Along with knowledge of Python, the basics of image processing, and computer vision, we will need the following Python libraries:

• NumPy
• Matplotlib

The codes used in the chapter have been added to the following GitHub repository:
https://github.com/PacktPublishing/Computer-Vision-Projects-with-OpenCV-and-Python-3

Image captioning is a process in which textual description is generated based on an image. To better understand image captioning, we need to first differentiate it from image classification.

Image classification is a relatively simple process that only tells us what is in an image. For example, if there is a boy on a bike, image classification will not give us a description; it will just provide the result as boy or bike. Image classification can tell us whether there is a woman or a dog in the image, or an action, such as snowboarding. This is not a desirable result as there is no description of what exactly is going on in the image...

Google Brain im2txt was used by Google in a paper 2015 MSCOCO Image Captioning Challenge, and will form the foundation of the image captioning code that we will implement in our project.

The Google's GitHub TensorFlow page can be found at https://github.com/tensorflow/models/tree/master/research/im2txt.

In the research directory, we will find the im2txt file, which was used by Google in the paper, 2015 MSCOCO Image Captioning Challenge, which is available for free at https://arxiv.org/abs/1609.06647. It covers RNNs, LSTM, and fundamental algorithms in detail.

We can check how CNNs are used for image classification and also learn how to use the LSTM RNNs for actually generating sequential caption outputs.

We can download the code from the GitHub link; however, it has not been set up to run easily as it does not include a pre-trained model...

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...

So, now that we have seen image captioning code in action, we are going to retrain the image captioner on our own desired data. However, we need to know that it will be very time consuming and will need over 100 GB of hard drive space for computations if we want it to process in a reasonable time. Even with a good GPU, it may take a few days or a week to complete the computation. Since we are inclined toward implementing it and have the resources, let's start retraining the model.

In the Notebook, the first step is to download the pre-trained Inception model. The webbrowser module will make it easy to open the URL and to download the file:

# First download pretrained Inception (v3) model

import webbrowser 
webbrowser.open("http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz")

# Completely unzip tar.gz file to get inception_v3...

In this chapter, we were introduced to different image captioning methods. We learned about the Google Brain im2txt captioning model. While working on the project, we were able to run our pre-trained model on a Jupyter Notebook and analyze the model based on the results. In the last section of the chapter, we retrained our image captioning model from scratch.

In the next chapter, we will cover reading license plates with OpenCV.