# -*- coding: utf-8 -*-
"""
Class definition of YOLO_v3 style detection model on image and video
"""

import colorsys

import numpy as np
from keras import backend as K
from keras.models import load_model
from keras.layers import Input

from .yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from .yolo3.utils import letterbox_image
import os
from keras.utils import multi_gpu_model


class YOLO(object):
  _defaults = {
    "model_path": 'project/detection/detectors/yolo3_keras/model_data/yolo.h5',
    "anchors_path": 'project/detection/detectors/yolo3_keras/model_data/yolo_anchors.txt',
    "classes_path": 'project/detection/detectors/yolo3_keras/model_data/coco_classes.txt',
    "score": 0.3,
    "iou": 0.45,
    "model_image_size": (416, 416),
    "gpu_num": 1,
  }

  @classmethod
  def get_defaults(cls, n):
    if n in cls._defaults:
      return cls._defaults[n]
    else:
      return "Unrecognized attribute name '" + n + "'"

  def __init__(self, **kwargs):
    self.__dict__.update(self._defaults)  # set up default values
    self.__dict__.update(kwargs)  # and update with user overrides
    self.class_names = self._get_class()
    self.anchors = self._get_anchors()
    self.sess = K.get_session()
    self.boxes, self.scores, self.classes = self.generate()

  def _get_class(self):
    classes_path = os.path.expanduser(self.classes_path)
    with open(classes_path) as f:
      class_names = f.readlines()
    class_names = [c.strip() for c in class_names]
    return class_names

  def _get_anchors(self):
    anchors_path = os.path.expanduser(self.anchors_path)
    with open(anchors_path) as f:
      anchors = f.readline()
    anchors = [float(x) for x in anchors.split(',')]
    return np.array(anchors).reshape(-1, 2)

  def generate(self):
    model_path = os.path.expanduser(self.model_path)
    assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'

    # Load model, or construct model and load weights.
    num_anchors = len(self.anchors)
    num_classes = len(self.class_names)
    is_tiny_version = num_anchors == 6  # default setting
    try:
      self.yolo_model = load_model(model_path, compile=False)
    except:
      self.yolo_model = tiny_yolo_body(Input(shape=(None, None, 3)), num_anchors // 2, num_classes) \
        if is_tiny_version else yolo_body(Input(shape=(None, None, 3)), num_anchors // 3, num_classes)
      self.yolo_model.load_weights(self.model_path)  # make sure model, anchors and classes match
    else:
      assert self.yolo_model.layers[-1].output_shape[-1] == \
             num_anchors / len(self.yolo_model.output) * (num_classes + 5), \
        'Mismatch between model and given anchor and class sizes'

    # print('{} model, anchors, and classes loaded.'.format(model_path))

    # Generate colors for drawing bounding boxes.
    hsv_tuples = [(x / len(self.class_names), 1., 1.)
                  for x in range(len(self.class_names))]
    self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
    self.colors = list(
      map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
          self.colors))
    np.random.seed(10101)  # Fixed seed for consistent colors across runs.
    np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.
    np.random.seed(None)  # Reset seed to default.

    # Generate output tensor targets for filtered bounding boxes.
    self.input_image_shape = K.placeholder(shape=(2,))
    if self.gpu_num >= 2:
      self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
    boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
                                       len(self.class_names), self.input_image_shape,
                                       score_threshold=self.score, iou_threshold=self.iou)
    return boxes, scores, classes

  def detect_image(self, image):

    if self.model_image_size != (None, None):
      assert self.model_image_size[0] % 32 == 0, 'Multiples of 32 required'
      assert self.model_image_size[1] % 32 == 0, 'Multiples of 32 required'
      boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
    else:
      new_image_size = (image.width - (image.width % 32),
                        image.height - (image.height % 32))
      boxed_image = letterbox_image(image, new_image_size)
    image_data = np.array(boxed_image, dtype='float32')


    image_data /= 255.
    image_data = np.expand_dims(image_data, 0)  # Add batch dimension.

    out_boxes, out_scores, out_classes = self.sess.run(
      [self.boxes, self.scores, self.classes],
      feed_dict={
        self.yolo_model.input: image_data,
        self.input_image_shape: [image.size[1], image.size[0]],
        K.learning_phase(): 0
      })


    return out_boxes, out_scores, out_classes

  def close_session(self):
    self.sess.close()
