OpenCV Computer Vision Examples with Python: A Complete Guide for Dummies

Packt Publishing

Reed Courses Certificate of Completion

Digital certificate - Included

• Course Intro and Table of Contents 08:10

  • 1: Course Intro and Table of Contents 08:10

• Introduction to OpenCV 10:40

  • 2: Introduction to OpenCV 10:40

• Installing Virtual Box and Ubuntu 18 13:12

  • 3: Installing Virtual Box and Ubuntu 18 - Part 1 08:23
  • 4: Installing Virtual Box and Ubuntu 18 - Part 2 04:49

• Installing Libraries and Dependencies 11:33

  • 5: Installing Libraries and Dependencies - Part 1 05:47
  • 6: Installing Libraries and Dependencies - Part 2 05:46

• Installing Sublime Text Editor for Ubuntu 04:46

  • 7: Installing Sublime Text Editor for Ubuntu 04:46

• Image Processing Concepts 08:49

  • 8: Image Processing Concepts 08:49

• OpenCV: Read Load and Save Image - Sample Program 20:10

  • 9: OpenCV: Read Load and Save Image - Sample Program - Part 1 11:41
  • 10: OpenCV: Read Load and Save Image - Sample Program - Part 2 08:29

• OpenCV Pixel and Area Manipulation 16:49

  • 11: OpenCV Pixel and Area Manipulation Part 1 09:56
  • 12: OpenCV Pixel and Area Manipulation Part 2 06:53

• OpenCV - Drawing Lines and Rectangles 10:57

  • 13: video9_1 10:57

• OpenCV - Drawing Circles - Simple and Concentric Circles 11:09

  • 14: OpenCV - Drawing Circles - Simple and Concentric Circles 11:09

• OpenCV - Drawing Random Circles 04:22

  • 15: OpenCV - Drawing Random Circles 04:22

• OpenCV Image Transformation - Translation 16:15

  • 16: OpenCV Image Transformation - Translation - Part 1 07:34
  • 17: OpenCV Image Transformation - Translation - Part 2 08:41

• OpenCV Image Transformation - Rotation 09:53

  • 18: OpenCV Image Transformation - Rotation 09:53

• OpenCV Image Transformation - Resizing 14:26

  • 19: OpenCV Image Transformation - Resizing - Part 1 07:29
  • 20: OpenCV Image Transformation - Resizing - Part 2 06:57

• OpenCV Image Transformation - Flipping 05:17

  • 21: OpenCV Image Transformation - Flipping 05:17

• OpenCV Image Transformation - Cropping 04:55

  • 22: OpenCV Image Transformation - Cropping 04:55

• OpenCV Image Arithmetic Operations 16:09

  • 23: OpenCV Image Arithmetic Operations - Part 1 10:46
  • 24: OpenCV Image Arithmetic Operations - Part 2 05:23

• OpenCV Image Bitwise / Logical Operations 12:52

  • 25: OpenCV Image Bitwise / Logical Operations - Part 1 05:09
  • 26: OpenCV Image Bitwise / Logical Operations - Part 2 07:43

• OpenCV - Image Masking 13:09

  • 27: OpenCV - Image Masking - Part 1 06:33
  • 28: OpenCV - Image Masking - Part 2 06:36

• Image Color Channels Merging and Splitting 15:16

  • 29: OpenCV Image Color Channels Merging and Splitting Part 1 05:54
  • 30: OpenCV Image Color Channels Merging and Splitting Part 2 09:22

• OpenCV - Other Color Spaces - GRAY, HSV, LAB 06:21

  • 31: OpenCV - Other Color Spaces - GRAY, HSV, LAB 06:21

• OpenCV - Gray scale Histograms 13:48

  • 32: OpenCV - Gray scale Histograms - Part 1 05:56
  • 33: OpenCV - Gray scale Histograms - Part 2 07:52

• OpenCV - Color Histograms 08:00

  • 34: OpenCV - Color Histograms 08:00

• OpenCV - Histogram Equalization 05:41

  • 35: OpenCV - Histogram Equalization 05:41

• OpenCV - Image Blurring 13:49

  • 36: OpenCV - Image Blurring - Part 1 08:23
  • 37: OpenCV - Image Blurring - Part 2 05:26

