Computer Vision Theory and Projects in Python for Beginners [Video]
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-
Free ChapterIntroduction to Course and Instructor
-
Introduction to Images
- Grayscale Image
- Quiz (Grayscale Image)
- Solution (Grayscale Image)
- Grayscale Spectrum
- Reading, Manipulating, and Saving Grayscale Image using Matplotlib Python
- Quiz (Reading, Manipulating, and Saving Grayscale Image using Matplotlib Python)
- Solution (Reading, Manipulating, and Saving Grayscale Image using Matplotlib Python)
- Reading, Manipulating, and Saving Grayscale Image using OpenCV Python
- Introduction to RGB Images
- Quiz (Introduction to RGB Images)
- Solution (Introduction to RGB Images)
- RGB Color Images Matplotlib and OpenCV
- Quiz (RGB Color Images Matplotlib and OpenCV)
- Solution (RGB Color Images Matplotlib and OpenCV)
- RGB to HSV theory and Algorithm
- RGB to HSV Algorithm Implementation using Python
- Quiz (RGB to HSV Algorithm Implementation using Python)
- Solution (RGB to HSV Algorithm Implementation using Python)
- Red Rose Extraction or Segmentation using HSV Python
- Quiz (Red Rose Extraction or Segmentation using HSV Python)
- Solution (Red Rose Extraction or Segmentation using HSV Python)
- Hyper Spectral Images
-
2D Scaling Transformations
- Introduction to Geometric Transformations
- Scaling Example in OpenCV
- Quiz (Scaling Example in OpenCV)
- Solution (Scaling Example in OpenCV)
- Scaling in Real Space
- Quiz (Scaling in Real Space)
- Solution (Scaling in Real Space)
- Linear Transformation Explained
- Scaling is a Linear Transformation
- Scaling as a Matrix Multiplication Example Python
- Quiz (Scaling as a Matrix Multiplication Example Python)
- Solution (Scaling as a Matrix Multiplication Example Python)
- Image Coordinate System
- Image Copy and Flipping Vertically
- Quiz 01 (Image Copy and Flipping Vertically)
- Solution 01 (Image Copy and Flipping Vertically)
- Quiz 02 (Image Copy and Flipping Vertically)
- Solution 02 (Image Copy and Flipping Vertically)
- Continuous Coordinates
- Saturations and Holes
- Image Doubling and Holes using Python
- Inverse Scaling and Quiz
- Solution and Nearest Neighbor Interpolation
- Inverse Scaling Python
- Quiz 01 (Inverse Scaling Python)
- Solution 01 (Inverse Scaling Python)
- Quiz 02 (Inverse Scaling Python)
- Solution 02 (Inverse Scaling Python)
- Nearest Neighbor Interpolation
- Weighted Average Versus Simple Average
- Bilinear Interpolation
- Bilinear Interpolation Implementation in Python
- Scaling Transformation with Bilinear Interpolation Implementation
- Scaling Transformation Algorithm(Recap)
- Exam
- Exam Solution 01
- Exam Solution 02
-
2D Geometric Transformations
- Rotation Introduction
- Optional Rotation is Linear Transform Proof
- Rotation can Result Negative Coordinates (Problem)
- Rotation Computing Width and Hight of Resultant Image(Solution)
- Rotation Index Shifting
- Quiz (Rotation Index Shifting)
- Solution (Rotation Index Shifting)
- Rotation Implementation Complete
- Quiz (Rotation Implementation Complete)
- Solution (Rotation Implementation Complete)
- Rotation Implementation (Good Coding Practice)
- Quiz: Rotation Implementation (Good Coding Practice)
- Solution: Rotation Implementation (Good Coding Practice)
- Reflection Introduction
- Quiz (Reflection Introduction)
- Solution (Reflection Introduction)
- Reflection Implementation
- Quiz 01 (Reflection Implementation)
- Solution 01 (Reflection Implementation)
- Quiz 02 (Reflection Implementation)
- Solution 02 (Reflection Implementation)
- Shear Introduction
- Shear Implementation and Quiz
- Translation and its Nonlinearity (Problem)
- Homogeneous Coordinates
- Translation as a Matrix (Solution)
- Homogeneous Representations of All Transformations
- Affine Transformation Implementation
- Quiz (Affine Transformation Implementation)
- Rotation about Any Point Theory
- Rotation about Any Point Implementation
- Reflection about a Line Quiz
- Solution (Reflection about a Line)
- Transformation Matrix Properties
- Transformation Matrix Properties Implementation
- Affine Transformation Hierarchy
- Optional Affine Transformation SVD
- Projective Transformation Homography
- Projective Transformation Implementation
- Projective Warping Algorithm
-
