Mastering OpenCV with Python

• Autore: Vaishya Ayush
• Pubblicazione: Delhi : , : Orange Education PVT Ltd, , 2023; ©2023
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (239 pages)
• Soggetto topico: Computer vision; Image processing
• Nota di contenuto: Intro -- 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.
• Sommario/riassunto: "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.
• Titolo autorizzato: Mastering OpenCV with Python
• ISBN: 9789390475797; 9390475791
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910915788203321
• Lo trovi qui: Univ. Federico II