Purpose of the article: To know about how we can achieve object detection techniques using semantic segmentation
Intended Audience: Python Developers, Web Developers, Mobile Developers, Backend Developers, All frontend Developers and Data Scientists, Data Analyst
Tools and Technology: Python, VS Code
Keywords: Semantic Segmentation, Python, OpenCV, Object Detection
Introduction
Semantic segmentation is a computer vision task that involves partitioning an image into multiple segments or regions, each of which is associated with a specific class label. Unlike simple object detection, where the goal is to identify the bounding boxes around objects in an image, semantic segmentation assigns a class label to every pixel in the image, thereby providing a detailed understanding of the image content at the pixel level.
So, I would like to help you with some NLP-related things where we can implement an object detection app using semantic segmentation. So, Let’s get started…
What is Semantic Segmentation?
Semantic segmentation is a computer vision task that involves partitioning an image into multiple segments or regions, each of which is associated with a specific class label. Unlike simple object detection, where the goal is to identify the bounding boxes around objects in an image, semantic segmentation assigns a class label to every pixel in the image, thereby providing a detailed understanding of the image content at the pixel level.
What is the Need for Semantic Segmentation?
Semantic segmentation serves several important needs in computer vision and related fields:
- Scene Understanding: Semantic segmentation provides a detailed understanding of the scene by labelling each pixel with a class label. This fine-grained analysis enables systems to comprehend complex visual scenes and make informed decisions.
- Object Detection and Localization: Semantic segmentation can aid in object detection and localization by precisely delineating object boundaries at the pixel level. This information can be used to improve the accuracy of object detection systems or to segment individual objects within a scene.
- Semantic Understanding for Robotics and Autonomous Vehicles: In robotics and autonomous vehicles, understanding the environment is crucial for safe navigation. Semantic segmentation helps in identifying obstacles, pedestrians, roads, and other relevant elements in the environment, allowing robots and vehicles to make informed decisions.
- Image Editing and Augmentation: Semantic segmentation can be used for advanced image editing tasks such as automatic object removal, image retouching, and background replacement. By segmenting objects or regions of interest, editing operations can be applied selectively to specific parts of the image.
- Medical Image Analysis: In medical imaging, semantic segmentation plays a vital role in tasks such as tumour detection, organ segmentation, and disease diagnosis. Accurate segmentation of anatomical structures or abnormalities helps clinicians in treatment planning and patient care.
- Remote Sensing and Satellite Imagery: Semantic segmentation is valuable in analyzing remote sensing and satellite imagery for applications such as land cover classification, urban planning, environmental monitoring, and disaster management.
How to Build a Semantic Segmentation Using Python
I have created a POC to detect an object using Semantic Segmentation technique. Let’s see how we can achieve this.
Step 1: Create a folder named with ProjectName and create one main.py file; after that go to the project directory terminal and install cv2, cvlib and cvlib.object_detection libraries and import it
import cv2
import cvlib as cv
from cvlib.object_detection import draw_bbox
Step 2: Now we must access the camera. So, we will open the camera and capture the video.
Step 3: For capturing the video, we can call the ‘VideoCapture’ function from the cv2 library.
video = cv2.VideoCapture(0)
Step 4: After detecting the object, we must give the name of that detected object; for that, we can create an array of labels like
labels = []
Step 5: Now, we must detect and identify the object from video; so for that, we will execute the following functionality
while True:
ret,frame = video.read()
bbox, label,conf = cv.detect_common_objects(frame)
output_image=draw_bbox(frame,bbox,label,conf)
cv2.imshow("Object Detected", output_image)
for item in label:
if item in labels:
pass
else:
labels.append(item)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
Step 6: From the above code, we can detect and identify the object from video along with highlighted object using red box.
Step 7: To run this project, you can go to the project directory and run python main.py.
Conclusion:
Semantic Segmentation offers a powerful approach to object detection, providing a detailed pixel-level understanding of images. Through this blog, we’ve explored how to detect an object using a live video camera and how to identify which object is appearing on the video by providing labels and a bounded box around it.
Author Bio:
Kalyani Hanumant Pawar
Digital Transformation - Software Engineer
I am an IT professional with 3+ years of experience in Mobile Application Development, I have also hands-on knowledge of JavaScript, Typescript, React Native, React JS, Basics of Python and NLP. I have developed a few mobile applications using react native and currently working as a Frontend Developer for Mobile App Development.
