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, opt_gpu=0):
    self.log = logger_utils.Logger.getLogger()
    if opt_gpu > 0:
      cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', '')
      os.environ['CUDA_VISIBLE_DEVICES'] = str(opt_gpu)
      self.predictor = dlib.shape_predictor(cfg.DIR_MODELS_DLIB_5PT)
      self.facerec = dlib.face_recognition_model_v1(cfg.DIR_MODELS_DLIB_FACEREC_RESNET)
      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

    scale = width / bbox.width
    im = cv.resize(im, (scale, scale), interploation=cv.INTER_LANCZOS4)
    bbox_dlib = bbox.to_dlib()
    face_shape = self.predictor(im, bbox_dlib)
    vec = self.facerec.compute_face_descriptor(im, face_shape, jitters, padding)
    return vec


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