def calculate_accuracy_for_matching(match_res_file, topk_acc):
max_k = max(topk_acc)
correct_counts = [0] * max_k
num_total = 0
all_gt = []
all_pred = []
for parts in tsv_io.tsv_reader(match_res_file):
num_total += 1
query_fea_idx = parts[0]
query_bbox = json.loads(parts[1])
pred_labels = json.loads(parts[2])
# calculate mAP
all_gt.append([query_fea_idx, qd_common.json_dump([query_bbox])])
all_pred.append([query_fea_idx, qd_common.json_dump(
[{"class": pred_labels[0][0], "conf": pred_labels[0][1]/1000.0, "rect": query_bbox["rect"]}])])
# calculate top k accuracy
gt_label = query_bbox["class"]
for i in range(min(max_k, len(pred_labels))):
cur_pred = pred_labels[i][0]
if cur_pred == gt_label:
correct_counts[i] += 1
break
map_report = match_res_file + ".eval.map"
pred_file = match_res_file + ".pred"
gt_file = match_res_file + ".gt"
tsv_io.tsv_writer(all_pred, pred_file)
tsv_io.tsv_writer(all_gt, gt_file)
deteval(truth=gt_file, dets=pred_file, report_file=map_report)
for i in range(1, len(correct_counts)):
correct_counts[i] += correct_counts[i-1]
return [c / float(num_total) for c in correct_counts]
def load_yaml_list(self, yaml_lst_file):
yaml_list = []
for parts in tsv_reader(yaml_lst_file):
f = parts[0]
assert (os.path.isfile(f))
yaml_list.append(f)
return yaml_list
def gen_rows():
for parts in tsv_reader(infile):
assert (len(parts) == 3)
pred_labels = [it.split(':') for it in parts[2].split(';')]
pred_labels = [[label, int(score), ""]
for label, score in pred_labels]
yield parts[0], parts[1], json_dump(pred_labels)
def label_counts(self):
assert not self._for_test
if self._label_counts is None:
self._label_counts = np.zeros(len(self.label_to_idx))
for parts in tsv_reader(self._bbox_idx_file):
self._label_counts[int(parts[5])] += 1
return self._label_counts
def _delf_feature_match(args):
query_fea_rows, all_query_fea, all_index_fea, outfile, max_k = args
# resume from last checkpoint
last_cache = {}
checkpoints = [outfile + ".tmp", outfile]
for cache_file in checkpoints:
if op.isfile(cache_file):
for parts in tsv_io.tsv_reader(cache_file):
if len(parts) == 3:
try:
json.loads(parts[1])
json.loads(parts[2])
except Exception:
continue
last_cache[int(parts[0])] = parts
def gen_rows():
for query_idx in query_fea_rows:
print(query_idx)
if query_idx in last_cache:
yield last_cache[query_idx]
else:
query_fea = all_query_fea[query_idx]
scores = []
for i in range(len(all_index_fea)):
index_fea = all_index_fea[i]
inliers, locations_1_to_use, locations_2_to_use = matcher.get_inliers(
query_fea['location_np_list'],
query_fea['descriptor_np_list'],
index_fea['location_np_list'],
index_fea['descriptor_np_list'])
if inliers is not None:
score = sum(inliers)
else:
score = 0
scores.append((i, score))
scores = sorted(scores, key=lambda t: t[1], reverse=True)
# use top1 matching image
pred_labels = []
for i, (matched_fea_idx, score) in enumerate(scores):
if i >= max_k:
break
cur_pred = get_bbox_from_fea(all_index_fea[matched_fea_idx])["class"]
pred_labels.append([cur_pred, score, matched_fea_idx])
query_bbox = get_bbox_from_fea(query_fea)
yield str(query_idx), qd_common.json_dump(query_bbox), qd_common.json_dump(pred_labels)
tsv_io.tsv_writer(gen_rows(), outfile)