def evaluate_all(distmat,
query=None,
gallery=None,
query_ids=None,
gallery_ids=None,
query_cams=None,
gallery_cams=None,
cmc_topk=(1, 5, 10),
dataset="veri"):
name = {"veri": "VeRi", "vihicleid": "VehicleID"}
if query is not None and gallery is not None:
query_ids = [label for _, label, _ in query]
gallery_ids = [label for _, label, _ in gallery]
query_cams = [cam for _, _, cam in query]
gallery_cams = [cam for _, _, cam in gallery]
else:
assert (query_ids is not None and gallery_ids is not None
and query_cams is not None and gallery_cams is not None)
# Compute mean AP
mAP = mean_ap(distmat, query_ids, gallery_ids, query_cams, gallery_cams)
dataName = name[dataset]
print("------------------------------")
print("The result of the dataset({})".format(dataName))
print('Mean AP: {:4.1%}'.format(mAP))
cmc_scores = cmc(distmat, query_ids, gallery_ids, query_cams, gallery_cams)
print('CMC Scores:{:4.1%}'.format(cmc_scores[0]))
for k in cmc_topk:
print(' top-{0:}: {1:4.1%}'.format(k, cmc_scores[k - 1]))
# Use the allshots cmc top-1 score for validation criterion
return cmc_scores[0]
def rank(dist):
r = cmc(dist, self.queryset.ids, self.testset.ids, self.queryset.cameras, self.testset.cameras,
separate_camera_set=False,
single_gallery_shot=False,
first_match_break=True)
m_ap = mean_ap(dist, self.queryset.ids, self.testset.ids, self.queryset.cameras, self.testset.cameras)
return r, m_ap
def evaluate_all(distmat,
query=None,
gallery=None,
query_ids=None,
gallery_ids=None,
query_cams=None,
gallery_cams=None,
cmc_topk=(1, 5, 10),
dataset="veri"):
name = {"veri": "VeRi", "vihicleid": "VehicleID"}
if query is not None and gallery is not None:
query_ids = [label for _, label, _ in query]
gallery_ids = [label for _, label, _ in gallery]
query_cams = [cam for _, _, cam in query]
gallery_cams = [cam for _, _, cam in gallery]
else:
assert (query_ids is not None and gallery_ids is not None
and query_cams is not None and gallery_cams is not None)
# Compute mean AP
mAP = mean_ap(distmat, query_ids, gallery_ids, query_cams, gallery_cams)
dataName = name[dataset]
print("------------------------------")
print("The result of the dataset({})".format(dataName))
print('Mean AP: {:4.1%}'.format(mAP))
# Compute all kinds of CMC scores
cmc_configs = {
'allshots':
dict(separate_camera_set=False,
single_gallery_shot=False,
first_match_break=False),
'cuhk03':
dict(separate_camera_set=True,
single_gallery_shot=True,
first_match_break=False),
'market1501':
dict(separate_camera_set=False,
single_gallery_shot=False,
first_match_break=True)
}
cmc_scores = {
name: cmc(distmat, query_ids, gallery_ids, query_cams, gallery_cams,
**params)
for name, params in cmc_configs.items()
}
print('CMC Scores{:>12}{:>12}{:>12}'.format('allshots', 'cuhk03',
'market1501'))
for k in cmc_topk:
print(' top-{:<4}{:12.1%}{:12.1%}{:12.1%}'.format(
k, cmc_scores['allshots'][k - 1], cmc_scores['cuhk03'][k - 1],
cmc_scores['market1501'][k - 1]))
# Use the allshots cmc top-1 score for validation criterion
return cmc_scores['allshots'][0]