链接旋转和平移变换正是您所需要的。
代替一个接一个地应用旋转和平移，我们可以应用具有等效链式变换矩阵的cv2.warpAffine。
仅使用cv2.warpAffine一次，可防止中间图像（仅中间旋转图像，带有切角）导致的切角。
链接两个变换矩阵是通过将两个矩阵相乘来完成的。
在OpenCV约定中，第一变换从左侧相乘。
2D仿射变换矩阵的最后一行总是[0，0，1]。
OpenCV转换省略了最后一行，因此M为2x3矩阵。
链接OpenCV转换M0和M1应用以下阶段：

• 将"省略最后一行" [0, 0, 1]插入M0和M1。

T0 = np.vstack((M0, np.array([0, 0, 1])))
 T1 = np.vstack((M1, np.array([0, 0, 1])))

• 矩阵乘法链变换：

T = T1 @ T0

• 删除最后一行（等于[0, 0, 1]）以匹配OpenCV 2x3约定：

M = T[0:2, :]

高级解决方案应用以下阶段：

• 计算旋转变换矩阵。
• 计算平移变换矩阵。
• 链接旋转和平移变换。
• 使用链接的变换矩阵应用仿射变换。

代码示例：

import numpy as np
import cv2

def get_rotation_mat(image, angle):
    w, h = (image.shape[1], image.shape[0])
    cx, cy = (w//2,h//2)

    M = cv2.getRotationMatrix2D((cx, cy), -1*angle, 1.0)
    #rotated = cv2.warpAffine(image, M, (w,h))
    return M

def get_translation_mat(d_x, d_y):
    M = np.float64([
        [1, 0, d_x],
        [0, 1, d_y]
    ])
    
    #return cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
    return M

def chain_affine_transformation_mats(M0, M1):
    """ 
    Chaining affine transformations given by M0 and M1 matrices.
    M0 - 2x3 matrix applying the first affine transformation (e.g rotation).
    M1 - 2x3 matrix applying the second affine transformation (e.g translation).
    The method returns M - 2x3 matrix that chains the two transformations M0 and M1 (e.g rotation then translation in a single matrix).
    """
    T0 = np.vstack((M0, np.array([0, 0, 1])))  # Add row [0, 0, 1] to the bottom of M0 ([0, 0, 1] applies last row of eye matrix), T0 is 3x3 matrix.
    T1 = np.vstack((M1, np.array([0, 0, 1])))  # Add row [0, 0, 1] to the bottom of M1.
    T = T1 @ T0  # Chain transformations T0 and T1 using matrix multiplication.
    M = T[0:2, :]  # Remove the last row from T (the last row of affine transformations is always [0, 0, 1] and OpenCV conversion is omitting the last row).
    return M

path = "dog.jpg"
image = cv2.imread(path)
angle = 30.0
d_x = 200
d_y = 300
#rotated = rotate_image(image, angle)
#translated = translate_image(rotated, d_x, d_y)
rotationM = get_rotation_mat(image, angle)  # Compute rotation transformation matrix
translationM = get_translation_mat(d_x, d_y)  # Compute translation transformation matrix

M = chain_affine_transformation_mats(rotationM, translationM)  # Chain rotation and translation transformations (translation after rotation)

transformed_image = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))  # Apply affine transformation with the chained (unified) matrix M.

cv2.imwrite("transformed_dog.jpg", transformed_image)  # Store output for testing

输出：