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

import cv2 as cv
import numpy as np
import dlib
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 RecognitionDLIB:

  # https://github.com/davisking/dlib/blob/master/python_examples/face_recognition.py
  # facerec.compute_face_descriptor(img, shape, 100, 0.25)

  def __init__(self, gpu=0):
    self.log = logger_utils.Logger.getLogger()
    
    if gpu > -1:
      cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', '')
      os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu)
    
    self.predictor = dlib.shape_predictor(cfg.DIR_MODELS_DLIB_5PT)
    self.facerec = dlib.face_recognition_model_v1(cfg.DIR_MODELS_DLIB_FACEREC_RESNET)
    
    if gpu > -1:
      os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices  # reset GPU env


  def vec(self, im, bbox, width=100, 
      jitters=cfg.DLIB_FACEREC_JITTERS, padding=cfg.DLIB_FACEREC_PADDING):
    # Converts image and bbox into 128d vector
    # scale the image so the face is always 100x100 pixels

    #self.log.debug('compute scale')
    scale = width / bbox.width
    #im = cv.resize(im, (scale, scale), cv.INTER_LANCZOS4)
    #self.log.debug('resize')
    cv.resize(im, None, fx=scale, fy=scale, interpolation=cv.INTER_LANCZOS4) 
    #self.log.debug('to dlib')
    bbox_dlib = bbox.to_dlib()
    #self.log.debug('precitor')
    face_shape = self.predictor(im, bbox_dlib)
    # vec = self.facerec.compute_face_descriptor(im, face_shape, jitters, padding)
    #self.log.debug('vec')
    vec = self.facerec.compute_face_descriptor(im, face_shape, jitters)
    #vec = self.facerec.compute_face_descriptor(im, face_shape)
    return vec


  def similarity(self, query_enc, known_enc):
    return np.linalg.norm(query_enc - known_enc, axis=1)