python opencv旋转图片

使用opencv函数的转置操作+翻转操作实现旋转
使用numpy.rot90实现

def rotateAntiClockWise90(img_file):  # 顺时针旋转90度img = cv2.imread(img_file)trans_img = cv2.transpose(img)img90 = cv2.flip(trans_img, 0)cv2.imshow("rotate", img90)cv2.waitKey(0)return img90def totateAntiClockWise90ByNumpy(img_file):  # np.rot90(img, -1) 顺时针旋转90度   旋转程度：1,2,-1,-2img = cv2.imread(img_file)img90 = np.rot90(img, -1)cv2.imshow("rotate", img90)cv2.waitKey(0)return img90

旋转180度、270度
使用numpy.rot90实现旋转180度、270度

img180 = np.rot90(img, 2)
cv2.imshow("rotate", img180)
cv2.waitKey(0)

旋转任意角度，以任意色值填充背景

import cv2
from math import *
import numpy as np# 旋转angle角度，缺失背景白色（255, 255, 255）填充
def rotate_bound_white_bg(image, angle):# grab the dimensions of the image and then determine the# center(h, w) = image.shape[: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# (i.e., the rotation components of the matrix)# -angle位置参数为角度参数负值表示顺时针旋转; 1.0位置参数scale是调整尺寸比例（图像缩放参数），建议0.75M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)cos = np.abs(M[0, 0])sin = np.abs(M[0, 1])# compute the new bounding dimensions of the imagenW = int((h * sin) + (w * cos))nH = int((h * cos) + (w * sin))# adjust the rotation matrix to take into account translationM[0, 2] += (nW / 2) - cXM[1, 2] += (nH / 2) - cY# perform the actual rotation and return the image# borderValue 缺失背景填充色彩，此处为白色，可自定义return cv2.warpAffine(image, M, (nW, nH),borderValue=(255,255,255))# borderValue 缺省，默认是黑色（0, 0 , 0）# return cv2.warpAffine(image, M, (nW, nH))img = cv2.imread("dog.png")
imgRotation = rotate_bound_white_bg(img, 45)cv2.imshow("img",img)
cv2.imshow("imgRotation",imgRotation)
cv2.waitKey(0)

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