"""
Utils to load and split image/video data.
"""

from __future__ import division
import math
import tensorflow as tf


def euler2mat(z, y, x):
    """Converts euler angles to rotation matrix
     TODO: remove the dimension for 'N' (deprecated for converting all source
           poses altogether)
     Reference: https://github.com/pulkitag/pycaffe-utils/blob/master/rot_utils.py#L174
    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 -- size = [B, N, 3, 3]
    """
    B = tf.shape(z)[0]
    N = 1
    z = tf.clip_by_value(z, -math.pi, math.pi)
    y = tf.clip_by_value(y, -math.pi, math.pi)
    x = tf.clip_by_value(x, -math.pi, math.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([B, N, 1, 1])
    ones = tf.ones([B, 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)

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


def pose_vec2mat(vec):
    """Converts 6DoF parameters to transformation matrix
    Args:
        vec: 6DoF parameters in the order of tx, ty, tz, rx, ry, rz -- [B, 6]
    Returns:
        A transformation matrix -- [B, 4, 4]
    """
    batch_size, _ = vec.get_shape().as_list()
    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, axis=[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 pixel2cam(depth, pixel_coords, intrinsics, is_homogeneous=True):
    """Transforms coordinates in the pixel frame to the camera frame.

    Args:
      depth: [batch, height, width]
      pixel_coords: homogeneous pixel coordinates [batch, 3, height, width]
      intrinsics: camera intrinsics [batch, 3, 3]
      is_homogeneous: return in homogeneous coordinates
    Returns:
      Coords in the camera frame [batch, 3 (4 if homogeneous), height, width]
    """
    batch, height, width = depth.get_shape().as_list()
    depth = tf.reshape(depth, [batch, 1, -1])
    pixel_coords = tf.reshape(pixel_coords, [batch, 3, -1])
    cam_coords = tf.matmul(tf.matrix_inverse(intrinsics), pixel_coords) * depth
    if is_homogeneous:
        ones = tf.ones([batch, 1, height*width])
        cam_coords = tf.concat([cam_coords, ones], axis=1)
    cam_coords = tf.reshape(cam_coords, [batch, -1, height, width])
    return cam_coords


def cam2pixel(cam_coords, proj):
    """Transforms coordinates in a camera frame to the pixel frame.

    Args:
      cam_coords: [batch, 4, height, width]
      proj: [batch, 4, 4]
    Returns:
      Pixel coordinates projected from the camera frame [batch, height, width, 2]
    """
    batch, _, height, width = cam_coords.get_shape().as_list()
    cam_coords = tf.reshape(cam_coords, [batch, 4, -1])
    unnormalized_pixel_coords = tf.matmul(proj, cam_coords)
    x_u = tf.slice(unnormalized_pixel_coords, [0, 0, 0], [-1, 1, -1])
    y_u = tf.slice(unnormalized_pixel_coords, [0, 1, 0], [-1, 1, -1])
    z_u = tf.slice(unnormalized_pixel_coords, [0, 2, 0], [-1, 1, -1])
    x_n = x_u / (z_u + 1e-10)
    y_n = y_u / (z_u + 1e-10)
    pixel_coords = tf.concat([x_n, y_n], axis=1)
    pixel_coords = tf.reshape(pixel_coords, [batch, 2, height, width])
    return tf.transpose(pixel_coords, perm=[0, 2, 3, 1])


def meshgrid(batch, height, width, is_homogeneous=True):
    """Construct a 2D meshgrid.

    Args:
      batch: batch size
      height: height of the grid
      width: width of the grid
      is_homogeneous: whether to return in homogeneous coordinates
    Returns:
      x,y grid coordinates [batch, 2 (3 if homogeneous), height, width]
    """
    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)
    if is_homogeneous:
        ones = tf.ones_like(x_t)
        coords = tf.stack([x_t, y_t, ones], axis=0)
    else:
        coords = tf.stack([x_t, y_t], axis=0)
    coords = tf.tile(tf.expand_dims(coords, 0), [batch, 1, 1, 1])
    return coords


def projective_inverse_warp(img, depth, pose, intrinsics):
    """Inverse warp a source image to the target image plane based on projection.

