python opencv rotate image usage

contexts

In image processing, there are times when there will be an angle rotation of the picture processing, especially in computer vision for image expansion, rotating the angle expansion of the picture is a common processing. This rotation of the image is also more application scenarios, such as when the user uploads a picture that is vertical, it is not good for processing, and it also needs to be rotated for the subsequent algorithm processing. There are two common ways to rotate the processing, one is converted to numpy matrix, numpy matrix processing, the other is to use opencv comes with the function of various transformations to achieve the results of the rotation angle.

Original image:

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opencv functions

The following functions are commonly used in rotation

: Transpose the image matrix

img = (origin_img_path)
img_transpose = (img)
('transpose', img_transpose)
(0)

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: The image matrix is flipped, and the parameters can be set to 1, 0, and -1, corresponding to horizontal flip, vertical flip, and horizontal-vertical flip, respectively.

img = (origin_img_path)
img_flip = (img, 1)
('flip', img_flip)
(0)

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cv2.getRotationMatrix2D: Construct the rotation matrix M. Subsequent rotations can be accomplished by simply multiplying with the rotation matrix.

The rotation matrix M

img = (origin_img_path)
rows, cols = 
# Here the first parameter is the center of rotation, the second is the angle of rotation, and the third is the scaling factor after rotation
# You can prevent rotations from going out of bounds by setting the center of rotation, the scaling factor, and the size of the window.
M = cv2.getRotationMatrix2D((cols/2,rows/2),45,0.6)

: perform affine transformations on images, usually translations or rotations

img = (origin_img_path)
(img, M,(lengh,lengh),borderValue=(255,255,255))  # M is the rotation matrix from above

numpy function

The numpy implementation of rotation is generally done usingnumpy.rot90Rotate images by a factor of 90 degrees

Official Description.

numpy.rot90(m, k=1, axes=(0, 1))[source]

Rotate an array by 90 degrees in the plane specified by axes.

Rotation direction is from the first towards the second axis.

k: Number of times the array is rotated by 90 degrees.

Key parameterskIndicates a multiple of 90 degrees of rotation, k is generally 1, 2, 3, respectively, 90 degrees of rotation, 180 degrees, 270 degrees; k can also take a negative number, -1, -2, -3. k takes a positive number of counterclockwise rotation, and take a negative number of clockwise rotation.

Rotate 90 degrees

counterclockwise

Rotate using the transpose operation + flip operation of the opencv function
Implemented using numpy.rot90

def rotateAntiClockWise90(img_file):  # Rotate 90 degrees counterclockwise
	img = (img_file)
    trans_img = (img)
    img90 = (trans_img, 0)
    ("rotate", img90)
    (0)
    return img90
    
def totateAntiClockWise90ByNumpy(img_file):  # np.rot90(img, -1) rotate 90 degrees counterclockwise
    img = (img_file)
    img90 = np.rot90(img, -1)
    ("rotate", img90)
    (0)
    return img90

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counterclockwise

def rotateClockWise90(self, img_file):
	img = (img_file)
    trans_img = ( img )
    img90 = (trans_img, 1)
    ("rotate", img90)
    (0)
    return img90

def totateClockWise90ByNumpy(img_file):  # np.rot90(img, 1) rotate 90 degrees clockwise
    img = (img_file)
    img90 = np.rot90(img, 1)
    ("rotate", img90)
    (0)
    return img90

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Rotate 180 degrees, 270 degrees

utilizationnumpy.rot90Realize rotation of 180 degrees, 270 degrees

180 degrees

img180 = np.rot90(img, 2)
("rotate", img180)
(0)

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270 degrees

img270 = np.rot90(img, 3)
("rotate", img270)
(0)

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Rotate at any angle to fill the background with any color value

import cv2
from math import *
import numpy as np
 
# rotate angle, missing background white (255, 255, 255) fill
def rotate_bound_white_bg(image, angle):
    # grab the dimensions of the image and then determine the
    # center
    (h, w) = [:2]
    (cX, cY) = (w // 2, h // 2)
 
    # grab the rotation matrix (applying the negative of the
    # angle to rotate clockwise), then grab the sine and cosine
    # (., the rotation components of the matrix)
    # -angle position parameter for the angle parameter negative value means clockwise rotation; 1.0 position parameter scale is to adjust the size of the proportion (image scaling parameters), it is recommended that the 0.75
    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
    cos = (M[0, 0])
    sin = (M[0, 1])
 
    # compute the new bounding dimensions of the image
    nW = int((h * sin) + (w * cos))
    nH = int((h * cos) + (w * sin))
 
    # adjust the rotation matrix to take into account translation
    M[0, 2] += (nW / 2) - cX
    M[1, 2] += (nH / 2) - cY
 
    # perform the actual rotation and return the image
    # borderValue missing background fill color, white here, customizable
    return (image, M, (nW, nH),borderValue=(255,255,255))
    # borderValue default, default is black (0, 0 , 0)
    # return (image, M, (nW, nH))
 
img = ("")
imgRotation = rotate_bound_white_bg(img, 45)
 
("img",img)
("imgRotation",imgRotation)
(0)

45 degrees

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60 degrees

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consultation

Introducing the cv2.getRotationMatrix2D blog!

Blog Introduction

numpy.rot90

Rotate any angle

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