您可以使用warpAffine函数围绕定义的中心点旋转图像。可以使用getRotationMatrix2D （其中theta以度为单位 ）生成合适的旋转矩阵。

然后，您可以使用Numpy切片来剪切图像。

import cv2
import numpy as np

def subimage(image, center, theta, width, height):

   ''' 
   Rotates OpenCV image around center with angle theta (in deg)
   then crops the image according to width and height.
   '''

   # Uncomment for theta in radians
   #theta *= 180/np.pi

   shape = ( image.shape[1], image.shape[0] ) # cv2.warpAffine expects shape in (length, height)

   matrix = cv2.getRotationMatrix2D( center=center, angle=theta, scale=1 )
   image = cv2.warpAffine( src=image, M=matrix, dsize=shape )

   x = int( center[0] - width/2  )
   y = int( center[1] - height/2 )

   image = image[ y:y+height, x:x+width ]

   return image

请记住， dsize是输出图像的形状。如果面片/角度足够大，则将原始形状用作上面的内容（为简单起见），边缘会被切除（比较上图）。在这种情况下，您可以引入比例因子来shape （以放大输出图像）和切片的参考点（此处为center ）。

上面的功能可以如下使用：

image = cv2.imread('owl.jpg')
image = subimage(image, center=(110, 125), theta=30, width=100, height=200)
cv2.imwrite('patch.jpg', image)

这是我执行相同任务的C ++版本。我注意到它有点慢。如果有人发现可以改善此功能性能的任何东西，请告诉我。 :)

bool extractPatchFromOpenCVImage( cv::Mat& src, cv::Mat& dest, int x, int y, double angle, int width, int height) {

  // obtain the bounding box of the desired patch
  cv::RotatedRect patchROI(cv::Point2f(x,y), cv::Size2i(width,height), angle);
  cv::Rect boundingRect = patchROI.boundingRect();

  // check if the bounding box fits inside the image
  if ( boundingRect.x >= 0 && boundingRect.y >= 0 &&
       (boundingRect.x+boundingRect.width) < src.cols &&  
       (boundingRect.y+boundingRect.height) < src.rows ) { 

    // crop out the bounding rectangle from the source image
    cv::Mat preCropImg = src(boundingRect);

    // the rotational center relative tot he pre-cropped image
    int cropMidX, cropMidY;
    cropMidX = boundingRect.width/2;
    cropMidY = boundingRect.height/2;

    // obtain the affine transform that maps the patch ROI in the image to the
    // dest patch image. The dest image will be an upright version.
    cv::Mat map_mat = cv::getRotationMatrix2D(cv::Point2f(cropMidX, cropMidY), angle, 1.0f);
    map_mat.at<double>(0,2) += static_cast<double>(width/2 - cropMidX);
    map_mat.at<double>(1,2) += static_cast<double>(height/2 - cropMidY);

    // rotate the pre-cropped image. The destination image will be
    // allocated by warpAffine()
    cv::warpAffine(preCropImg, dest, map_mat, cv::Size2i(width,height)); 

    return true;
  } // if
  else {
    return false;
  } // else
} // extractPatch

我在这里有错误的偏移量问题，并在类似的问题中发布了解决方案。因此，我进行了数学计算，并提出了以下可行的解决方案：

def subimage(self,image, center, theta, width, height):
    theta *= 3.14159 / 180 # convert to rad

    v_x = (cos(theta), sin(theta))
    v_y = (-sin(theta), cos(theta))
    s_x = center[0] - v_x[0] * ((width-1) / 2) - v_y[0] * ((height-1) / 2)
    s_y = center[1] - v_x[1] * ((width-1) / 2) - v_y[1] * ((height-1) / 2)

    mapping = np.array([[v_x[0],v_y[0], s_x],
                        [v_x[1],v_y[1], s_y]])

    return cv2.warpAffine(image,mapping,(width, height),flags=cv2.WARP_INVERSE_MAP,borderMode=cv2.BORDER_REPLICATE)

