Rotating Image
cv2.rotate() method is used to rotate a 2D array in multiples of 90 degrees. The function cv::rotate rotates the array in three different ways.
Example: Python OpenCV Rotate Image
Python3
# Python program to explain cv2.rotate() method # importing cv2 import cv2 # path path = 'geeks.png' # Reading an image in default mode src = cv2.imread(path) # Window name in which image is displayed window_name = 'Image' # Using cv2.rotate() method # Using cv2.ROTATE_90_CLOCKWISE rotate # by 90 degrees clockwise image = cv2.rotate(src, cv2.cv2.ROTATE_90_CLOCKWISE) # Displaying the image cv2.imshow(window_name, image) cv2.waitKey(0) |
Output:
The above functions restrict us to rotate the image in the multiple of 90 degrees only. We can also rotate the image to any angle by defining the rotation matrix listing rotation point, degree of rotation, and the scaling factor.
Example: Python OpenCV Rotate Image by any Angle
Python3
import cv2 import numpy as np FILE_NAME = 'geeks.png' # Read image from the disk. img = cv2.imread(FILE_NAME) # Shape of image in terms of pixels. (rows, cols) = img.shape[:2] # getRotationMatrix2D creates a matrix needed # for transformation. We want matrix for rotation # w.r.t center to 45 degree without scaling. M = cv2.getRotationMatrix2D((cols / 2, rows / 2), 45, 1) res = cv2.warpAffine(img, M, (cols, rows)) cv2.imshow("w3wiki", res) cv2.waitKey(0) cv2.destroyAllWindows() |
Output:
Essential OpenCV Functions to Get Started into Computer Vision
Computer vision is a process by which we can understand the images and videos how they are stored and how we can manipulate and retrieve data from them. Computer Vision is the base or mostly used for Artificial Intelligence. Computer-Vision is playing a major role in self-driving cars, robotics as well as in photo correction apps.
OpenCV is the huge open-source library for computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems. By using it, one can process images and videos to identify objects, faces, or even handwriting of a human. When it is integrated with various libraries, such as NumPy, python is capable of processing the OpenCV array structure for analysis. To Identify image patterns and their various features we use vector space and perform mathematical operations on these features.
In this article, we will discuss some commonly used functions in OpenCV along with their applications.
Note: The functions used in this article are common for different languages supported by OpenCV.