How to create a deep learning dataset using Google Images

PyImageSearch reader José asks:

Hey Adrian, thanks for putting together Deep Learning for Computer Vision with Python. This is by far the best resource I’ve seen for deep learning.

My question is this:

I’m working on a project where I need to classify the scenes of outdoor photographs into four distinct categories: cities, beaches, mountains, and forests.

I’ve found a small dataset (~100 images per class), but my models are quick to overfit and far from accurate.

I’m confident I can solve this project, but I need more data.

What do you suggest?

José has a point — without enough training data, your deep learning and machine learning models can’t learn the underlying, discriminative patterns required to make robust classifications.

Which begs the question:

How in the world do you gather enough images when training deep learning models?

Deep learning algorithms, especially Convolutional Neural Networks, can be data hungry beasts.

And to make matters worse, manually annotating an image dataset can be a time consuming, tedious, and even expensive process.

So is there a way to leverage the power of Google Images to quickly gather training images and thereby cut down on the time it takes to build your dataset?

You bet there is.

In the remainder of today’s blog post I’ll be demonstrating how you can use Google Images to quickly (and easily) gather training data for your deep learning models.

Looking for the source code to this post?
Jump right to the downloads section.

Deep learning and Google Images for training data

Today’s blog post is part one of a three part series on a building a Not Santa app, inspired by the Not Hotdog app in HBO’s Silicon Valley (Season 4, Episode 4).

As a kid Christmas time was my favorite time of the year — and even as an adult I always find myself happier when December rolls around.

Looking back on my childhood, my dad always went out well of his way to ensure Christmas was a magical time.

Without him I don’t think this time of year would mean as much to me (and I certainly wouldn’t be the person I am today).

In order to keep the magic of ole’ Saint Nicholas alive, we’re going to spend the next three blog posts building our Not Santa detector using deep learning:

• Part #1: Gather Santa Clause training data using Google Images (this post).
• Part #2: Train our Not Santa detector using deep learning, Python, and Keras.
• Part #3: Deploy our trained deep learning model to the Raspberry Pi.

Let’s go ahead and get started!

Using Google Images for training data and machine learning models

The method I’m about to share with you for gathering Google Images for deep learning is from a fellow deep learning practitioner and friend of mine, Michael Sollami.

He discussed the exact same technique I’m about to share with you in a blog post of his earlier this year.

I’m going to elaborate on these steps and provide further instructions on how you can use this technique to quickly gather training data for deep learning models using Google Images, JavaScript, and a bit of Python.

The first step in using Google Images to gather training data for our Convolutional Neural Network is to head to Google Images and enter a query.

In this case we’ll be using the query term “santa clause”:

Figure 1: The first step to downloading images from Google Image Search is to enter your query and let the pictures load in your browser. Santa Claus is visiting our computer screen!

As you can see from the example image above we have our search results.

The next step is to use a tiny bit of JavaScript to gather the image URLs (which we can then download using Python later in this tutorial).

Fire up the JavaScript console (I’ll assume you are using the Chrome web browser, but you can use Firefox as well) by clicking

View => Developer => JavaScript Console

 :

Figure 2: Opening Google Chrome’s JavaScript Console from the menu bar prior to performing the hack.

From there, click the

Console

  tab:

Figure 3: We will enter JavaScript in the Google Chrome JavaScript Console which is displayed in this figure.

This will enable you to execute JavaScript in a REPL-like manner. The next step is to start scrolling!

Figure 4: Keep scrolling through the Google Image search results until the results are no longer relevant.

Keep scrolling until you have found all relevant images to your query. From there, we need to grab the URLs for each of these images. Switch back to the JavaScript console and then copy and paste this JavaScript snippet into the Console:

// pull down jquery into the JavaScript console
var script = document.createElement('script');
script.src = "http://ift.tt/1V0HKB6";
document.getElementsByTagName('head')[0].appendChild(script);

The snippet above pulls down the jQuery JavaScript library, a common package used for nearly every JavaScript application.

