Breif Introduction of OpenCV Object Detection Library

Posted By :Vikas Kumar |29th September 2021

Computer vision is a technique for understanding how images and videos are stored, as well as modifying and retrieving data from them. Artificial intelligence is primarily dependent on computer vision. Self-driving cars, robotics, and photo editing apps all rely heavily on computer vision.

About OpenCV

OpenCV is a big open-source library for computer vision, machine learning, and image processing, and it presently plays a significant part in real-time operations, which are crucial in today's systems. It can distinguish objects, faces, and even human handwriting in photographs and movies. Python can process the OpenCV array structure for analysis when it is combined with other modules such as NumPy. We employ vector space and execute mathematical operations on these features to identify visual patterns and their various features.

 

Install OpenCV Library

pip install opencv-python

How to import  OpenCV for use

import cv2

How to read any image file for further process

cv2.imread(‘img name with path’, flag)

eg: img = cv2.imread('example.png', cv2.IMREAD_COLOR)

How to display image

cv2.imshow(‘name’,img)

 


OpenCV applications: OpenCV is used to solve a variety of problems, some of which are given below.

  • Detection of anamolies (defects) throughout the manufacturing process (the odd defective products)
  • Image stitching from a street view
  • Search and retrieval of video/images
  • Object recognition and navigation for robot and driverless cars
  • Image analysis in medicine
  • Movies - 3D structure derived from motion TV commercial recognition
  • recognition of people's faces
  • Inspection and surveillance by robots
  • Count the number of persons (foot traffic in a mall, etc)
  • Vehicles relying on highways, as well as their speeds
  • Installations of interactive art

 


Apart from this, you can play with images as per your requirements.

Grayscaling the image:

method 1:
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

method 2:
img = cv2.imread('tomatoes.jpg', 0)

** in both situation, you will get a grayscalled image.

 

Conclusion:

OpenCv has a large variety of functions that help you to manupulate images and achieve all the above listed goals. It is not possible to conclude such topics in a short piece of article. So, This article will continue further.


About Author

Vikas Kumar

Vikas is a seasoned backend developer with a strong expertise in Python.He possesses a wide range of skills such as Python, Django, Flask, HTML, CSS, Celery, Git, and AWS services and a solid understanding of various databases including MongoDB, PostgreSQL, MySQL, and DynamoDB. He has worked on several notable projects such as English-Chinese Language Translation, i_infinitytransformation, Political Content Moderation, Optical Character Recognition, Palmadoc: Document content extraction, Hey, Kaido, and ViralNation. Given his extensive experience and diverse skillset, he is adept at developing robust backend solutions.

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