Python for Computer Vision with OpenCV and Deep Learning
Learn the latest techniques in computer vision with Python , OpenCV , and Deep Learning!
Created by Jose Portilla
Last updated 12/2018
English
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.
Course content
Course Overview and Introduction
- Course Overview
- FAQ - Frequently Asked Questions
- 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
- Understanding a Neural Network
- Cost Functions
- Gradient Descent and Back Propagation
- Keras Basics
- MNIST Data Overview
- Convolutional Neural Networks Overview - Part One
- Convolutional Neural Networks Overview - Part Two
- Keras Convolutional Neural Networks with MNIST
- Keras Convolutional Neural Networks with CIFAR-10
- LINK FOR CATS AND DOGS ZIP
- Deep Learning on Custom Images - Part One
- Deep Learning on Custom Images - Part Two
- Deep Learning and Convolutional Neural Networks Assessment
- Deep Learning and Convolutional Neural Networks Assessment Solutions
- Introduction to YOLO v3
- YOLO Weights Download
- YOLO v3 with Python
Capstone Project
- Introduction to CapStone Project
- Capstone Part One - Variables and Background function
- Capstone Part Two - Segmentation
- Capstone Part Three - Counting and ConvexHull
- Capstone Part Four - Bringing it all together
Requirements
- Must have clear understanding of Python Basics
- Windows 10 or MacOS or Ubuntu
- Must have Install Permissions on Computer
- WebCam if you want to learn the video streaming content
Description
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.
About the instructor
Jose Portilla
Data Scientist
Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.
Total Size: 5.67GB
0 Comments