By Mohit
This ebook is a pragmatic advisor that indicates you some great benefits of utilizing Python for pentesting with the aid of distinctive code examples.
We begin through exploring the fundamentals of networking with Python after which continue to community hacking. subsequent, we delve into hacking the appliance layer the place we begin with accumulating details from an internet site. We then flow directly to strategies concerning site hacking equivalent to parameter tampering, DDoS, XSS, and SQL injection.
By examining this publication, you are going to study varied innovations and methodologies that would familiarize you with the artwork of pentesting in addition to growing computerized courses to discover the admin console, SQL injection, and XSS assault.
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Com> if you are having a problem with any aspect of the book, and we will do our best to address it. Fun with Filters The goal of this chapter is to develop a number of image processing filters and apply them to the video stream of a webcam in real time. These filters will rely on various OpenCV functions to manipulate matrices through splitting, merging, arithmetic operations, and applying lookup tables for complex functions. The three effects are as follows: Black-and-white pencil sketch: To create this effect, we will make use of two image blending techniques, known as dodging and burningWarming/cooling filters: To create these effects, we will implement our own curve filters using a lookup tableCartoonizer: To create this effect, we will combine a bilateral filter, a median filter, and adaptive thresholding OpenCV is such an advanced toolchain that often the question is not how to implement something from scratch, but rather which pre-canned implementation to choose for your needs.
Php. html. html. com/OpenKinect/libfreenect. org/wiki/Getting_Started. html) is highly recommended as it provides a flexible, interactive console interface. org/en/latest Who this book is for This book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. You should already have some experience of building simple applications, and you are expected to be familiar with OpenCV's concepts and Python libraries. Basic knowledge of Python programming is expected and assumed.
Chapter 5, Tracking Visually Salient Objects, helps you develop an app to track multiple visually salient objects in a video sequence (such as all the players on the field during a soccer match) at once. Chapter 6, Learning to Recognize Traffic Signs, shows you how to train a support vector machine to recognize traffic signs from the German Traffic Sign Recognition Benchmark (GTSRB) dataset. Chapter 7, Learning to Recognize Emotions on Faces, is where you develop an app that is able to both detect faces and recognize their emotional expressions in the video stream of a webcam in real time.