# Copyright 2018 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ==============================================================================

"""Geometry utilities for projecting frames based on depth and motion.

Modified from Spatial Transformer Networks:
https://github.com/tensorflow/models/blob/master/transformer/spatial_transformer.py
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from absl import logging
import numpy as np
import tensorflow as tf


def inverse_warp(img, depth, egomotion_mat, intrinsic_mat,
                 intrinsic_mat_inv):
  """Inverse warp a source image to the target image plane.

  Args:
    img: The source image (to sample pixels from) -- [B, H, W, 3].
    depth: Depth map of the target image -- [B, H, W].
    egomotion_mat: Matrix defining egomotion transform -- [B, 4, 4].
    intrinsic_mat: Camera intrinsic matrix -- [B, 3, 3].
    intrinsic_mat_inv: Inverse of the intrinsic matrix -- [B, 3, 3].
  Returns:
    Projected source image
  """
  dims = tf.shape(img)
  batch_size, img_height, img_width = dims[0], dims[1], dims[2]
  depth = tf.reshape(depth, [batch_size, 1, img_height * img_width])
  grid = _meshgrid_abs(img_height, img_width)
  grid = tf.tile(tf.expand_dims(grid, 0), [batch_size, 1, 1])
  cam_coords = _pixel2cam(depth, grid, intrinsic_mat_inv)
  ones = tf.ones([batch_size, 1, img_height * img_width])
  cam_coords_hom = tf.concat([cam_coords, ones], axis=1)

  # Get projection matrix for target camera frame to source pixel frame
  hom_filler = tf.constant([0.0, 0.0, 0.0, 1.0], shape=[1, 1, 4])
  hom_filler = tf.tile(hom_filler, [batch_size, 1, 1])
  intrinsic_mat_hom = tf.concat(
      [intrinsic_mat, tf.zeros([batch_size, 3, 1])], axis=2)
  intrinsic_mat_hom = tf.concat([intrinsic_mat_hom, hom_filler], axis=1)
  proj_target_cam_to_source_pixel = tf.matmul(intrinsic_mat_hom, egomotion_mat)
  source_pixel_coords = _cam2pixel(cam_coords_hom,
                                   proj_target_cam_to_source_pixel)
  source_pixel_coords = tf.reshape(source_pixel_coords,
                                   [batch_size, 2, img_height, img_width])
  source_pixel_coords = tf.transpose(source_pixel_coords, perm=[0, 2, 3, 1])
  projected_img, mask = _spatial_transformer(img, source_pixel_coords)
  return projected_img, mask


def get_transform_mat(egomotion_vecs, i, j):
  """Returns a transform matrix defining the transform from frame i to j."""
  egomotion_transforms = []
  batchsize = tf.shape(egomotion_vecs)[0]
  if i == j:
    return tf.tile(tf.expand_dims(tf.eye(4, 4), axis=0), [batchsize, 1, 1])
  for k in range(min(i, j), max(i, j)):
    transform_matrix = _egomotion_vec2mat(egomotion_vecs[:, k, :], batchsize)
    if i > j:  # Going back in sequence, need to invert egomotion.
      egomotion_transforms.insert(0, tf.linalg.inv(transform_matrix))
    else:  # Going forward in sequence
      egomotion_transforms.append(transform_matrix)

  # Multiply all matrices.
  egomotion_mat = egomotion_transforms[0]
  for i in range(1, len(egomotion_transforms)):
    egomotion_mat = tf.matmul(egomotion_mat, egomotion_transforms[i])
  return egomotion_mat


def _pixel2cam(depth, pixel_coords, intrinsic_mat_inv):
  """Transform coordinates in the pixel frame to the camera frame."""
  cam_coords = tf.matmul(intrinsic_mat_inv, pixel_coords) * depth
  return cam_coords


def _cam2pixel(cam_coords, proj_c2p):
  """Transform coordinates in the camera frame to the pixel frame."""
  pcoords = tf.matmul(proj_c2p, cam_coords)
  x = tf.slice(pcoords, [0, 0, 0], [-1, 1, -1])
  y = tf.slice(pcoords, [0, 1, 0], [-1, 1, -1])
  z = tf.slice(pcoords, [0, 2, 0], [-1, 1, -1])
  # Not tested if adding a small number is necessary
  x_norm = x / (z + 1e-10)
  y_norm = y / (z + 1e-10)
  pixel_coords = tf.concat([x_norm, y_norm], axis=1)
  return pixel_coords


def _meshgrid_abs(height, width):
  """Meshgrid in the absolute coordinates."""
  x_t = tf.matmul(
      tf.ones(shape=tf.stack([height, 1])),
      tf.transpose(tf.expand_dims(tf.linspace(-1.0, 1.0, width), 1), [1, 0]))
  y_t = tf.matmul(
      tf.expand_dims(tf.linspace(-1.0, 1.0, height), 1),
      tf.ones(shape=tf.stack([1, width])))
  x_t = (x_t + 1.0) * 0.5 * tf.cast(width - 1, tf.float32)
  y_t = (y_t + 1.0) * 0.5 * tf.cast(height - 1, tf.float32)
  x_t_flat = tf.reshape(x_t, (1, -1))
  y_t_flat = tf.reshape(y_t, (1, -1))
  ones = tf.ones_like(x_t_flat)
  grid = tf.concat([x_t_flat, y_t_flat, ones], axis=0)
  return grid


def _euler2mat(z, y, x):
  """Converts euler angles to rotation matrix.

