Python for Computer Vision with OpenCV and Deep Learning

About Course

Welcome to the ultimate online course on Python for Computer Vision!

This course is your best resource for learning how to use the Python programming language for Computer Vision.

We’ll be exploring how to use Python and the OpenCV (Open Computer Vision) library to analyze images and video data.

The most popular platforms in the world are generating never before seen amounts of image and video data. Every 60 seconds users upload more than 300 hours of video to Youtube, Netflix subscribers stream over 80,000 hours of video, and Instagram users like over 2 million photos! Now more than ever its necessary for developers to gain the necessary skills to work with image and video data using computer vision.

Computer vision allows us to analyze and leverage image and video data, with applications in a variety of industries, including self-driving cars, social network apps, medical diagnostics, and many more.

As the fastest growing language in popularity, Python is well suited to leverage the power of existing computer vision libraries to learn from all this image and video data.

In this course we’ll teach you everything you need to know to become an expert in computer vision! This $20 billion dollar industry will be one of the most important job markets in the years to come.

We’ll start the course by learning about numerical processing with the NumPy library and how to open and manipulate images with NumPy. Then will move on to using the OpenCV library to open and work with image basics. Then we’ll start to understand how to process images and apply a variety of effects, including color mappings, blending, thresholds, gradients, and more.

Then we’ll move on to understanding video basics with OpenCV, including working with streaming video from a webcam. Afterwards we’ll learn about direct video topics, such as optical flow and object detection. Including face detection and object tracking.

Then we’ll move on to an entire section of the course devoted to the latest deep learning topics, including image recognition and custom image classifications. We’ll even cover the latest deep learning networks, including the YOLO (you only look once) deep learning network.

This course covers all this and more, including the following topics:

NumPy
Images with NumPy
Image and Video Basics with NumPy
Color Mappings
Blending and Pasting Images
Image Thresholding
Blurring and Smoothing
Morphological Operators
Gradients
Histograms
Streaming video with OpenCV
Object Detection
Template Matching
Corner, Edge, and Grid Detection
Contour Detection
Feature Matching
WaterShed Algorithm
Face Detection
Object Tracking
Optical Flow
Deep Learning with Keras
Keras and Convolutional Networks
Customized Deep Learning Networks
State of the Art YOLO Networks
and much more!

Feel free to message me on Udemy if you have any questions about the course!

Thanks for checking out the course page, and I hope to see you inside!

