-
Notifications
You must be signed in to change notification settings - Fork 34
/
Copy pathtest_results.py
190 lines (162 loc) · 7.28 KB
/
test_results.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
import cv2
import os
from scipy.io import loadmat
import os.path as osp
import numpy as np
import json
from PIL import Image
import pickle
import sys
from sklearn.metrics import average_precision_score
from sklearn.preprocessing import normalize
from iou_utils import get_max_iou, get_good_iou
def compute_iou(a, b):
x1 = max(a[0], b[0])
y1 = max(a[1], b[1])
x2 = min(a[2], b[2])
y2 = min(a[3], b[3])
inter = max(0, x2 - x1) * max(0, y2 - y1)
union = (a[2] - a[0]) * (a[3] - a[1]) + (b[2] - b[0]) * (b[3] - b[1]) - inter
return inter * 1.0 / union
def set_box_pid(boxes, box, pids, pid):
for i in range(boxes.shape[0]):
if np.all(boxes[i] == box):
pids[i] = pid
return
print("Person: %s, box: %s cannot find in images." % (pid, box))
def image_path_at(data_path, image_index, i):
image_path = osp.join(data_path, image_index[i])
assert osp.isfile(image_path), "Path does not exist: %s" % image_path
return image_path
def load_image_index(root_dir, db_name):
"""Load the image indexes for training / testing."""
# Test images
test = loadmat(osp.join(root_dir, "annotation", "pool.mat"))
test = test["pool"].squeeze()
test = [str(a[0]) for a in test]
if db_name == "psdb_test":
return test
# All images
all_imgs = loadmat(osp.join(root_dir, "annotation", "Images.mat"))
all_imgs = all_imgs["Img"].squeeze()
all_imgs = [str(a[0][0]) for a in all_imgs]
# Training images = all images - test images
train = list(set(all_imgs) - set(test))
train.sort()
return train
if __name__ == "__main__":
db_name = "psdb_test"
# change to your own path
root_dir = '/home/yy1/2021/data/cuhk'
with open(root_dir + '/annotation/test_new.json', 'r') as fid:
test_det = json.load(fid)
id_to_img = dict()
img_to_id = dict()
for td in test_det['images']:
im_name = td['file_name'].split('/')[-1]
im_id = td['id']
id_to_img[im_id] = im_name
img_to_id[im_name] = im_id
# change to your own working dirs
results_path = '/home/yy1/2021/AlignPS/work_dirs/' + sys.argv[1]
#results_path = '/home/yy1/2021/mmdetection/work_dirs/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_4x4_1x_cuhk_reid_1500_stage1_fpncat_dcn_epoch24_singlescale_focal_x4_bg-2_lconv3dcn_sub_triqueue'
#results_path = '/raid/yy1/mmdetection/work_dirs/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_cuhk_reid_1000_fpncat'
with open(os.path.join(results_path, 'results_1000.pkl'), 'rb') as fid:
all_dets = pickle.load(fid)
gallery_dicts = {}
for i, dets in enumerate(all_dets):
image_id = i
gallery_dicts[image_id] = dict()
gallery_dicts[image_id]['bbox'] = dets[0][:, :4]
gallery_dicts[image_id]['scores'] = dets[0][:, 4]
gallery_dicts[image_id]['feats'] = dets[0][:, 5:]
all_thresh = [0.2]
#iou_thresh = 0.6
#all_thresh = [0.05, 0.1, 0.15, 0.18, 0.2, 0.22, 0.25]
#all_thresh = [0.15 + 0.01 * i for i in range(11)]
for thresh in all_thresh:
if db_name == "psdb_test":
gallery_size= 100
test = loadmat(osp.join(root_dir, "annotation/test/train_test/TestG{:d}.mat".format(gallery_size)))
test = test["TestG{:d}".format(gallery_size)].squeeze()
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,:], axis=1).ravel()
# gallery
gallery = item["Gallery"].squeeze()
for im_name, box, _ in gallery:
gallery_imname = str(im_name[0])
gt = box[0].astype(np.int32)
count_gt += gt.size > 0
img_id = img_to_id[gallery_imname]
#if img_id not in gallery_dicts:
# continue
det = np.asarray(gallery_dicts[img_id]['bbox'])
scores = np.asarray(gallery_dicts[img_id]['scores'])
keep_inds = np.where(scores >= thresh)
scores = scores[keep_inds]
det = det[keep_inds]
gallery_feat = gallery_dicts[img_id]['feats'][keep_inds]
if gallery_feat.shape[0] > 0:
gallery_feat = normalize(gallery_feat, axis=1)
else:
continue
sim = gallery_feat.dot(query_feat).ravel()
#Class Weighted Similarity
#print(scores)
#sim = sim * scores
label = np.zeros(len(sim), dtype=np.int32)
if gt.size > 0:
w, h = gt[2], gt[3]
gt[2:] += gt[:2]
iou_thresh = min(0.5, (w * h * 1.0) / ((w + 10) * (h + 10)))
inds = np.argsort(sim)[::-1]
sim = sim[inds]
det = det[inds]
# only set the first matched det as true positive
for j, roi in enumerate(det[:, :4]):
if compute_iou(roi, gt) >= iou_thresh:
label[j] = 1
count_tp += 1
break
y_true.extend(list(label))
y_score.extend(list(sim))
y_score = np.asarray(y_score)
y_true = np.asarray(y_true)
assert count_tp <= count_gt
recall_rate = count_tp * 1.0 / count_gt
ap = 0 if count_tp == 0 else average_precision_score(y_true, y_score) * recall_rate
aps.append(ap)
inds = np.argsort(y_score)[::-1]
y_score = y_score[inds]
y_true = y_true[inds]
accs.append([min(1, sum(y_true[:k])) for k in topk])
print("threshold: ", thresh)
print(" mAP = {:.2%}".format(np.mean(aps)))
accs = np.mean(accs, axis=0)
for i, k in enumerate(topk):
print(" Top-{:2d} = {:.2%}".format(k, accs[i]))