modified_coco/cocoeval.py

__author__ = 'tsungyi, [email protected]'

# This is a modified version of the original cocoeval.py
# In this version we are able to return the TP, FP, and FN values 
# along with the other default metrics.

import numpy as np
import datetime
import time
from collections import defaultdict
from pycocotools import mask as maskUtils
import copy

class COCOeval:
    # Interface for evaluating detection on the Microsoft COCO dataset.
    #
    # The usage for CocoEval is as follows:
    #  cocoGt=..., cocoDt=...       # load dataset and results
    #  E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object
    #  E.params.recThrs = ...;      # set parameters as desired
    #  E.evaluate();                # run per image evaluation
    #  E.accumulate();              # accumulate per image results
    #  E.summarize();               # display summary metrics of results
    # For example usage see evalDemo.m and http://mscoco.org/.
    #
    # The evaluation parameters are as follows (defaults in brackets):
    #  imgIds     - [all] N img ids to use for evaluation
    #  catIds     - [all] K cat ids to use for evaluation
    #  iouThrs    - [.5:.05:.95] T=10 IoU thresholds for evaluation
    #  recThrs    - [0:.01:1] R=101 recall thresholds for evaluation
    #  areaRng    - [...] A=4 object area ranges for evaluation
    #  maxDets    - [1 10 100] M=3 thresholds on max detections per image
    #  iouType    - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints'
    #  iouType replaced the now DEPRECATED useSegm parameter.
    #  useCats    - [1] if true use category labels for evaluation
    # Note: if useCats=0 category labels are ignored as in proposal scoring.
    # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified.
    #
    # evaluate(): evaluates detections on every image and every category and
    # concats the results into the "evalImgs" with fields:
    #  dtIds      - [1xD] id for each of the D detections (dt)
    #  gtIds      - [1xG] id for each of the G ground truths (gt)
    #  dtMatches  - [TxD] matching gt id at each IoU or 0
    #  gtMatches  - [TxG] matching dt id at each IoU or 0
    #  dtScores   - [1xD] confidence of each dt
    #  gtIgnore   - [1xG] ignore flag for each gt
    #  dtIgnore   - [TxD] ignore flag for each dt at each IoU
    #
    # accumulate(): accumulates the per-image, per-category evaluation
    # results in "evalImgs" into the dictionary "eval" with fields:
    #  params     - parameters used for evaluation
    #  date       - date evaluation was performed
    #  counts     - [T,R,K,A,M] parameter dimensions (see above)
    #  precision  - [TxRxKxAxM] precision for every evaluation setting
    #  recall     - [TxKxAxM] max recall for every evaluation setting
    #  TP         - [TxKxAxM] number of true positives for every eval setting  [NEW]
    #  FP         - [TxKxAxM] number of false positives for every eval setting [NEW]
    #  FN         - [TxKxAxM] number of false negatives for every eval setting [NEW]
    # Note: precision and recall==-1 for settings with no gt objects.
    #
    # See also coco, mask, pycocoDemo, pycocoEvalDemo
    #
    # Microsoft COCO Toolbox.      version 2.0
    # Data, paper, and tutorials available at:  http://mscoco.org/
    # Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
    # Licensed under the Simplified BSD License [see coco/license.txt]
    def __init__(self, cocoGt=None, cocoDt=None, iouType='segm'):
        '''
        Initialize CocoEval using coco APIs for gt and dt
        :param cocoGt: coco object with ground truth annotations
        :param cocoDt: coco object with detection results
        :return: None
        '''
        if not iouType:
            print('iouType not specified. use default iouType segm')
        self.cocoGt   = cocoGt              # ground truth COCO API
        self.cocoDt   = cocoDt              # detections COCO API
        self.evalImgs = defaultdict(list)   # per-image per-category evaluation results [KxAxI] elements
        self.eval     = {}                  # accumulated evaluation results
        self._gts = defaultdict(list)       # gt for evaluation
        self._dts = defaultdict(list)       # dt for evaluation
        self.params = Params(iouType=iouType) # parameters
        self._paramsEval = {}               # parameters for evaluation
        self.stats = []                     # result summarization
        self.ious = {}                      # ious between all gts and dts
        if not cocoGt is None:
            self.params.imgIds = sorted(cocoGt.getImgIds())
            self.params.catIds = sorted(cocoGt.getCatIds())


    def _prepare(self):
        '''
        Prepare ._gts and ._dts for evaluation based on params
        :return: None
        '''
        def _toMask(anns, coco):
            # modify ann['segmentation'] by reference
            for ann in anns:
                rle = coco.annToRLE(ann)
                ann['segmentation'] = rle
        p = self.params
        if p.useCats:
            gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
            dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
        else:
            gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))
            dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))

