Python for Computer Vision with OpenCV and Deep Learning

Python for Computer Vision with OpenCV and Deep Learning
Python for Computer Vision with OpenCV and Deep Learning

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 13.5 Hours | 5.41 GB
eLearning | Skill level: All Levels

Learn the latest techniques in computer vision with Python , OpenCV , and Deep Learning!

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!

What you’ll learn

  • 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.
+ Table of Contents

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
5 Introduction to Numpy and Image Section
6 NumPy Arrays
7 What is an image
8 Images and NumPy
9 NumPy and Image Assessment Test
10 NumPy and Image Assessment Test – Solutions

Image Basics with OpenCV
11 Introduction to Images and OpenCV Basics
12 Image Basics Assessment Solutions
13 Opening Image files in a notebook
14 Opening Image files with OpenCV
15 Drawing on Images – Part One – Basic Shapes
16 Drawing on Images Part Two – Text and Polygons
17 Direct Drawing on Images with a mouse – Part One
18 Direct Drawing on Images with a mouse – Part Two
19 Direct Drawing on Images with a mouse – Part Three
20 Image Basics Assessment

Image Processing
21 Introduction to Image Processing
22 Histograms – Part One
23 Histograms – Part Two – Histogram Eqaulization
24 Histograms Part Three – Histogram Equalization
25 Image Processing Assessment
26 Image Processing Assessment Solutions
27 Color Mappings
28 Blending and Pasting Images
29 Blending and Pasting Images Part Two – Masks
30 Image Thresholding
31 Blurring and Smoothing
32 Blurring and Smoothing – Part Two
33 Morphological Operators
34 Gradients

Video Basics with Python and OpenCV
35 Introduction to Video Basics
36 Connecting to Camera
37 Using Video Files
38 Drawing on Live Camera
39 Video Basics Assessment
40 Video Basics Assessment Solutions

Object Detection with OpenCV and Python
41 Introduction to Object Detection
42 Watershed Algorithm – Part One
43 Watershed Algorithm – Part Two
44 Custom Seeds with Watershed Algorithm
45 Introduction to Face Detection
46 Face Detection with OpenCV
47 Detection Assessment
48 Detection Assessment Solutions
49 Template Matching
50 Corner Detection – Part One – Harris Corner Detection
51 Corner Detection – Part Two – Shi-Tomasi Detection
52 Edge Detection
53 Grid Detection
54 Contour Detection
55 Feature Matching – Part One
56 Feature Matching – Part Two

Object Tracking
57 Introduction to Object Tracking
58 Optical Flow
59 Optical Flow Coding with OpenCV – Part One
60 Optical Flow Coding with OpenCV – Part Two
61 MeanShift and CamShift Tracking Theory
62 MeanShift and CamShift Tracking with OpenCV
63 Overview of various Tracking API Methods
64 Tracking APIs with OpenCV

Deep Learning for Computer Vision
65 Introduction to Deep Learning for Computer Vision
66 MNIST Data Overview
67 Convolutional Neural Networks Overview – Part One
68 Convolutional Neural Networks Overview – Part Two
69 Keras Convolutional Neural Networks with MNIST
70 Keras Convolutional Neural Networks with CIFAR-10
72 Deep Learning on Custom Images – Part One
73 Deep Learning on Custom Images – Part Two
74 Deep Learning and Convolutional Neural Networks Assessment
75 Deep Learning and Convolutional Neural Networks Assessment Solutions
76 Machine Learning Basics
77 Introduction to YOLO v3
78 YOLO Weights Download
79 YOLO v3 with Python
80 Understanding Classification Metrics
81 Introduction to Deep Learning Topics
82 Understanding a Neuron
83 Understanding a Neural Network
84 Cost Functions
85 Gradient Descent and Back Propagation
86 Keras Basics

87 Bonus Lecture Coupons

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