   From:
   https://github.com/pulkitag/pycaffe-utils/blob/master/rot_utils.py#L174

   TODO: Remove the dimension for 'N' (deprecated for converting all source
   poses altogether).

  Args:
    z: rotation angle along z axis (in radians) -- size = [B, n]
    y: rotation angle along y axis (in radians) -- size = [B, n]
    x: rotation angle along x axis (in radians) -- size = [B, n]

  Returns:
    Rotation matrix corresponding to the euler angles, with shape [B, n, 3, 3].
  """
  batch_size = tf.shape(z)[0]
  n = 1
  z = tf.clip_by_value(z, -np.pi, np.pi)
  y = tf.clip_by_value(y, -np.pi, np.pi)
  x = tf.clip_by_value(x, -np.pi, np.pi)

  # Expand to B x N x 1 x 1
  z = tf.expand_dims(tf.expand_dims(z, -1), -1)
  y = tf.expand_dims(tf.expand_dims(y, -1), -1)
  x = tf.expand_dims(tf.expand_dims(x, -1), -1)

  zeros = tf.zeros([batch_size, n, 1, 1])
  ones = tf.ones([batch_size, n, 1, 1])

  cosz = tf.cos(z)
  sinz = tf.sin(z)
  rotz_1 = tf.concat([cosz, -sinz, zeros], axis=3)
  rotz_2 = tf.concat([sinz, cosz, zeros], axis=3)
  rotz_3 = tf.concat([zeros, zeros, ones], axis=3)
  zmat = tf.concat([rotz_1, rotz_2, rotz_3], axis=2)

  cosy = tf.cos(y)
  siny = tf.sin(y)
  roty_1 = tf.concat([cosy, zeros, siny], axis=3)
  roty_2 = tf.concat([zeros, ones, zeros], axis=3)
  roty_3 = tf.concat([-siny, zeros, cosy], axis=3)
  ymat = tf.concat([roty_1, roty_2, roty_3], axis=2)

  cosx = tf.cos(x)
  sinx = tf.sin(x)
  rotx_1 = tf.concat([ones, zeros, zeros], axis=3)
  rotx_2 = tf.concat([zeros, cosx, -sinx], axis=3)
  rotx_3 = tf.concat([zeros, sinx, cosx], axis=3)
  xmat = tf.concat([rotx_1, rotx_2, rotx_3], axis=2)

  return tf.matmul(tf.matmul(xmat, ymat), zmat)


def _egomotion_vec2mat(vec, batch_size):
  """Converts 6DoF transform vector to transformation matrix.

  Args:
    vec: 6DoF parameters [tx, ty, tz, rx, ry, rz] -- [B, 6].
    batch_size: Batch size.

  Returns:
    A transformation matrix -- [B, 4, 4].
  """
  translation = tf.slice(vec, [0, 0], [-1, 3])
  translation = tf.expand_dims(translation, -1)
  rx = tf.slice(vec, [0, 3], [-1, 1])
  ry = tf.slice(vec, [0, 4], [-1, 1])
  rz = tf.slice(vec, [0, 5], [-1, 1])
  rot_mat = _euler2mat(rz, ry, rx)
  rot_mat = tf.squeeze(rot_mat, squeeze_dims=[1])
  filler = tf.constant([0.0, 0.0, 0.0, 1.0], shape=[1, 1, 4])
  filler = tf.tile(filler, [batch_size, 1, 1])
  transform_mat = tf.concat([rot_mat, translation], axis=2)
  transform_mat = tf.concat([transform_mat, filler], axis=1)
  return transform_mat


def _bilinear_sampler(im, x, y, name='blinear_sampler'):
  """Perform bilinear sampling on im given list of x, y coordinates.

  Implements the differentiable sampling mechanism with bilinear kernel
  in https://arxiv.org/abs/1506.02025.

  x,y are tensors specifying normalized coordinates [-1, 1] to be sampled on im.
  For example, (-1, -1) in (x, y) corresponds to pixel location (0, 0) in im,
  and (1, 1) in (x, y) corresponds to the bottom right pixel in im.

  Args:
    im: Batch of images with shape [B, h, w, channels].
    x: Tensor of normalized x coordinates in [-1, 1], with shape [B, h, w, 1].
    y: Tensor of normalized y coordinates in [-1, 1], with shape [B, h, w, 1].
    name: Name scope for ops.

