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