4.56 out of 5
4.56
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Python for Computer Vision with OpenCV and Deep Learning

Learn the latest techniques in computer vision with Python , OpenCV , and Deep Learning!
Instructor:
Jose Portilla
7,723 students enrolled
English [Auto-generated] More
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.

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

Course Overview and Introduction

1
Course Overview
2
FAQ - Frequently Asked Questions
3
Course Curriculum Overview
4
Getting Set-Up for the Course Content

NumPy and Image Basics

1
Introduction to Numpy and Image Section
2
NumPy Arrays
3
What is an image?
4
Images and NumPy
5
NumPy and Image Assessment Test
6
NumPy and Image Assessment Test - Solutions

Image Basics with OpenCV

1
Introduction to Images and OpenCV Basics
2
Opening Image files in a notebook
3
Opening Image files with OpenCV
4
Drawing on Images - Part One - Basic Shapes
5
Drawing on Images Part Two - Text and Polygons
6
Direct Drawing on Images with a mouse - Part One
7
Direct Drawing on Images with a mouse - Part Two
8
Direct Drawing on Images with a mouse - Part Three
9
Image Basics Assessment
10
Image Basics Assessment Solutions

Image Processing

1
Introduction to Image Processing
2
Color Mappings
3
Blending and Pasting Images
4
Blending and Pasting Images Part Two - Masks
5
Image Thresholding
6
Blurring and Smoothing
7
Blurring and Smoothing - Part Two
8
Morphological Operators
9
Gradients
10
Histograms - Part One
11
Histograms - Part Two - Histogram Eqaulization
12
Histograms Part Three - Histogram Equalization
13
Image Processing Assessment
14
Image Processing Assessment Solutions

Video Basics with Python and OpenCV

1
Introduction to Video Basics
2
Connecting to Camera
3
Using Video Files
4
Drawing on Live Camera
5
Video Basics Assessment
6
Video Basics Assessment Solutions

Object Detection with OpenCV and Python

1
Introduction to Object Detection
2
Template Matching
3
Corner Detection - Part One - Harris Corner Detection
4
Corner Detection - Part Two - Shi-Tomasi Detection
5
Edge Detection
6
Grid Detection
7
Contour Detection
8
Feature Matching - Part One
9
Feature Matching - Part Two
10
Watershed Algorithm - Part One
11
Watershed Algorithm - Part Two
12
Custom Seeds with Watershed Algorithm
13
Introduction to Face Detection
14
Face Detection with OpenCV
15
Detection Assessment
16
Detection Assessment Solutions

Object Tracking

1
Introduction to Object Tracking
2
Optical Flow
3
Optical Flow Coding with OpenCV - Part One
4
Optical Flow Coding with OpenCV - Part Two
5
MeanShift and CamShift Tracking Theory
6
MeanShift and CamShift Tracking with OpenCV
7
Overview of various Tracking API Methods
8
Tracking APIs with OpenCV

Deep Learning for Computer Vision

1
Introduction to Deep Learning for Computer Vision
2
Machine Learning Basics
3
Understanding Classification Metrics
4
Introduction to Deep Learning Topics
5
Understanding a Neuron
6
Understanding a Neural Network
7
Cost Functions
8
Gradient Descent and Back Propagation
9
Keras Basics
10
MNIST Data Overview
11
Convolutional Neural Networks Overview - Part One
12
Convolutional Neural Networks Overview - Part Two
13
Keras Convolutional Neural Networks with MNIST
14
Keras Convolutional Neural Networks with CIFAR-10
15
LINK FOR CATS AND DOGS ZIP
16
Deep Learning on Custom Images - Part One
17
Deep Learning on Custom Images - Part Two
18
Deep Learning and Convolutional Neural Networks Assessment
19
Deep Learning and Convolutional Neural Networks Assessment Solutions
20
Introduction to YOLO v3
21
YOLO Weights Download
22
YOLO v3 with Python

Capstone Project

1
Introduction to CapStone Project
2
Capstone Part One - Variables and Background function
3
Capstone Part Two - Segmentation
4
Capstone Part Three - Counting and ConvexHull
5
Capstone Part Four - Bringing it all together
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