OpenCV Python : rotate image without cropping sides

Question

Imagine I have these images :

I want the image from left to be rotated like the image of the middle, not the right one. How do I do this using Python and OpenCV. I looked at getRotationMatrix2D and warpAffine but the examples about it transform my image to the right one.

Answer 1

This is by far the best solution i have found for rotating images while avoiding cropping the image.

Rotate an image without cropping in OpenCV in C++

import cv2

def rotate_image(mat, angle):
    """
    Rotates an image (angle in degrees) and expands image to avoid cropping
    """

    height, width = mat.shape[:2] # image shape has 3 dimensions
    image_center = (width/2, height/2) # getRotationMatrix2D needs coordinates in reverse order (width, height) compared to shape

    rotation_mat = cv2.getRotationMatrix2D(image_center, angle, 1.)

    # rotation calculates the cos and sin, taking absolutes of those.
    abs_cos = abs(rotation_mat[0,0]) 
    abs_sin = abs(rotation_mat[0,1])

    # find the new width and height bounds
    bound_w = int(height * abs_sin + width * abs_cos)
    bound_h = int(height * abs_cos + width * abs_sin)

    # subtract old image center (bringing image back to origo) and adding the new image center coordinates
    rotation_mat[0, 2] += bound_w/2 - image_center[0]
    rotation_mat[1, 2] += bound_h/2 - image_center[1]

    # rotate image with the new bounds and translated rotation matrix
    rotated_mat = cv2.warpAffine(mat, rotation_mat, (bound_w, bound_h))
    return rotated_mat

You can add checks to avoid some calculations when the angles are 90*n, but this function will work for any angle as is.

Answer 2

If you only care about 90 degree rotations numpy instead. It's much easier and works on opencv input:

import numpy as np
rotated_image = np.rot90(im)

Answer 3

As I don't know your code I still would guess that using the imutils.rotate_bound function will solve the problem. Eg: rotate = imutils.rotate_bound(image, angle)

Answer 4

This is the easiest way to rotate image frames by using cv2.rotate(frame,rotateCode = 1) and rescale or resizing by using cv2.CAP_PROP_FRAME_WIDTH and cv2.CAP_PROP_FRAME_HEIGHT of the frame.

import numpy as np
import cv2

cam = cv2.VideoCapture(2)

while(True):
    # Capture frame-by-frame
    cam.set(cv2.CAP_PROP_FRAME_WIDTH, 640) # You can change frame width by chaning number.

    cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) # You can change frame height by chaning number.

    ret, frame = cam.read()

    new_frame=cv2.rotate(frame,rotateCode = 1) 

You can enter rotateCode= 0 or 1 or 2. Depends on your rotation. It's gonna give you 0 or 90 or 180 or 270 angles

    # Display the resulting frame
    cv2.imshow('frame',new_frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything done, release the capture
cam.release()
cv2.destroyAllWindows()

Hopefully, it with help you.

Answer 5

While this question was asked for CV2, you can do this with python's native image library.

rotate_degrees = -90
img = Image.open(input_file_path)
img2 = img.rotate(rotate_degrees, expand=True)
img2.save(output_file_path)

If you leave out expand=True in the rotate command you will get a result that looks like OP's right hand photo.

Answer 6

You can also use padding as well, that is, add borders to the sides of the image, then rotate it to avoid cropping from the original image.

def rotate_im(image, angle)
    image_height = image.shape[0]
    image_width = image.shape[1]
    diagonal_square = (image_width*image_width) + (
        image_height* image_height
    )
    #
    diagonal = round(sqrt(diagonal_square))
    padding_top = round((diagonal-image_height) / 2)
    padding_bottom = round((diagonal-image_height) / 2)
    padding_right = round((diagonal-image_width) / 2)
    padding_left = round((diagonal-image_width) / 2)
    padded_image = cv2.copyMakeBorder(image,
                                      top=padding_top,
                                      bottom=padding_bottom,
                                      left=padding_left,
                                      right=padding_right,
                                      borderType=cv2.BORDER_CONSTANT,
                                      value=0
            )
    padded_height = padded_image.shape[0]
    padded_width = padded_image.shape[1]
    transform_matrix = cv2.getRotationMatrix2D(
                (padded_height/2,
                 padded_width/2), # center
                angle, # angle
      1.0) # scale
    rotated_image = cv2.warpAffine(padded_image,
                                   transform_matrix,
                                   (diagonal, diagonal),
                                   flags=cv2.INTER_LANCZOS4)
    return rotated_image

Answer 7

its simple no need of any warpaffine or any calculation check this code out

import numpy as np
from PIL import ImageGrab
import cv2

angle = -90
scale = 1.0

while True:
    img = ImageGrab.grab()
    img_np = np.array(img)
    frame = cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB)
    new = cv2.rotate(frame,rotateCode = 0)# this is the line to rotate the image
    true = cv2.resize(new, (0,0), fx = 0.6, fy = 0.6) # with fxand fy u can control the size
    cv2.imshow('output', true)
    if cv2.waitKey(1) == 27:
        break


cv2.destroyAllWindows()

Answer 8

Here is an alternative from ndimage.rotate from SciPy

Related documentation

from scipy.ndimage import rotate as rotate_image

#rotation angle in degree
rotated_img1 = rotate_image(img,90)

rotated_img2 = rotate_image(img,-110)

rotated_img3 = rotate_image(img,-45)

# angles extending beyond 360 are calculated appropriately:
rotated_img4 = rotate_image(img,390)