--- ../pycocotools/PythonAPI/pycocotools/cocoeval.py    2020-04-02 12:41:09.770697735 +0200
+++ pycoco.py   2021-02-16 13:16:39.248434628 +0100
@@ -1,12 +1,11 @@
-__author__ = 'tsungyi'
-
 import numpy as np
 import datetime
 import time
 from collections import defaultdict
-from . import mask as maskUtils
+from pycocotools import mask as maskUtils
 import copy
 
+
 class COCOeval:
     # Interface for evaluating detection on the Microsoft COCO dataset.
     #
@@ -68,7 +67,6 @@
             print('iouType not specified. use default iouType segm')
         self.cocoGt   = cocoGt              # ground truth COCO API
         self.cocoDt   = cocoDt              # detections COCO API
-        self.params   = {}                  # evaluation parameters
         self.evalImgs = defaultdict(list)   # per-image per-category evaluation results [KxAxI] elements
         self.eval     = {}                  # accumulated evaluation results
         self._gts = defaultdict(list)       # gt for evaluation
@@ -203,21 +202,26 @@
         if len(gts) == 0 or len(dts) == 0:
             return []
         ious = np.zeros((len(dts), len(gts)))
-        sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62,.62, 1.07, 1.07, .87, .87, .89, .89])/10.0
+        sigmas = p.kpt_oks_sigmas
         vars = (sigmas * 2)**2
         k = len(sigmas)
         # compute oks between each detection and ground truth object
         for j, gt in enumerate(gts):
             # create bounds for ignore regions(double the gt bbox)
             g = np.array(gt['keypoints'])
-            xg = g[0::3]; yg = g[1::3]; vg = g[2::3]
+            xg = g[0::3];
+            yg = g[1::3];
+            vg = g[2::3]
             k1 = np.count_nonzero(vg > 0)
             bb = gt['bbox']
-            x0 = bb[0] - bb[2]; x1 = bb[0] + bb[2] * 2
-            y0 = bb[1] - bb[3]; y1 = bb[1] + bb[3] * 2
+            x0 = bb[0] - bb[2];
+            x1 = bb[0] + bb[2] * 2
+            y0 = bb[1] - bb[3];
+            y1 = bb[1] + bb[3] * 2
             for i, dt in enumerate(dts):
                 d = np.array(dt['keypoints'])
-                xd = d[0::3]; yd = d[1::3]
+                xd = d[0::3];
+                yd = d[1::3]
                 if k1>0:
                     # measure the per-keypoint distance if keypoints visible
                     dx = xd - xg
@@ -334,6 +338,7 @@
         M           = len(p.maxDets)
         precision   = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories
         recall      = -np.ones((T,K,A,M))
+        scores = -np.ones((T, R, K, A, M))
 
         # create dictionary for future indexing
         _pe = self._paramsEval
@@ -364,6 +369,7 @@
                     # different sorting method generates slightly different results.
                     # mergesort is used to be consistent as Matlab implementation.
                     inds = np.argsort(-dtScores, kind='mergesort')
+                    dtScoresSorted = dtScores[inds]
 
                     dtm  = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds]
                     dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet]  for e in E], axis=1)[:,inds]
@@ -383,6 +389,7 @@
                         rc = tp / npig
                         pr = tp / (fp+tp+np.spacing(1))
                         q  = np.zeros((R,))
+                        ss = np.zeros((R,))
 
                         if nd:
                             recall[t,k,a,m] = rc[-1]
@@ -391,7 +398,8 @@
 
                         # numpy is slow without cython optimization for accessing elements
                         # use python array gets significant speed improvement
-                        pr = pr.tolist(); q = q.tolist()
+                        pr = pr.tolist();
+                        q = q.tolist()
 
                         for i in range(nd-1, 0, -1):
                             if pr[i] > pr[i-1]:
@@ -401,15 +409,18 @@
                         try:
                             for ri, pi in enumerate(inds):
                                 q[ri] = pr[pi]
+                                ss[ri] = dtScoresSorted[pi]
                         except:
                             pass
                         precision[t,:,k,a,m] = np.array(q)
+                        scores[t, :, k, a, m] = np.array(ss)
         self.eval = {
             'params': p,
             'counts': [T, R, K, A, M],
             'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
             'precision': precision,
             'recall':   recall,
+            'scores': scores,
         }
         toc = time.time()
         print('DONE (t={:0.2f}s).'.format( toc-tic))
@@ -419,7 +430,12 @@
         Compute and display summary metrics for evaluation results.
         Note this functin can *only* be applied on the default parameter setting
         '''
+
+        self.per_class_precisions = []
+        self.ap_per_class_columns = []
+
         def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ):
+            print(ap, iouThr, areaRng, maxDets)
             p = self.params
             iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}'
             titleStr = 'Average Precision' if ap == 1 else 'Average Recall'
@@ -448,8 +464,22 @@
                 mean_s = -1
             else:
                 mean_s = np.mean(s[s>-1])
+
+                # cacluate AP(average precision) for each category
+                num_classes = 80
+                avg_ap = 0.0
+                if ap == 1:
+                    pcp = {}
+                    for i, c in enumerate(sorted(list(self.cocoDt.cats.values()), key=lambda x: x['id'])):
+                        pcp[c['name']] = np.mean(s[:, :, i, :])
+
+                    self.per_class_precisions.append(pcp)
+                    self.ap_per_class_columns.append(f"ap={ap} iouThr={iouThr or '0.5:0.95'} area={areaRng} maxDets={maxDets}")
+
+
             print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))
             return mean_s
+
         def _summarizeDets():
             stats = np.zeros((12,))
             stats[0] = _summarize(1)
@@ -494,12 +527,13 @@
     '''
     Params for coco evaluation api
     '''
+
     def setDetParams(self):
         self.imgIds = []
         self.catIds = []
         # np.arange causes trouble.  the data point on arange is slightly larger than the true value
-        self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True)
-        self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True)
+        self.iouThrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05) + 1), endpoint=True)
+        self.recThrs = np.linspace(.0, 1.00, int(np.round((1.00 - .0) / .01) + 1), endpoint=True)
         self.maxDets = [1, 10, 100]
         self.areaRng = [[0 ** 2, 1e5 ** 2], [0 ** 2, 32 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]]
         self.areaRngLbl = ['all', 'small', 'medium', 'large']
@@ -509,12 +543,14 @@
         self.imgIds = []
         self.catIds = []
         # np.arange causes trouble.  the data point on arange is slightly larger than the true value
-        self.iouThrs = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True)
-        self.recThrs = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True)
+        self.iouThrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05) + 1), endpoint=True)
+        self.recThrs = np.linspace(.0, 1.00, int(np.round((1.00 - .0) / .01) + 1), endpoint=True)
         self.maxDets = [20]
         self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]]
         self.areaRngLbl = ['all', 'medium', 'large']
         self.useCats = 1
+        self.kpt_oks_sigmas = np.array(
+            [.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0
 
     def __init__(self, iouType='segm'):
         if iouType == 'segm' or iouType == 'bbox':