OpenCV Python围绕特定点将图像旋转X度

Question

我很难找到使用 OpenCV 在 Python 中将图像围绕特定点旋转特定（通常非常小）角度的示例。

这是我目前所拥有的，但它产生了一个非常奇怪的结果图像，但它有点旋转：

def rotateImage( image, angle ):
    if image != None:
        dst_image = cv.CloneImage( image )

        rotate_around = (0,0)
        transl = cv.CreateMat(2, 3, cv.CV_32FC1 )

        matrix = cv.GetRotationMatrix2D( rotate_around, angle, 1.0, transl )
        cv.GetQuadrangleSubPix( image, dst_image, transl )
        cv.GetRectSubPix( dst_image, image, rotate_around )

    return dst_image

Answer 1

import numpy as np
import cv2

def rotate_image(image, angle):
  image_center = tuple(np.array(image.shape[1::-1]) / 2)
  rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)
  result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR)
  return result

假设您使用的是 cv2 版本，该代码会找到您要旋转的图像的中心，计算变换矩阵并应用于图像。

Answer 2

或者更容易使用 SciPy

from scipy import ndimage

#rotation angle in degree
rotated = ndimage.rotate(image_to_rotate, 45)

见 here 了解更多使用信息。

Answer 3

def rotate(image, angle, center = None, scale = 1.0):
    (h, w) = image.shape[:2]

    if center is None:
        center = (w / 2, h / 2)

    # Perform the rotation
    M = cv2.getRotationMatrix2D(center, angle, scale)
    rotated = cv2.warpAffine(image, M, (w, h))

    return rotated

Answer 4

cv2.warpAffine 函数以相反的顺序获取形状参数： (col,row) 上面的答案没有提到。这对我有用：

import numpy as np

def rotateImage(image, angle):
    row,col = image.shape
    center=tuple(np.array([row,col])/2)
    rot_mat = cv2.getRotationMatrix2D(center,angle,1.0)
    new_image = cv2.warpAffine(image, rot_mat, (col,row))
    return new_image

Answer 5

在获取正确的“bounding_box”或图像的新尺寸时，我遇到了上述一些解决方案的问题。因此这是我的版本

def rotation(image, angleInDegrees):
    h, w = image.shape[:2]
    img_c = (w / 2, h / 2)

    rot = cv2.getRotationMatrix2D(img_c, angleInDegrees, 1)

    rad = math.radians(angleInDegrees)
    sin = math.sin(rad)
    cos = math.cos(rad)
    b_w = int((h * abs(sin)) + (w * abs(cos)))
    b_h = int((h * abs(cos)) + (w * abs(sin)))

    rot[0, 2] += ((b_w / 2) - img_c[0])
    rot[1, 2] += ((b_h / 2) - img_c[1])

    outImg = cv2.warpAffine(image, rot, (b_w, b_h), flags=cv2.INTER_LINEAR)
    return outImg

Answer 6

import imutils

vs = VideoStream(src=0).start()
...

while (1):
   frame = vs.read()
   ...

   frame = imutils.rotate(frame, 45)

更多：https://github.com/jrosebr1/imutils

Answer 7

您可以简单地使用 imutils 包进行旋转。它有两种方法

1. rotate：以指定角度旋转图像。但是缺点是如果不是方形图像，图像可能会被裁剪。
2. Rotate_bound：它克服了旋转时出现的问题。它会在旋转图像时相应地调整图像的大小。

您可以在此博客上获得更多信息： https://www.pyimagesearch.com/2017/01/02/rotate-images-correctly-with-opencv-and-python/

Answer 8

快速调整@alex-rodrigues 的答案...处理形状，包括通道数。

import cv2
import numpy as np

def rotateImage(image, angle):
    center=tuple(np.array(image.shape[0:2])/2)
    rot_mat = cv2.getRotationMatrix2D(center,angle,1.0)
    return cv2.warpAffine(image, rot_mat, image.shape[0:2],flags=cv2.INTER_LINEAR)

Answer 9

您可以使用 opencv python 轻松旋转图像-

def funcRotate(degree=0):
    degree = cv2.getTrackbarPos('degree','Frame')
    rotation_matrix = cv2.getRotationMatrix2D((width / 2, height / 2), degree, 1)
    rotated_image = cv2.warpAffine(original, rotation_matrix, (width, height))
    cv2.imshow('Rotate', rotated_image)

