# --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import xml.etree.ElementTree as ET

import numpy as np

from alpharotate.libs.label_name_dict.label_dict import LabelMap
from alpharotate.utils import tools


class EVAL(object):
  def __init__(self, cfgs):
      self.cfgs = cfgs
      label_map = LabelMap(cfgs)
      self.name_label_map, self.label_name_map = label_map.name2label(), label_map.label2name()

  def write_voc_results_file(self, all_boxes, test_imgid_list, det_save_dir):
    '''

    :param all_boxes: is a list. each item reprensent the detections of a img.
    the detections is a array. shape is [-1, 6]. [category, score, xmin, ymin, xmax, ymax]
    Note that: if none detections in this img. that the detetions is : []

    :param test_imgid_list:
    :param det_save_path:
    :return:
    '''
    for cls, cls_id in self.name_label_map.items():
      if cls == 'back_ground':
        continue
      print("Writing {} VOC resutls file".format(cls))

      tools.makedirs(det_save_dir)
      det_save_path = os.path.join(det_save_dir, "det_"+cls+".txt")
      with open(det_save_path, 'wt') as f:
        for index, img_name in enumerate(test_imgid_list):
          this_img_detections = all_boxes[index]

          if this_img_detections.shape[0] == 0:
            continue

          this_cls_detections = this_img_detections[this_img_detections[:, 0]==cls_id]
          if this_cls_detections.shape[0] == 0:
            continue # this cls has none detections in this img
          for a_det in this_cls_detections:
            f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
                    format(img_name, a_det[1],
                           a_det[2], a_det[3],
                           a_det[4], a_det[5]))  # that is [img_name, score, xmin, ymin, xmax, ymax]

  def parse_rec(self, filename):
    """ Parse a PASCAL VOC xml file """
    tree = ET.parse(filename)
    objects = []
    for obj in tree.findall('object'):
      obj_struct = {}
      obj_struct['name'] = obj.find('name').text
      obj_struct['pose'] = obj.find('pose').text
      obj_struct['truncated'] = int(obj.find('truncated').text)
      obj_struct['difficult'] = int(obj.find('difficult').text)
      bbox = obj.find('bndbox')
      obj_struct['bbox'] = [int(bbox.find('xmin').text),
                            int(bbox.find('ymin').text),
                            int(bbox.find('xmax').text),
                            int(bbox.find('ymax').text)]
      objects.append(obj_struct)

    return objects

  def voc_ap(self, rec, prec, use_07_metric=False):
    """ ap = voc_ap(rec, prec, [use_07_metric])
    Compute VOC AP given precision and recall.
    If use_07_metric is true, uses the
    VOC 07 11 point method (default:False).
    """
    if use_07_metric:
      # 11 point metric
      ap = 0.
      for t in np.arange(0., 1.1, 0.1):
        if np.sum(rec >= t) == 0:
          p = 0
        else:
          p = np.max(prec[rec >= t])
        ap = ap + p / 11.
    else:
      # correct AP calculation
      # first append sentinel values at the end
      mrec = np.concatenate(([0.], rec, [1.]))
      mpre = np.concatenate(([0.], prec, [0.]))

      # compute the precision envelope
      for i in range(mpre.size - 1, 0, -1):
        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])

      # to calculate area under PR curve, look for points
      # where X axis (recall) changes value
      i = np.where(mrec[1:] != mrec[:-1])[0]

      # and sum (\Delta recall) * prec
      ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
    return ap

  def voc_eval(self, detpath, annopath, test_imgid_list, cls_name, ovthresh=0.5,
                   use_07_metric=False, use_diff=False):
    '''

    :param detpath:
    :param annopath:
    :param test_imgid_list: it 's a list that contains the img_name of test_imgs
    :param cls_name:
    :param ovthresh:
    :param use_07_metric:
    :param use_diff:
    :return:
    '''
    # 1. parse xml to get gtboxes

    # read list of images
    imagenames = test_imgid_list

    recs = {}
    for i, imagename in enumerate(imagenames):
      recs[imagename] = self.parse_rec(os.path.join(annopath, imagename+'.xml'))
      # if i % 100 == 0:
      #   print('Reading annotation for {:d}/{:d}'.format(
      #     i + 1, len(imagenames)))

    # 2. get gtboxes for this class.
    class_recs = {}
    num_pos = 0
    # if cls_name == 'person':
    #   print ("aaa")
    for imagename in imagenames:
      R = [obj for obj in recs[imagename] if obj['name'] == cls_name]
      bbox = np.array([x['bbox'] for x in R])
      if use_diff:
        difficult = np.array([False for x in R]).astype(np.bool)
      else:
        difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
      det = [False] * len(R)
      num_pos = num_pos + sum(~difficult)  # ignored the diffcult boxes
      class_recs[imagename] = {'bbox': bbox,
                               'difficult': difficult,
                               'det': det} # det means that gtboxes has already been detected

