The Future Of Your Brain

Unlocking Visual Insights: OpenCV Made Simple and Powerful. Key Features ● OpenCV Mastery: Harness the full potential of OpenCV. ● Comprehensive Coverage: From fundamentals to advanced techniques. ● Practical Exercises: Apply knowledge through hands-on tasks. Book Description "Mastering OpenCV with Python" immerses you in the captivating realm of computer vision, with a structured approach that equips you with the knowledge and skills essential for success in this rapidly evolving field. From grasping the fundamental concepts of image processing and OpenCV to mastering advanced techniques such as neural networks and object detection, you will gain a comprehensive understanding. Each chapter is enriched with hands-on exercises and real-world projects, ensuring the acquisition of practical skills that can be immediately applied in your professional journey. This book not only elevates your technical proficiency but also prepares you for a rewarding career. The technological job landscape is constantly evolving, and professionals who can harness the potential of computer vision are in high demand. By mastering the skills and insights contained within these pages, you will be well-prepared to explore exciting career opportunities, ranging from machine learning engineering to computer vision research. This book is your ticket to a future filled with innovation and professional advancement within the dynamic world of computer vision. What you will learn ● Master Image Processing and Machine Learning with OpenCV using advanced Tools and Libraries. ● Create Real-World Projects with Hands-On Experience. ● Explore Machine Learning for Computer Vision. ● Develop Confidence in Practical Computer Vision Projects. ● Conquer Real-World Image Processing Challenges. ● Apply Computer Vision Across Diverse Industries. ● Boost Your Career in Computer Vision. ● Become an Expert in Computer Vision for Career Advancement. Who is this book for? This beginner-friendly book in computer vision requires no prior experience, making it accessible to newcomers. While a basic programming understanding is helpful, it's designed to guide individuals from diverse backgrounds into the captivating realms of AI, computer vision, and image processing. It's equally valuable for aspiring tech professionals, students, and enthusiasts seeking rewarding careers and knowledge in these cutting-edge fields. Cover Page Title Page Copyright Page Dedication Page About the Author About the Technical Reviewer Acknowledgements Preface Errata Table of Contents 1. Introduction to Computer Vision Introduction Structure Introduction to Computer Vision Applications of Computer Vision Python OpenCV. Brief history of OpenCV OpenCV 4.7 Supporting Libraries NumPy Matplotlib SciPy Scikit-Learn Scikit-Image Mahotas TensorFlow Keras Dlib Environment Setup Installing Python Installing Python on Windows Installing Python on Ubuntu and Mac Package Manager Installing libraries Installing Mahotas Installing OpenCV Verifying our installation IDE Documentation Conclusion Test Your Understanding 2. Getting Started with Images Structure Introduction to images and pixels Loading and displaying images Imread() Imshow Imwrite WaitKey DestroyAllWindows Manipulating images with pixels Accessing individual pixels Accessing a region of interest (ROI) Drawing in OpenCV Line Rectangle Circle Text Conclusion Points to remember Test your understanding 3. Image Processing Fundamentals Structure Geometric transformations Image translation Rotation Scaling Flipping Shearing Cropping Arithmetic Operations Addition Subtraction Multiplication and division Bitwise operations AND OR XOR NOT Channels and color spaces Red Green Blue (RGB) color space Blue Green Red (BGR) color space Hue Saturation Value (HSV) color space Hue Saturation Lightness (HSL) color space cvtColor() 67 Hue Saturation Lightness (HSL) color space LAB color space YCbCr color space Conclusion Points to Remember Test Your Understanding 4. Image Operations Structure Morphological operations on images Erosion cv2.Erode() Dilation cv2.Dilate() Opening Cv2.morphologyex() Closing Morphological gradient Top hat Bottom hat Smoothing and blurring Average blurring Cv2.blur() Median blur cv2.medianBlur() Gaussian blur cv2.gaussianBlur() Bilateral filter cv2.bilateralFilter() Conclusion Points to remember Test your understanding 5. Image Histograms Structure Introduction to histograms cv2.calcHist() Matplotlib helper functions Histogram for colored images Two-dimensional histograms Histogram with masks Histogram equalization cv2.equalizeHist() Histogram equalization on colored images Adaptive histogram equalization Contrast limited adaptive histogram equalization (CLAHE) cv2.createCLAHE() Histograms for feature extraction Conclusion Points