Geometric Transformation Estimation (Panorama)
- Goal
- Affine Transformation Estimation Introduction
- Quiz (Affine Transformation Estimation Introduction)
- Solution (Affine Transformation Estimation Introduction)
- Affine Transformation Estimation Points Correspondences
- Estimation Points Marking using Python and Quiz
- Affine Transformation Min Number of Points Needed
- Affine Transformation Estimation using Python
- Affine Transformation Estimation Verification using Python
- Affine Transformation Estimation with More Than Three Points
- Quiz (Affine Transformation Estimation with More Than Three Points)
- Solution (Affine Transformation Estimation with More Than Three Points)
- Affine Transformation Estimation with More Than Three Points Implementation
- Quiz (Affine Transformation Estimation with More Than Three Points Implementation)
- Solution (Affine Transformation Estimation with More Than Three Points Implementation)
- Optional Affine Transformation Estimation with LeastSquared
- Projective Transformation Estimation Introduction
- Projective Transformation Estimation First Implementation having Bug
- Projective Transformation Estimation Reason of the Bug
- Projective Transformation Estimation Removing Scale Factor
- Projective Transformation Estimation DLT
- Projective Transformation Estimation DLT Nullspace and Why Four Points
- Projective Transformation Estimation DLT Nullspace Implementation
- DLT Implementation
- Quiz (DLT Implementation)
- Panorama Stitching
- Panorama Stitching Implementation in OpenCV
- How Projective Transformation Helps in Panorama
-
Binary Morphology
- Binary Images Theory
- Binary Images Python
- Structuring Element Kernel and Sliding Window Theory
- Structuring Element Python
- Erosion Theory
- Quiz 01 (Erosion Theory)
- Solution 01 (Erosion Theory)
- Quiz 02 (Erosion Theory)
- Solution 02 (Erosion Theory)
- Erosion Python
- Dilation Theory
- Quiz 01 (Dilation Theory)
- Solution 01 (Dilation Theory)
- Quiz 02 (Dilation Theory)
- Solution 02 (Dilation Theory)
- Dilation Python
- Opening Theory
- Opening Python
- Closing Theory
- Closing Python
- Gradient Morphology
- Gradient Morphology Python
- Top Hat and Black Hat
-
Image Filtering
-
Canny Edge Detector
- Canny Edge Detector Algorithm Introduction
- Canny Edge Detector OpenCV
- Quiz (Canny Edge Detector OpenCV)
- Solution (Canny Edge Detector OpenCV)
- Gaussian Filter Introduction
- Gaussian Filter to Mask Computation
- Gaussian Filter Window Size
- Gaussian Filter Implementation
- Quiz (Gaussian Filter Implementation)
- Solution (Gaussian Filter Implementation)
- Gaussian Filter Smoothing Implementation
- Quiz (Gaussian Filter Smoothing Implementation)
- Solution (Gaussian Filter Smoothing Implementation)
- Image Gradients Theory
- Image Gradients Implementation
- Image Gradients Implementation Datatype Bug
- Derivative of Gaussian
- Derivative of Gaussian Expression
- Derivative of Gaussian Implementation
- Applying DOG Filters
- Gradient Vector
- Gradient Magnitude and Gradient Direction
- Non-Maxima Suppression
- Gradient Direction Quantization
- Quiz (Gradient Direction Quantization)
- Solution (Gradient Direction Quantization)
- Gradient Direction Quantization Implementation
- Gradient Direction Quantization Implementation Better Way
- NMS Implementation
- Quiz 01 (NMS Implementation)
- Solution 01 (NMS Implementation)
- Quiz 02 (NMS Implementation)
- Solution 02 (NMS Implementation)
- Last Step Thresholding
- Hysteresis Thresholding
- Hysteresis Thresholding Implementation
-
Shape Detection
- Shape Detection Introduction
- Why Edge Detection is not Enough
- RANSAC Introduction
- RANSAC For Lines Coordinate Arrays
- RANSAC for Lines Sampling Points Randomly Implementation
- Quiz (RANSAC for Lines Sampling Points Randomly Implementation)
- Solution (RANSAC for Lines Sampling Points Randomly Implementation)
- RANSAC for Lines - Fitting Line with Two Points
- RANSAC for Lines - Fitting Line with Two Points Implementation
- Quiz (RANSAC for Lines Fitting Line with Two Points Implementation)
- Solution (RANSAC for Lines Fitting Line with Two Points Implementation)
- RANSAC for Lines Computing Consistency Score
- RANSAC For Lines Computing Consistency Score Implementation
- RANSAC for Lines Implementation
- RANSAC for Lines Implementation Test on Real Image
