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 []
dt = sorted(dt, key=lambda x: -x['score'])
if len(dt) > p.maxDets[-1]:
dt=dt[0:p.maxDets[-1]]
if p.useSegm:
g = [g['segmentation'] for g in gt]
d = [d['segmentation'] for d in dt]
else:
g = [g['bbox'] for g in gt]
d = [d['bbox'] for d in dt]
# compute iou between each dt and gt region
iscrowd = [int(o['iscrowd']) for o in gt]
ious = mask.iou(d,g,iscrowd)
return ious
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 []
dt = sorted(dt, key=lambda x: -x['score'])
if len(dt) > p.maxDets[-1]:
dt = dt[0:p.maxDets[-1]]
if p.useSegm:
g = [g['segmentation'] for g in gt]
d = [d['segmentation'] for d in dt]
else:
g = [g['bbox'] for g in gt]
d = [d['bbox'] for d in dt]
# compute iou between each dt and gt region
iscrowd = [int(o['iscrowd']) for o in gt]
ious = mask.iou(d, g, iscrowd)
return ious
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 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 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]]
# for multiple gt/annotator case, use gt[0] for now.
dt = dt[0]['regions']
gt = gt[0]['regions']
dt = sorted(dt, key=lambda x: -x['score'])
if len(dt) > p.maxDets[-1]:
dt = dt[0:p.maxDets[-1]]
if p.useSegm:
if p.useAmodalGT:
g = [g['segmentation'] for g in gt]
else:
g = [
g['visible_mask']
if 'visible_mask' in g else g['segmentation'] for g in gt
]
if p.useAmodalDT:
d = [
d['amodal_mask']
if 'amodal_mask' in d else d['segmentation'] for d in dt
]
else:
d = [d['segmentation'] for d in dt]
else:
g = [g['bbox'] for g in gt]
d = [d['bbox'] for d in dt]
# compute iou between each dt and gt region
iscrowd = [0 for o in gt]
ious = mask.iou(d, g, iscrowd)
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 'ignore' not in g:
g['ignore'] = 0
if g['iscrowd'] == 1 or 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
gt = sorted(gt, key=lambda x: x['_ignore'])
dt = sorted(dt, key=lambda x: -x['score'])
if len(dt) > maxDet:
dt = dt[0:maxDet]
if p.useSegm:
g = [g['segmentation'] for g in gt]
d = [d['segmentation'] for d in dt]
else:
g = [g['bbox'] for g in gt]
d = [d['bbox'] for d in dt]
# compute iou between each dt and gt region
iscrowd = [int(o['iscrowd']) for o in gt]
ious = mask.iou(d, g, iscrowd)
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
iou_tmp = ious[dind, gind]
if ious[dind, gind] < iou:
continue
# 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 {
'imgId': imgId,
'catId': catId,
'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,
'ious': ious,
'areaRng': aRng,
'maxDet': maxDet,
}