        # convert ground truth to mask if iouType == 'segm'
        if p.iouType == 'segm':
            _toMask(gts, self.cocoGt)
            _toMask(dts, self.cocoDt)
        # set ignore flag
        for gt in gts:
            gt['ignore'] = gt['ignore'] if 'ignore' in gt else 0
            gt['ignore'] = 'iscrowd' in gt and gt['iscrowd']
            if p.iouType == 'keypoints':
                gt['ignore'] = (gt['num_keypoints'] == 0) or gt['ignore']
        self._gts = defaultdict(list)       # gt for evaluation
        self._dts = defaultdict(list)       # dt for evaluation
        for gt in gts:
            self._gts[gt['image_id'], gt['category_id']].append(gt)
        for dt in dts:
            self._dts[dt['image_id'], dt['category_id']].append(dt)
        self.evalImgs = defaultdict(list)   # per-image per-category evaluation results
        self.eval     = {}                  # accumulated evaluation results

    def evaluate(self):
        '''
        Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
        :return: None
        '''
        tic = time.time()
        print('Running per image evaluation...')
        p = self.params
        # add backward compatibility if useSegm is specified in params
        if not p.useSegm is None:
            p.iouType = 'segm' if p.useSegm == 1 else 'bbox'
            print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType))
        print('Evaluate annotation type *{}*'.format(p.iouType))
        p.imgIds = list(np.unique(p.imgIds))
        if p.useCats:
            p.catIds = list(np.unique(p.catIds))
        p.maxDets = sorted(p.maxDets)
        self.params=p

        self._prepare()
        # loop through images, area range, max detection number
        catIds = p.catIds if p.useCats else [-1]

        if p.iouType == 'segm' or p.iouType == 'bbox':
            computeIoU = self.computeIoU
        elif p.iouType == 'keypoints':
            computeIoU = self.computeOks
        self.ious = {(imgId, catId): computeIoU(imgId, catId) \
                        for imgId in p.imgIds
                        for catId in catIds}

        evaluateImg = self.evaluateImg
        maxDet = p.maxDets[-1]
        self.evalImgs = [evaluateImg(imgId, catId, areaRng, maxDet)
                 for catId in catIds
                 for areaRng in p.areaRng
                 for imgId in p.imgIds
             ]
        self._paramsEval = copy.deepcopy(self.params)
        toc = time.time()
        print('DONE (t={:0.2f}s).'.format(toc-tic))

    def computeIoU(self, imgId, catId):
        p = self.params
        if p.useCats:
            gt = self._gts[imgId,catId]
            dt = self._dts[imgId,catId]
        else:
            gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]]
            dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]]
        if len(gt) == 0 and len(dt) ==0:
            return []
        inds = np.argsort([-d['score'] for d in dt], kind='mergesort')
        dt = [dt[i] for i in inds]
        if len(dt) > p.maxDets[-1]:
            dt=dt[0:p.maxDets[-1]]

        if p.iouType == 'segm':
            g = [g['segmentation'] for g in gt]
            d = [d['segmentation'] for d in dt]
        elif p.iouType == 'bbox':
            g = [g['bbox'] for g in gt]
            d = [d['bbox'] for d in dt]
        else:
            raise Exception('unknown iouType for iou computation')

