blocks|key|451678|text|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|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|451679|假设您使用的是cv2版本，该代码会找到您想要旋转的图像的中心，计算转换矩阵并应用于该图像。|unstyled|451680|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|J|8|@]|9|@]|A|$]]|$1|G|3|-4|5|F|7|K|8|@]|9|@]|A|$]]]|H|$]]

<pre><code>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
</code></pre>
Assuming you're using the cv2 version, that code finds the center of the image you want to rotate, calculates the transformation matrix and applies to the image.

blocks|key|665959|text|或者更容易使用SciPy|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|665960|from+scipy+import+ndimage

#rotation+angle+in+degree
rotated+=+ndimage.rotate(image_to_rotate,+45)|code-block|syntax|javascript|665961|有关更多用法信息，请参阅here+。|665962|entityMap|0|LINK|mutability|MUTABLE|url|http://sourceforge.net/projects/scipy/|1|http://scipy-lectures.github.io/advanced/image_processing/#id5^0|7|5|0|0|0|C|5|1|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|F|7|Y|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|Z|8|@]|9|@$A|10|B|11|1|12]]|C|$]]|$1|K|3|-4|5|6|7|13|8|@]|9|@]|C|$]]]|L|$M|$5|N|O|P|C|$Q|R]]|S|$5|N|O|P|C|$Q|T]]]]

Or much easier use
<a href="http://sourceforge.net/projects/scipy/" rel="noreferrer">SciPy</a>

<pre><code>from scipy import ndimage

#rotation angle in degree
rotated = ndimage.rotate(image_to_rotate, 45)
</code></pre>

see
<a href="http://scipy-lectures.github.io/advanced/image_processing/#id5" rel="noreferrer">here </a>
for more usage info.

blocks|key|451869|text|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|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|451870|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>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
</code></pre>

blocks|key|666035|text|cv2.warpAffine函数以相反的顺序接受形状参数：(col，row)，上面的答案没有提到这一点。以下是对我有效的方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|666036|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|code-block|syntax|javascript|666037|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

The cv2.warpAffine function takes the shape parameter in reverse order: (col,row) which the answers above do not mention. Here is what worked for me: 

<pre><code>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
</code></pre>

blocks|key|666130|text|我在上面的一些解决方案中遇到了一些问题，比如获取正确的"bounding_box“或图像的新大小。因此，以下是我的版本|type|unstyled|depth|inlineStyleRanges|entityRanges|data|666131|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))+%2B+(w+*+abs(cos)))
++++b_h+=+int((h+*+abs(cos))+%2B+(w+*+abs(sin)))

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

++++outImg+=+cv2.warpAffine(image,+rot,+(b_w,+b_h),+flags=cv2.INTER_LINEAR)
++++return+outImg|code-block|syntax|javascript|666132|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I had issues with some of the above solutions, with getting the correct "bounding_box" or new size of the image. Therefore here is my version

<pre><code>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
</code></pre>

blocks|key|666081|text|import+imutils

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

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

+++frame+=+imutils.rotate(frame,+45)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|666082|更多信息：https://github.com/jrosebr1/imutils|unstyled|offset|length|666083|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/jrosebr1/imutils^0|0|5|Z|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|R|8|@]|9|@$G|S|H|T|1|U]]|A|$]]|$1|I|3|-4|5|F|7|V|8|@]|9|@]|A|$]]]|J|$K|$5|L|M|N|A|$O|P]]]]

<pre><code>import imutils

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

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

 frame = imutils.rotate(frame, 45)
</code></pre>

More: <a href="https://github.com/jrosebr1/imutils" rel="noreferrer">https://github.com/jrosebr1/imutils</a>

blocks|key|451745|text|快速调整到@alex-rodrigues的答案...处理形状，包括通道的数量。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|451746|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)|code-block|syntax|javascript|451747|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Quick tweak to @alex-rodrigues answer... deals with shape including the number of channels.

