def main(det_thresh=0.05,
gallery_size=-1,
ignore_cam_id=True,
input_path=None):
#results_path = '/raid/ljp/code/chao_mmdetection/jobs/dcn_base_focal/'
# change here
results_path = '/home/yy1/2021/mmdetection-public/work_dirs/' + input_path
data_root = '/home/yy1/2021/data/prw/PRW-v16.04.20/'
probe_set = load_probes(data_root)
gallery_set = gt_roidbs(data_root)
name_id = dict()
for i, gallery in enumerate(gallery_set):
name = gallery['im_name']
name_id[name] = i
# print(name_id)
with open(os.path.join(results_path, 'results_1000.pkl'), 'rb') as fid:
all_dets = pickle.load(fid)
gallery_det, gallery_feat = [], []
for det in all_dets:
gallery_det.append(det[0][:, :5])
if det[0].shape[0] > 0:
feat = normalize(det[0][:, 5:], axis=1)
else:
feat = det[0][:, 5:]
# feat = normalize(det[0][:, 5:], axis=1)
gallery_feat.append(feat)
probe_feat = []
for probe in probe_set:
name = probe['im_name']
query_gt_box = probe['boxes'][0]
id = name_id[name]
det = gallery_det[id]
feat = gallery_feat[id]
iou, iou_max, nmax = get_max_iou(det, query_gt_box)
if iou_max < 0.1:
print("not detected", name, iou_max)
feat = feat[nmax]
probe_feat.append(feat)
# gallery_det, gallery_feat = [], []
# for det in all_dets:
# det[0] = det[0][det[0][:, 4]>thresh]
# gallery_det.append(det[0][:, :5])
# if det[0].shape[0] > 0:
# feat = normalize(det[0][:, 5:], axis=1)
# else:
# feat = det[0][:, 5:]
# feat = normalize(det[0][:, 5:], axis=1)
# gallery_feat.append(feat)
search_performance_calc(gallery_set, probe_set, gallery_det, gallery_feat,
probe_feat, det_thresh, gallery_size,
ignore_cam_id)
aps = []
accs = []
topk = [1, 5, 10]
for index, item in enumerate(test):
# query
y_true, y_score = [], []
count_gt, count_tp = 0, 0
im_name = str(item["Query"][0, 0][0][0])
query_gt_box = item["Query"][0,
0][1].squeeze().astype(np.int32)
query_gt_box[2:] += query_gt_box[:2]
query_dict = gallery_dicts[img_to_id[im_name]]
query_boxes = query_dict['bbox']
iou, iou_max, nmax = get_max_iou(query_boxes, query_gt_box)
#print(iou_max)
'''
if iou_max <= iou_thresh:
query_feat = query_dict['feats'][nmax]
#print("not detected", im_name, iou_max)
#continue
else:
iou_good, good_idx = get_good_iou(query_boxes, query_gt_box, iou_thresh)
query_feats = query_dict['feats'][good_idx]
query_feat = iou_good[np.newaxis,:].dot(query_feats) / np.sum(iou_good)
query_feat = query_feat.ravel()
'''
query_feat = query_dict['feats'][nmax]
query_feat = normalize(query_feat[np.newaxis, :],
def main(det_thresh=0.05,
gallery_size=-1,
ignore_cam_id=True,
input_path=None):
#results_path = '/raid/ljp/code/chao_mmdetection/jobs/dcn_base_focal/'
results_path = '/home/jx1/yy1/mmdetection/work_dirs/' + input_path
# thresh = 0.2
data_root = '/home/jx1/yy1/data/PRW-v16.04.20/'
probe_set = load_probes(data_root)
gallery_set = gt_roidbs(data_root)
name_id = dict()
for i, gallery in enumerate(gallery_set):
name = gallery['im_name']
name_id[name] = i
# print(name_id)
with open(os.path.join(results_path, 'results_1000.pkl'), 'rb') as fid:
all_dets = pickle.load(fid)
# print(len(all_dets))
all_dets1 = all_dets[0]
all_dets2 = all_dets[1]
print(len(all_dets1), len(all_dets2))
gallery_det1, gallery_feat1 = [], []
gallery_det2, gallery_feat2 = [], []
gallery_det3, gallery_feat3 = [], []
for det1, det2 in zip(all_dets1, all_dets2):
gallery_det1.append(det1[0][:, :5])
feat1 = normalize(det1[0][:, 5:], axis=1)
# print('feat1', feat1.shape)
gallery_feat1.append(feat1)
gallery_det2.append(det2[:, :5])
feat2 = normalize(det2[:, 5:], axis=1)
# print('feat2', feat2.shape)
gallery_feat2.append(feat2)
gallery_det3 = gallery_det1
gallery_feat3.append(np.concatenate((feat1, feat2), axis=1))
probe_feat1 = []
probe_feat2 = []
probe_feat3 = []
for probe in probe_set:
name = probe['im_name']
query_gt_box = probe['boxes'][0]
id = name_id[name]
det1 = gallery_det1[id]
feat1 = gallery_feat1[id]
iou, iou_max, nmax = get_max_iou(det1, query_gt_box)
if iou_max < 0.1:
print("not detected", name, iou_max)
feat1 = feat1[nmax]
probe_feat1.append(feat1)
feat3 = gallery_feat3[id]
feat3 = feat3[nmax]
probe_feat3.append(feat3)
det2 = gallery_det2[id]
feat2 = gallery_feat2[id]
iou, iou_max, nmax = get_max_iou(det2, query_gt_box)
if iou_max < 0.1:
print("not detected", name, iou_max)
feat2 = feat2[nmax]
probe_feat2.append(feat2)
# gallery_det, gallery_feat = [], []
# for det in all_dets:
# # det[0] = det[0][det[0][:, 4]>thresh]
# gallery_det.append(det[:, :5])
# # if det[0].shape[0] > 0:
# # feat = normalize(det[0][:, 5:], axis=1)
# # else:
# # feat = det[0][:, 5:]
# feat = normalize(det[:, 5:], axis=1)
# gallery_feat.append(feat)
all_gallery_det = [gallery_det1, gallery_det2, gallery_det3]
all_gallery_feat = [gallery_feat1, gallery_feat2, gallery_feat3]
all_probe_feat = [probe_feat1, probe_feat2, probe_feat3]
# all_gallery_det = [gallery_det1, gallery_det2]
# all_gallery_feat = [gallery_feat1, gallery_feat2]
# all_probe_feat = [probe_feat1, probe_feat2]
for gallery_det, gallery_feat, probe_feat in zip(all_gallery_det,
all_gallery_feat,
all_probe_feat):
search_performance_calc(gallery_set, probe_set, gallery_det,
gallery_feat, probe_feat, det_thresh,
gallery_size, ignore_cam_id)