Practical Image Processing with OpenCV & Python with Project from Udemy

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.

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 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 ( 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.

Data Science Anywhere

What's inside

Learning objectives

Learn opencv with python
9 opencv project
Image processing with opencv
Image translation

Smoothing filters
Bitwise operations and masking
Convolution process
Thresholding concepts

Learn opencv with python
9 opencv project
Image processing with opencv
Image translation
Smoothing filters
Bitwise operations and masking
Convolution process
Thresholding concepts

Syllabus

Introduction

Install Python

Install OpenCV & Requirements

Facing Any Issue with the Course ? Here is the solution

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Includes projects on face detection using both the Viola-Jones algorithm and deep neural networks, offering a comprehensive introduction to object detection techniques

Covers image processing techniques from basic to advanced, including applied machine learning algorithms and models, which is useful for building a strong foundation

Features projects involving GUI-based brightness control, providing practical experience in creating interactive image processing applications

Emphasizes project-based learning, where learners complete a project after each module, reinforcing concepts and building practical skills

Highlights that the code is designed for direct integration into real-time applications, enabling learners to quickly deploy their image processing solutions

May use older versions of OpenCV, which could present compatibility issues with the latest software and libraries, requiring careful version management

Reviews summary

Practical opencv & python with projects

According to learners, this course offers a highly practical and project-based approach to image processing using OpenCV and Python. Students frequently highlight the hands-on nature and the inclusion of real-world projects, particularly the face detection modules, as major strengths. The course is often described as a solid introduction for beginners, covering fundamental concepts effectively. While the core content is well-received, some reviewers note potential challenges with library compatibility and code setup, which may require extra troubleshooting. Overall, it's seen as a valuable resource for gaining practical skills in computer vision.

Concepts are explained clearly.

"The explanations for core concepts were very clear and easy to follow."

"Instructor does a good job explaining the 'why' behind the code."

"I appreciated how the mathematical background was briefly touched upon when needed."

"Found the breakdown of different filters particularly well explained."

Solid introduction to OpenCV basics.

"As a beginner, this course provided a great starting point with OpenCV and Python."

"The course breaks down complex ideas into understandable steps for newcomers."

"I had very little experience before, and now I feel comfortable with image processing basics."

"It's a good foundational course if you're new to the field."

Projects are highlight, hands-on coding.

"The hands-on coding and projects are the strongest part of the course for me."

"I really liked the projects, especially the face detection parts. They made the concepts concrete."

"The project-based approach is fantastic for learning by doing."

"Building practical projects gave me confidence to apply what I learned."

Library compatibility issues reported.

"Ran into issues with OpenCV library versions during setup. Needed extra troubleshooting."

"Some code examples required minor tweaks to work with current Python/library versions."

"It took me some time to get the environment set up correctly due to dependencies."

"While the content is good, expect to do some debugging related to code environments."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Practical Image Processing with OpenCV & Python with Project with these activities:

Review Linear Algebra Fundamentals

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Reinforce your understanding of linear algebra concepts, which are foundational for image processing transformations and matrix operations used in OpenCV.

Browse courses on Linear Algebra

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Review matrix operations such as addition, subtraction, and multiplication.
Practice solving systems of linear equations.
Understand vector spaces and linear transformations.

Read 'Practical Python and OpenCV'

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Supplement your learning with a comprehensive guide to OpenCV and Python, providing practical examples and in-depth explanations of key concepts.

View Putting Knowledge to Work on Amazon

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Read the chapters relevant to the current course module.
Try out the code examples provided in the book.
Adapt the examples to solve similar image processing problems.

Implement Image Filtering Techniques

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Solidify your understanding of image filtering by implementing various filters from scratch using NumPy and comparing the results with OpenCV's built-in functions.

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Implement average blur, Gaussian blur, and median blur filters.
Compare your implementations with OpenCV's functions.
Analyze the performance differences.

Four other activities

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Create a Blog Post on Image Segmentation Techniques

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Deepen your understanding of image segmentation by researching and writing a blog post explaining different segmentation techniques and their applications.

