Preparing images for #DeepLearning by removing background
There are a number of methods available for preparing images for input to a variety of purposes. For example, for input to deep learning, other image processing models/applications/systems, etc. But sometimes you just need a quick tool to perform a certain task. An example of this is I regularly have to edit images to extract just a certain part of it, or to filter out all the background colors and/or objects etc. There are a a variety of tools available to help you with this kind of task. For me, I’m a Mac user, so I use the instant alpha feature available in some of the Mac products. But what if you are not a Mac user, what can you use.
I’ve recently come across a very useful Python library that takes all or most of the hard work out of doing such tasks, and has proved to be extremely useful for some demos and projects I’ve been working on. The Python library I’m using is remgb (Remove Background). It isn’t perfect, but it does a pretty good job and only in a small number of modified images, did I need to do some additional processing.
Let’s get started with setting things up to use remgb. I did encounter some minor issues installing it, and I’ve give the workarounds below, just in case you encounter the same.
pip3 install remgb
This will install lots of required libraries and will check for compatibility with what you have installed. The first time I ran the install it generated some errors. It also suggested I update my version of pip, which I did, then uninstalled the remgb library and installed again. No errors this time.
When I ran the code below, I got some errors about accessing a document on google drive or it had reached the maximum number of views/downloads. The file it is trying to access is an onix model. If you click on the link, you can download the file. Create a directory called .u2net (in your home directory) and put the onix file into it. Make sure the directory is readable. After doing that everything runs smoothly for me.
The code I’ve given below is typical of what I’ve been doing on some projects. I have a folder with lots of images where I want to remove the background and only keep the key foreground object. Then save the modified images to another directory. It is these image that can be used in products like Amazon Rekognition, Oracle AI Services, and lots of other similar offerings.
from rembg import remove from PIL import Image import os from colorama import Fore, Back, Style sourceDir = '/Users/brendan.tierney/Dropbox/4-Datasets/F1-Drivers/' destDir = '/Users/brendan.tierney/Dropbox/4-Datasets/F1-Drivers-NewImages/' print('Searching = ', sourceDir) files = os.listdir(sourceDir) for file in files: try: inputFile = sourceDir + file outputFile = destDir + file with open(inputFile, 'rb') as i: print(Fore.BLACK + '..reading file : ', file) input = i.read() print(Fore.CYAN + '...removing background...') output = remove(input) try: with open(outputFile, 'wb') as o: print(Fore.BLUE + '.....writing file : ', outputFile) o.write(output) except: print(Fore.RED + 'Error writing file :', outputFile) except: print(Fore.RED + 'Error processing file :', file) print(Fore.BLACK + '---') print(Fore.BLACK + 'Finished processing all files') print(Fore.BLACK + '---')
For this demonstration I’ve used images of the F1 drivers for 2022. I had collected five images of each driver with different backgrounds including, crowds, pit-lane, giving media interviews, indoor and outdoor images.
Generally the results were very good. Here are some results with the before and after.
As you can see from these image there are some where a shadow remains and the library wasn’t able to remove it. The following images gives some additional examples of this. The first is with Bottas and his car, where the car wasn’t removed. The second driver is Vettel where the library captures his long hair and keeps it in the filtered image.
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