        # compute iou between each dt and gt region
        iscrowd = [int(o['iscrowd']) for o in gt]
        ious = maskUtils.iou(d,g,iscrowd)
        return ious

    def computeOks(self, imgId, catId):
        p = self.params
        # dimention here should be Nxm
        gts = self._gts[imgId, catId]
        dts = self._dts[imgId, catId]
        inds = np.argsort([-d['score'] for d in dts], kind='mergesort')
        dts = [dts[i] for i in inds]
        if len(dts) > p.maxDets[-1]:
            dts = dts[0:p.maxDets[-1]]
        # if len(gts) == 0 and len(dts) == 0:
        if len(gts) == 0 or len(dts) == 0:
            return []
        ious = np.zeros((len(dts), len(gts)))
        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]
            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
            for i, dt in enumerate(dts):
                d = np.array(dt['keypoints'])
                xd = d[0::3]; yd = d[1::3]
                if k1>0:
                    # measure the per-keypoint distance if keypoints visible
                    dx = xd - xg
                    dy = yd - yg
                else:
                    # measure minimum distance to keypoints in (x0,y0) & (x1,y1)
                    z = np.zeros((k))
                    dx = np.max((z, x0-xd),axis=0)+np.max((z, xd-x1),axis=0)
                    dy = np.max((z, y0-yd),axis=0)+np.max((z, yd-y1),axis=0)
                e = (dx**2 + dy**2) / vars / (gt['area']+np.spacing(1)) / 2
                if k1 > 0:
                    e=e[vg > 0]
                ious[i, j] = np.sum(np.exp(-e)) / e.shape[0]
        return ious
    
    def is_bbox1_inside_bbox2(self, bbox1, bbox2):
        '''
        Check if bbox1 is inside bbox2. Bbox is in the format [x, y, w, h]
        Returns:
            - True if bbox1 is inside bbox2, False otherwise
            - How much bbox1 is inside bbox2 (number between 0 and 1)
        '''
        x1_1, y1_1, w1_1, h1_1 = bbox1
        x1_2, y1_2, w1_2, h1_2 = bbox2

        # Convert xywh to (x, y, x2, y2) format
        x2_1, y2_1 = x1_1 + w1_1, y1_1 + h1_1
        x2_2, y2_2 = x1_2 + w1_2, y1_2 + h1_2

        # Calculate the coordinates of the intersection rectangle
        x_left, y_top = max(x1_1, x1_2), max(y1_1, y1_2)
        x_right, y_bottom = min(x2_1, x2_2), min(y2_1, y2_2)
        print(f"{x_left=}, {x_right=}, {y_top=}, {y_bottom=}")
        if x_right < x_left or y_bottom < y_top:
            return False, 0

        intersection_area = (x_right - x_left) * (y_bottom - y_top)      
        print(f"{intersection_area=}")  
        return True, intersection_area / (w1_1 * h1_1)

    def evaluateImg(self, imgId, catId, aRng, maxDet):
        '''
        perform evaluation for single category and image
        :return: dict (single image results)
        '''
        p = self.params
        if p.useCats:
            gt = self._gts[imgId,catId]
            dt = self._dts[imgId,catId]
        else:
            gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]]
            dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]]
        if len(gt) == 0 and len(dt) ==0:
            return None

        for g in gt:
            if g['ignore'] or (g['area']<aRng[0] or g['area']>aRng[1]):
                g['_ignore'] = 1
            else:
                g['_ignore'] = 0

        # sort dt highest score first, sort gt ignore last
        gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort')
        gt = [gt[i] for i in gtind]
        dtind = np.argsort([-d['score'] for d in dt], kind='mergesort')
        dt = [dt[i] for i in dtind[0:maxDet]]
        iscrowd = [int(o['iscrowd']) for o in gt]
        # load computed ious
        ious = self.ious[imgId, catId][:, gtind] if len(self.ious[imgId, catId]) > 0 else self.ious[imgId, catId]

        T = len(p.iouThrs)
        G = len(gt)
        D = len(dt)
        gtm  = np.zeros((T,G))
        dtm  = np.zeros((T,D))
        gtIg = np.array([g['_ignore'] for g in gt])
        dtIg = np.zeros((T,D))
        dtDup = np.zeros((T,D))