如果您正在考虑创建一个轨迹栏，那么只需使用cv2.createTrackbar() 创建一个轨迹栏，然后从您的主脚本中调用funcRotate()fucntion。然后，您可以轻松地将其旋转到您想要的任何角度。有关实现的完整详细信息也可以在这里找到-Rotate images at any degree using Trackbars in opencv

Answer 10

这是一个仅使用 openCV 围绕任意点 (x,y) 旋转的示例

def rotate_about_point(x, y, degree, image):
    rot_mtx = cv.getRotationMatrix2D((x, y), angle, 1)
    abs_cos = abs(rot_mtx[0, 0])
    abs_sin = abs(rot_mtx[0, 1])
    rot_wdt = int(frm_hgt * abs_sin + frm_wdt * abs_cos)
    rot_hgt = int(frm_hgt * abs_cos + frm_wdt * abs_sin)
    rot_mtx += np.asarray([[0, 0, -lftmost_x],
                           [0, 0, -topmost_y]])
    rot_img = cv.warpAffine(image, rot_mtx, (rot_wdt, rot_hgt),
                            borderMode=cv.BORDER_CONSTANT)
    return rot_img

Answer 11

您可以使用以下代码：

import numpy as np
from PIL import Image
import math
def shear(angle,x,y):

tangent=math.tan(angle/2)
new_x=round(x-y*tangent)
new_y=y

#shear 2
new_y=round(new_x*math.sin(angle)+new_y)     
#since there is no change in new_x according to the shear matrix

#shear 3
new_x=round(new_x-new_y*tangent)            
#since there is no change in new_y according to the shear matrix

return new_y,new_x




image = np.array(Image.open("test.png"))            
# Load the image
angle=-int(input("Enter the angle :- "))               
# Ask the user to enter the angle of rotation

# Define the most occuring variables
angle=math.radians(angle)                             
#converting degrees to radians
cosine=math.cos(angle)
sine=math.sin(angle)

height=image.shape[0]                                
#define the height of the image
width=image.shape[1]                                    
#define the width of the image

# Define the height and width of the new image that is to be formed
new_height  = round(abs(image.shape[0]*cosine)+abs(image.shape[1]*sine))+1
new_width  = round(abs(image.shape[1]*cosine)+abs(image.shape[0]*sine))+1


output=np.zeros((new_height,new_width,image.shape[2]))
image_copy=output.copy()


# Find the centre of the image about which we have to rotate the image
original_centre_height   = round(((image.shape[0]+1)/2)-1)    
#with respect to the original image
original_centre_width = round(((image.shape[1]+1)/2)-1)   
#with respect to   the original image

# Find the centre of the new image that will be obtained
new_centre_height= round(((new_height+1)/2)-1)        
#with respect to the new image
new_centre_width= round(((new_width+1)/2)-1)          
#with respect to the new image


for i in range(height):
 for j in range(width):
    #co-ordinates of pixel with respect to the centre of original image
    y=image.shape[0]-1-i-original_centre_height                   
    x=image.shape[1]-1-j-original_centre_width 

    #Applying shear Transformation                     
    new_y,new_x=shear(angle,x,y)

   
    new_y=new_centre_height-new_y
    new_x=new_centre_width-new_x
    
    output[new_y,new_x,:]=image[i,j,:]                        

    pil_img=Image.fromarray((output).astype(np.uint8))                       
    pil_img.save("rotated_image.png")       

Answer 12

您需要一个大小为 2x3 的齐次矩阵。第一个 2x2 是旋转矩阵，最后一列是平移向量。

以下是构建转换矩阵的方法：

# Exemple with img center point:
# angle = np.pi/6
# specific_point = np.array(img.shape[:2][::-1])/2

def rotate(img: np.ndarray, angle: float, specific_point: np.ndarray) -> np.ndarray:
    warp_mat = np.zeros((2,3))
    cos, sin = np.cos(angle), np.sin(angle)
    warp_mat[:2,:2] = [[cos, -sin],[sin, cos]]
    warp_mat[:2,2] = specific_point - np.matmul(warp_mat[:2,:2], specific_point)
    return cv2.warpAffine(img, warp_mat, img.shape[:2][::-1])