import cv2 as cv
import numpy as np
img = cv.imread('tekapo.bmp')
if img is None:
    print("文件未找到")
rows, cols, ch = img.shape
# 初始化 x、y 值和默认值
c = (int(cols/2), int(rows/2))
def draw_line(input_img):  # 在 input_img 上绘制线条、圆并返回转换后的图像
    (x, y) = c
    c_img = input_img.copy()
    cv.line(c_img, c, (640, y), (0, 0, 255), 1)  # x 轴辅助线
    cv.line(c_img, c, (x, 480), (0, 255, 0), 1)  # y 轴辅助线
    cv.circle(c_img, c, 5, (0, 0, 255), -1)
    return c_img
def imshow(input_img):  # 在 input_img 上显示点并输出，input_img 未经变换
    c_img = input_img.copy()
    cv.imshow('C_img', draw_line(c_img))
def click_mouse(e, x, y, d, p):
    if e == cv.EVENT_LBUTTONDOWN:
        global c
        c = (int(x), int(y))  # 保存坐标
        imshow(copy_img)
# def translate(input_img, dx, dy):
#     rows, cols, ch = input_img.shape
#     M = np.float32([[1, 0, dx], [0, 1, dy]])
#     c_img = input_img.copy()
#     c_img2 = cv.warpAffine(c_img, M, (cols, rows))
#     img = c_img2.copy()
#     imshow(c_img2)
#     return img
def translate_mat(M, dx, dy):
    # 而不是更新图像，更新平移矩阵 M
    T0 = np.vstack((M, np.array([0, 0, 1])))  # 在 M 底部添加行 [0, 0, 1]（[0, 0, 1] 应用于眼矩阵的最后一行），T0 是 3x3 矩阵。
    # 平移矩阵（3x3）
    T1 = np.float64([[1, 0, dx],
                     [0, 1, dy],
                     [0, 0, 1 ]])
    T = T1 @ T0  # 链式变换（将平移矩阵 T1 与输入平移矩阵相乘）。
    M = T[0:2, :]  # 从 T 中删除最后一行（仿射变换的最后一行始终为 [0, 0, 1]，OpenCV 转换会省略最后一行）。
    return M  # 返回更新后的 M（应用平移于输入 M 后的 M）.
copy_img = img.copy()  # 存储要变换的图像的变量
imshow(copy_img)
cv.setMouseCallback('C_img', click_mouse, copy_img)
# 使用“eye”变换初始化 M（去除第三行）。
M = np.float64([[1, 0, 0],
                [0, 1, 0]])
rows, cols, ch = img.shape
while True:
    key = cv.waitKey()
    if key == ord('t'):  # 向右下移动功能
        # copy_img = translate(copy_img, 5, 5)
        M = translate_mat(M, 5, 5)  # 将变换应用于变换矩阵 M，而不是修改图像。
        copy_img = cv.w
<details>
<summary>英文:</summary>
Instead of updating the image, we may update the transformation matrix `M`, and apply `warpAffine` to the original image.  
That way we are preserving the original image and only modifying the transformation matrix each time the relevant key is pressed.
---
 - Initialize matrix `M` with &quot;eye&quot; transformation (without the third row):  
        M = np.float64([[1, 0, 0], 
                        [0, 1, 0]])
 - Instead of using `translate`, implement a method that updates matrix `M`.  
`M` is updated by changing the translation and the current `M`.  
Changing is applied by multiplying the translation matrix from the left, as described in my [previous answer][1]:  
        def translate_mat(M, dx, dy):
            # Instead of updating the image, update the transformation matrix M
            T0 = np.vstack((M, np.array([0, 0, 1])))  # Add row [0, 0, 1] to the bottom of M ([0, 0, 1] applies last row of eye matrix), T0 is 3x3 matrix.
            
