def single_evaluate(net, probe_path, gallery_path, adv_index, target_index,
adv_id, target_id, graph, res):
#load the bobo dataset. 1)adv images; 2) target images; 3)other pid images
#and their infos (pid, cid)
img_names = sorted(os.listdir(probe_path))
imgs, infos = load(probe_path, img_names)
# divide the adv images into 2 part. 1) probe 2)gallery_add
rand_index = np.arange(len(adv_index))
#np.random.shuffle(rand_index)
adv_index = adv_index[rand_index]
adv_probe_imgs = imgs[adv_index[0:1]]
adv_probe_infos = [infos[i] for i in adv_index[0:1]]
# construct the gallery_add imgs and the infos
num_per_pc = 6
gal_add_pid = sorted(list(set(range(1502, 1513)) - {adv_id, target_id}))
otherpid_index = np.random.randint(0, 24, size=3 * (11-2) * num_per_pc) + \
np.array([np.arange(3)+(i-1502)*3 for i in gal_add_pid]).flatten().repeat(num_per_pc)*24
gal_add_index = np.concatenate(
(adv_index[1:], target_index, otherpid_index))
gallery_imgs_add = imgs[gal_add_index]
gallery_infos_add = [infos[i] for i in gal_add_index]
#a. untarget test
# the probe adv serving as probe
# gallery is obtained by concat target image, other pid, gallery_add adv
# do the test:
result_untarget = eval_without_noise(net, adv_probe_imgs, adv_probe_infos,
gallery_imgs_add, gallery_infos_add,
graph, res)
#b. target test
# change the adv infos to (target_id, cid)
# gallery is obtained by concat target image, other pid, gallery_add adv
# do the test
# noted that if the probe comes from the camera of target, if would cause error when we change the id
# because there is no correct img in gallery matched to the probe
for i in adv_index:
if infos[i][1] == infos[adv_index[0]][
1]: # means we set the first img of adv_index as the probe
infos[i] = (target_id, infos[i][1])
adv_probe_infos = [infos[i] for i in adv_index[0:1]]
gal_add_index = np.concatenate(
(adv_index[1:], target_index, otherpid_index))
gallery_infos_add = [infos[i] for i in gal_add_index]
result_target = eval_without_noise(net, adv_probe_imgs, adv_probe_infos,
gallery_imgs_add, gallery_infos_add,
graph, res)
res.put((result_untarget, result_target))
return [result_untarget, result_target]
def bobo_evaluate(net, probe_path, gallery_path):
img_names = sorted(os.listdir(probe_path))
imgs, infos = load(probe_path, img_names)
index = np.arange(len(imgs))
probe_index = index[::4] #only the forward
gal_add_index = index[2::4]
probe_imgs = imgs[probe_index]
probe_infos = [infos[i] for i in probe_index]
gallery_imgs_add = imgs[gal_add_index]
gallery_infos_add = [infos[i] for i in gal_add_index]
eval_without_noise(net, probe_imgs, probe_infos, gallery_imgs_add,
gallery_infos_add)
def single_evaluate(net, probe_path, gallery_path, adv_index, adv_id, target_id, num_target):
#load the bobo dataset. 1)adv images; 2) target images; 3)other pid images
#and their infos (pid, cid)
img_names = sorted(os.listdir(probe_path))
imgs, infos = load(probe_path, img_names)
# divide the adv images into 2 part. 1) probe 2)gallery_add
adv_probe_imgs = imgs[adv_index[0:1]]
adv_probe_infos = [infos[i] for i in adv_index[0:1]]
# construct the gallery_add imgs and the infos
num_per_pc = 6
gal_add_pid = sorted(list(set(range(1502,1513))-{adv_id}))
otherpid_index = np.random.randint(0, 24, size=3 * (11-1) * num_per_pc) + \
np.array([np.arange(3)+(i-1502)*3 for i in gal_add_pid]).flatten().repeat(num_per_pc)*24
gal_add_index = np.concatenate(
(adv_index[1:], otherpid_index)
)
gallery_imgs_add = imgs[gal_add_index]
gallery_infos_add = [infos[i] for i in gal_add_index]
#a. untarget test
result_untarget = eval_without_noise(net, adv_probe_imgs, adv_probe_infos, gallery_path, gallery_imgs_add, gallery_infos_add,
target_id, num_target)
#b. target test
for i in adv_index:
if infos[i][1] == infos[adv_index[0]][1]: #means we set the first img of adv_index as the probe
infos[i] = (target_id, infos[i][1])
adv_probe_infos = [infos[i] for i in adv_index[0:1]]
gal_add_index = np.concatenate(
(adv_index[1:], otherpid_index)
)
gallery_infos_add = [infos[i] for i in gal_add_index]
result_target = eval_without_noise(net, adv_probe_imgs, adv_probe_infos, gallery_path, gallery_imgs_add, gallery_infos_add,
target_id, num_target)
return [result_untarget, result_target]