        if not len(ious)==0:
            for tind, t in enumerate(p.iouThrs):
                for dind, d in enumerate(dt):
                    # information about best match so far (m=-1 -> unmatched)
                    iou = min([t,1-1e-10])
                    m   = -1
                    for gind, g in enumerate(gt):
                        # if this gt already matched, iou>iouThr, and not a crowd
                        # store detection as duplicate
                        if gtm[tind,gind]>0 and ious[dind,gind]>t and not iscrowd[gind]:
                            dtDup[tind, dind] = d['id']
                        # if this gt already matched, and not a crowd, continue
                        if gtm[tind,gind]>0 and not iscrowd[gind]:
                            continue
                        # if dt matched to reg gt, and on ignore gt, stop
                        if m > -1 and gtIg[m]==0 and gtIg[gind]==1:
                            break
                        # continue to next gt unless better match made
                        if ious[dind,gind] < iou:
                            continue
                        # if match successful and best so far, store appropriately
                        iou=ious[dind,gind]
                        m=gind
                    # if match made store id of match for both dt and gt
                    if m ==-1:
                        continue
                    dtIg[tind,dind] = gtIg[m]
                    dtm[tind,dind]  = gt[m]['id']
                    gtm[tind,m]     = d['id']
        # set unmatched detections outside of area range to ignore
        a = np.array([d['area']<aRng[0] or d['area']>aRng[1] for d in dt]).reshape((1, len(dt)))
        dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0)))
        # only consider duplicates if dets are inside the area range
        dtDup = np.logical_and(dtDup, np.logical_and(dtm==0, np.logical_not(np.repeat(a,T,0))))
        # false positive img (fpi) when all gt are ignored and there remain detections
        fpi = (gtIg.sum() == G) and np.any(dtIg == 0)

        # store results for given image and category
        return {
                'image_id':     imgId,
                'category_id':  catId,
                'aRng':         aRng,
                'maxDet':       maxDet,
                'dtIds':        [d['id'] for d in dt],
                'gtIds':        [g['id'] for g in gt],
                'dtMatches':    dtm,
                'gtMatches':    gtm,
                'dtScores':     [d['score'] for d in dt],
                'gtIgnore':     gtIg,
                'dtIgnore':     dtIg,
                'dtDuplicates': dtDup,
                'fpi':          fpi,
            }

    def accumulate(self, p = None):
        '''
        Accumulate per image evaluation results and store the result in self.eval
        :param p: input params for evaluation
        :return: None
        '''
        print('Accumulating evaluation results...')
        tic = time.time()
        if not self.evalImgs:
            print('Please run evaluate() first')
        # allows input customized parameters
        if p is None:
            p = self.params
        p.catIds = p.catIds if p.useCats == 1 else [-1]
        T           = len(p.iouThrs)
        R           = len(p.recThrs)
        K           = len(p.catIds) if p.useCats else 1
        A           = len(p.areaRng)
        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))
        TP          = -np.ones((T,K,A,M))
        FP          = -np.ones((T,K,A,M))
        FN          = -np.ones((T,K,A,M))
        duplicates  = -np.ones((T,K,A,M))
        FPI         = -np.ones((T,K,A,M))

        # matrix of arrays
        TPC         = np.empty((T,K,A,M), dtype=object)
        FPC         = np.empty((T,K,A,M), dtype=object)
        sorted_conf = np.empty((K,A,M), dtype=object)

        # create dictionary for future indexing
        _pe = self._paramsEval
        catIds = _pe.catIds if _pe.useCats else [-1]
        setK = set(catIds)
        setA = set(map(tuple, _pe.areaRng))
        setM = set(_pe.maxDets)
        setI = set(_pe.imgIds)
        # get inds to evaluate
        k_list = [n for n, k in enumerate(p.catIds)  if k in setK]
        m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
        a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA]
        i_list = [n for n, i in enumerate(p.imgIds)  if i in setI]
        I0 = len(_pe.imgIds)
        A0 = len(_pe.areaRng)
        # retrieve E at each category, area range, and max number of detections
        for k, k0 in enumerate(k_list):
            Nk = k0*A0*I0
            for a, a0 in enumerate(a_list):
                Na = a0*I0
                for m, maxDet in enumerate(m_list):
                    E = [self.evalImgs[Nk + Na + i] for i in i_list]
                    E = [e for e in E if not e is None]
                    if len(E) == 0:
                        continue
                    dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E])

