A Comprehensive Guide to OpenCV in Python.
1 min readJan 2, 2024
Introduction:
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. It contains more than 2500 optimized algorithms for image and video analysis.
Installation:
You can install OpenCV using pip:
pip install opencv-python
Reading, Writing and Displaying Images
To read an image, we use the imread()
function:
import cv2
img = cv2.imread('image.jpg')
To display an image, we use the imshow()
function:
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
To write or save an image, we use the imwrite()
function:
cv2.imwrite('output.jpg', img)
Basic Operations on Images:
1. Image Resizing
resized_img = cv2.resize(img, (new_width, new_height))
2. Image Rotation
(h, w) = img.shape[:2]
center = (w / 2, h / 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(img, M, (w, h))
Image Processing:
1. Edge Detection
edges = cv2.Canny(img, threshold1, threshold2)
2. Image Thresholding
ret,thresh1 = cv2.threshold(img,threshold_value,max_value, cv2.THRESH_BINARY)
Conclusion
OpenCV offers a wide array of functionalities for image processing. It’s a powerful tool for developers and researchers in the field of computer vision.