Practical Image Processing with OpenCV Python with Project

Practical Image Processing with OpenCV & Python with Project, available at $64.99, has an average rating of 4.35, with 103 lectures, 2 quizzes, based on 84 reviews, and has 5853 subscribers.

You will learn about Learn OpenCV with Python 9 OpenCV Project Image Processing with OpenCV Image Translation Smoothing Filters Bitwise Operations and Masking Convolution Process Thresholding Concepts This course is ideal for individuals who are Anyone who are passionate to learn image Processing with OpenCV It is particularly useful for Anyone who are passionate to learn image Processing with OpenCV.

Enroll now: Practical Image Processing with OpenCV & Python with Project

Summary

Title: Practical Image Processing with OpenCV & Python with Project

Price: $64.99

Average Rating: 4.35

Number of Lectures: 103

Number of Quizzes: 2

Number of Published Lectures: 103

Number of Published Quizzes: 1

Number of Curriculum Items: 105

Number of Published Curriculum Objects: 104

Number of Practice Tests: 2

Number of Published Practice Tests: 1

Original Price: $149.99

Quality Status: approved

Status: Live

What You Will Learn

Learn OpenCV with Python

9 OpenCV Project

Image Processing with OpenCV

Image Translation

Smoothing Filters

Bitwise Operations and Masking

Convolution Process

Thresholding Concepts

Who Should Attend

Anyone who are passionate to learn image Processing with OpenCV

Target Audiences

Anyone who are passionate to learn image Processing with OpenCV

Welcome to “Image Processing using OpenCV from Zero to Hero” !!!

Image Processing is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course is completely project-based learning. Where you will do the project after completion of every module. Here I will cover the image processing from basics to advanced techniques including applied machine learningalgorithms and models to images.

WHAT YOU WILL LEARN?

Image Basics

Drawings

Image Translation

Image Processing Techniques

Smoothing Filters

Filters

Graphical Use Interphase  (GUI) in OpenCV

Thresholding

Key Highlights in Section 1 to 7

We will start the course with very basic like load, display images. With that, we will understand the basic mathematics background behind the images. Also, I will teach you the concepts of Drawingsand Videos.

Projects(Object Detection):

1. Face Detectionusing Viola-Jones Algorithm

2. Face Detection using Deep Neural Networks (SSD ResNet 10, Caffe Implementation)

3. Real-Time Face Detection

4. Facial LandmarkDetection

Key Highlights in Section 8 to 11

We will slowly move into image processing concepts related to image transformations like image translation, flipping, rotating, and cropping.I will also teach arithmetic operations in OpenCV.

Project (Brightness Control):

  5. GUI based Brightness Control in Images

  6. Real-Time Brightness Control

Key Highlights in Section 12,13

In these sections, I will introduce new concepts on bitwise operations and masking, where you will learn the truth table and different bitwise operations like “AND“, “OR“, “NOT“, “XOR“.

Key Highlights in Section 14

Then we will extend our discussion on Smoothing Filter which is a very important image processing technique. In this section, I will teach smoothing techniques like Average Blur, Gaussian Blur, Median Blur& Bilateral Filter.

Key Highlights in Section 15

Project on automatics facial blur

Key Highlights in Section 16

Thresholding filter: Here we will deep dive into thresholding concepts (BINARY, TOZERO, TRUNC, ADAPTIVE MEAN, ADAPTIVE GAUSSIAN) and implement with OpenCV and Python

You will have complete access to Images, Data, Jupyter Notebook files that are used in this course. The code used in this course is written in such a way that you can directly plug the function into the real-time scenario and get the output. 

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Data Science Anywhere

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Install Python

Lecture 3: Install OpenCV & Requirements

Lecture 4: Facing Any Issue with the Course ? Here is the solution

Lecture 5: Load Display Save Image

Lecture 6: What is Pixel ?

