Tensorflow framework to rotate images at given start and end angle with total number of images to produce

rotate_images.py

from math import pi

def rotate_images(X_imgs, start_angle, end_angle, n_images):
    X_rotate = []
    iterate_at = (end_angle - start_angle) / (n_images - 1)
    
    tf.reset_default_graph()
    X = tf.placeholder(tf.float32, shape = (None, IMAGE_SIZE, IMAGE_SIZE, 3))
    radian = tf.placeholder(tf.float32, shape = (len(X_imgs)))
    tf_img = tf.contrib.image.rotate(X, radian)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
    
        for index in range(n_images):
            degrees_angle = start_angle + index * iterate_at
            radian_value = degrees_angle * pi / 180  # Convert to radian
            radian_arr = [radian_value] * len(X_imgs)
            rotated_imgs = sess.run(tf_img, feed_dict = {X: X_imgs, radian: radian_arr})
            X_rotate.extend(rotated_imgs)

    X_rotate = np.array(X_rotate, dtype = np.float32)
    return X_rotate
	
# Start rotation at -90 degrees, end at 90 degrees and produce totally 14 images
rotated_imgs = rotate_images(X_imgs, -90, 90, 14)