Now that jQuery is pulled down we can use a CSS selector to grab a list of URLs:

// grab the URLs
var urls = $('.rg_di .rg_meta').map(function() { return JSON.parse($(this).text()).ou; });

Note: Make sure you expand the code block above using the “<=>” button — this will ensure you copy and pate the entire JavaScript function call.

And then finally write the URLs to file (one per line):

// write the URls to file (one per line)
var textToSave = urls.toArray().join('\n');
var hiddenElement = document.createElement('a');
hiddenElement.href = 'data:attachment/text,' + encodeURI(textToSave);
hiddenElement.target = '_blank';
hiddenElement.download = 'urls.txt';
hiddenElement.click();

After executing the above snippet you’ll have a file named

urls.txt

  in your default Downloads directory.

If you are having trouble following this guide, please see the video at the very top of this blog post where I provide step-by-step instructions.

Downloading Google Images using Python

Now that we have our

urls.txt

  file, we need to download each of the individual images.

Using Python and the requests library, this is quite easy.

If you don’t already have requests installed on your machine you’ll want to install it now (taking care to use the

workon

  command first if you are using Python virtual environments):

$ workon cv
$ pip install requests

From there, open up a new file, name it

download_images.py

 , and insert the following code:

# import the necessary packages
from imutils import paths
import argparse
import requests
import cv2
import os

Here we are just importing required packages. Notice

requests

  on Line 4 — this will be the package we use for downloading the image content.

Next, we’ll parse command line arguments and load our

urls

  from disk into memory:

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-u", "--urls", required=True,
        help="path to file containing image URLs")
ap.add_argument("-o", "--output", required=True,
        help="path to output directory of images")
args = vars(ap.parse_args())

# grab the list of URLs from the input file, then initialize the
# total number of images downloaded thus far
rows = open(args["urls"]).read().strip().split("\n")
total = 0

Command line argument parsing is handled on Lines 9-14 — we only require two:

• --urls
  

   : The path to the file containing image URLs generated by the Javascript trick above.

• --output
  

   : The path to the output directory where we’ll store our images downloaded from Google Images.

From there, we load each URL from the file into a list on Line 18. We also initialize a counter,

total

 , to count the files we’ve downloaded.

Next we’ll loop over the URLs and attempt to download each image:

# loop the URLs
for url in rows:
        try:
                # try to download the image
                r = requests.get(url, timeout=60)

                # save the image to disk
                p = os.path.sep.join([args["output"], "{}.jpg".format(
                        str(total).zfill(8))])
                f = open(p, "wb")
                f.write(r.content)
                f.close()

                # update the counter
                print("[INFO] downloaded: {}".format(p))
                total += 1

        # handle if any exceptions are thrown during the download process
        except:
                print("[INFO] error downloading {}...skipping".format(p))

Using

requests

 , we just need to specify the

url

  and a timeout for the download. We attempt to download the image file into a variable,

r

, which holds the binary file (along with HTTP headers, etc.) in memory temporarily (Line 25).

Let’s go ahead and save the image to disk.

The first thing we’ll need is a valid path and filename. Lines 28 and 29 generate a path + filename,

p

 , which will count up incrementally from

00000000.jpg

 .

We then create a file pointer,

f

 , specifying our path, 

p

 , and indicating that we want write mode in binary format (

"wb"

 ) on Line 30.

Subsequently, we write our files contents (

r.content

 ) and then close the file (Lines 31 and 32).

And finally, we update our total count of downloaded images.

If any errors are encountered along the way (and there will be some errors — you should expect them whenever trying to automatically download unconstrained images/pages on the web), the exception is handled and a message is printed to the terminal (Lines 39 and 40).

Now we’ll do a step that shouldn’t be left out!

