img = cv2.imread(origin_img_path)
img_transpose = cv2.transpose(img)
cv2.imshow('transpose', img_transpose)
cv2.waitKey(0)

img = cv2.imread(origin_img_path)
img_flip = cv2.flip(img, 1)
cv2.imshow('flip', img_flip)
cv2.waitKey(0)

img = cv2.imread(origin_img_path)
rows, cols = img.shape
# 这里的第一个参数为旋转中心，第二个为旋转角度，第三个为旋转后的缩放因子
# 可以通过设置旋转中心，缩放因子以及窗口大小来防止旋转后超出边界的问题
M = cv2.getRotationMatrix2D((cols/2,rows/2),45,0.6)

img = cv2.imread(origin_img_path)
cv2.warpAffine(img, M,(lengh,lengh),borderValue=(255,255,255))  # M为上面的旋转矩阵

def rotateAntiClockWise90(img_file):  # 逆时针旋转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 img90
    
def totateAntiClockWise90ByNumpy(img_file):  # np.rot90(img, -1) 逆时针旋转90度
    img = cv2.imread(img_file)
    img90 = np.rot90(img, -1)
    cv2.imshow("rotate", img90)
    cv2.waitKey(0)
    return img90

def rotateClockWise90(self, img_file):
	img = cv2.imread(img_file)
    trans_img = cv2.transpose( img )
    img90 = cv2.flip(trans_img, 1)
    cv2.imshow("rotate", img90)
    cv2.waitKey(0)
    return img90

def totateClockWise90ByNumpy(img_file):  # np.rot90(img, 1) 顺时针旋转90度
    img = cv2.imread(img_file)
    img90 = np.rot90(img, 1)
    cv2.imshow("rotate", img90)
    cv2.waitKey(0)
    return img90

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

img270 = np.rot90(img, 3)
cv2.imshow("rotate", img270)
cv2.waitKey(0)

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0.75
    M = 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 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 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)