opencv-python-圖像旋轉-rotation-image

def rotate_im(image, angle, crop=True):
    """Rotate the image.
    
    Rotate the image such that the rotated image is enclosed inside the tightest
    rectangle. The area not occupied by the pixels of the original image is colored
    black. 
    
    Parameters
    ----------
    
    image : numpy.ndarray
        numpy image
    
    angle : float
        angle by which the image is to be rotated
    crop: bool
    	crop the image to the original size
    
    Returns
    -------
    
    numpy.ndarray
        Rotated Image
    
    """
    # grab the dimensions of the image and then determine the
    # centre
    (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)
    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
    image = cv2.warpAffine(image, M, (nW, nH))
    
    # if crop the image to the original size
    if crop:
        (new_h, new_w) = image.shape[:2]
        h_delta_half = (new_h - h)/ 2.0
        w_delta_half = (new_w - w)/ 2.0

        image = image[math.ceil(h_delta_half):new_h - math.floor(h_delta_half), 
                                math.ceil(w_delta_half):new_w -math.floor(w_delta_half),:]
    
    
 
   
    return image