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از ساعت 7 صبح تا 10 شب
ویرایش: [2 ed.]
نویسندگان: Паджанкар Э.
سری:
ناشر:
سال نشر: 2022
تعداد صفحات: [288]
زبان: Russian
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 13 Mb
در صورت تبدیل فایل کتاب Программирование компьютерного зрения с Raspberry Pi. به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب برنامه نویسی بینایی کامپیوتر با Raspberry Pi. نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
Cover Title Page Copyright and Credits About Packt Contributors Table of Contents Preface Chapter 1: Introduction to Computer Vision and the Raspberry Pi Understanding computer vision OpenCV Single-board computers The Beagleboard family ASUS Tinkerboard NVIDIA Jetson Intel boards Raspberry Pi Raspberry Pi models OSes for Raspberry Pi Setting up Raspbian on a Raspberry Pi Downloading the necessary software Preparing the microSD card manually Booting up the Raspberry Pi for the first time Connecting various RPi board models to the internet Updating the RPi Summary Chapter 2: Preparing the Raspberry Pi for Computer Vision Remotely logging into the RPi with SSH Remote desktop access Installing OpenCV on an RPi board Heatsinks and overclocking RPi 4B Summary Chapter 3: Introduction to Python Programming Technical requirements Understanding Python 3 Python on RPi and Raspberry Pi OS Python 3 IDEs on Raspberry Pi OS Working with Python 3 in interactive mode The basics of Python 3 programming The SciPy ecosystem The basics of NumPy Matplotlib RPi GPIO programming with Python 3 LED programming with GPIO Push-button programming with GPIO Summary Chapter 4: Getting Started with Computer Vision Technical requirements Exploring image datasets Working with images using OpenCV Using matplotlib to visualize images Drawing geometric shapes with OpenCV and NumPy Working with a GUI Event handling and a primitive paint application Working with a USB webcam Capturing images with the webcam Timelapse photography Webcam video recording Capturing images with the webcam using Python and OpenCV Live videos with the webcam using Python and OpenCV Webcam resolution FPS of the webcam Saving webcam videos Playing back the video with OpenCV The Pi camera module Capturing images and videos with the raspistill and raspivid utilities Using picamera with Python 3 Using the RPi camera module and Python 3 to record videos Summary Chapter 5: Basics of Image Processing Technical requirements Retrieving image properties Basic operations on images Splitting the image into channels Adding a border to an image Arithmetic operations on images Blending and transitioning images Multiplying images by a constant and one another Creating a negative of an image Bitwise logical operations on images Summary Chapter 6: Colorspaces, Transformations, and Thresholding Technical requirements Colorspaces and converting them HSV colorspace Tracking in real time based on color Performing transformation operations on images Scaling The translation, rotation, and affine transformation of images Perspective transformation of images Thresholding images Otsu's binarization method Adaptive thresholding Summary Chapter 7: Let's Make Some Noise Technical requirements Noise Introducing noise to an image Working with kernels 2D convolution with the signal processing module in SciPy Filtering and blurring with OpenCV 2D convolution filtering Low-pass filtering Summary Chapter 8: High-Pass Filters and Feature Detection Technical requirements Exploring high-pass filters Working with the Canny edge detector Finding circles and lines with Hough transforms Harris corner detection Exercise Summary Chapter 9: Image Restoration, Segmentation, and Depth Maps Technical requirements Restoring damaged images using inpainting Segmenting images Mean shift algorithm segmentation K-means clustering and image quantization Comparison of k-means and the mean shift algorithm Disparity maps and depth estimation Summary Chapter 10: Histograms, Contours, and Morphological Transformations Technical requirements Computing and visualizing histograms Histogram equalization Visualizing image contours Applying morphological transformations to images Summary Chapter 11: Real-Life Applications of Computer Vision Technical requirements Implementing the Max RGB filter Implementing background subtraction Computing the optical flow Detecting and tracking motion Detecting barcodes in images Implementing the chroma key effect Summary Chapter 12: Working with Mahotas and Jupyter Technical requirements Processing images with Mahotas Reading images and built-in images Thresholding images The distance transform Colorspace Combining Mahotas and OpenCV Other popular image processing libraries Exploring the Jupyter Notebook for Python 3 programming Summary Chapter 13: Appendix Technical requirements Performance measurement and the management of OpenCV Reusing a Raspbian OS microSD card Formatting the SD card using the SD card formatter The Disk Management utility in Windows Tour of the raspi-config command-line utility Installation and the environment setup on Windows, Debian, and Ubuntu Python implementations and Python distributions Other Books You May Enjoy Leave a review - let other readers know what you think Index