This course is designed for those interested to learn the basics of computer vision and OpenCV, how to manipulate images within OpenCV using Python, and advanced techniques such as thresholding and histogram equalization.
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I am a Post Graduate Masters Degree holder in Computer Science and Engineering with experience in Android/iOS Mobile and PHP/Python Web Developer Apps
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Who this course is for:
- Beginners or those who want to start with Python Computer Vision using OpenCV
What you’ll learn:Â
- An overview of computer vision and the amazing OpenCV
- Installing OpenCV and trying a one-liner code to check if everything is working fine
- Python assignment, flow-control, functions and data structures
- The theory of how a digital image is organized, concept of pixels, color and grayscale channels, color codes etc
- Manipulating the individual pixels of an image and modifying it
- Selecting a collective area of pixels and manipulating it by trying to change the color and also get the properties of the image
- Separating and extracting color channels and merging them back to form the original image
- The popular color spaces and switching an image between different color spaces
- Creating and drawing simple geometric shapes like line, rectangle, circle, ellipse, polygon, etc. into an image canvas, and inserting a text into the canvas using OpenCV
- Morphological transformations including erosion, dilation, white noise removal, black point noise removal, gradient transformation, and top hat and black hat morphological image transformations
- Geometric transformations which includes scaling or resizing the image, translating or place shifting the image, flipping or changing sides, rotating the image by fixing an axis, and cropping the image to extract the region of interest
Welcome to my course Computer Vision: OpenCV Fundamentals Using Python. OpenCV is an open-source computer vision library.
Even if you do not have a Python background, the course includes lessons that will help you get the basic Python programming skills to proceed with the rest of the course.Â
The code and the images used are a part of the course and you are free to use the code in your projects with no questions asked.
Happy learning and have a great time.
Our Promise to You
By the end of this course, you will have learned more about OpenCV.
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Get started today and learn more about the functionalities of OpenCV.
Course Curriculum
| Section 1 - Computer Vision OpenCV | |||
| Introduction To OpenCV | 00:00:00 | ||
| Downloadable Materials | 00:00:00 | ||
| Environment Setup | 00:00:00 | ||
| Python Basics – Assignment | 00:00:00 | ||
| Image Concepts | 00:00:00 | ||
| Read, Display And Write Images | 00:00:00 | ||
| Pixel Access And Modification | 00:00:00 | ||
| Area Manipulation And Image Properties | 00:00:00 | ||
| Color Channels – Split And Merge | 00:00:00 | ||
| Color Spaces Conversion | 00:00:00 | ||
| Drawing Geometric Shapes And Text Part 1 – Create Drawing Canvas | 00:00:00 | ||
| Drawing Geometric Shapes And Text Part 2 – Drawing Line, Circle, Rectangle | 00:00:00 | ||
| Drawing Geometric Shapes And Text Part 3 – Ellipse, Polygon And Text | 00:00:00 | ||
| Morphological Transformation Part 1 – Erosion | 00:00:00 | ||
| Morphological Transformation Part 2 – Dilation And Opening | 00:00:00 | ||
| Morphological Transformation Part 3 – Closing, Gradient, Tophat, Blackhat | 00:00:00 | ||
| Geometric Transformations Part 1 – Scaling | 00:00:00 | ||
| Geometric Transformations Part 2 – Translation | 00:00:00 | ||
| Geometric Transformations Part 3 – Rotation | 00:00:00 | ||
| Geometric Transformations Part 4 – Flipping | 00:00:00 | ||
| Geometric Transformations Part 5 – Cropping | 00:00:00 | ||
| Arithmetic Operations – Addition And Subtraction | 00:00:00 | ||
| Bitwise Operations – And, Or, Xor, Not | 00:00:00 | ||
| Image Masking | 00:00:00 | ||
| Custom Filter Image Smoothing | 00:00:00 | ||
| Average And Gaussian Image Smoothing | 00:00:00 | ||
| Median And Bilateral Smoothing | 00:00:00 | ||
| Image Thresholding | 00:00:00 | ||
| Otsu Thresholding | 00:00:00 | ||
| Adaptive Thresholding | 00:00:00 | ||
| Histograms | 00:00:00 | ||
| Histogram Equalization | 00:00:00 | ||
| Image Pyramids | 00:00:00 | ||
| Canny Edge Detection | 00:00:00 | ||
| Image Gradients – Laplace And Sobel | 00:00:00 | ||
| Image Contours | 00:00:00 | ||
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