Practical OpenCV with Python from Zero to Hero
Learn Practical Python OpenCV concepts and develop projects on completion of every module.
Updated on Sep, 2023
Language - English
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 learning algorithms and models to images.
WHAT YOU WILL LEARN?
- Image Basics
- Image Translation
- Image Processing Techniques
- Smoothing Filters
- Graphical Use Interphase (GUI) in OpenCV
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 Drawings and Videos.
Projects (Object Detection):
Face Detection using Viola-Jones Algorithm
Face Detection using Deep Neural Networks (SSD ResNet 10, Caffe Implementation)
Real-Time Face Detection
Facial Landmark Detection
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.
What will you learn in this course:
- Learn OpenCV with Python
- 9 OpenCV Project
- Image Processing with OpenCV
- Image Translation
- Smoothing Filters
- Bitwise Operations and Masking
- Convolution Process
- Thresholding Concepts
What are the prerequisites for this course?
- At least should be beginner to Python
- A Personal Desktop/Laptop.
- At least 4GB RAM, 250 GB HDD
Check out the detailed breakdown of what’s inside the course
- Introduction 05:19 05:19
- Install Python 02:23 02:23
- Install Requirements 04:47 04:47
- Download Complete Materials
- Read, Display and Save Image 12:42 12:42
- Pixel 04:55 04:55
- Convert RBG / BGR Image to Grayscale 11:34 11:34
- Access and Manipulate Pixels 08:06 08:06
Drawings with OpenCV
Working with Videos
Project - 1 : Face Detection with OpenCV
Project -2: Real - Time Face Detection with OpenCV
Project -3: Face Detection with Deep Neural Network (DNN) OpenCV
Project-4: Real Time Face Detection with DNN OpenCV
Arithmetic Operations in Images
Project - 5: Controlling Brightness of Image with GUI using OpenCV
Project - 6: Real Time Brightness Control with GUI using OpenCV
Project-7: Pencil Sketch Image in Real Time
Project - 8: Automatic Facial Blur
Srikanth GuskraData Scientist
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