Python for Computer Vision with OpenCV and Deep Learning
- Offered byUDEMY
Python for Computer Vision with OpenCV and Deep Learning at UDEMY Overview
Duration | 14 hours |
Total fee | ₹499 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Python for Computer Vision with OpenCV and Deep Learning at UDEMY Highlights
- Compatible on Mobile and TV
- Earn a Cerificate on successful completion
- Get Full Lifetime Access
- Course Instructor
- Jose Portilla
Python for Computer Vision with OpenCV and Deep Learning at UDEMY Course details
- Python Developers interested in Computer Vision and Deep Learning. This course is not for complete python beginners.
- 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
Python for Computer Vision with OpenCV and Deep Learning at UDEMY Curriculum
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
BONUS SECTION: THANK YOU!
BONUS LECTURE
Other courses offered by UDEMY
Student Forum
Anything you would want to ask experts?