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Research different image segmentation techniques (e.g., thresholding, clustering, edge-based).
Write a clear and concise explanation of each technique.
Include examples of how each technique can be applied using OpenCV.
Publish your blog post online.

Build an Automated License Plate Recognition System

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Apply your knowledge to a real-world problem by building a system that automatically detects and recognizes license plates in images or videos.

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Collect a dataset of license plate images.
Implement license plate detection using techniques like edge detection and contour analysis.
Use OCR (Optical Character Recognition) to extract the license plate text.
Evaluate the system's accuracy and performance.

Study 'Computer Vision: Algorithms and Applications'

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Enhance your theoretical understanding of computer vision algorithms by studying a comprehensive textbook that covers the mathematical foundations and applications.

View Computer Vision on Amazon

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Read chapters related to topics covered in the course.
Work through the mathematical derivations and examples.
Relate the theoretical concepts to the practical implementations in OpenCV.

Contribute to an OpenCV Project

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Gain practical experience and contribute to the open-source community by identifying and fixing bugs, improving documentation, or adding new features to an OpenCV project.

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Explore OpenCV's GitHub repository and identify areas for improvement.
Contribute by fixing bugs, improving documentation, or adding new features.
Submit your changes as a pull request.

Career center

Learners who complete Practical Image Processing with OpenCV & Python with Project will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

A Computer Vision Engineer is responsible for developing algorithms and systems that enable computers to "see" and interpret images, much like humans do. This course helps build a foundation in the practical aspects of image processing using OpenCV and Python, vital tools in a computer vision engineer's toolkit. The course's focus on image basics, transformations, and filtering helps one understand how to manipulate and enhance images for better analysis. Furthermore, the project-based approach, including face detection and real-time applications, provides hands-on experience directly applicable to computer vision projects. Learning concepts such as bitwise operations and masking also contributes towards a computer vision engineer's ability to isolate and analyze specific regions of interest within images.

See salaries and explore the career path for Computer Vision Engineer

Image Processing Specialist

An Image Processing Specialist focuses on enhancing, analyzing, and manipulating digital images using various software and techniques. This course provides in-depth knowledge and practical skills directly relevant to this role. This course explores key image processing techniques using OpenCV and Python, including smoothing filters, thresholding, and image transformations which helps one refine images for specific applications. The course's project-based learning helps develop hands-on skills, such as creating a GUI-based brightness control and implementing automatic facial blur, which are directly applicable to real-world image processing tasks. The knowledge of bitwise operations and masking also gives the image processing specialist the ability to isolate regions of interest for more refined analysis.

See salaries and explore the career path for Image Processing Specialist

Robotics Engineer

A Robotics Engineer designs, develops, and tests robots for various applications, often incorporating computer vision for navigation and object recognition. This course helps build essential image processing skills needed in robotics. Learning OpenCV and Python, as well as applying image processing techniques like smoothing filters and thresholding, enables a robotics engineer to integrate visual input into robot control systems. The project-based approach, especially the face detection projects, helps one understand how to implement computer vision algorithms in real-time. The understanding of image transformations and bitwise operations enhances a robotics engineer's ability to process and interpret visual data, leading to more intelligent and responsive robots.

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Machine Learning Engineer

Machine learning engineers design and implement machine learning algorithms. The course may be useful for those focusing on computer vision applications. With its practical approach to image processing using OpenCV and Python, the course is a potential starting point to this career. The course covers image processing techniques, applied machine learning algorithms, and models to images. Topics such as image translation and smoothing filters may be helpful. Projects include face detection and real-time brightness control, offering practical experience.

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Data Scientist

Data Scientists analyze data to extract meaningful insights and develop data-driven solutions. The course may be useful for data scientists who work with image data. The course introduces image processing with OpenCV and Python, covering topics from image basics to advanced techniques using applied machine learning algorithms. The course includes projects such as face detection and real-time brightness control which may contribute hands-on experience. The use of smoothing filters and thresholding techniques covered in the course may be beneficial to a data scientist.

