Hands on Computer Vision with OpenCV & Python
4.2 (21 ratings)
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Hands on Computer Vision with OpenCV & Python

A comprehensive & easy to understand foundation to Computer Vision
4.2 (21 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
200 students enrolled
Created by Shrobon Biswas
Last updated 5/2017
English
Current price: $10 Original price: $195 Discount: 95% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 3.5 hours on-demand video
  • 2 Articles
  • 12 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • After completing this course, you will have strong foundations in Image processing techniques
  • You will be confident in coding your own algorithms in python & OpenCV
  • Through the course you will build # interesting real-life projects
  • Application centered teaching approach : WONT BORE YOU with DIFFICULT MATH
View Curriculum
Requirements
  • Basic programming skills in any language (not necessarily python) would be beneficial.
  • Have an openminded attitude towards learning
Description
  • Wanted to learn Computer Vision but hurdled by the MATH HEAVY articles ? 
  • Enthusiastic about learning OpenCv and don’t know where to start ?
  • Want to learn about Object Tracking but bogged down by too much theory ? 
  • Wanting to build strong portfolio with Computer Vision & Image Processing Projects ? 
  • Looking to add Computer Vision algorithms in your current software project ?

Whatever be your motivation to learn OpenCV, i can assure you that you’ve come to the right course.

Hands on Computer Vision with OpenCV & Python is THE most comprehensive and cost-effective video course you will find on the web right now. 

This course is tailor made for an individual who wishes to transition quickly from an absolute beginner to an OpenCV expert in just three weeks. I ensure this by breaking down and articulating the most difficult concepts in plain and simple manner, replacing tough equations by examples and concepts by using small code snippets. This course covers topics using a methodical step-by-step approach with increasing difficulty, starting outright with the very basics and fundamentals.

My approach is simple - Don’t parrot rote code , rather Understand. 

I personally guarantee this is the number one course for you. This may not be your first OpenCV course, but trust me - It will definitely be your last. 

I assure you, that you will receive fast, friendly, responsive support by email, and on the Udemy.

Don't believe me? I offer a full money back guarantee, so long as you request it within 30 days of your purchase of the course.

If you're looking for a genuinely effective course that equips you all the tools, and more importantly the knowhow and behind the scenes magic of OpenCV, then look no further. 

Also the course is updated on a regular basis to add more new and exciting content.


Join the course right now. 

So what are you waiting for ?

Let’s meet at the other side of the course. 

Who is the target audience?
  • Anyone who is interested in Computer Vision
  • Students who wish to pursue Image Processing & Computer Vision in the future
  • Hobbyists who wish to learn about Object Tracking and Face detection
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Curriculum For This Course
39 Lectures
03:32:58
+
Introduction & Installation
2 Lectures 02:08

Installation
00:48
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Image Basics
5 Lectures 37:43

After completing this lecture , the students will be able to read any image file on their computer and display it on a titled window on the screen.

This lecture introduces the 2 functions :

  1. imread() - Used for reading an image
  2. imshow()- Used to Display an image


Reading & displaying an image
05:24

In this lecture the students learn how to convert a colour image into a gray image and write the gray image into a file on the computer for viewing later.

The following functions are introduced in this lecture :

  1. cvtColor() - for converting the colorspace
  2. imwrite() - for saving an image as a file on the computer
Writing an image file to disk & RGB to Grayscale conversion
05:07

In this lecture, the concept of channels is demystified. The 3 Channels of an RGB image are clearly explained and then the RGB image is decomposed into its constituent ( Red ,Green , & Blue ) channels respectively using the split() function. Next we merge the separate channels obtained using the merge() function, to reproduce the original image. 

The key takeaways : To visualise that an RGB image (colour image) has 3 Channels , while a GrayscaleImage has just one channel.

Preview 12:53

This lecture introduces the concept of subplots and explains why they are so important. The video also explains the difference in conventions followed by matplotlib package and the cv2 library, when reading OR displaying an RGB image.

Preview 08:58

This video demonstrates, how to read and display a picture , from the provided url.

Getting an image from a URL
05:21
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Histograms
3 Lectures 17:57

Histograms tell us a lot about an image. This lecture uses a grayscale picture as a sample and explains its histogram plot , detailing each aspect of the plot and the information it conveys.

After going through this lecture, the student will be more adept in analysing images solely based on its histograms. 

Understanding histograms (conceptual)
05:19

Computing the histogram of a Grayscale image
07:06

Computing the histogram of an RGB image [Coding Exercise ]
05:32
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Pixel Manipulation & Geometry
6 Lectures 32:15
Accessing & Modifying Pixels
05:52

Lines , Circles , Squares & Rectangles
05:55

Image Transformation : Flipping
03:54

Image Transformations : Scaling / Resizing
07:24

Image Transformations : Rotation
03:15

Thresholding Basics : Global Thresholding
05:55
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Project 1 : Making an Image Snipping utility using Mouse EVENTS
2 Lectures 12:01

This video will serve as an introduction for the Snipping utility project. 

This fun project will help students experiment hands-on, utilising all the knowledge they have gained till this section.

Project 1 : Introduction
01:27

Programming the snipping utility [CODE SECTION]
10:34
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Filtering , Blurring & Noise Removal
2 Lectures 06:41
Median Filter : Intro
00:58

Median Filter : Explanation & Code
05:43
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All about Thresholding
2 Lectures 08:50

In this video, the students will attempt to perform thresholding without using the cv2.threshold() function.

Write your own thresholding program [Coding Exercise]
05:11

Adaptive Thresholding
03:39
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Project 2 : Making a custom Glitter Filter of an Image
2 Lectures 06:16
Project 2 : Introduction
00:48

Programming the Custom Glitter Filter [CODE SECTION]
05:28
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Having fun with images
2 Lectures 06:15

In this video the students will learn two superimpose one image on top of another image. This is a fun exercise , but the concept of this lesson is very important and is used in many applications.

Students can use this idea to make a photo watermarking tool in python.

Superimposing two Images
06:10

This lecture contains a simple rudimentary code to have a nice trackbar to control the amount of superimposition of an image.

Simple Superimposing Tool using Trackbar
00:05
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Working with Videos
3 Lectures 21:34

This is the introduction to the videos section. Students are taught, how to access the webcam using code, and how to release the webcam. 

Accessing the Webcam
04:12

In this video we will learn how to change colourspaces in a live video feed using users choice of colourspace.


Mini Challenge 1: Changing colourspaces in Video
07:09

In this video we will show, how to track an object in realtime using the object's colour.

Object Tracking using its colour
10:13
2 More Sections
About the Instructor
Shrobon Biswas
4.2 Average rating
20 Reviews
200 Students
1 Course
Researcher & OpenCV Lover

Shrobon is carrying his research in High Performance Computer Vision, and is well abreast with the latest developments in this field. Apart from computer vision, Shrobon has years of experience in developing system softwares, and working on embedded and web projects. 

His growing interest in Computer Vision, Machine Learning and Medical imaging has led him to pursue a masters degree in Computing Science (specialization in multimedia) from University of Alberta, Canada. Prior to this, Shrobon completed his bachelors degree (B.Tech) in Computer Science & Engineering from West Bengal University of Technology, he where excelled as a student and contributed to several projects in domains of Computer Vision, Image Processing, Internet Of Things , and Cyber Security. 

The quality of his research work, and his proficiency in articulating the very difficult concepts in a simple & easy to understand manner, makes him the instructor of choice where Computer Vision is concerned.