Images and masks splitting into multiple pieces in Python

A practical example of images and masks splitting into smaller parts. Data labelers use special annotation tools for objects annotation. For example, the Computer Vision Annotation Tool (CVAT) is widely known in computer vision.

mages and masks splitting into multiple pieces in Python with Google Colab.

Data labelers use special annotation tools for objects annotation. For example, the Computer Vision Annotation Tool (CVAT) is widely known in computer vision. Naturally, it is more convenient for labelers to work with high-resolution images. This is especially true when you need to mark a large number of objects.

In one of the roof segmentation tasks that I participated in, it was necessary to highlight triangular segments, quadrangular segments, other segments and edges of the roof. An example of such markup is shown in the following figure (white color for edges, red color for triangles, green color for quadrangles, blue color for other polygons):

Image is created by Oleksii Sheremet with matplotlib module

The original images were obtained from Google Earth at 2048x1208 pixels. The masks were annotated by data labelers using CVAT at the same resolution. To train the model, images and masks should be in a lower resolution (from 128x128 to 512x512 pixels). It is well known that image splitting is a technique most often used to slice a large image into smaller parts. Thus, the logical solution was to split the images and their corresponding masks into the parts with the same resolution.

All code for splitting was implemented in Google Colab. Let’s take a closer look. Import libraries:

import os
import sys
import shutil
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from PIL import Image

Mount the Google Drive (with images and masks) to Google Colab:

from google.colab import drive
drive.mount('/content/gdrive')
%cd "gdrive/My Drive/File Folder"

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