tensorflow_affine_to_projective.py

import tensorflow as tf
from tensorflow.contrib.image import matrices_to_flat_transforms, transform

sess = tf.Session()


# affine_M = [[  0.14,   0.  , -18.4 ],
#             [ -0.  ,   0.14, -16.28],
#             [0,        0,     1]]

affine_M = np.vstack([tfm_mean, # <- M which might be used in cv2.warpAffine
                      np.array([[0, 0, 1]])])

projective_M = sess.run(matrices_to_flat_transforms(tf.linalg.inv(affine_M)))


wraped_image = sess.run(transform(np.array(img),
                                  projective_M,
                                  output_shape=(crop_size, crop_size)))