Book Image

Computer Vision Projects with OpenCV and Python 3

By : Matthew Rever
Book Image

Computer Vision Projects with OpenCV and Python 3

By: Matthew 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)

Running the captioning code on Jupyter

Let's now run our own version of the code on a Jupyter Notebook. We can start up own own Jupyter Notebook and load the Section_1-Tensorflow_Image_Captioning.ipynb file from the GitHub repository (https://github.com/PacktPublishing/Computer-Vision-Projects-with-OpenCV-and-Python-3/blob/master/Chapter01/Section_1-Tensorflow_Image_Captioning.ipynb).

Once we load the file on a Jupyter Notebook, it will look something like this:

In the first part, we are going to load some essential libraries, including math, os, and tensorflow. We will also use our handy utility function, %pylab inline, to easily read and display images within the Notebook.

Select the first code block:

# load essential libraries
import math
import os

import tensorflow as tf

%pylab inline

When we hit Ctrl + Enter to execute the code in the cell, we will get the following output...