Python for Computer Vision with OpenCV and Deep Learning

Python for Computer Vision with OpenCV and Deep Learning, available at $109.99, has an average rating of 4.59, with 92 lectures, based on 11221 reviews, and has 63306 subscribers.

You will learn about 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. This course is ideal for individuals who are Python Developers interested in Computer Vision and Deep Learning. This course is not for complete python beginners. It is particularly useful for Python Developers interested in Computer Vision and Deep Learning. This course is not for complete python beginners.

Enroll now: Python for Computer Vision with OpenCV and Deep Learning

Summary

Title: Python for Computer Vision with OpenCV and Deep Learning

Price: $109.99

Average Rating: 4.59

Number of Lectures: 92

Number of Published Lectures: 92

Number of Curriculum Items: 92

Number of Published Curriculum Objects: 92

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

Python Developers interested in Computer Vision and Deep Learning. This course is not for complete python beginners.

Target Audiences

Python Developers interested in Computer Vision and Deep Learning. This course is not for complete python beginners.

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 Curriculum

Chapter 1: Course Overview and Introduction

Lecture 1: Course Overview

Lecture 2: FAQ – Frequently Asked Questions

Lecture 3: Course Curriculum Overview

Lecture 4: Getting Set-Up for the Course Content

Chapter 2: NumPy and Image Basics

Lecture 1: Introduction to Numpy and Image Section

Lecture 2: NumPy Arrays

Lecture 3: What is an image?

Lecture 4: Images and NumPy

Lecture 5: NumPy and Image Assessment Test

Lecture 6: NumPy and Image Assessment Test – Solutions

Chapter 3: Image Basics with OpenCV

Lecture 1: Introduction to Images and OpenCV Basics

Lecture 2: Opening Image files in a notebook

Lecture 3: Opening Image files with OpenCV

Lecture 4: Drawing on Images – Part One – Basic Shapes

Lecture 5: Drawing on Images Part Two – Text and Polygons

Lecture 6: Direct Drawing on Images with a mouse – Part One

Lecture 7: Direct Drawing on Images with a mouse – Part Two

Lecture 8: Direct Drawing on Images with a mouse – Part Three

Lecture 9: Image Basics Assessment

Lecture 10: Image Basics Assessment Solutions

Chapter 4: Image Processing

Lecture 1: Introduction to Image Processing

Lecture 2: Color Mappings

Lecture 3: Blending and Pasting Images

Lecture 4: Blending and Pasting Images Part Two – Masks

Lecture 5: Image Thresholding

Lecture 6: Blurring and Smoothing

Lecture 7: Blurring and Smoothing – Part Two

Lecture 8: Morphological Operators

Lecture 9: Gradients

Lecture 10: Histograms – Part One

Lecture 11: Histograms – Part Two – Histogram Eqaulization

Lecture 12: Histograms Part Three – Histogram Equalization

Lecture 13: Image Processing Assessment

Lecture 14: Image Processing Assessment Solutions

Chapter 5: Video Basics with Python and OpenCV

Lecture 1: Introduction to Video Basics

Lecture 2: Connecting to Camera

Lecture 3: Using Video Files

Lecture 4: Drawing on Live Camera

Lecture 5: Video Basics Assessment

Lecture 6: Video Basics Assessment Solutions

Chapter 6: Object Detection with OpenCV and Python

Lecture 1: Introduction to Object Detection

Lecture 2: Template Matching

Lecture 3: Corner Detection – Part One – Harris Corner Detection

Lecture 4: Corner Detection – Part Two – Shi-Tomasi Detection

Lecture 5: Edge Detection

Lecture 6: Grid Detection

Lecture 7: Contour Detection

Lecture 8: Feature Matching – Part One

Lecture 9: Feature Matching – Part Two

Lecture 10: Watershed Algorithm – Part One

Lecture 11: Watershed Algorithm – Part Two

Lecture 12: Custom Seeds with Watershed Algorithm

Lecture 13: Introduction to Face Detection

Lecture 14: Face Detection with OpenCV

Lecture 15: Detection Assessment

Lecture 16: Detection Assessment Solutions

Chapter 7: Object Tracking

Lecture 1: Introduction to Object Tracking

Lecture 2: Optical Flow

Lecture 3: Optical Flow Coding with OpenCV – Part One

Lecture 4: Optical Flow Coding with OpenCV – Part Two

Lecture 5: MeanShift and CamShift Tracking Theory

Lecture 6: MeanShift and CamShift Tracking with OpenCV

Lecture 7: Overview of various Tracking API Methods

Lecture 8: Tracking APIs with OpenCV

Chapter 8: Deep Learning for Computer Vision

Lecture 1: Introduction to Deep Learning for Computer Vision

Lecture 2: Machine Learning Basics

Lecture 3: Understanding Classification Metrics

Lecture 4: Introduction to Deep Learning Topics

Lecture 5: Understanding a Neuron

Lecture 6: Understanding a Neural Network

Lecture 7: Cost Functions

Lecture 8: Gradient Descent and Back Propagation

Lecture 9: Keras Basics

Lecture 10: MNIST Data Overview

Lecture 11: Convolutional Neural Networks Overview – Part One

Lecture 12: Convolutional Neural Networks Overview – Part Two

Lecture 13: Keras Convolutional Neural Networks with MNIST

Lecture 14: Keras Convolutional Neural Networks with CIFAR-10

Lecture 15: LINK FOR CATS AND DOGS ZIP

Lecture 16: Deep Learning on Custom Images – Part One

Lecture 17: Deep Learning on Custom Images – Part Two

Lecture 18: Deep Learning and Convolutional Neural Networks Assessment

Lecture 19: Deep Learning and Convolutional Neural Networks Assessment Solutions

Lecture 20: Introduction to YOLO v3

Lecture 21: YOLO Weights Download

Lecture 22: YOLO v3 with Python

Chapter 9: Capstone Project

Lecture 1: Introduction to CapStone Project

Lecture 2: Capstone Part One – Variables and Background function

Lecture 3: Capstone Part Two – Segmentation

Lecture 4: Capstone Part Three – Counting and ConvexHull

Lecture 5: Capstone Part Four – Bringing it all together

Instructors

Jose Portilla
Head of Data Science at Pierian Training

Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 77 votes

2 stars: 119 votes

3 stars: 772 votes

4 stars: 3791 votes

5 stars: 6460 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!