<pre><code>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)
</code></pre>

blocks|key|666172|text|您可以使用opencv+python轻松地旋转图像-|type|unstyled|depth|inlineStyleRanges|entityRanges|data|666173|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)|code-block|syntax|javascript|666174|如果你正在考虑创建一个跟踪栏，那么只需使用cv2.createTrackbar()并从你的主脚本调用funcRotate()函数来创建一个跟踪栏。然后你可以很容易地将它旋转到任何你想要的程度。有关实现的完整详细信息也可以在此处找到-+Rotate+images+at+any+degree+using+Trackbars+in+opencv|offset|length|style|CODE|666175|entityMap|0|LINK|mutability|MUTABLE|url|https://www.life2coding.com/how-to-rotate-images-at-any-degree-usingtrackbar-in-opencv-python/^0|0|0|L|K|1E|C|39|1H|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|W|8|@$I|X|J|Y|K|L]|$I|Z|J|10|K|L]]|9|@$I|11|J|12|1|13]]|A|$]]|$1|M|3|-4|5|6|7|14|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|T]]]]

You can easily rotate the images using opencv python-

<pre><code>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)
</code></pre>

If you are thinking of creating a trackbar, then simply create a trackbar using <code>cv2.createTrackbar()</code> and the call the <code>funcRotate()</code>fucntion from your main script. Then you can easily rotate it to any degree you want. Full details about the implementation can be found here as well- <a href="https://www.life2coding.com/how-to-rotate-images-at-any-degree-usingtrackbar-in-opencv-python/" rel="nofollow noreferrer" title="Rotate images at any degree with opencv python">Rotate images at any degree using Trackbars in opencv</a>

blocks|key|666254|text|以下是仅使用openCV围绕任意点(x，y)旋转的示例|type|unstyled|depth|inlineStyleRanges|entityRanges|data|666255|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+%2B+frm_wdt+*+abs_cos)
++++rot_hgt+=+int(frm_hgt+*+abs_cos+%2B+frm_wdt+*+abs_sin)
++++rot_mtx+%2B=+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|code-block|syntax|javascript|666256|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Here's an example for rotating about an arbitrary point (x,y) using only openCV
<pre><code>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
</code></pre>

blocks|key|452066|text|您可以使用以下代码：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|452067|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)%2Bnew_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)%2Babs(image.shape[1]*sine))%2B1
new_width++=+round(abs(image.shape[1]*cosine)%2Babs(image.shape[0]*sine))%2B1


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]%2B1)/2)-1)++++
#with+respect+to+the+original+image
original_centre_width+=+round(((image.shape[1]%2B1)/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%2B1)/2)-1)++++++++
#with+respect+to+the+new+image
new_centre_width=+round(((new_width%2B1)/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")+++++++|code-block|syntax|javascript|452068|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

you can use the following code:
<pre><code>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(&quot;test.png&quot;)) 
# Load the image
angle=-int(input(&quot;Enter the angle :- &quot;)) 
# 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(&quot;rotated_image.png&quot;) 
</code></pre>

blocks|key|666382|text|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。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|666383|​|666384|📷|atomic|offset|length|666385|666386|下面是如何构建转换矩阵的方法：|666387|#+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])|code-block|syntax|javascript|666388|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/812028d51a9627dc0f593e49f66f88a3.gif|imageAlt^0|0|0|0|1|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|10|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|11|8|@]|9|@$G|12|H|13|1|14]]|A|$]]|$1|I|3|C|5|6|7|15|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|16|8|@]|9|@]|A|$]]|$1|L|3|M|5|N|7|17|8|@]|9|@]|A|$O|P]]|$1|Q|3|-4|5|6|7|18|8|@]|9|@]|A|$]]]|R|$S|$5|T|U|V|A|$W|X|Y|-4]]]]

You need a homogenous matrix of size 2x3. First 2x2 is the rotation matrix and last column is a translation vector.
<a href="https://i.stack.imgur.com/VkRVA.gif" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/VkRVA.gif" alt="enter image description here" /></a>
Here's how to build your transformation matrix:
<pre class="lang-py prettyprint-override"><code># 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) -&gt; 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])
</code></pre>

I'm having a hard time finding examples for rotating an image around a specific point by a specific (often very small) angle in Python using OpenCV.

This is what I have so far, but it produces a very strange resulting image, but it is rotated somewhat:

<pre><code>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
</code></pre>

OpenCV Python rotate image by X degrees around specific point

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我很难找到使用OpenCV在Python语言中将图像围绕特定点旋转特定角度(通常非常小)的示例。这就是我到目前为止所得到的，但它产生了一个非常奇怪的结果图像，但它有些旋转：def rotateImage( image, angle ):    if image != None:        dst_image = cv.CloneImage( image )        rotate_arou

问OpenCV Python将图像围绕特定点旋转X度
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问OpenCV Python将图像围绕特定点旋转X度
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