text: from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import division
  
  import numpy as np
  from collections import defaultdict
  import warnings
  
  try:
      from reid.metrics.rank_cylib.rank_cy import evaluate_cy
      IS_CYTHON_AVAI = True
  except ImportError:
      IS_CYTHON_AVAI = False
      warnings.warn(
          'Cython evaluation (very fast so highly recommended) is '
          'unavailable, now use python evaluation.'
      )
  
  
  def eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
      """Evaluation with cuhk03 metric
      Key: one image for each gallery identity is randomly sampled for each query identity.
      Random sampling is performed num_repeats times.
      """
      num_repeats = 10
      num_q, num_g = distmat.shape
      
      if num_g < max_rank:
          max_rank = num_g
          print('Note: number of gallery samples is quite small, got {}'.format(num_g))
      
      indices = np.argsort(distmat, axis=1)
      matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
  
      # compute cmc curve for each query
      all_cmc = []
      all_AP = []
      num_valid_q = 0. # number of valid query
      
      for q_idx in range(num_q):
          # get query pid and camid
          q_pid = q_pids[q_idx]
          q_camid = q_camids[q_idx]
  
          # remove gallery samples that have the same pid and camid with query
          order = indices[q_idx]
          remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
          keep = np.invert(remove)
  
          # compute cmc curve
          raw_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
          if not np.any(raw_cmc):
              # this condition is true when query identity does not appear in gallery
              continue
  
          kept_g_pids = g_pids[order][keep]
          g_pids_dict = defaultdict(list)
          for idx, pid in enumerate(kept_g_pids):
              g_pids_dict[pid].append(idx)
  
          cmc = 0.
          for repeat_idx in range(num_repeats):
              mask = np.zeros(len(raw_cmc), dtype=np.bool)
              for _, idxs in g_pids_dict.items():
                  # randomly sample one image for each gallery person
                  rnd_idx = np.random.choice(idxs)
                  mask[rnd_idx] = True
              masked_raw_cmc = raw_cmc[mask]
              _cmc = masked_raw_cmc.cumsum()
              _cmc[_cmc > 1] = 1
              cmc += _cmc[:max_rank].astype(np.float32)
          
          cmc /= num_repeats
          all_cmc.append(cmc)
          # compute AP
          num_rel = raw_cmc.sum()
          tmp_cmc = raw_cmc.cumsum()
          tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
          tmp_cmc = np.asarray(tmp_cmc) * raw_cmc
          AP = tmp_cmc.sum() / num_rel
          all_AP.append(AP)
          num_valid_q += 1.
  
      assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
  
      all_cmc = np.asarray(all_cmc).astype(np.float32)
      all_cmc = all_cmc.sum(0) / num_valid_q
      mAP = np.mean(all_AP)
  
      return all_cmc, mAP
  
  
  def eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
      """Evaluation with market1501 metric
      Key: for each query identity, its gallery images from the same camera view are discarded.
      """
      num_q, num_g = distmat.shape
      
      if num_g < max_rank:
          max_rank = num_g
          print('Note: number of gallery samples is quite small, got {}'.format(num_g))
      
      indices = np.argsort(distmat, axis=1)
      matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
  
      # compute cmc curve for each query
      all_cmc = []
      all_AP = []
      num_valid_q = 0. # number of valid query
      
      for q_idx in range(num_q):
          # get query pid and camid
          q_pid = q_pids[q_idx]
          q_camid = q_camids[q_idx]
  
          # remove gallery samples that have the same pid and camid with query
          order = indices[q_idx]
          remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
          keep = np.invert(remove)
  
          # compute cmc curve
          raw_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
          if not np.any(raw_cmc):
              # this condition is true when query identity does not appear in gallery
              continue
  
          cmc = raw_cmc.cumsum()
          cmc[cmc > 1] = 1
  
          all_cmc.append(cmc[:max_rank])
          num_valid_q += 1.
  
          # compute average precision
          # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
          num_rel = raw_cmc.sum()
          tmp_cmc = raw_cmc.cumsum()
          tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
          tmp_cmc = np.asarray(tmp_cmc) * raw_cmc
          AP = tmp_cmc.sum() / num_rel
          all_AP.append(AP)
  
      assert num_valid_q > 0, 'Error: all query identities do not appear in gallery'
  
      all_cmc = np.asarray(all_cmc).astype(np.float32)
      all_cmc = all_cmc.sum(0) / num_valid_q
      mAP = np.mean(all_AP)
  
      return all_cmc, mAP
  
  
  def evaluate_py(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03):
      if use_metric_cuhk03:
          return eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
      else:
          return eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
  
  
  def evaluate_rank(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50,
                    use_metric_cuhk03=False, use_cython=True):
      """Evaluates CMC rank.
  
      Args:
          distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).
          q_pids (numpy.ndarray): 1-D array containing person identities
              of each query instance.
          g_pids (numpy.ndarray): 1-D array containing person identities
              of each gallery instance.
          q_camids (numpy.ndarray): 1-D array containing camera views under
              which each query instance is captured.
          g_camids (numpy.ndarray): 1-D array containing camera views under
              which each gallery instance is captured.
          max_rank (int, optional): maximum CMC rank to be computed. Default is 50.
          use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.
              Default is False. This should be enabled when using cuhk03 classic split.
          use_cython (bool, optional): use cython code for evaluation. Default is True.
              This is highly recommended as the cython code can speed up the cmc computation
              by more than 10x. This requires Cython to be installed.
      """
      if use_cython and IS_CYTHON_AVAI:
          return evaluate_cy(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03)
      else:
          return evaluate_py(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, use_metric_cuhk03)