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Surveillance Systems Developer

Surveillance Systems Developers create and maintain systems that monitor and record activities, often using computer vision for automated analysis. This course helps build foundational skills needed in surveillance technology. The course helps one learn how to process and analyze video feeds using OpenCV and Python, including implementing face detection and real-time analysis. The knowledge of image transformations and filtering techniques helps enhance the quality and clarity of surveillance footage. Furthermore, the ability to create GUI-based controls, as demonstrated in the brightness control project, may be useful for developing user-friendly surveillance interfaces.

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Augmented Reality Developer

An Augmented Reality Developer creates applications that overlay digital information onto the real world. The course helps build skills needed for the image processing aspects of AR. The course introduces image processing with OpenCV and Python and may give one the ability to manipulate and analyze camera input in real-time. The course's coverage of image transformations, filtering, face detection, and real-time brightness control may prove valuable in creating interactive AR experiences. The knowledge of bitwise operations and masking may improve the ability to isolate and enhance specific elements within a live video feed.

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Quality Assurance Engineer

A Quality Assurance Engineer ensures the quality of products. The course may be useful for testing image-based systems. The course introduces image processing with OpenCV and Python, which may help one understand how to evaluate the performance of image processing algorithms and systems. The course's focus on image transformations and filtering helps an engineer assess image quality under different conditions. The project-based approach, with examples like face detection, may provide a hands-on understanding of potential failure points in image processing applications.

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Biomedical Imaging Specialist

A Biomedical Imaging Specialist works with medical images to aid in diagnosis and treatment. This role typically requires an advanced degree (master's or phd). The course may be useful for understanding the fundamentals of image processing that are applicable to medical imaging. The course's exploration of image transformations, filtering, and thresholding with OpenCV and Python may help one enhance and analyze medical images. While biomedical imaging often involves specialized techniques, a solid foundation in basic image processing, as may be provided by this course, can be a helpful starting point.

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Geospatial Analyst

A Geospatial Analyst analyzes geographic data, often including satellite and aerial imagery. The course may be useful for preprocessing and enhancing these images. The course's coverage of image processing techniques using OpenCV and Python, such as image translation, smoothing filters, and bitwise operations, helps one prepare geospatial data for analysis. While geospatial analysis involves specific tools and methods, foundational image processing skills, as may be learned in this course, can be valuable for improving image quality. The knowledge of masking may also allow for better extraction of regions of interest in satellite images.

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Game Developer

Game developers create video games for computers and consoles. This course may be useful for those who want to work with game graphics and special visual effects. This course uses OpenCV and Python and covers the fundamentals of image processing. It may help improve the rendering of visual elements in a game. The course includes smoothing filters and thresholding concepts.

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Web Developer

A Web Developer is in charge of designing and developing web applications. The course may be useful for web developers that need to manipulate images for their websites. The course focuses on image processing using OpenCV and Python, covering topics like image transformations, smoothing filters, and thresholding. The course may help one to optimize images for web display and create dynamic visual effects. The knowledge of bitwise operations and masking may also be relevant for creating interactive image-based content.

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Technical Support Specialist

Technical Support Specialists provide assistance to users experiencing technical issues. The course may be useful for those supporting software related to image processing. Covering image processing techniques using OpenCV and Python may give one a better understanding of the software's capabilities and potential problems. Knowing how the software load, display, and process images can help one troubleshoot issues related to image quality and performance. The project-based approach in this course may improve one's ability to diagnose and resolve user problems effectively.

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Technical Writer

Technical writers create documentation for technical products. This course may be useful for those who specialize in writing documentation for image processing software. The course introduces image processing with OpenCV and Python, which may help an individual better understand the concepts. Knowing the software can load, display, and save images could allow technical writers to create guides.

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Photographer

Photographers capture images for various purposes. The course may be useful for enhancing images. The course helps one understand image transformations, bitwise operations, and masking, which may allow a photographer to edit photographs. Learning to control brightness is also beneficial. Applying smoothing filters may allow photographers to reduce image noise.

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Practical Image Processing with OpenCV & Python with Project

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Reviews summary

Practical opencv & python with projects

Activities

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