AI with Python – Computer Vision training

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Duration: 2 Days

Course fee:$399.00 (₹28,402.93)

Description

Modeling and Replication of human vision with the help of computer software and hardware is possible using Computer Vision(CV). AI with Python using OpenCV is primarily focusing on discipline that studies how to reconstruct, interrupt and understand a 3d scene from its 2d images, in terms of the properties of the structure present in the scene. Using Computer Vision terminology, large number of applications can be easily created like Robotics, Medicine, Security, Transportation, Industrial Automation Applications and many more.

Objectives
1. Basic Understanding on OpenCV for Computer Vision
2. Practical Implementation of AI Activities using Python
3. Learning some advanced terminologies using OpenCV platform

Advantages:
1. Getting expertised knowledge on working with AI
2. Hands-on labs for Image Processing activities
3. Understanding some geometric transformation using OpenCV

Additional information

Course Content

OpenCV

Scikit-image

Introduction to OpenCV

– Basic Installation of OpenCV
– Understanding Technical Requirements
– Understanding the concept of OpenCV Library

Introduction to Anaconda

Basics of Images in OpenCV

– Understanding main problems with Image Processing
– Steps for Image Processing
– Understanding Image Formulation

Basic Concept of Pixels, Colors, Channels, Images and Color Space

Understanding Coordinate System using OpenCV

Accessing and Manipulating Pixels in OpenCV

Understanding the concept of Handling Files and Images

– Working with Reading and Writing Images
– Concept of Reading Camera frames and video files
– How to write a video file
– Playing with video capture properties

Basic Shape Construction using OpenCV

– Concept of drawing shapes – lines, rectangles and circles
– Concept of drawing advanced shapes -clip lines, arrows, ellipses, polygon
– Concept of writing text
– Dynamic Drawing using Mouse Events

Understanding Image Processing Techniques

– Geometric Transformation of Images
– Images Filtering Techniques
– Arithmetic operations using images
– Performing Morphological Transformation
– Using Color spaces and Color Maps

Building and Constructing Histograms

– Understanding Grayscale Histograms
– Working with Color Histograms
– Using Custom visualization of Histograms

Understanding Thresholding Techniques

– Basic Installation of scikit-image
– Basic Installation of scipy

Basics of Simple Thresholding

Using Adaptive Thresholding

Concept of Thresholding Color Images

Understanding Contour Detection, Filtering and Drawing

Understanding Face Detection

Understanding Edge Detection

Understanding Eye Detection

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