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

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.

Or much easier use SciPy

from scipy import ndimage

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

see here for more usage info.

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

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:

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

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

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

import imutils

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

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

   frame = imutils.rotate(frame, 45)

More:

You can simply use the imutils package to do the rotation. it has two methods

1. rotate: Rotate the image at specified angle. however the drawback is image might get cropped if it is not a square image.
2. Rotate_bound: it overcomes the problem happened with rotate. It adjusts the size of the image accordingly while rotating the image.

more info you can get on this blog:

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

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)

You can easily rotate the images using opencv python-

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)

If you are thinking of creating a trackbar, then simply create a trackbar using cv2.createTrackbar() and the call the funcRotate()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- Rotate images at any degree using Trackbars in opencv

Here's an example for rotating about an arbitrary point (x,y) using only openCV

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

you can use the following 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("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")       

You need a homogenous matrix of size 2x3. First 2x2 is the rotation matrix and last column is a translation vector.

Here's how to build your transformation matrix:

# Exemple with img center point:
# angle = 
# 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])