def tracking(self, image):
self.i += 1
local_state1, self.score_map, update, score_max, dis = self.local_track(
image)
gt_err = self.groundtruth[self.i, 2] < 3 or self.groundtruth[self.i,
3] < 3
gt_nan = any(np.isnan(self.groundtruth[self.i]))
if gt_err:
iou = -1
elif gt_nan:
iou = 0
else:
iou = compute_iou(self.groundtruth[self.i], local_state1)
width = self.last_gt[3] - self.last_gt[1]
height = self.last_gt[2] - self.last_gt[0]
if self.p.visualization:
show_res(cv2.cvtColor(image, cv2.COLOR_RGB2BGR),
np.array(self.last_gt, dtype=np.int32),
'2',
groundtruth=self.groundtruth,
update=update,
frame_id=self.i,
score=max(self.score_map.flatten()))
return [
float(self.last_gt[1]),
float(self.last_gt[0]),
float(width),
float(height)
], self.score_map, iou, score_max, dis
def eval_tracking(Dataset, video_spe=None, save=False, p=None):
if Dataset == 'otb':
data_dir = '/media/zj/4T/Dataset/OTB-100'
elif Dataset == "lasot":
data_dir = '/media/zj/4T/Dataset/LaSOT/dataset/images'
tmp = video_spe.split('-')
data_dir = os.path.join(data_dir, tmp[0])
elif Dataset == 'uav123':
data_dir = '/media/zj/4T/Dataset/UAV123/Dataset_UAV123/UAV123'
if video_spe is not None:
sequence_list = [video_spe]
else:
sequence_list = os.listdir(data_dir)
sequence_list.sort()
sequence_list = [
title for title in sequence_list if not title.endswith("txt")
]
base_save_path = p.save_path
for seq_id, video in enumerate(sequence_list):
if Dataset == "otb" or Dataset == "uav123":
sequence_path = video['path']
nz = video['nz']
ext = video['ext']
start_frame = video['startFrame']
end_frame = video['endFrame']
init_omit = 0
if 'initOmit' in video:
init_omit = video['initOmit']
image_list = [
'{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(
base_path=data_dir,
sequence_path=sequence_path,
frame=frame_num,
nz=nz,
ext=ext)
for frame_num in range(start_frame + init_omit, end_frame + 1)
]
anno_path = '{}/{}'.format(data_dir, video['anno_path'])
try:
groundtruth = np.loadtxt(str(anno_path), dtype=np.float64)
except:
groundtruth = np.loadtxt(str(anno_path),
delimiter=',',
dtype=np.float64)
result_save_path = os.path.join(base_save_path,
video['name'] + '.txt')
image_dir = image_list[0]
elif Dataset == "lasot":
sequence_dir = data_dir + '/' + video + '/img/'
gt_dir = data_dir + '/' + video + '/groundtruth.txt'
image_list = os.listdir(sequence_dir)
image_list.sort()
image_list = [
im for im in image_list
if im.endswith("jpg") or im.endswith("jpeg")
]
try:
groundtruth = np.loadtxt(gt_dir, delimiter=',')
except:
groundtruth = np.loadtxt(gt_dir)
result_save_path = os.path.join(base_save_path, video + '.txt')
image_dir = sequence_dir + image_list[0]
if os.path.exists(result_save_path):
continue
region = Region(groundtruth[0, 0], groundtruth[0, 1],
groundtruth[0, 2], groundtruth[0, 3])
# image = cv.cvtColor(cv.imread(image_dir), cv.COLOR_BGR2RGB)
image = Image.open(image_dir).convert('RGB')
tracker = metric_tracker(image, region, video=video,
p=p) #,groundtruth=groundtruth
num_frames = len(image_list)
bBoxes = np.zeros((num_frames, 4))
bBoxes2 = np.zeros((num_frames, 4))
bBoxes[0, :] = groundtruth[0, :]
bBoxes2[0, :] = groundtruth[0, :]
for im_id in range(1, len(image_list)):
if Dataset == 'lasot':
imagefile = sequence_dir + image_list[im_id]
else:
imagefile = image_list[im_id]
# image = cv.cvtColor(cv.imread(imagefile), cv.COLOR_BGR2RGB)
image = Image.open(imagefile).convert('RGB')
# print("%d: " % seq_id + video + ": %d /" % im_id + "%d" % len(image_list))
if p.base_tracker == 'pymdnet':
region = tracker.pymdnet_track(image)
elif p.base_tracker == 'metricnet':
