text: # -----------------------------------------------------
  # Copyright (c) Shanghai Jiao Tong University. All rights reserved.
  # Written by Jiefeng Li (jeff.lee.sjtu@gmail.com)
  # -----------------------------------------------------
  
  import torch
  import json
  import os
  import numpy as np
  
  ''' Constant Configuration '''
  delta1 = 1
  mu = 1.7
  delta2 = 1.3
  gamma = 22.48
  scoreThreds = 0.15
  matchThreds = 5
  alpha = 0.1
  
  
  def pose_nms(bboxes, pose_preds, pose_scores):
      '''
      Parametric Pose NMS algorithm
      bboxes:         bbox locations list (n, 4)
      bbox_scores:    bbox scores list (n,)
      pose_preds:     pose locations list (n, 50, 2)
      pose_scores:    pose scores list    (n, 50, 1)
      '''
      pose_scores[pose_scores <= scoreThreds] = 1e-5
      pose_scores[pose_scores > 1] = 1
      final_result = []
  
      ori_pose_preds = pose_preds.clone()
      ori_pose_scores = pose_scores.clone()
  
      xmax = bboxes[:, 2]
      xmin = bboxes[:, 0]
      ymax = bboxes[:, 3]
      ymin = bboxes[:, 1]
  
      widths = xmax - xmin
      heights = ymax - ymin
      ref_dists = alpha * np.maximum(widths, heights)
  
      nsamples = bboxes.shape[0]
      human_scores = pose_scores.mean(dim=1)
  
      human_ids = np.arange(nsamples)
      # Do pPose-NMS
      pick = []
      merge_ids = []
      while(human_scores.shape[0] != 0):
          # Pick the one with highest score
          pick_id = torch.argmax(human_scores)
          pick.append(human_ids[pick_id])
          # num_visPart = torch.sum(pose_scores[pick_id] > 0.2)
  
          # Get numbers of match keypoints by calling PCK_match
          ref_dist = ref_dists[human_ids[pick_id]]
          simi = get_parametric_distance(
              pick_id, pose_preds, pose_scores, ref_dist)
          num_match_keypoints = PCK_match(
              pose_preds[pick_id], pose_preds, ref_dist)
  
          # Delete humans who have more than matchThreds keypoints overlap and high similarity
          delete_ids = torch.from_numpy(np.arange(human_scores.shape[0]))[
              (simi > gamma) | (num_match_keypoints >= matchThreds)]
  
          if delete_ids.shape[0] == 0:
              delete_ids = pick_id
          #else:
          #    delete_ids = torch.from_numpy(delete_ids)
  
          merge_ids.append(human_ids[delete_ids])
          pose_preds = np.delete(pose_preds, delete_ids, axis=0)
          pose_scores = np.delete(pose_scores, delete_ids, axis=0)
          human_ids = np.delete(human_ids, delete_ids)
          human_scores = np.delete(human_scores, delete_ids, axis=0)
  
      assert len(merge_ids) == len(pick)
      preds_pick = ori_pose_preds[pick]
      scores_pick = ori_pose_scores[pick]
  
      for j in range(len(pick)):
          ids = np.arange(50)
          max_score = torch.max(scores_pick[j, ids, 0])
  
          if max_score < scoreThreds:
              continue
  
          # Merge poses
          merge_id = merge_ids[j]
          merge_pose, merge_score = p_merge_fast(
              preds_pick[j], ori_pose_preds[merge_id], ori_pose_scores[merge_id], ref_dists[pick[j]])
  
          max_score = torch.max(merge_score[ids])
          if max_score < scoreThreds:
              continue
  
          xmax = max(merge_pose[:, 0])
          xmin = min(merge_pose[:, 0])
          ymax = max(merge_pose[:, 1])
          ymin = min(merge_pose[:, 1])
  
          if (1.5 ** 2 * (xmax - xmin) * (ymax - ymin) < 40 * 40.5):
              continue
  
          final_result.append({
              'keypoints': merge_pose - 0.3,
              'kp_score': merge_score,
              'proposal_score': torch.mean(merge_score) + 1.25 * max(merge_score)
          })
  
      return final_result
  
  
  def filter_result(args):
      score_pick, merge_id, pred_pick, pick, bbox_score_pick = args
      global ori_pose_preds, ori_pose_scores, ref_dists
      ids = np.arange(50)
      max_score = torch.max(score_pick[ids, 0])
  
      if max_score < scoreThreds:
          return None
  
      # Merge poses
      merge_pose, merge_score = p_merge_fast(
          pred_pick, ori_pose_preds[merge_id], ori_pose_scores[merge_id], ref_dists[pick])
  
      max_score = torch.max(merge_score[ids])
      if max_score < scoreThreds:
          return None
  
      xmax = max(merge_pose[:, 0])
      xmin = min(merge_pose[:, 0])
      ymax = max(merge_pose[:, 1])
      ymin = min(merge_pose[:, 1])
  
      if (1.5 ** 2 * (xmax - xmin) * (ymax - ymin) < 40 * 40.5):
          return None
  
      return {
          'keypoints': merge_pose - 0.3,
          'kp_score': merge_score,
          'proposal_score': torch.mean(merge_score) + bbox_score_pick + 1.25 * max(merge_score)
      }
  
  
  def p_merge(ref_pose, cluster_preds, cluster_scores, ref_dist):
      '''
      Score-weighted pose merging
      INPUT:
          ref_pose:       reference pose          -- [50, 2]
          cluster_preds:  redundant poses         -- [n, 50, 2]
          cluster_scores: redundant poses score   -- [n, 50, 1]
          ref_dist:       reference scale         -- Constant
      OUTPUT:
          final_pose:     merged pose             -- [50, 2]
          final_score:    merged score            -- [50]
      '''
      dist = torch.sqrt(torch.sum(
          torch.pow(ref_pose[np.newaxis, :] - cluster_preds, 2),
          dim=2
      ))  # [n, 50]
  
