Detect an object with OpenCV-Python

In this tutorial you are going to learn about how to Detect an object with OpenCV. By using it, one can process images and videos to identify objects, faces, or even the handwriting of a human. And the purpose of detection is to recognize the presence of an object in a location or ambiance.

What is OpenCV:

OpenCV-Python is a library of Python bindings to solve computer vision problems. OpenCV is a great tool for image processing and performing computer vision tasks.

Object Detection:

Object Detection is a computer technology related to computer vision, image processing, and deep learning that deals with detecting instances of objects in image and videos. We will do object detection in this article using something known as haar cascades.

Haar Cascades:

Haar Cascades classifiers are an effective way for object detection.  And this method was proposed by Paul Viola and Michael Jones in their paper. The first step involves training a cascade  function with a large amount of negative and positive labeled images. Once the classifier is trained, identifying features, namely “Haar Cascades” are extracted from these training images.

1. Positive Images: These images contain the images that we want our classifier to identify.
2. Negative Images: Images of everything else, which do not contain the object we want to detect.

Example:

import cv2
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
image = cv2.imread('image.jpg')
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
faces = face_cascode.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minsize=(30,30))
for(x, y, w, h) in faces:
   cv2.rectangle(image, (x, y) + (x+w, y+h), (0, 255, 0), 2)
cv2.imshow("Face Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

Conclusion:

Object detection using OpenCV in Python involves loading an image, preprocessing it, applying edge detection, and finding contours to identify object boundaries. Bounding boxes are then drawn around detected objects, and the results are displayed. This basic approach can be extended for more complex tasks like face detection or real-time object detection using advanced models. Open CV provides a powerful toolkit for various computer vision applications, making it a versatile choice for object detection tasks.