                    # 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]
                    sorted_conf[k,a,m] = dtScoresSorted.copy()

                    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]
                    dtDups = np.concatenate([e['dtDuplicates'][:,0:maxDet] for e in E], axis=1)[:,inds]
                    gtIg = np.concatenate([e['gtIgnore'] for e in E])
                    npig = np.count_nonzero(gtIg==0) # number of not ignored gt objects
                    fpi = np.array([e['fpi'] for e in E]) # false positive image (no gt objects)
                    # if npig == 0:
                    #     print("No ground truth objects, continuing...")
                    #     continue
                    tps = np.logical_and(               dtm,  np.logical_not(dtIg) )
                    fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) )

                    tp_sum = np.cumsum(tps, axis=1).astype(dtype=float)
                    fp_sum = np.cumsum(fps, axis=1).astype(dtype=float)
                    fpi_sum = np.cumsum(fpi).astype(dtype=int)
                    for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
                        tp = np.array(tp)
                        fp = np.array(fp)
                        fn = npig - tp # difference between gt and tp
                        nd = len(tp)
                        rc = tp / npig if npig else [0]
                        pr = tp / (fp+tp+np.spacing(1))
                        q  = np.zeros((R,))
                        ss = np.zeros((R,)) # 

                        if nd:
                            recall[t,k,a,m] = rc[-1]
                        else:
                            recall[t,k,a,m] = 0

                        TP[t,k,a,m] = tp[-1] if nd else 0
                        FP[t,k,a,m] = fp[-1] if nd else 0
                        FN[t,k,a,m] = fn[-1] if nd else npig
                        duplicates[t,k,a,m] = np.sum(dtDups[t, :])
                        FPI[t,k,a,m] = fpi_sum[-1]
                        TPC[t,k,a,m] = tp.copy()
                        FPC[t,k,a,m] = fp.copy()

                        # numpy is slow without cython optimization for accessing elements
                        # use python array gets significant speed improvement
                        pr = pr.tolist(); q = q.tolist()

                        for i in range(nd-1, 0, -1):
                            if pr[i] > pr[i-1]:
                                pr[i-1] = pr[i]

                        inds = np.searchsorted(rc, p.recThrs, side='left')
                        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,
            'TP': TP,
            'FP': FP,
            'FN': FN,
            'duplicates': duplicates,
            'support': TP + FN,
            'FPI': FPI,
            'TPC': TPC,
            'FPC': FPC,
            'sorted_conf': sorted_conf,
        }
        toc = time.time()
        print('DONE (t={:0.2f}s).'.format( toc-tic))

    def summarize(self):
        results = {}
        max_dets = self.params.maxDets[-1]
        min_iou = self.params.iouThrs[0]

        results['params'] = self.params
        results['eval'] = self.eval
        results['metrics'] = {}

        # for area_lbl in self.params.areaRngLbl:
        #     results.append(self._summarize('ap', iouThr=min_iou,
        #                  areaRng=area_lbl, maxDets=max_dets))

        # for area_lbl in self.params.areaRngLbl:
        #     results.append(self._summarize('ar', iouThr=min_iou,
        #                  areaRng=area_lbl, maxDets=max_dets))

        metrics_str = f"{'tp':>6}, {'fp':>6}, {'fn':>6}, {'dup':>6}, "
        metrics_str += f"{'pr':>5.2}, {'rec':>5.2}, {'f1':>5.2}, {'supp':>6}"
        metrics_str += f", {'fpi':>6}, {'nImgs':>6}"
        print('{:>51} {}'.format('METRIC', metrics_str))
        for area_lbl in self.params.areaRngLbl:
            results['metrics'][area_lbl] = self._summarize(
                'pr_rec_f1',
                iouThr=min_iou,
                areaRng=area_lbl,
                maxDets=max_dets
            )
            
        return results

    def _summarize(self, metric_type='ap', iouThr=None, areaRng='all', maxDets=100):
        """
        Helper function to print and obtain metrics of types:
        - ap: average precision
        - ar: average recall
        - cf: tp, fp, fn, precision, recall, f1 
        values from COCOeval object
        """
        def _summarize_ap_ar(ap=1, iouThr=None, areaRng='all', maxDets=100):
            iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}'
            titleStr = 'Average Precision' if ap == 1 else 'Average Recall'
            typeStr = '(AP)' if ap == 1 else '(AR)'
            iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \
                if iouThr is None else '{:0.2f}'.format(iouThr)

            aind = [i for i, aRng in enumerate(
                p.areaRngLbl) if aRng == areaRng]
            mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]

            if ap == 1:
                # dimension of precision: [TxRxKxAxM]
                s = self.eval['precision']
                # IoU
                if iouThr is not None:
                    t = np.where(iouThr == p.iouThrs)[0]
                    s = s[t]
                s = s[:, :, :, aind, mind]
            else:
                # dimension of recall: [TxKxAxM]
                s = self.eval['recall']
                if iouThr is not None:
                    t = np.where(iouThr == p.iouThrs)[0]
                    s = s[t]
                s = s[:, :, aind, mind]
            if len(s[s > -1]) == 0:
                mean_s = -1
            else:
                mean_s = np.mean(s[s > -1])
            print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))
            return mean_s

        def _summarize_pr_rec_f1(iouThr=None, areaRng='all', maxDets=100):
            aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]
            mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]