      kp_num = 50
      ref_dist = min(ref_dist, 15)
  
      mask = (dist <= ref_dist)
      final_pose = torch.zeros(kp_num, 2)
      final_score = torch.zeros(kp_num)
  
      if cluster_preds.dim() == 2:
          cluster_preds.unsqueeze_(0)
          cluster_scores.unsqueeze_(0)
      if mask.dim() == 1:
          mask.unsqueeze_(0)
  
      for i in range(kp_num):
          cluster_joint_scores = cluster_scores[:, i][mask[:, i]]  # [k, 1]
          cluster_joint_location = cluster_preds[:, i, :][mask[:, i].unsqueeze(
              -1).repeat(1, 2)].view((torch.sum(mask[:, i]), -1))
  
          # Get an normalized score
          normed_scores = cluster_joint_scores / torch.sum(cluster_joint_scores)
  
          # Merge poses by a weighted sum
          final_pose[i, 0] = torch.dot(
              cluster_joint_location[:, 0], normed_scores.squeeze(-1))
          final_pose[i, 1] = torch.dot(
              cluster_joint_location[:, 1], normed_scores.squeeze(-1))
  
          final_score[i] = torch.dot(cluster_joint_scores.transpose(
              0, 1).squeeze(0), normed_scores.squeeze(-1))
  
      return final_pose, final_score
  
  
  def p_merge_fast(ref_pose, cluster_preds, cluster_scores, ref_dist):
      '''
      Score-weighted pose merging
      INPUT:
          ref_pose:       reference pose          -- [50, 2]
          cluster_preds:  redundant poses         -- [n, 50, 2]
          cluster_scores: redundant poses score   -- [n, 50, 1]
          ref_dist:       reference scale         -- Constant
      OUTPUT:
          final_pose:     merged pose             -- [50, 2]
          final_score:    merged score            -- [50]
      '''
      dist = torch.sqrt(torch.sum(
          torch.pow(ref_pose[np.newaxis, :] - cluster_preds, 2),
          dim=2
      ))
  
      kp_num = 50
      ref_dist = min(ref_dist, 15)
  
      mask = (dist <= ref_dist)
      final_pose = torch.zeros(kp_num, 2)
      final_score = torch.zeros(kp_num)
  
      if cluster_preds.dim() == 2:
          cluster_preds.unsqueeze_(0)
          cluster_scores.unsqueeze_(0)
      if mask.dim() == 1:
          mask.unsqueeze_(0)
  
      # Weighted Merge
      masked_scores = cluster_scores.mul(mask.float().unsqueeze(-1))
      normed_scores = masked_scores / torch.sum(masked_scores, dim=0)
  
      final_pose = torch.mul(
          cluster_preds, normed_scores.repeat(1, 1, 2)).sum(dim=0)
      final_score = torch.mul(masked_scores, normed_scores).sum(dim=0)
      return final_pose, final_score
  
  
  def get_parametric_distance(i, all_preds, keypoint_scores, ref_dist):
      pick_preds = all_preds[i]
      pred_scores = keypoint_scores[i]
      dist = torch.sqrt(torch.sum(
          torch.pow(pick_preds[np.newaxis, :] - all_preds, 2),
          dim=2
      ))
      mask = (dist <= 1)
  
      # Define a keypoints distance
      score_dists = torch.zeros(all_preds.shape[0], 50)
      keypoint_scores.squeeze_()
      if keypoint_scores.dim() == 1:
          keypoint_scores.unsqueeze_(0)
      if pred_scores.dim() == 1:
          pred_scores.unsqueeze_(1)
      # The predicted scores are repeated up to do broadcast
      pred_scores = pred_scores.repeat(1, all_preds.shape[0]).transpose(0, 1)
  
      score_dists[mask] = torch.tanh(
          pred_scores[mask] / delta1) * torch.tanh(keypoint_scores[mask] / delta1)
  
      point_dist = torch.exp((-1) * dist / delta2)
      final_dist = torch.sum(score_dists, dim=1) + mu * \
          torch.sum(point_dist, dim=1)
  
      return final_dist
  
  
  def PCK_match(pick_pred, all_preds, ref_dist):
      dist = torch.sqrt(torch.sum(
          torch.pow(pick_pred[np.newaxis, :] - all_preds, 2),
          dim=2
      ))
      ref_dist = min(ref_dist, 7)
      num_match_keypoints = torch.sum(
          dist / ref_dist <= 1,
          dim=1
      )
  
      return num_match_keypoints
  
  
  def write_json(all_results, outputpath, for_eval=False):
      '''
      all_result: result dict of predictions
      outputpath: output directory
      '''
      json_results = []
      for im_res in all_results:
          im_name = im_res['imgname']
          for human in im_res['result']:
              keypoints = []
              result = {}
              if for_eval:
                  result['image_id'] = int(im_name.split(
                      '/')[-1].split('.')[0].split('_')[-1])
              else:
                  result['image_id'] = im_name.split('/')[-1]
              result['category_id'] = 1
  
              kp_preds = human['keypoints']
              kp_scores = human['kp_score']
              pro_scores = human['proposal_score']
              for n in range(kp_scores.shape[0]):
                  keypoints.append(float(kp_preds[n, 0]))
                  keypoints.append(float(kp_preds[n, 1]))
                  keypoints.append(float(kp_scores[n]))
              result['keypoints'] = keypoints
              result['score'] = float(pro_scores)
  
              json_results.append(result)
  
      with open(os.path.join(outputpath, 'alphapose-results.json'), 'w') as json_file:
          json_file.write(json.dumps(json_results))
  