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.
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"
Clustering parts of an image together which belong to the same object class. In the research and application of images, people are often only interested in certain parts of the images.
Image annotation is an integral part of the machine learning process for numerous computer vision models. This video will introduce five of the most common t...
Image recognition and annotation technologies are evolving. New techniques that allow you to solve a wide variety of tasks quickly appear. We are happy to present five major trends in image recognition and annotation.
In this article, a few image processing/computer vision problems and their solutions with python libraries (scikit-image, PIL, opencv-python) will be discussed. Some of the problems are from the exercises from this book (available on Amazon).
Everything we see around its nothing but an Image. we capture them using our mobile camera. Image is nothing but a signal which conveys.