import os
from os.path import join
from pathlib import Path
import math

import cv2 as cv
import numpy as np
import imutils

from app.utils import im_utils, logger_utils
from app.models.bbox import BBox
from app.settings import app_cfg as cfg
from app.settings import types

class Landmarks3D:

  def __init__(self):
    self.log = logger_utils.Logger.getLogger()

  def landmarks(self, im, bbox):
    pass

    
class FaceAlignment3D(Landmarks3D):

  # Estimates 3D facial landmarks
  import face_alignment

  def __init__(self, gpu=0, flip_input=False):
    super().__init__()
    device = f'cuda:{gpu}' if gpu > -1 else 'cpu'
    self.fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device=device, flip_input=flip_input)

  def landmarks(self, im, as_type=str):
    '''Calculates the 3D facial landmarks
    :param im: (numpy.ndarray) image
    :param as_type: (str) or (list) type to return data
    '''
    preds = self.fa.get_landmarks(im)
    # convert to comma separated ints
    # storing data as "[1,2], [3,4]" is larger file size than storing as "1,2,3,4"
    # storing a list object in Pandas seems to result in 30% larger CSV files
    # TODO optimize this
    preds_int = [list(map(int, x)) for x in preds[0]]  # list of ints
    if as_type is str:
      return ','.join([','.join(list(map(str,[x,y]))) for x,y in preds_int])
    else
      return preds_int


  def draw(self, im):
    '''draws landmarks in 3d scene'''

    # TODO
    '''
    import face_alignment
    import numpy as np
    from mpl_toolkits.mplot3d import Axes3D
    import matplotlib.pyplot as plt
    from skimage import io

    # Run the 3D face alignment on a test image, without CUDA.
    fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device='cuda:0', flip_input=True)

    input = io.imread('../test/assets/aflw-test.jpg')
    preds = fa.get_landmarks(input)[-1]

    #TODO: Make this nice
    fig = plt.figure(figsize=plt.figaspect(.5))
    ax = fig.add_subplot(1, 2, 1)
    ax.imshow(input)
    ax.plot(preds[0:17,0],preds[0:17,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
    ax.plot(preds[17:22,0],preds[17:22,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
    ax.plot(preds[22:27,0],preds[22:27,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
    ax.plot(preds[27:31,0],preds[27:31,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
    ax.plot(preds[31:36,0],preds[31:36,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
    ax.plot(preds[36:42,0],preds[36:42,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
    ax.plot(preds[42:48,0],preds[42:48,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
    ax.plot(preds[48:60,0],preds[48:60,1],marker='o',markersize=6,linestyle='-',color='w',lw=2)
    ax.plot(preds[60:68,0],preds[60:68,1],marker='o',markersize=6,linestyle='-',color='w',lw=2) 
    ax.axis('off')

    ax = fig.add_subplot(1, 2, 2, projection='3d')
    surf = ax.scatter(preds[:,0]*1.2,preds[:,1],preds[:,2],c="cyan", alpha=1.0, edgecolor='b')
    ax.plot3D(preds[:17,0]*1.2,preds[:17,1], preds[:17,2], color='blue' )
    ax.plot3D(preds[17:22,0]*1.2,preds[17:22,1],preds[17:22,2], color='blue')
    ax.plot3D(preds[22:27,0]*1.2,preds[22:27,1],preds[22:27,2], color='blue')
    ax.plot3D(preds[27:31,0]*1.2,preds[27:31,1],preds[27:31,2], color='blue')
    ax.plot3D(preds[31:36,0]*1.2,preds[31:36,1],preds[31:36,2], color='blue')
    ax.plot3D(preds[36:42,0]*1.2,preds[36:42,1],preds[36:42,2], color='blue')
    ax.plot3D(preds[42:48,0]*1.2,preds[42:48,1],preds[42:48,2], color='blue')
    ax.plot3D(preds[48:,0]*1.2,preds[48:,1],preds[48:,2], color='blue' )

    ax.view_init(elev=90., azim=90.)
    ax.set_xlim(ax.get_xlim()[::-1])
    plt.show()
    '''
    return im