Jose

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 Content

Course Overview and Introduction

A Message from the Professor
Course Overview

04:54
Course Curriculum Overview

04:39
Getting Set-Up for the Course Content

12:45

NumPy and Image Basics

Image Basics with OpenCV

Introduction to Images and OpenCV Basics

02:37
Opening Image files in a notebook

19:29
Opening Image files with OpenCV

10:49
Drawing on Images – Part One – Basic Shapes

10:00
Drawing on Images Part Two – Text and Polygons

09:29
Direct Drawing on Images with a mouse – Part One

09:36
Direct Drawing on Images with a mouse – Part Two

02:41
Direct Drawing on Images with a mouse – Part Three

10:26
Image Basics Assessment

05:15
Image Basics Assessment Solutions

13:26

Image Processing

Introduction to Image Processing

00:39
Color Mappings

06:47
Blending and Pasting Images

14:15
Blending and Pasting Images Part Two – Masks

15:55
Image Thresholding

17:41
Blurring and Smoothing

06:43
Blurring and Smoothing – Part Two

19:45
Morphological Operators

15:27
Gradients

13:40
Histograms – Part One

12:34
Histograms – Part Two – Histogram Eqaulization

12:19
Histograms Part Three – Histogram Equalization

08:12
Image Processing Assessment

03:52
Image Processing Assessment Solutions

08:31

Video Basics with Python and OpenCV

Object Detection with OpenCV and Python

Introduction to Object Detection

02:27
Template Matching

17:41
Corner Detection – Part One – Harris Corner Detection

14:09
Corner Detection – Part Two – Shi-Tomasi Detection

06:26
Edge Detection

09:28
Grid Detection

08:16
Contour Detection

11:11
Feature Matching – Part One

12:25
Feature Matching – Part Two

18:28
Watershed Algorithm – Part One

11:49
Watershed Algorithm – Part Two

20:14
Custom Seeds with Watershed Algorithm

18:55
Introduction to Face Detection

09:11
Face Detection with OpenCV

14:30
Detection Assessment

03:27
Detection Assessment Solutions

07:10

Object Tracking

Deep Learning for Computer Vision

Introduction to Deep Learning for Computer Vision

02:29
Machine Learning Basics

06:54
Understanding Classification Metrics

14:12
Introduction to Deep Learning Topics

01:24
Understanding a Neuron

05:12
Understanding a Neural Network

06:30
Understanding a Neural Network

06:30
Cost Functions

03:40
Gradient Descent and Back Propagation

03:20
Keras Basics

18:02
MNIST Data Overview

04:41
Convolutional Neural Networks Overview – Part One

18:53
Convolutional Neural Networks Overview – Part Two

04:23
Keras Convolutional Neural Networks with MNIST

17:08
Keras Convolutional Neural Networks with CIFAR-10

11:59
Deep Learning on Custom Images – Part One

14:50
Deep Learning on Custom Images – Part Two

19:34
Deep Learning and Convolutional Neural Networks Assessment

02:37
Deep Learning and Convolutional Neural Networks Assessment Solutions

07:07
Introduction to YOLO v3

03:16
YOLO v3 with Python

17:05

Capstone Project

Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.

Student Ratings & Reviews

5.0

Total 2 Ratings

2 Ratings

0 Rating

Rayan Malik

4 months ago

Excellent Course! Explains everything perfectly!

Ahmad Mushtaq

11 months ago

Very nice course. Covered almost all the essential topics in a very short-time. I really appreciate how well the course material and python notebooks were organized by Jose. Much recommended to anyone starting with computer vision

About Course

What Will You Learn?

Course Content

Course Overview and Introduction

A Message from the Professor

Course Overview

Course Curriculum Overview

Getting Set-Up for the Course Content

NumPy and Image Basics

Introduction to Numpy and Image Section

NumPy Arrays

What is an image

Images and NumPy

NumPy and Image Assessment Test

NumPy and Image Assessment Test – Solutions

Image Basics with OpenCV

Introduction to Images and OpenCV Basics

Opening Image files in a notebook

Opening Image files with OpenCV

Drawing on Images – Part One – Basic Shapes

Drawing on Images Part Two – Text and Polygons

Direct Drawing on Images with a mouse – Part One

Direct Drawing on Images with a mouse – Part Two

Direct Drawing on Images with a mouse – Part Three

Image Basics Assessment

Image Basics Assessment Solutions

Image Processing

Introduction to Image Processing

Color Mappings

Blending and Pasting Images

Blending and Pasting Images Part Two – Masks

Image Thresholding

Blurring and Smoothing

Blurring and Smoothing – Part Two

Morphological Operators

Gradients

Histograms – Part One

Histograms – Part Two – Histogram Eqaulization

Histograms Part Three – Histogram Equalization

Image Processing Assessment

Image Processing Assessment Solutions

Video Basics with Python and OpenCV

Introduction to Video Basics

Connecting to Camera

Using Video Files

Drawing on Live Camera

Video Basics Assessment

Video Basics Assessment Solutions

Object Detection with OpenCV and Python

Introduction to Object Detection

Template Matching

Corner Detection – Part One – Harris Corner Detection

Corner Detection – Part Two – Shi-Tomasi Detection

Edge Detection

Grid Detection

Contour Detection

Feature Matching – Part One

Feature Matching – Part Two

Watershed Algorithm – Part One

Watershed Algorithm – Part Two

Custom Seeds with Watershed Algorithm

Introduction to Face Detection

Face Detection with OpenCV

Detection Assessment

Detection Assessment Solutions

Object Tracking

Introduction to Object Tracking

Optical Flow

Optical Flow Coding with OpenCV – Part One

Optical Flow Coding with OpenCV – Part Two

MeanShift and CamShift Tracking Theory

MeanShift and CamShift Tracking with OpenCV

Overview of various Tracking API Methods

Tracking APIs with OpenCV

Deep Learning for Computer Vision

Introduction to Deep Learning for Computer Vision

Machine Learning Basics

Understanding Classification Metrics

Introduction to Deep Learning Topics

Understanding a Neuron