    # 3. read the detection file
    detfile = os.path.join(detpath, "det_"+cls_name+".txt")
    with open(detfile, 'r') as f:
      lines = f.readlines()

    # for a line. that is [img_name, confidence, xmin, ymin, xmax, ymax]
    splitlines = [x.strip().split(' ') for x in lines]  # a list that include a list
    image_ids = [x[0] for x in splitlines]  # img_id is img_name
    confidence = np.array([float(x[1]) for x in splitlines])
    BB = np.array([[float(z) for z in x[2:]] for x in splitlines])

    nd = len(image_ids) # num of detections. That, a line is a det_box.
    tp = np.zeros(nd)
    fp = np.zeros(nd)

    if BB.shape[0] > 0:
      # sort by confidence
      sorted_ind = np.argsort(-confidence)
      sorted_scores = np.sort(-confidence)
      BB = BB[sorted_ind, :]
      image_ids = [image_ids[x] for x in sorted_ind]  #reorder the img_name

      # go down dets and mark TPs and FPs
      for d in range(nd):
        R = class_recs[image_ids[d]]  # img_id is img_name
        bb = BB[d, :].astype(float)
        ovmax = -np.inf
        BBGT = R['bbox'].astype(float)

        if BBGT.size > 0:
          # compute overlaps
          # intersection
          ixmin = np.maximum(BBGT[:, 0], bb[0])
          iymin = np.maximum(BBGT[:, 1], bb[1])
          ixmax = np.minimum(BBGT[:, 2], bb[2])
          iymax = np.minimum(BBGT[:, 3], bb[3])
          iw = np.maximum(ixmax - ixmin + 1., 0.)
          ih = np.maximum(iymax - iymin + 1., 0.)
          inters = iw * ih

          # union
          uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
                 (BBGT[:, 2] - BBGT[:, 0] + 1.) *
                 (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)

          overlaps = inters / uni
          ovmax = np.max(overlaps)
          jmax = np.argmax(overlaps)

        if ovmax > ovthresh:
          if not R['difficult'][jmax]:
            if not R['det'][jmax]:
              tp[d] = 1.
              R['det'][jmax] = 1
            else:
              fp[d] = 1.
        else:
          fp[d] = 1.

    # 4. get recall, precison and AP
    fp = np.cumsum(fp)
    tp = np.cumsum(tp)
    rec = tp / float(num_pos)
    # avoid divide by zero in case the first detection matches a difficult
    # ground truth
    prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
    ap = self.voc_ap(rec, prec, use_07_metric)

    return rec, prec, ap

  def do_python_eval(self, test_imgid_list, test_annotation_path):
    AP_list = []
    # import matplotlib.pyplot as plt
    # import matplotlib.colors as colors
    # color_list = colors.cnames.keys()[::6]

    for cls, index in self.name_label_map.items():
      if cls == 'back_ground':
        continue
      recall, precision, AP = self.voc_eval(detpath=os.path.join(self.cfgs.EVALUATE_DIR,
                                                                 self.cfgs.VERSION),
                                            test_imgid_list=test_imgid_list,
                                            cls_name=cls,
                                            annopath=test_annotation_path,
                                            use_07_metric=self.cfgs.USE_07_METRIC,
                                            ovthresh=self.cfgs.EVAL_THRESHOLD)

      AP_list += [AP]
      print("cls : {}|| Recall: {} || Precison: {}|| AP: {}".format(cls, recall[-1], precision[-1], AP))
      # plt.plot(recall, precision, label=cls, color=color_list[index])
      # plt.legend(loc='upper right')
      # print(10*"__")
    # plt.show()
    # plt.savefig(cfgs.VERSION+'.jpg')
    print("mAP is : {}".format(np.mean(AP_list)))

  def voc_evaluate_detections(self, all_boxes, test_annotation_path, test_imgid_list):
    '''

    :param all_boxes: is a list. each item reprensent the detections of a img.

    The detections is a array. shape is [-1, 6]. [category, score, xmin, ymin, xmax, ymax]
    Note that: if none detections in this img. that the detetions is : []
    :return:
    '''
    test_imgid_list = [item.split('.')[0] for item in test_imgid_list]

    self.write_voc_results_file(all_boxes, test_imgid_list=test_imgid_list,
                           det_save_dir=os.path.join(self.cfgs.EVALUATE_DIR, self.cfgs.VERSION))
    self.do_python_eval(test_imgid_list, test_annotation_path=test_annotation_path)







