how to rotate image python manually code example

Example 1: rotate image python

import skimage
import skimage.transform
rotated_img=skimage.transform.rotate(img,-60, resize=True)

Example 2: how to rotate image python manually

def rotate_image(img, degrees, output_scale="crop"):

    assert output_scale in ["crop", "full"], "output_scale should be either 'crop' or 'full'"
    #  convert rotation amount to radian
    rot_rad = degrees * np.pi / 180.0
    rotate_m = np.array([[np.cos(rot_rad), np.sin(rot_rad)],
                         [- np.sin(rot_rad), np.cos(rot_rad)]])

    # If output_scale = "full", the image must be inserted into a bigger frame, so the coordinates would be translated
    # appropriately.
    gray_scale = False
    if len(img.shape) < 3:
        img = img.reshape(*img.shape, 1)
        gray_scale = True

    h, w, c = img.shape
    if output_scale == "full":
        diagonal = int(np.sqrt(h * h + w * w))   # Pytagoras theorm - the diagonal is the longest line in the rectangle
        im_padded = np.zeros((diagonal, diagonal, c))
        center_h = int((diagonal - h) // 2)
        center_w = int((diagonal - w) // 2)
        im_padded[center_h:-center_h, center_w:-center_w, :] = img
        img = im_padded
        rotated_image = np.zeros((diagonal, diagonal, c))
        h, w, c = img.shape
    else:
        rotated_image = np.zeros((h, w, c))

    # Rotate and shift the indices, from PICTURE to SOURCE (and NOT the intuitive way)
    indices_org = np.array(np.meshgrid(np.arange(h), np.arange(w))).reshape(2, -1)
    indices_new = indices_org.copy()
    indices_new = np.dot(rotate_m, indices_new).astype(int)   # Apply the affineWrap
    mu1 = np.mean(indices_new, axis=1).astype(int).reshape((-1, 1))
    mu2 = np.mean(indices_org, axis=1).astype(int).reshape((-1, 1))
    indices_new += (mu2-mu1)   # Shift the image back to the center

    # Remove the pixels in the rotated image, that are now out of the bounds of the result image
    # (Note that the result image is a rectangle of shape (h,w,c) that the rotated image is inserted into, so in the
    # case of a "full" output_scale, these are just black pixels from the padded image...).
    t0, t1 = indices_new
    t0 = (0 <= t0) & (t0 < h)
    t1 = (0 <= t1) & (t1 < w)
    valid = t0 & t1
    indices_new = indices_new.T[valid].T
    indices_org = indices_org.T[valid].T

    #
    xind, yind = indices_new
    xi, yi = indices_org
    rotated_image[xi, yi, :] = img[xind, yind, :]

    if gray_scale:
        rotated_image = rotated_image.reshape((h, w))

    return rotated_image.astype(np.uint8)
  
img = cv2.imread(image_path)
rotated = rotate_image(img, 45)
cv2.imshow("Rotated_Image", rotated)
cv2.waitkey(0)