作为参考，下面的图片说明了其背后的数学原理：

注意

w_dst = width-1
h_dst = height-1

那是因为最后一个坐标的值为width-1而不是width ；或height 。

如果对数学有疑问，请以评论的形式提出，我将尽力回答。

其他方法仅在旋转后矩形的内容在旋转后的图像中有效，否则在其他情况下将严重失败 。如果某些部分丢失了怎么办？请参阅以下示例：

如果要使用上述方法裁剪旋转的矩形文本区域，

import cv2
import numpy as np


def main():
    img = cv2.imread("big_vertical_text.jpg")
    cnt = np.array([
            [[64, 49]],
            [[122, 11]],
            [[391, 326]],
            [[308, 373]]
        ])
    print("shape of cnt: {}".format(cnt.shape))
    rect = cv2.minAreaRect(cnt)
    print("rect: {}".format(rect))

    box = cv2.boxPoints(rect)
    box = np.int0(box)

    print("bounding box: {}".format(box))
    cv2.drawContours(img, [box], 0, (0, 0, 255), 2)

    img_crop, img_rot = crop_rect(img, rect)

    print("size of original img: {}".format(img.shape))
    print("size of rotated img: {}".format(img_rot.shape))
    print("size of cropped img: {}".format(img_crop.shape))

    new_size = (int(img_rot.shape[1]/2), int(img_rot.shape[0]/2))
    img_rot_resized = cv2.resize(img_rot, new_size)
    new_size = (int(img.shape[1]/2)), int(img.shape[0]/2)
    img_resized = cv2.resize(img, new_size)

    cv2.imshow("original contour", img_resized)
    cv2.imshow("rotated image", img_rot_resized)
    cv2.imshow("cropped_box", img_crop)

    # cv2.imwrite("crop_img1.jpg", img_crop)
    cv2.waitKey(0)


def crop_rect(img, rect):
    # get the parameter of the small rectangle
    center = rect[0]
    size = rect[1]
    angle = rect[2]
    center, size = tuple(map(int, center)), tuple(map(int, size))

    # get row and col num in img
    height, width = img.shape[0], img.shape[1]
    print("width: {}, height: {}".format(width, height))

    M = cv2.getRotationMatrix2D(center, angle, 1)
    img_rot = cv2.warpAffine(img, M, (width, height))

    img_crop = cv2.getRectSubPix(img_rot, size, center)

    return img_crop, img_rot


if __name__ == "__main__":
    main()

这是您将得到的：

显然，有些零件被切掉了！为什么不直接扭曲旋转的矩形，因为我们可以使用cv.boxPoints()方法获得其四个角点？

import cv2
import numpy as np


def main():
    img = cv2.imread("big_vertical_text.jpg")
    cnt = np.array([
            [[64, 49]],
            [[122, 11]],
            [[391, 326]],
            [[308, 373]]
        ])
    print("shape of cnt: {}".format(cnt.shape))
    rect = cv2.minAreaRect(cnt)
    print("rect: {}".format(rect))

    box = cv2.boxPoints(rect)
    box = np.int0(box)
    width = int(rect[1][0])
    height = int(rect[1][1])

    src_pts = box.astype("float32")
    dst_pts = np.array([[0, height-1],
                        [0, 0],
                        [width-1, 0],
                        [width-1, height-1]], dtype="float32")
    M = cv2.getPerspectiveTransform(src_pts, dst_pts)
    warped = cv2.warpPerspective(img, M, (width, height))

现在裁剪的图像变为

好多了，不是吗？如果仔细检查，您会发现裁切后的图像中存在一些黑色区域。这是因为检测到的矩形的一小部分超出了图像的范围。为了解决这个问题，您可以稍稍填充图像 ，然后裁剪。 这个答案有一个例子。

现在，我们比较两种方法从图像中裁剪旋转的矩形。此方法不需要旋转图像，并且可以用更少的代码更好地处理此问题。

openCV版本3.4.0的相似配方。

from cv2 import cv
import numpy as np

def getSubImage(rect, src):
    # Get center, size, and angle from rect
    center, size, theta = rect
    # Convert to int 
    center, size = tuple(map(int, center)), tuple(map(int, size))
    # Get rotation matrix for rectangle
    M = cv2.getRotationMatrix2D( center, theta, 1)
    # Perform rotation on src image
    dst = cv2.warpAffine(src, M, src.shape[:2])
    out = cv2.getRectSubPix(dst, size, center)
    return out

img = cv2.imread('img.jpg')
# Find some contours
thresh2, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Get rotated bounding box
rect = cv2.minAreaRect(contours[0])
# Extract subregion
out = getSubImage(rect, img)
# Save image
cv2.imwrite('out.jpg', out)