learn Computer Vision with OpenCV and Python

Summary

Title: Learn Computer Vision with OpenCV and Python

Price: $54.99

Average Rating: 4.35

Number of Lectures: 54

Number of Published Lectures: 54

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 55

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Understanding the fundamentals of computer vision & image processing

Build computer vision applications using OpenCV

Improve programming skills in Python

Object detection and tracking examples

Deep Learning for Computer Vision

Beside learning some OpenCV functions, Also you will have many special examples with own algorithm

Who Should Attend

Passion to learn computer vision from scratch

For students looking for computer vision applications

Target Audiences

Passion to learn computer vision from scratch

For students looking for computer vision applications

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 )

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction-1 (for the first state of the course)

Lecture 2: Introduction-2 (for the richest content of the course)

Chapter 2: Basic Image Processing

Lecture 1: Histogram equalization

Lecture 2: Thresholding

Lecture 3: Convolution

Lecture 4: Sharpening and edge detection

Lecture 5: Morphological tranformations

Lecture 6: Image pyramid

Chapter 3: Special App : Mini Game – Hit the Ball with Key Point Detection

Lecture 1: Corner detection

Lecture 2: Keypoint detection and Feature matching

Lecture 3: Mini hit the ball game

Chapter 4: Image Segmentation

Lecture 1: Contours and segmentation

Lecture 2: Contour properties

Lecture 3: Blob detection

Lecture 4: Circle detection

Lecture 5: Line detection

Lecture 6: Watershed segmentation

Chapter 5: Special App – People Counter

Lecture 1: People counting

Chapter 6: Object Tracking

Lecture 1: Tracking APIs

Lecture 2: Filtering by Color

Lecture 3: Special App: Moving object tracking

Chapter 7: Object Detection

Lecture 1: Haarcascade – face and eye detection

Lecture 2: HOG – Pedestrian detection

Lecture 3: Special App: Search team logos

Chapter 8: How to Prepare dataset and Train Your Deep Learning Model

Lecture 1: How to install Keras with tensorflow

Lecture 2: Automatically download images from Google

Lecture 3: Prepare dataset with LabelImg

Lecture 4: How to get selected ROI information for labeled data

Lecture 5: Special App: Train your model with a simple dataset

Lecture 6: Test trained model for bird detection

Chapter 9: Detect and Track Object with YOLO

Lecture 1: Tracker for YOLOv5 object detector

Chapter 10: Object detection with Deep Learning

Lecture 1: Object detection with trained caffe model

Lecture 2: Train model with YOLOV5

Chapter 11: Special Apps – Missing and Abandoned Object Detection

Lecture 1: Missing object detection

Lecture 2: Abandoned object detection

Chapter 12: Facial Landmarks and Special Applications

Lecture 1: Facial Landmarks detection

Lecture 2: Facial regions identification

Lecture 3: Special App: Smile detection

Lecture 4: Special App: Sleep detection

Chapter 13: Assignments

Chapter 14: Different Special Applications

Lecture 1: Soccer players detection

Lecture 2: API for object detection

Lecture 3: Detect objects and eliminate overlapping rectangles

Lecture 4: Play dino runner with your hand movements

Lecture 5: Head angle detection

Lecture 6: Rotate image and apply OCR (to fix not straight text)

Lecture 7: Draw moving object history and action according to detecting circle shape

Lecture 8: Detect if you are wearing a hat or not!

Lecture 9: Skin detection

Lecture 10: Finger sign detection

Lecture 11: Cigarette burner!

Lecture 12: Optical flow

Lecture 13: Dense-optical flow

Lecture 14: Prepare dataset for SVM classifier

Lecture 15: Train SVM with HOG features

Instructors

Ibrahim Delibasoglu
Lecturer at Sakarya University

Rating Distribution

1 stars: 8 votes

2 stars: 16 votes

3 stars: 28 votes

4 stars: 37 votes

5 stars: 51 votes

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