to remember Test your understanding 6. Image Segmentation Structure Introduction to Image Segmentation Basic Segmentation Techniques Image thresholding Simple Thresholding cv2.threshold() Adaptive Thresholding cv2.adaptiveThreshold() Otsu’s Thresholding Edge and contour-based segmentation Advanced Segmentation Techniques Watershed Algorithm GrabCut algorithm cv2.grabCut() Clustering-based Segmentation Deep Learning-based Segmentation Conclusion Points to Remember Test your understanding 7. Edges and Contours Structure Introduction to edges Image gradients Filters for image gradients Sobel Filters cv2.Sobel() Scharr Operator cv2.filter2D Laplacian Operators Canny Edge Detector cv2.Canny() Introduction to Contours Contour Hierarchy Extracting and Visualizing Contours cv2.findContours() cv2.drawContours() Contour Moments cv2.Moments() Properties of Contours Area cv2.contourArea() Perimeter Centroid/Center Of mass Bounding Rectangle cv2.boundingRect() cv2.minAreaRect() cv2.boxPoints() Extent Convex Hull cv2.convexHull() cv2.polyLines() Solidity Contour Approximation cv2.approxPolyDP() Contour Filtering and Selection Conclusion Points to Remember Test your understanding 8. Machine Learning with Images Structure Introduction to Machine Learning Overfitting and Underfitting Evaluation Metrics Hyperparameters and Tuning KMeans Clustering cv2.kmeans() k-Nearest Neighbors (k-NN) Feature Scaling Hyperparameters Logistic Regression Hyperparameters Decision Trees Hyperparameters Ensemble Learning Random Forest Randomness Hyperparameters Support Vector Machines Conclusion Points to Remember Test your understanding 9. Advanced Computer Vision Algorithms Structure FAST (Features from Accelerated Segment Test) cv2.FastFeatureDetector_create Harris Keypoint Detection cv2.cornerHarris BRIEF (Binary Robust Independent Elementary Features) cv2.ORB_create ORB (Oriented FAST and Rotated BRIEF) SIFT (Scale-Invariant Feature Transform) cv2.SIFT_create RootSIFT (Root Scale-Invariant Feature Transform) SURF (Speeded-Up Robust Features) Local Binary Patterns Histogram of Oriented Gradients Conclusion Points to Remember Test Your Understanding 10. Neural Networks Structure Introduction to Neural Networks Design of a Neural Network Activation Functions Training a Neural Network Gradient descent Convolutional neural networks Layers in a CNN Convolutional Layer Pooling Layer Fully Connected Layer Activation Layer First Neural Network Model Data Loading Model Instantiation Results Dropout Regularization Neural network architectures LeNet AlexNet VGGNET Transfer Learning Other Network Architectures GoogleNet Inception Module Architecture ResNet Conclusion Points to remember Test your understanding 11. Object Detection Using OpenCV Structure Introduction to object detection Detecting objects using sliding windows Template matching using OpenCV cv2.matchTemplate Haar cascades Feature extraction for object detection Image pyramids Facial landmarks with DLIB Object tracking using OpenCV Conclusion Points to remember Test your understanding 12.Projects Using OpenCV Structure Automated book inventory system Document scanning using OpenCV and OCR Face recognition Drowsiness detection Conclusion Index Unlocking Visual Insights: OpenCV Made Simple and Powerful. Book Description "Mastering OpenCV with Python" immerses you in the captivating realm of computer vision, with a structured approach that equips you with the knowledge and skills essential for success in this rapidly evolving field. From grasping the fundamental concepts of image processing and OpenCV to mastering advanced techniques such as neural networks and object detection, you will gain a comprehensive understanding. Each chapter is enriched with hands-on exercises and real-world projects, ensuring the acquisition of practical skills that can be immediately applied in your professional journey. This book not only elevates your technical proficiency but also prepares you for a rewarding career. The technological job landscape is constantly evolving, and professionals who can harness the potential of computer vision are in high demand. By mastering the skills and insights contained within these pages, you will be well-prepared to explore exciting career opportunities, ranging from machine learning engineering to computer vision research. This book is your ticket to a future filled with innovation and professional advancement within the dynamic world of computer vision. Table of Contents 1. Introduction to Computer Vision 2. Getting Started with Images 3. Image Processing Fundamentals 4. Image Operations 5. Image Histograms 6. Image Segmentation 7. Edges and Contours 8. Machine Learning with Images 9. Advanced Computer Vision Algorithms 10. Neural Networks 11. Object Detection Using OpenCV 12. Projects Using OpenCV