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

import cv2 as cv
import numpy as np
import imutils
import operator

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 DetectorMTCNN:
  
  # https://github.com/ipazc/mtcnn
  # pip install mtcnn

  dnn_size = (300, 300)

  def __init__(self, size=(400,400)):
    from mtcnn.mtcnn import MTCNN
    self.detector = MTCNN()

  def detect(self, im, size=(400,400), conf_thresh=None, pyramids=None, largest=False):
    '''Detects face using MTCNN and returns (list) of BBox
    :param im: (numpy.ndarray) image
    :returns list of BBox
    '''
    bboxes = []
    #conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
    #pyramids = self.pyramids if pyramids is None else pyramids
    dnn_size = self.dnn_size if size is None else size

    im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
    dim = im.shape[:2][::-1]
    dets = self.detector.detect_faces(im)
    for det in dets:
      rect = det['box']
      #keypoints = det['keypoints']  # not using here. see 'face_landmarks.py'
      bbox = BBox.from_xywh_dim(*rect, dim)
      bboxes.append(bbox)

    if largest and len(bboxes) > 1:
      # only keep largest
      bboxes.sort(key=operator.attrgetter('area'), reverse=True)
      bboxes = [bboxes[0]]

    return bboxes


class DetectorHaar:

  im_size = (400, 400)
  cascade_name = types.HaarCascade.FRONTAL

  def __init__(self, cascade=types.HaarCascade.FRONTAL):
    self.log = logger_utils.Logger.getLogger()

  def detect(self, im, scale_factor=1.05, overlaps=5):
    pass
    

class DetectorDLIBCNN:
  

  dnn_size = (300, 300)
  pyramids = 0
  conf_thresh = 0.85

  def __init__(self, gpu=0):
    import dlib
    self.log = logger_utils.Logger.getLogger()
    cuda_visible_devices = os.getenv('CUDA_VISIBLE_DEVICES', '')
    os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu)
    self.log.info('load model: {}'.format(cfg.DIR_MODELS_DLIB_CNN))
    self.detector = dlib.cnn_face_detection_model_v1(cfg.DIR_MODELS_DLIB_CNN)
    os.environ['CUDA_VISIBLE_DEVICES'] = cuda_visible_devices  # reset

  def detect(self, im, size=None, conf_thresh=None, pyramids=None, largest=False):
    bboxes = []
    conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
    pyramids = self.pyramids if pyramids is None else pyramids
    dnn_size = self.dnn_size if size is None else size
    # resize image
    im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
    dim = im.shape[:2][::-1]
    im = im_utils.bgr2rgb(im)  # convert to RGB for dlib
    # run detector
    mmod_rects = self.detector(im, pyramids)
    # sort results
    for mmod_rect in mmod_rects:
      if mmod_rect.confidence > conf_thresh:
        bbox = BBox.from_dlib_dim(mmod_rect.rect, dim)
        bboxes.append(bbox)

    if largest and len(bboxes) > 1:
      # only keep largest
      bboxes.sort(key=operator.attrgetter('area'), reverse=True)
      bboxes = [bboxes[0]]

    return bboxes


class DetectorDLIBHOG:

  size = (320, 240)
  pyramids = 0
  conf_thresh = 0.85

  def __init__(self):
    import dlib
    self.log = logger_utils.Logger.getLogger()
    self.detector = dlib.get_frontal_face_detector()

  def detect(self, im, size=None, conf_thresh=None, pyramids=0, largest=False):
    conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
    dnn_size = self.size if size is None else size
    pyramids = self.pyramids if pyramids is None else pyramids
    
    im = im_utils.resize(im, width=dnn_size[0], height=dnn_size[1])
    dim = im.shape[:2][::-1]
    im = im_utils.bgr2rgb(im)  # ?
    hog_results = self.detector.run(im, pyramids)
    
    bboxes = []
    if len(hog_results[0]) > 0:
      for rect, score, direction in zip(*hog_results):
        if score > conf_thresh:
          bbox = BBox.from_dlib_dim(rect, dim)
          bboxes.append(bbox)
    
    if largest and len(bboxes) > 1:
      # only keep largest
      bboxes.sort(key=operator.attrgetter('area'), reverse=True)
      bboxes = [bboxes[0]]

    return bboxes

class DetectorCVDNN:

  dnn_scale = 1.0  # fixed
  dnn_mean = (104.0, 177.0, 123.0)  # fixed
  dnn_crop = False  # crop or force resize
  size = (300, 300)
  conf_thresh = 0.85
  
  def __init__(self):
    import dlib
    fp_prototxt = join(cfg.DIR_MODELS_CAFFE, 'face_detect', 'opencv_face_detector.prototxt')
    fp_model = join(cfg.DIR_MODELS_CAFFE, 'face_detect', 'opencv_face_detector.caffemodel')
    self.net = cv.dnn.readNet(fp_prototxt, fp_model)
    self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
    self.net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)

  def detect(self, im, size=None, conf_thresh=None, largest=False, pyramids=None):
    """Detects faces and returns (list) of (BBox)"""
    conf_thresh = self.conf_thresh if conf_thresh is None else conf_thresh
    dnn_size = self.size if size is None else size
    im = cv.resize(im, dnn_size)
    blob = cv.dnn.blobFromImage(im, self.dnn_scale, dnn_size, self.dnn_mean)
    self.net.setInput(blob)
    net_outputs = self.net.forward()

    bboxes = []
    for i in range(0, net_outputs.shape[2]):
      conf = net_outputs[0, 0, i, 2]
      if conf > conf_thresh:
        rect_norm = net_outputs[0, 0, i, 3:7]
        bboxes.append(BBox(*rect_norm))
    
    if largest and len(bboxes) > 1:
      # only keep largest
      bboxes.sort(key=operator.attrgetter('area'), reverse=True)
      bboxes = [bboxes[0]]

    return bboxes