"""
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


def compute_smooth_loss(self, pred_disp):
    def gradient(pred):
        D_dy = pred[:, 1:, :, :] - pred[:, :-1, :, :]
        D_dx = pred[:, :, 1:, :] - pred[:, :, :-1, :]
        return D_dx, D_dy
    dx, dy = gradient(pred_disp)
    dx2, dxdy = gradient(dx)
    dydx, dy2 = gradient(dy)
    return tf.reduce_mean(tf.abs(dx2)) + \
        tf.reduce_mean(tf.abs(dxdy)) + \
        tf.reduce_mean(tf.abs(dydx)) + \
        tf.reduce_mean(tf.abs(dy2))


def get_reference_explain_mask(self, downscaling):
    opt = self.opt
    tmp = np.array([0, 1])
    ref_exp_mask = np.tile(tmp,
                           (opt.batch_size,
                            int(opt.img_height/(2**downscaling)),
                            int(opt.img_width/(2**downscaling)),
                            1))
    ref_exp_mask = tf.constant(ref_exp_mask, dtype=tf.float32)
    return ref_exp_mask


def get_sfm_loss_fn(opt):
    def sfm_loss_fn(y, y_pred):
        # TODO: Correctly format a batch that is required for this loss function
        pixel_loss = 0
        exp_loss = 0
        smooth_loss = 0
        tgt_image_all = []
        src_image_stack_all = []
        proj_image_stack_all = []
        proj_error_stack_all = []
        exp_mask_stack_all = []
        for s in range(opt.num_scales):
            if opt.explain_reg_weight > 0:
                # Construct a reference explainability mask (i.e. all
                # pixels are explainable)
                ref_exp_mask = get_reference_explain_mask(s)
            # Scale the source and target images for computing loss at the
            # according scale.
            curr_tgt_image = tf.image.resize_area(tgt_image,
                                                  [int(opt.img_height/(2**s)), int(opt.img_width/(2**s))])
            curr_src_image_stack = tf.image.resize_area(src_image_stack,
                                                        [int(opt.img_height/(2**s)), int(opt.img_width/(2**s))])

            if opt.smooth_weight > 0:
                smooth_loss += opt.smooth_weight/(2**s) * \
                    compute_smooth_loss(pred_disp[s])

            for i in range(opt.num_source):
                # Inverse warp the source image to the target image frame
                curr_proj_image = projective_inverse_warp(
                    curr_src_image_stack[:, :, :, 3*i:3*(i+1)],
                    tf.squeeze(pred_depth[s], axis=3),
                    pred_poses[:, i, :],
                    intrinsics[:, s, :, :])
                curr_proj_error = tf.abs(curr_proj_image - curr_tgt_image)
                # Cross-entropy loss as regularization for the
                # explainability prediction
                if opt.explain_reg_weight > 0:
                    curr_exp_logits = tf.slice(pred_exp_logits[s],
                                               [0, 0, 0, i*2],
                                               [-1, -1, -1, 2])
                    exp_loss += opt.explain_reg_weight * \
                        self.compute_exp_reg_loss(curr_exp_logits,
                                                  ref_exp_mask)
                    curr_exp = tf.nn.softmax(curr_exp_logits)
                # Photo-consistency loss weighted by explainability
                if opt.explain_reg_weight > 0:
                    pixel_loss += tf.reduce_mean(curr_proj_error *
                                                 tf.expand_dims(curr_exp[:, :, :, 1], -1))
                else:
                    pixel_loss += tf.reduce_mean(curr_proj_error)
                # Prepare images for tensorboard summaries
                if i == 0:
                    proj_image_stack = curr_proj_image
                    proj_error_stack = curr_proj_error
                    if opt.explain_reg_weight > 0:
                        exp_mask_stack = tf.expand_dims(
                            curr_exp[:, :, :, 1], -1)
                else:
                    proj_image_stack = tf.concat([proj_image_stack,
                                                  curr_proj_image], axis=3)
                    proj_error_stack = tf.concat([proj_error_stack,
                                                  curr_proj_error], axis=3)
                    if opt.explain_reg_weight > 0:
                        exp_mask_stack = tf.concat([exp_mask_stack,
                                                    tf.expand_dims(curr_exp[:, :, :, 1], -1)], axis=3)
            tgt_image_all.append(curr_tgt_image)
            src_image_stack_all.append(curr_src_image_stack)
            proj_image_stack_all.append(proj_image_stack)
            proj_error_stack_all.append(proj_error_stack)
            if opt.explain_reg_weight > 0:
                exp_mask_stack_all.append(exp_mask_stack)
        total_loss = pixel_loss + smooth_loss + exp_loss
        return total_loss
        return sfm_loss_fn


def photometric_reconstruction_loss(tgt_img, ref_imgs, intrinsics,
                                    depth, explainability_mask, pose,
                                    rotation_mode='euler', padding_mode='zeros'):
    def one_scale(d, mask):
        assert(mask is None or d.size()
               [2:] == mask.size()[2:])
        assert(pose.size(1) == len(ref_imgs))

        reconstruction_loss = 0
        b, _, h, w = d.size()
        downscale = tgt_img.size(2)/h

        tgt_img_scaled = F.interpolate(tgt_img, (h, w), mode='area')
        ref_imgs_scaled = [F.interpolate(
            ref_img, (h, w), mode='area') for ref_img in ref_imgs]
        intrinsics_scaled = tf.concat(
            (intrinsics[:, 0:2]/downscale, intrinsics[:, 2:]), dim=1)

        warped_imgs = []
        diff_maps = []

        for i, ref_img in enumerate(ref_imgs_scaled):
            current_pose = pose[:, i]

            ref_img_warped, valid_points = inverse_warp(ref_img, depth[:, 0], current_pose,
                                                        intrinsics_scaled,
                                                        rotation_mode, padding_mode)
            diff = (tgt_img_scaled - ref_img_warped) * \
                valid_points.unsqueeze(1).float()

            if explainability_mask is not None:
                diff = diff * explainability_mask[:, i:i+1].expand_as(diff)

            reconstruction_loss += diff.abs().mean()
            assert((reconstruction_loss == reconstruction_loss).item() == 1)

            warped_imgs.append(ref_img_warped[0])
            diff_maps.append(diff[0])

        return reconstruction_loss, warped_imgs, diff_maps

    warped_results, diff_results = [], []
    if type(explainability_mask) not in [tuple, list]:
        explainability_mask = [explainability_mask]
    if type(depth) not in [list, tuple]:
        depth = [depth]

    total_loss = 0
    for d, mask in zip(depth, explainability_mask):
        loss, warped, diff = one_scale(d, mask)
        total_loss += loss
        warped_results.append(warped)
        diff_results.append(diff)
    return total_loss, warped_results, diff_results