            # dimension of TP, FP, FN [TxKxAxM]
            tp = self.eval['TP']
            fp = self.eval['FP']
            fn = self.eval['FN']
            dup = self.eval['duplicates']
            fpi = self.eval['FPI']
            nImgs = len(p.imgIds)

            # filter by IoU
            if iouThr is not None:
                t = np.where(iouThr == p.iouThrs)[0]
                tp, fp, fn = tp[t], fp[t], fn[t]
                dup = dup[t]
                fpi = fpi[t]

            # filter by area and maxDets
            tp = tp[:, :, aind, mind].squeeze()
            fp = fp[:, :, aind, mind].squeeze()
            fn = fn[:, :, aind, mind].squeeze()
            dup = dup[:, :, aind, mind].squeeze()
            fpi = fpi[:, :, aind, mind].squeeze()

            # handle case where tp, fp, fn and dup are empty (no gt and no dt)
            if all([not np.any(m) for m in [tp, fp, fn, dup, fpi]]):
                tp, fp, fn, dup, fpi =[-1] * 5
            else:
                tp, fp, fn, dup, fpi = [e.item() for e in [tp, fp, fn, dup, fpi]]
            
            # compute precision, recall, f1
            if tp == -1 and fp == -1 and fn == -1:
                pr, rec, f1 = -1, -1, -1
                support, fpi = 0, 0
            else:
                pr = 0 if tp + fp == 0 else tp / (tp + fp)
                rec = 0 if tp + fn == 0 else tp / (tp + fn)
                f1 = 0 if pr + rec == 0 else 2 * pr * rec / (pr + rec)
                support = tp + fn
            # print(f"{tp=}, {fp=}, {fn=}, {dup=}, {pr=}, {rec=}, {f1=}, {support=}, {fpi=}")

            iStr = '@[ IoU={:<9} | area={:>9s} | maxDets={:>3d} ] = {}'
            iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \
                if iouThr is None else '{:0.2f}'.format(iouThr)
            metrics_str = f"{tp:>6.0f}, {fp:>6.0f}, {fn:>6.0f}, {dup:>6.0f}, "
            metrics_str += f"{pr:>5.2f}, {rec:>5.2f}, {f1:>5.2f}, {support:>6.0f}, "
            metrics_str += f"{fpi:>6.0f}, {nImgs:>6.0f}"
            print(iStr.format(iouStr, areaRng, maxDets, metrics_str))

            return {
                'range': p.areaRng[aind[0]],
                'iouThr': iouStr,
                'maxDets': maxDets,
                'tp': int(tp),
                'fp': int(fp),
                'fn': int(fn),
                'duplicates': int(dup),
                'precision': pr,
                'recall': rec,
                'f1': f1,
                'support': int(support),
                'fpi': int(fpi),
                'nImgs': nImgs,
            }

        p = self.params
        if metric_type in ['ap', 'ar']:
            ap = 1 if metric_type == 'ap' else 0
            return _summarize_ap_ar(ap, iouThr=iouThr, areaRng=areaRng, maxDets=maxDets)
        
        # return tp, fp, fn, pr, rec, f1, support, fpi, nImgs
        return _summarize_pr_rec_f1(iouThr=iouThr, areaRng=areaRng, maxDets=maxDets)

    def __str__(self):
        self.summarize()

class Params:
    '''
    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, 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']
        self.useCats = 1

    def setKpParams(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, 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':
            self.setDetParams()
        elif iouType == 'keypoints':
            self.setKpParams()
        else:
            raise Exception('iouType not supported')
        self.iouType = iouType
        # useSegm is deprecated
        self.useSegm = None

    def __repr__(self) -> str:
        return str(self.__dict__)

    def __iter__(self):
        return iter(self.__dict__.items())