Python for Computer Vision with OpenCV and Deep Learning
4.5 (3,636 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
20,084 students enrolled

Python for Computer Vision with OpenCV and Deep Learning

Learn the latest techniques in computer vision with Python , OpenCV , and Deep Learning!
Bestseller
4.5 (3,636 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
20,084 students enrolled
Created by Jose Portilla
Last updated 9/2019
English
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Current price: $129.99 Original price: $199.99 Discount: 35% off
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This course includes
  • 14 hours on-demand video
  • 4 articles
  • 3 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Expand all 92 lectures 14:06:26
+ NumPy and Image Basics
6 lectures 47:10
Introduction to Numpy and Image Section
00:41
NumPy Arrays
16:49
What is an image?
05:53
Images and NumPy
12:23
NumPy and Image Assessment Test
02:39
NumPy and Image Assessment Test - Solutions
08:45
+ Image Basics with OpenCV
10 lectures 01:33:47
Introduction to Images and OpenCV Basics
02:37
Opening Image files in a notebook
19:29
Opening Image files with OpenCV
10:49
Drawing on Images - Part One - Basic Shapes
10:00
Drawing on Images Part Two - Text and Polygons
09:29
Direct Drawing on Images with a mouse - Part One
09:36
Direct Drawing on Images with a mouse - Part Two
02:41
Direct Drawing on Images with a mouse - Part Three
10:25
Image Basics Assessment
05:15
Image Basics Assessment Solutions
13:26
+ Image Processing
14 lectures 02:36:20
Introduction to Image Processing
00:39
Color Mappings
06:47
Blending and Pasting Images
14:15
Blending and Pasting Images Part Two - Masks
15:55
Image Thresholding
17:41
Blurring and Smoothing
06:43
Blurring and Smoothing - Part Two
19:45
Morphological Operators
15:27
Gradients
13:40
Histograms - Part One
12:34
Histograms - Part Two - Histogram Eqaulization
12:19
Histograms Part Three - Histogram Equalization
08:12
Image Processing Assessment
03:52
Image Processing Assessment Solutions
08:31
+ Video Basics with Python and OpenCV
6 lectures 45:40
Introduction to Video Basics
01:05
Connecting to Camera
14:14
Using Video Files
07:00
Drawing on Live Camera
16:45
Video Basics Assessment
01:36
Video Basics Assessment Solutions
05:00
+ Object Detection with OpenCV and Python
16 lectures 03:05:44
Introduction to Object Detection
02:27
Template Matching
17:41
Corner Detection - Part One - Harris Corner Detection
14:08
Corner Detection - Part Two - Shi-Tomasi Detection
06:25
Edge Detection
09:28
Grid Detection
08:16
Contour Detection
11:11
Feature Matching - Part One
12:25
Feature Matching - Part Two
18:28
Watershed Algorithm - Part One
11:49
Watershed Algorithm - Part Two
20:14
Custom Seeds with Watershed Algorithm
18:54
Introduction to Face Detection
09:11
Detection Assessment Solutions
07:10
+ Object Tracking
8 lectures 01:09:53
Introduction to Object Tracking
00:34
Optical Flow
05:37
Optical Flow Coding with OpenCV - Part Two
10:57
MeanShift and CamShift Tracking Theory
05:47
MeanShift and CamShift Tracking with OpenCV
14:41
Overview of various Tracking API Methods
06:50
Tracking APIs with OpenCV
06:52
+ Deep Learning for Computer Vision
22 lectures 03:03:29
Introduction to Deep Learning for Computer Vision
02:29
Machine Learning Basics
06:54
Understanding Classification Metrics
14:12
Introduction to Deep Learning Topics
01:24
Understanding a Neuron
05:12
Understanding a Neural Network
06:30
Cost Functions
03:40
Gradient Descent and Back Propagation
03:20
Keras Basics
18:02
MNIST Data Overview
04:41
Convolutional Neural Networks Overview - Part One
18:53
Convolutional Neural Networks Overview - Part Two
04:23
Keras Convolutional Neural Networks with MNIST
17:08
Keras Convolutional Neural Networks with CIFAR-10
11:59
LINK FOR CATS AND DOGS ZIP
00:05
Deep Learning on Custom Images - Part One
14:50
Deep Learning on Custom Images - Part Two
19:34
Deep Learning and Convolutional Neural Networks Assessment
02:37
Deep Learning and Convolutional Neural Networks Assessment Solutions
07:07
Introduction to YOLO v3
03:16
YOLO Weights Download
00:08
YOLO v3 with Python
17:05
+ Capstone Project
5 lectures 41:10
Introduction to CapStone Project
00:50
Capstone Part One - Variables and Background function
07:47
Capstone Part Two - Segmentation
06:01
Capstone Part Three - Counting and ConvexHull
14:17
Capstone Part Four - Bringing it all together
12:15
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.

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

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