We’ll loop through all files we’ve just downloaded and try to open them with OpenCV. If the file can’t be opened with OpenCV, we delete it and move on. This is covered in our last code block:

# loop over the image paths we just downloaded
for imagePath in paths.list_images(args["output"]):
        # initialize if the image should be deleted or not
        delete = False

        # try to load the image
        try:
                image = cv2.imread(imagePath)

                # if the image is `None` then we could not properly load it
                # from disk, so delete it
                if image is None:
                        delete = True

        # if OpenCV cannot load the image then the image is likely
        # corrupt so we should delete it
        except:
                print("Except")
                delete = True

        # check to see if the image should be deleted
        if delete:
                print("[INFO] deleting {}".format(imagePath))
                os.remove(imagePath)

As we loop over each file, we’ll initialize a

delete

  flag to

False

 (Line 45).

Then we’ll

try

  to load the image file on Line 49.

If the

image

  is loaded as

None

 , or if there’s an exception, we’ll set

delete = True

  (Lines 53 and 54 and Lines 58-60).

Common reasons for an image being unable to load include an error during the download (such as a file not downloading completely), a corrupt image, or an image file format that OpenCV cannot read.

Lastly if the

delete

  flag was set, we call 

os.remove

  to delete the image on Lines 63-65.

That’s all there is to the Google Images downloader script — it’s pretty self-explanatory.

To download our example images, make sure you use the “Downloads” section of this blog post to download the script and example

urls.txt

  file.

From there, open up a terminal and execute the following command:

$ python download_images.py --urls urls.txt --output images/santa
[INFO] downloaded: images/santa/00000000.jpg
[INFO] downloaded: images/santa/00000001.jpg
[INFO] downloaded: images/santa/00000002.jpg
[INFO] downloaded: images/santa/00000003.jpg
...
[INFO] downloaded: images/santa/00000519.jpg
[INFO] error downloading images/santa/00000519.jpg...skipping
[INFO] downloaded: images/santa/00000520.jpg
...
[INFO] deleting images/santa/00000211.jpg
[INFO] deleting images/santa/00000199.jpg
...

As you can see, example images from Google Images are being downloaded to my machine as training data.

The error you see in the output is normal — you should expect these. You should also expect some images to be corrupt and unable to open — these images get deleted from our dataset.

Pruning irrelevant images from our dataset

Of course, not every image we downloaded is relevant.

To resolve this, we need to do a bit of manual inspection.

My favorite way to do this is to use the default tools on my macOS machine. I can open up Finder and browse the images in the “Cover Flow” view:

Figure 5: The macOS “Cover Flow” view allows us to quickly check each downloaded image to make sure it’s Santa. We’ll want to be sure we’re training our deep learning detector (which we’ll cover next week) with valid Santa pictures.

I can then easily scroll through my downloaded images.

Images that are not relevant can easily moved to the Trash using 

<cmd> + <delete>

  — similar shortcuts exist on other operating systems as well. After pruning my downloaded images I have a total of 461 images as training to our Not Santa app.

In next week’s blog post I’ll demonstrate how we can use Python and Keras to train a Convolutional Neural Network to detect if Santa Clause is in an input image.

The complete Google Images + deep learning pipeline

I have put together a step-by-step video that demonstrates me performing the above steps to gather deep learning training data using Google Images.

Be sure to take a look!

Summary

In today’s blog post you learned how to:

1. Use Google Images to search for example images.
2. Grab the image URLs via a small amount of JavaScript.
3. Download the images using Python and the requests library.

Using this method we downloaded ~550 images.

We then manually inspected the images and removed non-relevant ones, trimming the dataset down to ~460 images.

In next week’s blog post we’ll learn how to train a deep learning model that will be used in our Not Santa app.

To be notified when the next post in this series goes live, be sure to enter your email address in the form below!

Downloads:

If you would like to download the code and images used in this post, please enter your email address in the form below. Not only will you get a .zip of the code, I’ll also send you a FREE 11-page Resource Guide on Computer Vision and Image Search Engines, including exclusive techniques that I don’t post on this blog! Sound good? If so, enter your email address and I’ll send you the code immediately!

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