#!/usr/bin/env python3

import os
import time

import numpy as np

import common.transformations.orientation as orient
from selfdrive.locationd.kalman.helpers.sympy_helpers import quat_matrix_l
from selfdrive.locationd.kalman.helpers import TEMPLATE_DIR, write_code, load_code


def sane(track):
  img_pos = track[1:,2:4]
  diffs_x = abs(img_pos[1:,0] - img_pos[:-1,0])
  diffs_y = abs(img_pos[1:,1] - img_pos[:-1,1])
  for i in range(1, len(diffs_x)):
    if ((diffs_x[i] > 0.05 or diffs_x[i-1] > 0.05) and \
       (diffs_x[i] > 2*diffs_x[i-1] or \
        diffs_x[i] < .5*diffs_x[i-1])) or \
       ((diffs_y[i] > 0.05 or diffs_y[i-1] > 0.05) and \
       (diffs_y[i] > 2*diffs_y[i-1] or \
        diffs_y[i] < .5*diffs_y[i-1])):
      return False
  return True


class FeatureHandler():
  name = 'feature_handler'

  @staticmethod
  def generate_code(K=5):
    # Wrap c code for slow matching
    c_header = "\nvoid merge_features(double *tracks, double *features, long long *empty_idxs);"

    c_code = "#include <math.h>\n"
    c_code += "#include <string.h>\n"
    c_code += "#define K %d\n" % K
    c_code += "\n" + open(os.path.join(TEMPLATE_DIR, "feature_handler.c")).read()

    filename = f"{FeatureHandler.name}_{K}"
    write_code(filename, c_code, c_header)

  def __init__(self, K=5):
    self.MAX_TRACKS = 6000
    self.K = K

    #Array of tracks, each track
    #has K 5D features preceded
    #by 5 params that inidicate
    #[f_idx, last_idx, updated, complete, valid]
    # f_idx: idx of current last feature in track
    # idx of of last feature in frame
    # bool for whether this track has been update
    # bool for whether this track is complete
    # bool for whether this track is valid
    self.tracks = np.zeros((self.MAX_TRACKS, K+1, 5))
    self.tracks[:] = np.nan

    name = f"{FeatureHandler.name}_{K}"
    ffi, lib = load_code(name)

    def merge_features_c(tracks, features, empty_idxs):
      lib.merge_features(ffi.cast("double *", tracks.ctypes.data),
                         ffi.cast("double *", features.ctypes.data),
                         ffi.cast("long long *", empty_idxs.ctypes.data))

    #self.merge_features = self.merge_features_python
    self.merge_features = merge_features_c

  def reset(self):
    self.tracks[:] = np.nan

  def merge_features_python(self, tracks, features, empty_idxs):
    empty_idx = 0
    for f in features:
      match_idx = int(f[4])
      if tracks[match_idx, 0, 1] == match_idx and tracks[match_idx, 0 ,2] == 0:
        tracks[match_idx, 0, 0] += 1
        tracks[match_idx, 0, 1] = f[1]
        tracks[match_idx, 0, 2] = 1
        tracks[match_idx, int(tracks[match_idx, 0, 0])] = f
        if tracks[match_idx, 0, 0] == self.K:
          tracks[match_idx, 0, 3] = 1
          if sane(tracks[match_idx]):
            tracks[match_idx, 0, 4] = 1
      else:
        if empty_idx == len(empty_idxs):
          print('need more empty space')
          continue
        tracks[empty_idxs[empty_idx], 0, 0] = 1
        tracks[empty_idxs[empty_idx], 0, 1] = f[1]
        tracks[empty_idxs[empty_idx], 0, 2] = 1
        tracks[empty_idxs[empty_idx], 1] = f
        empty_idx += 1

  def update_tracks(self, features):
    t0 = time.time()
    last_idxs = np.copy(self.tracks[:,0,1])
    real = np.isfinite(last_idxs)
    self.tracks[last_idxs[real].astype(int)] = self.tracks[real]
    mask = np.ones(self.MAX_TRACKS, np.bool)
    mask[last_idxs[real].astype(int)] = 0
    empty_idxs = np.arange(self.MAX_TRACKS)[mask]
    self.tracks[empty_idxs] = np.nan
    self.tracks[:,0,2] = 0
    self.merge_features(self.tracks, features, empty_idxs)

  def handle_features(self, features):
    self.update_tracks(features)
    valid_idxs = self.tracks[:,0,4] == 1
    complete_idxs = self.tracks[:,0,3] == 1
    stale_idxs = self.tracks[:,0,2] == 0
    valid_tracks = self.tracks[valid_idxs]
    self.tracks[complete_idxs] = np.nan
    self.tracks[stale_idxs] = np.nan
    return valid_tracks[:,1:,:4].reshape((len(valid_tracks), self.K*4))



def generate_orient_error_jac(K):
  import sympy as sp
  from common.sympy_helpers import quat_rotate
  x_sym = sp.MatrixSymbol('abr', 3,1)
  dtheta = sp.MatrixSymbol('dtheta', 3,1)
  delta_quat = sp.Matrix(np.ones(4))
  delta_quat[1:,:] = sp.Matrix(0.5*dtheta[0:3,:])
  poses_sym = sp.MatrixSymbol('poses', 7*K,1)
  img_pos_sym = sp.MatrixSymbol('img_positions', 2*K,1)
  alpha, beta, rho = x_sym
  to_c = sp.Matrix(orient.rot_matrix(-np.pi/2, -np.pi/2, 0))
  pos_0 = sp.Matrix(np.array(poses_sym[K*7-7:K*7-4])[:,0])
  q = quat_matrix_l(poses_sym[K*7-4:K*7])*delta_quat
  quat_rot = quat_rotate(*q)
  rot_g_to_0 = to_c*quat_rot.T
  rows = []
  for i in range(K):
    pos_i = sp.Matrix(np.array(poses_sym[i*7:i*7+3])[:,0])
    q = quat_matrix_l(poses_sym[7*i+3:7*i+7])*delta_quat
    quat_rot = quat_rotate(*q)
    rot_g_to_i = to_c*quat_rot.T
    rot_0_to_i = rot_g_to_i*(rot_g_to_0.T)
    trans_0_to_i = rot_g_to_i*(pos_0 - pos_i)
    funct_vec = rot_0_to_i*sp.Matrix([alpha, beta, 1]) + rho*trans_0_to_i
    h1, h2, h3 = funct_vec
    rows.append(h1/h3 - img_pos_sym[i*2 +0])
    rows.append(h2/h3 - img_pos_sym[i*2 + 1])
  img_pos_residual_sym = sp.Matrix(rows)

  # sympy into c
  sympy_functions = []
  sympy_functions.append(('orient_error_jac', img_pos_residual_sym.jacobian(dtheta), [x_sym, poses_sym, img_pos_sym, dtheta]))

  return sympy_functions


if __name__ == "__main__":
  # TODO: get K from argparse
  FeatureHandler.generate_code()