region = tracker.metricnet_track(image)
if p.visualization:
show_res(cv.cvtColor(np.array(image), cv.COLOR_RGB2BGR),
np.array(region).astype('int16'), '1')
bBoxes[im_id, :] = region
# bbox_overlap[im_id] = overlap_ratio(groundtruth[im_id], bBoxes[im_id, :])[0]
#print(region)
fps = tracker.i / tracker.spf_total
print('fps', fps)
if save:
np.savetxt(result_save_path, bBoxes, fmt="%.6f,%.6f,%.6f,%.6f")
def tracking(self, image):
self.i += 1
mask = None
candidate_bboxes = None
# state, pyscore = self.pymdnet_track(image)
# self.last_gt = [state[1], state[0], state[1] + state[3], state[0] + state[2]]
self.local_Tracker.pos = torch.FloatTensor([
(self.last_gt[0] + self.last_gt[2] - 1) / 2,
(self.last_gt[1] + self.last_gt[3] - 1) / 2
])
self.local_Tracker.target_sz = torch.FloatTensor([
(self.last_gt[2] - self.last_gt[0]),
(self.last_gt[3] - self.last_gt[1])
])
tic = time.time()
local_state, self.score_map, update, local_score, dis, flag, update_score = self.local_track(
image)
md_score = self.pymdnet_eval(image,
np.array(local_state).reshape([-1, 4]))[0]
self.score_max = md_score
if md_score > 0 and flag == 'normal':
self.flag = 'found'
if self.p.use_mask:
self.siamstate['target_pos'] = self.local_Tracker.pos.numpy(
)[::-1]
self.siamstate[
'target_sz'] = self.local_Tracker.target_sz.numpy()[::-1]
siamscore, mask = self.siammask_track(
cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
self.local_Tracker.pos = torch.FloatTensor(
self.siamstate['target_pos'][::-1].copy())
self.local_Tracker.target_sz = torch.FloatTensor(
self.siamstate['target_sz'][::-1].copy())
local_state = torch.cat(
(self.local_Tracker.pos[[1, 0]] -
(self.local_Tracker.target_sz[[1, 0]] - 1) / 2,
self.local_Tracker.target_sz[[1, 0]])).data.cpu().numpy()
self.last_gt = np.array([
local_state[1], local_state[0],
local_state[1] + local_state[3],
local_state[0] + local_state[2]
])
elif md_score < 0 or flag == 'not_found':
self.count += 1
self.flag = 'not_found'
candidate_bboxes = self.Global_Track_eval(image, 10)
candidate_scores = self.pymdnet_eval(image, candidate_bboxes)
max_id = np.argmax(candidate_scores)
if candidate_scores[max_id] > 0:
redet_bboxes = candidate_bboxes[max_id]
if self.count >= 5:
self.last_gt = np.array([
redet_bboxes[1], redet_bboxes[0],
redet_bboxes[1] + redet_bboxes[3],
redet_bboxes[2] + redet_bboxes[0]
])
self.local_Tracker.pos = torch.FloatTensor([
(self.last_gt[0] + self.last_gt[2] - 1) / 2,
(self.last_gt[1] + self.last_gt[3] - 1) / 2
])
self.local_Tracker.target_sz = torch.FloatTensor([
(self.last_gt[2] - self.last_gt[0]),
(self.last_gt[3] - self.last_gt[1])
])
self.score_max = candidate_scores[max_id]
self.count = 0
if update:
self.collect_samples_pymdnet(image)
self.pymdnet_long_term_update()
width = self.last_gt[3] - self.last_gt[1]
height = self.last_gt[2] - self.last_gt[0]
toc = time.time() - tic
print(toc)
# if self.flag == 'found' and self.score_max > 0:
# confidence_score = 0.99
# elif self.flag == 'not_found':
# confidence_score = 0.0
# else:
# confidence_score = np.clip((local_score+np.arctan(0.2*self.score_max)/math.pi+0.5)/2, 0, 1)
confidence_score = np.clip(
(local_score + np.arctan(0.2 * self.score_max) / math.pi + 0.5) /
2, 0, 1)
if self.p.visualization:
show_res(cv2.cvtColor(image, cv2.COLOR_RGB2BGR),
np.array(self.last_gt, dtype=np.int32),
'2',
groundtruth=self.groundtruth,
update=update_score,
can_bboxes=candidate_bboxes,
frame_id=self.i,
tracker_score=md_score,
mask=mask)
return [
float(self.last_gt[1]),
float(self.last_gt[0]),
float(width),
float(height)
], self.score_map, 0, confidence_score, 0