Lecture 7: Converting Color

Lecture 8: Accessing and Manipulate Pixels

Chapter 2: Drawing

Lecture 1: Download the Resources

Lecture 2: Line

Lecture 3: Rectangle

Lecture 4: Cricle

Lecture 5: Abstract Circles

Chapter 3: Working on Videos

Lecture 1: Download the Resources

Lecture 2: Load and Display Video

Lecture 3: Frames Per Seconds (FPS) & Controlling FPS

Lecture 4: Accessing Web Camera

Lecture 5: Stacking Multiple Web Cameras

Chapter 4: Project -1: Face Detection with OpenCV

Lecture 1: Download the Resources

Lecture 2: Download Cascade Classifier

Lecture 3: Load Image and Cascade Classifier using OpenCV

Lecture 4: Apply Viola-Jone Framework (cascade classifier) to Image

Lecture 5: Draw Bounding Box

Lecture 6: Face Detection Function

Chapter 5: Project -2: Real Time Face Detection with OpenCV

Lecture 1: Real Time Face Detection with OpenCV

Chapter 6: Project -3: Face Detection with Deep Neural Network (DNN) OpenCV

Lecture 1: Download the Resources

Lecture 2: Face Detection with DNN Module

Lecture 3: Load SSD ResNet 10 Caffe Model with OpenCV

Lecture 4: Calculate Blob from Image

Lecture 5: Get Face Detections Bounding Boxes from the DNN Model

Lecture 6: Bounding Box : Set the threshold Confidence Score

Lecture 7: Bounding Box: De-Normalize Bounding Box Co-ordinates

Lecture 8: Bounding Box: Draw Rectangle and Put Text (confidence score)

Lecture 9: Create Face Detection Function

Chapter 7: Project-4: Real Time Face Detection with DNN OpenCV

Lecture 1: Real Time Face Detection with DNN and OpenCV

Chapter 8: Image Transformations

Lecture 1: Download the Resources

Lecture 2: Image Translation or Shifting

Lecture 3: Rotating Image

Lecture 4: Resizing Image

Lecture 5: Flipping Image

Lecture 6: Cropping Image

Chapter 9: Arithmetic Operations in Images

Lecture 1: Download the Resources

Lecture 2: Addition in Image

Lecture 3: Subtraction in Image

Lecture 4: Blending Image Idea

Lecture 5: Blending Image – OpenCV Python

Chapter 10: Project – 5: Controlling Brightness of Image with GUI using OpenCV

Lecture 1: Download the Resources

Lecture 2: What we will develop ?

Lecture 3: Controlling Brightness in Image

Chapter 11: Project – 6: Real Time Brightness Control with GUI using OpenCV

Lecture 1: Controlling Brightness in Videos

Chapter 12: Bitwise Operations

Lecture 1: Download the Resources

Lecture 2: Truth Table for AND, OR, NOT, XOR

Lecture 3: Bitwise AND

Lecture 4: Bitwise OR

Lecture 5: Bitwise NOT

Lecture 6: Bitwise XOR

Chapter 13: Masking

Lecture 1: Download the Resources

Lecture 2: Masking Image

Lecture 3: Preparing Mask Image

Lecture 4: Masking Image using mask

Lecture 5: Example-2: Mask image with different shape

Lecture 6: Example-3: Masking circle shape

Chapter 14: Smoothing Filters

Lecture 1: Download the Resources

Lecture 2: Average Blur & Convolution Process

Lecture 3: OpenCV: Average Blur

Lecture 4: Gaussian Blur

Lecture 5: OpenCV: Gaussian Blur

Lecture 6: Median Blur

Lecture 7: OpenCV: Median Blur for Salt Pepper Noise

Chapter 15: Project-7: Pencil Sketch Image in Real Time

Lecture 1: What will you Develop ?

Lecture 2: Load Image and Flow

Lecture 3: Convert image into grayscale

Lecture 4: Apply Gaussian Blur to Gray Scale Image

Lecture 5: Divide Grayscale image and Gaussian Blur Image

Lecture 6: Adjust Gamma to Division Image

Lecture 7: Pencil Sketch Function

Lecture 8: GUI Control Panel

Lecture 9: Calibrate k-size to odd numbers

Lecture 10: Calibrate Gamma to 0 to 1 Scale

Lecture 11: Pencil Sketch in Real Time

Chapter 16: Project – 8: Automatic Facial Blur

Lecture 1: Project Flow

Lecture 2: Load Image

Lecture 3: Step-1, Face Detection: Get Detections

Instructors

datascience Anywhere
Team of Engineers

G Sudheer
Instructor

Brightshine Learn
Instructor Team

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 8 votes

4 stars: 28 votes

5 stars: 46 votes

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