def random_rotation(img: tf.Tensor, max_rotation: float=0.1, crop: bool=True) -> tf.Tensor: # from SeguinBe
with tf.name_scope('RandomRotation'):
rotation = tf.random_uniform([], -max_rotation, max_rotation)
rotated_image = tf.contrib.image.rotate(img, rotation, interpolation='BILINEAR')
if crop:
rotation = tf.abs(rotation)
original_shape = tf.shape(rotated_image)[:2]
h, w = original_shape[0], original_shape[1]
# see https://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders for formulae
old_l, old_s = tf.cond(h > w, lambda: [h, w], lambda: [w, h])
old_l, old_s = tf.cast(old_l, tf.float32), tf.cast(old_s, tf.float32)
new_l = (old_l * tf.cos(rotation) - old_s * tf.sin(rotation)) / tf.cos(2*rotation)
new_s = (old_s - tf.sin(rotation) * new_l) / tf.cos(rotation)
new_h, new_w = tf.cond(h > w, lambda: [new_l, new_s], lambda: [new_s, new_l])
new_h, new_w = tf.cast(new_h, tf.int32), tf.cast(new_w, tf.int32)
bb_begin = tf.cast(tf.ceil((h-new_h)/2), tf.int32), tf.cast(tf.ceil((w-new_w)/2), tf.int32)
rotated_image_crop = rotated_image[bb_begin[0]:h - bb_begin[0], bb_begin[1]:w - bb_begin[1], :]
# If crop removes the entire image, keep the original image
rotated_image = tf.cond(tf.equal(tf.size(rotated_image_crop), 0),
true_fn=lambda: img,
false_fn=lambda: rotated_image_crop)
return rotated_image
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