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Computer Vision Projects with OpenCV and Python 3

Computer Vision Projects with OpenCV and Python 3

By : Rever
1 (1)
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Computer Vision Projects with OpenCV and Python 3

Computer Vision Projects with OpenCV and Python 3

1 (1)
By: Rever

Overview of this book

Python is the ideal programming language for rapidly prototyping and developing production-grade codes for image processing and Computer Vision with its robust syntax and wealth of powerful libraries. This book will help you design and develop production-grade Computer Vision projects tackling real-world problems. With the help of this book, you will learn how to set up Anaconda and Python for the major OSes with cutting-edge third-party libraries for Computer Vision. You'll learn state-of-the-art techniques for classifying images, finding and identifying human postures, and detecting faces within videos. You will use powerful machine learning tools such as OpenCV, Dlib, and TensorFlow to build exciting projects such as classifying handwritten digits, detecting facial features,and much more. The book also covers some advanced projects, such as reading text from license plates from real-world images using Google’s Tesseract software, and tracking human body poses using DeeperCut within TensorFlow. By the end of this book, you will have the expertise required to build your own Computer Vision projects using Python and its associated libraries.
Table of Contents (9 chapters)
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Finding 68 facial landmarks in images

In this section, we're going to see our first example, where we find 68 facial landmarks and images with single people and with multiple people. So, let's open our Jupyter Notebook for this section. Take a look at this first cell:

%pylab notebook

import dlib
import cv2
import os
import tkinter
from tkinter import filedialog
from IPython import display
root = tkinter.Tk()
root.withdraw()
#Go to your working directory (will be different for you)
%cd /home/test/13293

We've got to do some basic setup, as we did in the previous chapters. We're going to initialize %pylab notebook. Again, that will load NumPy and PyPlot and some other stuff, and we're going to perform notebook for now, which will be good for close-up views of images, though we're going to switch it to inline for the second example because we'll need that...

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