"""
Trainer to learn depth information on unlabeled data (raw images/videos)

Allows pluggable depth networks for differing performance (including fast-depth)
"""

import tensorflow as tf
import tensorflow.keras as keras


class SFMLearner(keras.Model):

    def __init__(depth_model, pose_model):
        pass

    def train_step(self, data):
        pass


def projective_inverse_warp(depth, pose, t_img, s_imgs, intrinsics):
    '''
    SFM Learner inverse warp step
    ps ~ K.T(t->s).Dt(pt).K^-1.pt

    projected source pixel 
    '''
    pass


def bilinear_sample(projected_coords, s_img):
    '''
    Sample the 4 closest pixels in the source image via the projected coordinates
    to get the source image warped to the target image
    '''
    pass


def make_sfm_learner_pose_net(input_shape=(224, 224, 3)):
    pass