    Args:
      img: the source image [batch, height_s, width_s, 3]
      depth: depth map of the target image [batch, height_t, width_t]
      pose: target to source camera transformation matrix [batch, 6], in the
            order of tx, ty, tz, rx, ry, rz
      intrinsics: camera intrinsics [batch, 3, 3]
    Returns:
      Source image inverse warped to the target image plane [batch, height_t,
      width_t, 3]
    """
    batch, height, width, _ = img.get_shape().as_list()
    # Convert pose vector to matrix
    pose = pose_vec2mat(pose)
    # Construct pixel grid coordinates
    pixel_coords = meshgrid(batch, height, width)
    # Convert pixel coordinates to the camera frame
    cam_coords = pixel2cam(depth, pixel_coords, intrinsics)
    # Construct a 4x4 intrinsic matrix (TODO: can it be 3x4?)
    filler = tf.constant([0.0, 0.0, 0.0, 1.0], shape=[1, 1, 4])
    filler = tf.tile(filler, [batch, 1, 1])
    intrinsics = tf.concat([intrinsics, tf.zeros([batch, 3, 1])], axis=2)
    intrinsics = tf.concat([intrinsics, filler], axis=1)
    # Get a 4x4 transformation matrix from 'target' camera frame to 'source'
    # pixel frame.
    proj_tgt_cam_to_src_pixel = tf.matmul(intrinsics, pose)
    src_pixel_coords = cam2pixel(cam_coords, proj_tgt_cam_to_src_pixel)
    output_img = bilinear_sampler(img, src_pixel_coords)
    return output_img


def bilinear_sampler(imgs, coords):
    """Construct a new image by bilinear sampling from the input image.

    Points falling outside the source image boundary have value 0.

    Args:
      imgs: source image to be sampled from [batch, height_s, width_s, channels]
      coords: coordinates of source pixels to sample from [batch, height_t,
        width_t, 2]. height_t/width_t correspond to the dimensions of the output
        image (don't need to be the same as height_s/width_s). The two channels
        correspond to x and y coordinates respectively.
    Returns:
      A new sampled image [batch, height_t, width_t, channels]
    """
    def _repeat(x, n_repeats):
        rep = tf.transpose(
            tf.expand_dims(tf.ones(shape=tf.stack([
                n_repeats,
            ])), 1), [1, 0])
        rep = tf.cast(rep, 'float32')
        x = tf.matmul(tf.reshape(x, (-1, 1)), rep)
        return tf.reshape(x, [-1])

    with tf.name_scope('image_sampling'):
        coords_x, coords_y = tf.split(coords, [1, 1], axis=3)
        inp_size = imgs.get_shape()
        coord_size = coords.get_shape()
        out_size = coords.get_shape().as_list()
        out_size[3] = imgs.get_shape().as_list()[3]

        coords_x = tf.cast(coords_x, 'float32')
        coords_y = tf.cast(coords_y, 'float32')

        x0 = tf.floor(coords_x)
        x1 = x0 + 1
        y0 = tf.floor(coords_y)
        y1 = y0 + 1

        y_max = tf.cast(tf.shape(imgs)[1] - 1, 'float32')
        x_max = tf.cast(tf.shape(imgs)[2] - 1, 'float32')
        zero = tf.zeros([1], dtype='float32')

        x0_safe = tf.clip_by_value(x0, zero, x_max)
        y0_safe = tf.clip_by_value(y0, zero, y_max)
        x1_safe = tf.clip_by_value(x1, zero, x_max)
        y1_safe = tf.clip_by_value(y1, zero, y_max)

        # bilinear interp weights, with points outside the grid having weight 0
        # wt_x0 = (x1 - coords_x) * tf.cast(tf.equal(x0, x0_safe), 'float32')
        # wt_x1 = (coords_x - x0) * tf.cast(tf.equal(x1, x1_safe), 'float32')
        # wt_y0 = (y1 - coords_y) * tf.cast(tf.equal(y0, y0_safe), 'float32')
        # wt_y1 = (coords_y - y0) * tf.cast(tf.equal(y1, y1_safe), 'float32')

        wt_x0 = x1_safe - coords_x
        wt_x1 = coords_x - x0_safe
        wt_y0 = y1_safe - coords_y
        wt_y1 = coords_y - y0_safe

