English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 20m | 271 MB
Detect and track objects in images and videos. Perform accurate and reliable processing tasks with Computer vision using OpenCV
Computer vision solves imaging problems that cannot be solved using ordinary systems and sensors. OpenCV is one of the most popular Computer Vision libraries and helps you write faster code.
This course begins with the basics of loading and working with images. You will detect colored objects in your images easily. You will also use tools to build and apply filters in your photos and track objects in a video.
By the end of the course, you will have a firm grasp of Computer Vision techniques using OpenCV libraries. This course will be your gateway to the world of data science.
This course will teach you the skills required to develop computer vision applications using Python with practical examples.
What You Will Learn
- Set up and use OpenCV 3.3 with Python 3 from a Jupyter Notebook within a Docker container
- Perform simple Computer Vision tasks using manipulation techniques
- Build Instagram-style image filters
- Adjust brightness, saturation, and image hue to create Instagram-style filters
- Bulk-apply image manipulation operations to a folder of images
- Separate moving foreground objects from the background of a video
- Track objects in a video
- Work with binary images and use morphological operations and contours to extract colored objects from an image
Table of Contents
Get Started with OpenCV Libraries
1 The Course Overview
2 OpenCV Installation and Its Prerequisites
3 Exploring Images Using OpenCV
4 Mathematical Operations on Images
5 Image Transformations – Color Spaces, Resizing, Rotation, Thresholding
6 Image Transformations – Smoothing and Histograms
Detect Colored Objects in Images
7 Fourier Transform on Images
8 Morphological Operations for Cleaning
9 Drawing Geometric Functions on Images
10 Image Gradients and Edge Detection
11 Image Contours
12 Image Segmentation Using Watershed Algorithm
Feature Detection Algorithms
13 Contrast Enhancement Using Histogram Equalization
14 Understanding Features
15 Harris Corner Detection
16 Face Detection Using Haar Cascades
Video Analysis
17 Getting Started with Videos
18 Background Subtraction
19 Theory Behind Optical Flow
20 Optical Flow Using Lucas-Kanade and Dense Optical Flow
21 Object Tracking Using Meanshift and Camshift
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