__author__ = 'tsungyi'

import numpy as np
import datetime
import time
from collections import defaultdict
from . 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
 # 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.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
 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 = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62,.62, 1.07, 1.07, .87, .87, .89, .89])/10.0
 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 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))
 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, 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)))
 # 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,
 }

 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))

 # 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]

 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]
 gtIg = np.concatenate([e['gtIgnore'] for e in E])
 npig = np.count_nonzero(gtIg==0 )
 if npig == 0:
 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=np.float)
 fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
 for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
 tp = np.array(tp)
 fp = np.array(fp)
 nd = len(tp)
 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]
 else:
 recall[t,k,a,m] = 0

 # 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,
 }
 toc = time.time()
 print('DONE (t={:0.2f}s).'.format( toc-tic))

 def summarize(self):
 '''
 Compute and display summary metrics for evaluation results.
 Note this functin can *only* be applied on the default parameter setting
 '''
 def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ):
 p = self.params
 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 _summarizeDets():
 stats = np.zeros((12,))
 stats[0] = _summarize(1)
 stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2])
 stats[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2])
 stats[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2])
 stats[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2])
 stats[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2])
 stats[6] = _summarize(0, maxDets=self.params.maxDets[0])
 stats[7] = _summarize(0, maxDets=self.params.maxDets[1])
 stats[8] = _summarize(0, maxDets=self.params.maxDets[2])
 stats[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2])
 stats[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2])
 stats[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2])
 return stats
 def _summarizeKps():
 stats = np.zeros((10,))
 stats[0] = _summarize(1, maxDets=20)
 stats[1] = _summarize(1, maxDets=20, iouThr=.5)
 stats[2] = _summarize(1, maxDets=20, iouThr=.75)
 stats[3] = _summarize(1, maxDets=20, areaRng='medium')
 stats[4] = _summarize(1, maxDets=20, areaRng='large')
 stats[5] = _summarize(0, maxDets=20)
 stats[6] = _summarize(0, maxDets=20, iouThr=.5)
 stats[7] = _summarize(0, maxDets=20, iouThr=.75)
 stats[8] = _summarize(0, maxDets=20, areaRng='medium')
 stats[9] = _summarize(0, maxDets=20, areaRng='large')
 return stats
 if not self.eval:
 raise Exception('Please run accumulate() first')
 iouType = self.params.iouType
 if iouType == 'segm' or iouType == 'bbox':
 summarize = _summarizeDets
 elif iouType == 'keypoints':
 summarize = _summarizeKps
 self.stats = summarize()

 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, 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.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, 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.maxDets = [20]
 self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]]
 self.areaRngLbl = ['all', 'medium', 'large']
 self.useCats = 1

 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