• OpenCV - Image Threshold 21:09

  • 38: OpenCV - Image Blurring - Part 1 07:50
  • 39: OpenCV - Image Blurring - Part 2 06:08
  • 40: OpenCV - Image Blurring - Part 3 07:11

• OpenCV - Image Gradient Detection 13:55

  • 41: OpenCV - Image Gradient Detection - Part 1 07:06
  • 42: OpenCV - Image Gradient Detection - Part 2 06:49

• OpenCV- Canny Edge Detection 06:40

  • 43: OpenCV- Canny Edge Detection 06:40

• OpenCV - Image Contours 15:17

  • 44: OpenCV - Image Contours - Part 1 06:47
  • 45: OpenCV - Image Contours - Part 2 08:30

• Face Detection using OpenCV 17:28

  • 46: Face Detection using OpenCV - Part 1 05:12
  • 47: Face Detection using OpenCV - Part 2 06:54
  • 48: Face Detection using OpenCV - Part 3 05:22

• Face Recognition using Machine Learning 16:29

  • 49: Face Recognition using Machine Learning - Part 1 06:10
  • 50: Face Recognition using Machine Learning - Part 2 06:44
  • 51: Face Recognition using Machine Learning - Part 3 03:35

• Digital Face Makeup 13:47

  • 52: Digital Face Makeup - Part 1 05:26
  • 53: Digital Face Makeup - Part 2 08:21

• Face Distance Value of Face Recognition 10:22

  • 54: Face Distance Value of Face Recognition 10:22

• Real Time Face Recognition 13:07

  • 55: Real Time Face Recognition 13:07

• OpenCV - Real time Facial Expression Recognition 21:15

  • 56: Real time Facial Expression Recognition - Part 1 10:16
  • 57: Real time Facial Expression Recognition - Part 2 10:59

• A Basic Motion Detection System using Open CV 20:06

  • 58: A Basic Motion Detection System using Open CV 20:06

• Optical Character Recognition - OCR using PyTesseract Library 16:18

  • 59: Optical Character Recognition - OCR - Part 1 06:24
  • 60: Optical Character Recognition - OCR - Part 2 06:08
  • 61: Optical Character Recognition - OCR - Part 3 03:46

• System Preparation - Object Detection using Pre-Trained Models - Introduction 06:56

  • 62: System Preparation - Object Detection using Pre-Trained Models - Introduction 06:56

• SSD MobileNet - Object Detection using Pre-Trained Models 20:07

  • 63: Object Detection using Pre-Trained Models - SSD MobileNet - Part 1 05:26
  • 64: Object Detection using Pre-Trained Models - SSD MobileNet - Part 2 14:41

• Mask R-CNN - Object Detection using Pre-Trained Models 23:58

  • 65: Mask R-CNN - Object Detection using Pre-Trained Models - Part 1 04:24
  • 66: Mask R-CNN - Object Detection using Pre-Trained Models - Part 2 11:48
  • 67: Mask R-CNN - Object Detection using Pre-Trained Models - Part 3 07:46

• YOLO - Object Detection using Pre-Trained Models 16:23

  • 68: YOLO - Object Detection using Pre-Trained Models 16:23

Computer Vision is an AI based, that is, Artificial Intelligence-based technology that allows computers to understand and label images. So, learning and mastering this fantastic world of Computer Vision-based technology is surely up-market. It will make you proficient in competing with the swiftly changing Image Processing technology arena.
And this course is designed in such a way that even the very beginner to programming can master the Computer Vision-based technology.
So, overall this is a complete package in which you can learn Computer Vision-based Technology, Deep Learning-based Face Detection, then Face Recognition and Optical Character Recognition.
And by the end of this course, we will provide you with a course completion certificate which you can keep with you and mention it in your portfolio so that you will be having more weight when you are dealing with jobs based on Computer Vision Technology.
So without wasting much time, let’s dive into this magical world. See you soon in the class room. Have a great time.

What You Will Learn

• Learn all the important functionalities of OpenCV Library
• Implement Face Detection, Face Recognition and Optical Character Recognition

About the Author

Abhilash Nelson - Senior Programmer at Dubai
The author is a pioneering, talented, and security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing, and supporting impact-full web and mobile applications. His experience with PHP/Python Programming is an added advantage for server-based Android and iOS Client Applications.

Beginners who are interested in Computer Vision based technology. Developers who wish to use Computer Vision in their applications.

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