- Drawback
- RANSAC for Lines Implementation Test on Real Image Drawing and Quiz
- RANSAC for Circles
- RANSAC for Circles Consistency Score
- RANSAC for Circles Implementation
- RANSAC for Circles Implementation Real Image
- Drawback
- RANSAC for Circles Implementation Real Image Drawing
- RANSAC General
- RANSAC Quiz
- RANSAC Quiz Solution
-
Shape Detection Hough Transform
- Hough Transform Introduction
- Hough Transform as Voting
- Hough Transform as Voting Loop
- Hough Transform Polar Representation
- Hough Transform Polar Representation Benefits
- Hough Transform Polar Representation Implementation
- Hough Transform Lines Implementation Real Image
- Hough Transform Lines Parameters Conversion
- Hough Transform Lines Drawing
- Solution (Hough Transform Lines Drawing)
- Hough Transform Fast Version
- Hough Transform Circles
- Hough Transform Circles Implementation
- Hough Transform Circles Implementation Drawing
- Solution (Hough Transform Circles Implementation Drawing)
-
Corner Detection
- Corner Definition
- Why Corner
- Corner Measure
- SSD
- Why SSD to be Muted Somewhere
- Corner Detection Implementation 01
- Corner Detection Implementation 02
- Corner Detection Implementation 03
- Moravec Corner Detector
- Scale Space
- Infinite Directions Towards Harris Corner Detector
- Harris Corner Detector 01
- Harris Corner Detector 02
- Harris Corner Detector 03
- Harris Corner Detector 04 Structure Tensor
- Harris Corner Detector 05 Final Expression
- Harris Corner Detector Implementation Speedup Convolution
- Harris Corner Detector Implementation 01
- Harris Corner Detector Implementation 02
- Harris Corner Detector as Edge Detector
-
Automatic Panorama SIFT
-
Object Detection
-
YOLO Object Detector
- CNNS Introduction
- Face Detection Implementation
- YOLO Implementation
- YOLO Image Classification Revisited
- YOLO Sliding Window Object Localization
- YOLO Sliding Window Efficient Implementation
- YOLO Introduction
- YOLO Training Data Generation
- YOLO Anchor Boxes
- YOLO Algorithm
- YOLO Non-Maxima Suppression
- YOLO RCNN
-
Motion
-
Object Tracking
-
3D Reconstruction
-
Smart CCTV Project
- Introduction to the Project
- Introduction to Data
- Reading a Video File
- Change Detection Frame Differencing
- Change Detection Frame Differencing Implementation
- Change Detection Background Subtraction
- Change Detection Background Subtraction MOG
- Denoising using Morphology
- Connected Components
- Connected Components Filtering
- Tracking Change
- Saving Segments
- Saving and Viewing Segments
- Saving and Viewing Segments with Object Detection
- Applications
About this
video
The high-quality content of the Mastering Computer Vision from the Absolute Beginning Using Python course presents you with a great opportunity to learn and become an expert. You will learn the core concepts of the CV field. This course will also help you understand the digital imaging process and identify the key application areas of CV. The course is easy to understand, descriptive, comprehensive, practical with live coding, and rich with state-of-the-art and updated knowledge of this field.
Although this course is a compilation of all the basic concepts of CV, you are encouraged to step up and experience more than what you learn. Your understanding of every concept is tested at the end of each section. The homework assignments/tasks/activities/quizzes along with solutions will assess your learning. Several of these activities are focused on coding so that you are ready to run with implementations.
The two hands-on projects in the last section—Change Detection in CCTV Cameras (Real-Time) and Smart DVRs (Real-Time)—make up the most important learning element of this course. They will help you sharpen your practical skills. Successful completion of these two projects will help you enrich your portfolio and kick-start your career in the CV field.
By the end of the course, you will have a strong understanding of Computer Vision concepts and will be ready to apply them in your future projects.
Code bundles are available here: https://github.com/PacktPublishing/Computer-Vision-Theory-and-Projects-in-Python-for-Beginners
- Publication date:
- September 2021
- Publisher
- Packt
- Duration
- 26 hours 49 minutes
- ISBN
- 9781801815949