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Update cocoeval.py #7

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153 changes: 152 additions & 1 deletion PythonAPI/pycocotools/cocoeval.py
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Original file line number Diff line number Diff line change
Expand Up @@ -496,6 +496,157 @@ def _summarizeKps():
def __str__(self):
self.summarize()

# add for per category metric from here
def summarize_per_category(self):
'''
Compute and display summary metrics for evaluation results *per category*.
Note this functin can *only* be applied on the default parameter setting
'''
def _summarize_single_category(ap=1, iouThr=None, categoryId=None, areaRng='all', maxDets=100):
p = self.params
iStr = ' {:<18} {} @[ CategoryId={:>3d} | 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]
if categoryId is not None:
category_index = [i for i, i_catId in enumerate(p.catIds) if i_catId == categoryId]
s = s[:,:,category_index,aind,mind]
else:
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]
if categoryId is not None:
category_index = [i for i, i_catId in enumerate(p.catIds) if i_catId == categoryId]
s = s[:,category_index,aind,mind]
else:
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, catId, iouStr, areaRng, maxDets, mean_s))
return mean_s

def _summarizeDets_per_category():
category_stats = np.zeros((12,len(self.params.catIds)))
for category_index, category_id in enumerate(self.params.catIds):
category_stats[0][category_index] = _summarize_single_category(1,
categoryId=category_id)
category_stats[1][category_index] = _summarize_single_category(1,
iouThr=.5,
maxDets=self.params.maxDets[2],
categoryId=category_id)
category_stats[2][category_index] = _summarize_single_category(1,
iouThr=.75,
maxDets=self.params.maxDets[2],
categoryId=category_id)
category_stats[3][category_index] = _summarize_single_category(1,
areaRng='small',
maxDets=self.params.maxDets[2],
categoryId=category_id)
category_stats[4][category_index] = _summarize_single_category(1,
areaRng='medium',
maxDets=self.params.maxDets[2],
categoryId=category_id)
category_stats[5][category_index] = _summarize_single_category(1,
areaRng='large',
maxDets=self.params.maxDets[2],
categoryId=category_id)
category_stats[6][category_index] = _summarize_single_category(0,
maxDets=self.params.maxDets[0],
categoryId=category_id)
category_stats[7][category_index] = _summarize_single_category(0,
maxDets=self.params.maxDets[1],
categoryId=category_id)
category_stats[8][category_index] = _summarize_single_category(0,
maxDets=self.params.maxDets[2],
categoryId=category_id)
category_stats[9][category_index] = _summarize_single_category(0,
areaRng='small',
maxDets=self.params.maxDets[2],
categoryId=category_id)
category_stats[10][category_index] = _summarize_single_category(0,
areaRng='medium',
maxDets=self.params.maxDets[2],
categoryId=category_id)
category_stats[11][category_index] = _summarize_single_category(0,
areaRng='large',
maxDets=self.params.maxDets[2],
categoryId=category_id)
return category_stats

def _summarizeKps_per_category():
category_stats = np.zeros((10,len(self.params.catIds)))
for category_index, category_id in self.params.catIds:
category_stats[0][category_index] = _summarize_single_category(1,
maxDets=20,
categoryId=category_id)
category_stats[1][category_index] = _summarize_single_category(1,
maxDets=20,
iouThr=.5,
categoryId=category_id)
category_stats[2][category_index] = _summarize_single_category(1,
maxDets=20,
iouThr=.75,
categoryId=category_id)
category_stats[3][category_index] = _summarize_single_category(1,
maxDets=20,
areaRng='medium',
categoryId=category_id)
category_stats[4][category_index] = _summarize_single_category(1,
maxDets=20,
areaRng='large',
categoryId=category_id)
category_stats[5][category_index] = _summarize_single_category(0,
maxDets=20,
categoryId=category_id)
category_stats[6][category_index] = _summarize_single_category(0,
maxDets=20,
iouThr=.5,
categoryId= category_id)
category_stats[7][category_index] = _summarize_single_category(0,
maxDets=20,
iouThr=.75,
categoryId=category_id)
category_stats[8][category_index] = _summarize_single_category(0,
maxDets=20,
areaRng='medium',
categoryId=category_id)
category_stats[9][category_index] = _summarize_single_category(0,
maxDets=20,
areaRng='large',
categoryId=category_id)
return category_stats

if not self.eval:
raise Exception('Please run accumulate() first')
iouType = self.params.iouType
if iouType == 'segm' or iouType == 'bbox':
summarize_per_category = _summarizeDets_per_category
elif iouType == 'keypoints':
summarize_per_category = _summarizeKps_per_category
self.category_stats = summarize_per_category()

def __str__(self):
self.summarize_per_category()
# add for metric per category end here

class Params:
'''
Params for coco evaluation api
Expand Down Expand Up @@ -531,4 +682,4 @@ def __init__(self, iouType='segm'):
raise Exception('iouType not supported')
self.iouType = iouType
# useSegm is deprecated
self.useSegm = None
self.useSegm = None