            # Translation matrix (3x3)
            T1 = np.float64([[1, 0, dx], 
                             [0, 1, dy],
                             [0, 0, 1 ]])
        
            T = T1 @ T0  # Chain transformations (multiply translation matrix T1 by the input translation matrix).
            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  # Return updated M (after applying the translation on the input M).
 - In the while loop, update `M` and apply `warpAffine` to `img` (instead of updating `copy_img`):  
        while True:
            key = cv.waitKey()
            if key == ord(&#39;t&#39;):#오른쪽 아래로 가는 기능
                # copy_img = translate(copy_img, 5, 5)
                M = translate_mat(M, 5, 5)  # Apply transformation to transformation matrix M instead of modifying the image.
                copy_img = cv.warpAffine(img, M, (cols, rows))  # Apply warp to the original image
                imshow(copy_img)
---
Code sample:  
    import cv2 as cv
    import numpy as np
    
    img = cv.imread(&#39;tekapo.bmp&#39;)
    
    if img is None:
        print(&quot;file not found&quot;)
        
    rows, cols, ch = img.shape
    
    #초기 x, y값 및 기본값 설정
    c = (int(cols/2), int(rows/2))
    
    def draw_line(input_img): #input_img에 선, 원 그리고 변형된 이미지 반환
        (x, y) = c
        c_img = input_img.copy()
        cv.line(c_img, c, (640, y), (0, 0, 255), 1) #x축 보조선
        cv.line(c_img, c, (x, 480), (0, 255, 0), 1) #y축 보조선
        cv.circle(c_img, c, 5, (0, 0, 255), -1)
        return c_img
        
    def imshow(input_img): #input_img에 point그리고 출력, input_img는 변형 X
        c_img = input_img.copy()
        cv.imshow(&#39;C_img&#39;, draw_line(c_img))
        
    
    def click_mouse(e, x, y, d, p):
        if e == cv.EVENT_LBUTTONDOWN:
            global c
            c = (int(x), int(y)) #좌표 저장
            imshow(copy_img)
            
        
    #def translate(input_img, dx, dy):
    #    rows, cols, ch = input_img.shape
    #    M = np.float32([[1, 0, dx], [0, 1, dy]])
    #    c_img = input_img.copy()
    #    c_img2 = cv.warpAffine(c_img, M, (cols, rows))
    #    img = c_img2.copy()
    #    imshow(c_img2)
    #    return img
    
    def translate_mat(M, dx, dy):
        # Instead of uprating the image, update the translation matrix M
        T0 = np.vstack((M, np.array([0, 0, 1])))  # Add row [0, 0, 1] to the bottom of M ([0, 0, 1] applies last row of eye matrix), T0 is 3x3 matrix.
    
        # Translation matrix (3x3)
        T1 = np.float64([[1, 0, dx], 
                         [0, 1, dy],
                         [0, 0, 1 ]])
    
        T = T1 @ T0  # Chain transformations (multiply translation matrix T1 by the input translation matrix).
        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  # Return updated M (after applying the translation on the input M).
    
    
    
    
    copy_img = img.copy() #변형시킬 이미지가 담길 변수
    imshow(copy_img)
    
    cv.setMouseCallback(&#39;C_img&#39;, click_mouse, copy_img)
    
    # Initialize M to &quot;eye&quot; transformation (without the third row).
    M = np.float64([[1, 0, 0], 
                    [0, 1, 0]])
    
    rows, cols, ch = img.shape
    
    while True:
        key = cv.waitKey()
        if key == ord(&#39;t&#39;):#오른쪽 아래로 가는 기능
            # copy_img = translate(copy_img, 5, 5)
            M = translate_mat(M, 5, 5)  # Apply transformation to transformation matrix M instead of modifying the image.
            copy_img = cv.warpAffine(img, M, (cols, rows))  # Apply warp to the original image
            imshow(copy_img)
        elif key == ord(&#39;e&#39;):#왼쪽 위로 가는 기능
            # copy_img = translate(copy_img, -5, -5)
            M = translate_mat(M, -5, -5)  # Apply transformation to transformation matrix M instead of modifying the image.
            copy_img = cv.warpAffine(img, M, (cols, rows))  # Apply warp to the original image
            imshow(copy_img)
        elif key == 27:
            print(c)
            break
    
    cv.destroyAllWindows()
---
Sample image after pressing `&#39;t&#39;` 10 times:  
[![enter image description here][2]][2]
Sample image after pressing `&#39;t&#39;` 10 times and then pressing `&#39;e&#39;` 20 times:  
[![enter image description here][3]][3]
  [1]: https://stackoverflow.com/a/75391691/4926757
  [2]: https://i.stack.imgur.com/Pav7o.png
  [3]: https://i.stack.imgur.com/1DXMP.png
</details>