        # indices in the flat image to sample from
        dim2 = tf.cast(inp_size[2], 'float32')
        dim1 = tf.cast(inp_size[2] * inp_size[1], 'float32')
        base = tf.reshape(
            _repeat(
                tf.cast(tf.range(coord_size[0]), 'float32') * dim1,
                coord_size[1] * coord_size[2]),
            [out_size[0], out_size[1], out_size[2], 1])

        base_y0 = base + y0_safe * dim2
        base_y1 = base + y1_safe * dim2
        idx00 = tf.reshape(x0_safe + base_y0, [-1])
        idx01 = x0_safe + base_y1
        idx10 = x1_safe + base_y0
        idx11 = x1_safe + base_y1

        # sample from imgs
        imgs_flat = tf.reshape(imgs, tf.stack([-1, inp_size[3]]))
        imgs_flat = tf.cast(imgs_flat, 'float32')
        im00 = tf.reshape(
            tf.gather(imgs_flat, tf.cast(idx00, 'int32')), out_size)
        im01 = tf.reshape(
            tf.gather(imgs_flat, tf.cast(idx01, 'int32')), out_size)
        im10 = tf.reshape(
            tf.gather(imgs_flat, tf.cast(idx10, 'int32')), out_size)
        im11 = tf.reshape(
            tf.gather(imgs_flat, tf.cast(idx11, 'int32')), out_size)

        w00 = wt_x0 * wt_y0
        w01 = wt_x0 * wt_y1
        w10 = wt_x1 * wt_y0
        w11 = wt_x1 * wt_y1

        output = tf.add_n([
            w00 * im00, w01 * im01,
            w10 * im10, w11 * im11
        ])
        return output

# Spatial transformer network bilinear sampler, taken from https://github.com/kevinzakka/spatial-transformer-network/blob/master/stn/transformer.py


def stn_bilinear_sampler(img, x, y):
    """
    Performs bilinear sampling of the input images according to the
    normalized coordinates provided by the sampling grid. Note that
    the sampling is done identically for each channel of the input.
    To test if the function works properly, output image should be
    identical to input image when theta is initialized to identity
    transform.
    Input
    -----
    - img: batch of images in (B, H, W, C) layout.
    - grid: x, y which is the output of affine_grid_generator.
    Returns
    -------
    - out: interpolated images according to grids. Same size as grid.
    """
    H = tf.shape(img)[1]
    W = tf.shape(img)[2]
    max_y = tf.cast(H - 1, 'int32')
    max_x = tf.cast(W - 1, 'int32')
    zero = tf.zeros([], dtype='int32')

    # rescale x and y to [0, W-1/H-1]
    x = tf.cast(x, 'float32')
    y = tf.cast(y, 'float32')
    x = 0.5 * ((x + 1.0) * tf.cast(max_x-1, 'float32'))
    y = 0.5 * ((y + 1.0) * tf.cast(max_y-1, 'float32'))

    # grab 4 nearest corner points for each (x_i, y_i)
    x0 = tf.cast(tf.floor(x), 'int32')
    x1 = x0 + 1
    y0 = tf.cast(tf.floor(y), 'int32')
    y1 = y0 + 1

    # clip to range [0, H-1/W-1] to not violate img boundaries
    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)

    # get pixel value at corner coords
    Ia = get_pixel_value(img, x0, y0)
    Ib = get_pixel_value(img, x0, y1)
    Ic = get_pixel_value(img, x1, y0)
    Id = get_pixel_value(img, x1, y1)

    # recast as float for delta calculation
    x0 = tf.cast(x0, 'float32')
    x1 = tf.cast(x1, 'float32')
    y0 = tf.cast(y0, 'float32')
    y1 = tf.cast(y1, 'float32')

    # calculate deltas
    wa = (x1-x) * (y1-y)
    wb = (x1-x) * (y-y0)
    wc = (x-x0) * (y1-y)
    wd = (x-x0) * (y-y0)

    # add dimension for addition
    wa = tf.expand_dims(wa, axis=3)
    wb = tf.expand_dims(wb, axis=3)
    wc = tf.expand_dims(wc, axis=3)
    wd = tf.expand_dims(wd, axis=3)

    # compute output
    out = tf.add_n([wa*Ia, wb*Ib, wc*Ic, wd*Id])

    return out