![]() From the unzipped contents, move the data\negatives folder so that it becomes Chapter003\cascade_training\urtho_negatives. From the unzipped contents, move the VOC2007 folder so that it becomes Chapter003\cascade_training\VOC2007. Unzip it as Chapter003\cascade_training\faces. Unzip it as Chapter003\cascade_training\CAT_DATASET_02. Unzip it as Chapter003\cascade_training\CAT_DATASET_01. ![]() On Windows, download and unzip the datasets manually, as described in the following steps:.They can be obtained in any of the following ways: We use them as positive and negative training sets for a cat-face detector. Several third-party datasets provide us with sample images of cats and other subjects. Moreeover, the cascade-training script at Chapter003/cascade_training/train.bat (for Windows) or Chapter003/cascade_training/train.sh (for Mac or Linux) depends on tools that are not yet part of OpenCV 4. The data-description script at Chapter003/cascade_training/describe.py depends on datasets that are not part of this repository. External datasets and tools to train Haar and LBP cascades * Most of the book's code should also work with OpenCV 3.4. OpenCV 4.x*, Python 2.7 or 3.x, NumPy, SciPy, WxPython, PyFFTW, Spinnaker SDK (optional) plus PySpin (optional) OpenCV 4.x*, Python 2.7 or 3.x, NumPy, SciPy, WxPython, PyFFTW OpenCV 4.x*, Python 2.7 or 3.x, NumPy, WxPython OpenCV or 4.x* (plus optional cascade training tools from OpenCV 3.4), Python 2.7 or 3.x, NumPy, WxPython, PyInstaller (optional) OpenCV or 4.x*, Python 2.7 or 3.x, NumPy, SciPy, Requests, WxPython, PyInstaller (optional) The following list shows the software dependencies for each chapter's code. The code is organized into folders, such as Chapter002, each corresponding to a chapter in the book. General familiarity with object-oriented programming, application development, and usage of operating systems (OS), developer tools, and the command line is required. The book will also help existing OpenCV users who want upgrade their projects to OpenCV 4 and new versions of other libraries, languages, tools, and operating systems. If you are an experienced software developer who is new to computer vision or machine learning, and wants to study these topics through creative projects, then this book is for you. If you feel this book is for you, get your copy today! Build OpenCV 4 Android applications in Android Studio and Unity.Build OpenCV 4 projects in Python 3 for desktops and Raspberry Pi.Make a physics simulation that detects shapes in a real-world drawing.Amplify motion in real-time video to show heartbeats and breaths. ![]() Detect and recognize human and cat faces to trigger an alarm.Detect car headlights and estimate distances to them.Detect motion and recognize gestures to control a smartphone game.This book covers the following exciting features: To target Android, the book supports Java in Android Studio, as well as C# in the Unity game engine. To target diverse desktop systems and Raspberry Pi, the book supports multiple Python versions (from 2.7 to 3.7). What a lot of gadgets we can build with such a handy library! OpenCV 4 for Secret Agents is a broad selection of projects based on computer vision, machine learning, and several application frameworks. It is open source, it supports many programming languages and platforms, and it is fast enough for many real-time applications. OpenCV 4 is a grand collection of image processing functions and computer vision algorithms. Use OpenCV 4 in secret projects to classify cats, reveal the unseen, and react to rogue drivers. This is the code repository for OpenCV 4 for Secret Agents - Second Edition, published by Packt. The idea of adding packages to Python is central to how an agent will leverage these tools for data processing.OpenCV 4 for Secret Agents - Second Edition Furthermore, this book shows you how to add tools to a Python environment, work with images, and parse HTML web pages to extract meaningful data. You will then learn how to use polynomials to encode and decode data in different representations. It starts by covering the basics and then moves on to sections such as file exchange, image processing, geocoding, simple trigonometry, and more sensitive statistical processing. This book will guide new field agent trainees through putting together a Python-based toolset to gather, analyze, and communicate data. It gives beginners a simple way to start programming, but Python's standard library also provides numerous packages that allow Python-using secret agents to easily utilize very sophisticated information processing. Python is an easy-to-learn and extensible programming language that allows secret agents to work with a wide variety of data in a number of ways.
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