  Returns:
    Sampled image with shape [B, h, w, channels].
    Principled mask with shape [B, h, w, 1], dtype:float32.  A value of 1.0
      in the mask indicates that the corresponding coordinate in the sampled
      image is valid.
  """
  with tf.variable_scope(name):
    x = tf.reshape(x, [-1])
    y = tf.reshape(y, [-1])

    # Constants.
    batch_size = tf.shape(im)[0]
    _, height, width, channels = im.get_shape().as_list()

    x = tf.to_float(x)
    y = tf.to_float(y)
    height_f = tf.cast(height, 'float32')
    width_f = tf.cast(width, 'float32')
    zero = tf.constant(0, dtype=tf.int32)
    max_y = tf.cast(tf.shape(im)[1] - 1, 'int32')
    max_x = tf.cast(tf.shape(im)[2] - 1, 'int32')

    # Scale indices from [-1, 1] to [0, width - 1] or [0, height - 1].
    x = (x + 1.0) * (width_f - 1.0) / 2.0
    y = (y + 1.0) * (height_f - 1.0) / 2.0

    # Compute the coordinates of the 4 pixels to sample from.
    x0 = tf.cast(tf.floor(x), 'int32')
    x1 = x0 + 1
    y0 = tf.cast(tf.floor(y), 'int32')
    y1 = y0 + 1

    mask = tf.logical_and(
        tf.logical_and(x0 >= zero, x1 <= max_x),
        tf.logical_and(y0 >= zero, y1 <= max_y))
    mask = tf.to_float(mask)

    x0 = tf.clip_by_value(x0, zero, max_x)
    x1 = tf.clip_by_value(x1, zero, max_x)
    y0 = tf.clip_by_value(y0, zero, max_y)
    y1 = tf.clip_by_value(y1, zero, max_y)
    dim2 = width
    dim1 = width * height

    # Create base index.
    base = tf.range(batch_size) * dim1
    base = tf.reshape(base, [-1, 1])
    base = tf.tile(base, [1, height * width])
    base = tf.reshape(base, [-1])

    base_y0 = base + y0 * dim2
    base_y1 = base + y1 * dim2
    idx_a = base_y0 + x0
    idx_b = base_y1 + x0
    idx_c = base_y0 + x1
    idx_d = base_y1 + x1

    # Use indices to lookup pixels in the flat image and restore channels dim.
    im_flat = tf.reshape(im, tf.stack([-1, channels]))
    im_flat = tf.to_float(im_flat)
    pixel_a = tf.gather(im_flat, idx_a)
    pixel_b = tf.gather(im_flat, idx_b)
    pixel_c = tf.gather(im_flat, idx_c)
    pixel_d = tf.gather(im_flat, idx_d)

    x1_f = tf.to_float(x1)
    y1_f = tf.to_float(y1)

    # And finally calculate interpolated values.
    wa = tf.expand_dims(((x1_f - x) * (y1_f - y)), 1)
    wb = tf.expand_dims((x1_f - x) * (1.0 - (y1_f - y)), 1)
    wc = tf.expand_dims(((1.0 - (x1_f - x)) * (y1_f - y)), 1)
    wd = tf.expand_dims(((1.0 - (x1_f - x)) * (1.0 - (y1_f - y))), 1)

    output = tf.add_n([wa * pixel_a, wb * pixel_b, wc * pixel_c, wd * pixel_d])
    output = tf.reshape(output, tf.stack([batch_size, height, width, channels]))
    mask = tf.reshape(mask, tf.stack([batch_size, height, width, 1]))
    return output, mask


def _spatial_transformer(img, coords):
  """A wrapper over binlinear_sampler(), taking absolute coords as input."""
  img_height = tf.cast(tf.shape(img)[1], tf.float32)
  img_width = tf.cast(tf.shape(img)[2], tf.float32)
  px = coords[:, :, :, :1]
  py = coords[:, :, :, 1:]
  # Normalize coordinates to [-1, 1] to send to _bilinear_sampler.
  px = px / (img_width - 1) * 2.0 - 1.0
  py = py / (img_height - 1) * 2.0 - 1.0
  output_img, mask = _bilinear_sampler(img, px, py)
  return output_img, mask


def get_cloud(depth, intrinsics_inv, name=None):
  """Convert depth map to 3D point cloud."""
  with tf.name_scope(name):
    dims = depth.shape.as_list()
    batch_size, img_height, img_width = dims[0], dims[1], dims[2]
    depth = tf.reshape(depth, [batch_size, 1, img_height * img_width])
    grid = _meshgrid_abs(img_height, img_width)
    grid = tf.tile(tf.expand_dims(grid, 0), [batch_size, 1, 1])
    cam_coords = _pixel2cam(depth, grid, intrinsics_inv)
    cam_coords = tf.transpose(cam_coords, [0, 2, 1])
    cam_coords = tf.reshape(cam_coords, [batch_size, img_height, img_width, 3])
    logging.info('depth -> cloud: %s', cam_coords)
    return cam_coords