def tracking(im, state, cap):
"""Return cx, cy"""
state = SiamRPN_track(state, im) # track
res = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
cx, cy = res[0] + res[2] / 2, res[1] + res[3] / 2
res = [int(l) for l in res]
cv2.rectangle(im, (res[0], res[1]), (res[0] + res[2], res[1] + res[3]),
(0, 255, 255), 3)
cv2.imshow('SiamRPN', im)
cap.write(im)
cv2.waitKey(1)
return cx, cy
def test(score):
net = SiamRPNvot()
net.load_state_dict(
torch.load('/home/traker_hao/code/learn/train_RPN/model/30.model'))
net.eval().cuda()
version_name = 'jiasu'
sequence_path = '/media/traker_hao/data/dataset/UAV1/sequences'
init_path = '/media/traker_hao/data/dataset/UAV1/annotations'
result_path = '/home/traker_hao/result/visdrone/' + version_name
if os.path.exists(result_path) is False:
os.mkdir(result_path)
sequence_names = os.listdir(sequence_path)
random.shuffle(sequence_names)
#sequence_names.sort()
i = 0
for sequence_name in sequence_names:
print(sequence_name)
#if sequence_name != 'Suv':
#continue
#sequence_name='uav0000054_00000_s'
imagenames = os.listdir(sequence_path + '/' + sequence_name)
imagenames.sort()
print(i)
i = i + 1
print(sequence_path + '/' + sequence_name)
f = open(
result_path + '/' + sequence_name + '_' + version_name + '.txt',
'w')
inited = False
fp = open(init_path + '/' + sequence_name + '.txt')
j = 0
for imagename in imagenames:
j = j + 1
image = cv2.imread(sequence_path + '/' + sequence_name + '/' +
imagename)
#init the tracker
if inited is False:
data = fp.readline()
data = data.strip('\n')
data = data.split(',')
[cx, cy, w,
h] = (int(data[0]) + int(data[2]) // 2,
int(data[1]) + int(data[3]) // 2, int(data[2]),
int(data[3]))
#f.write(str(annos[0]['bbox'][0])+','+str(annos[0]['bbox'][1])+','+str(annos[0]['bbox'][2])+','+str(annos[0]['bbox'][3])+','+str(1.00)+'\n')
f.write(data[0] + ',' + data[1] + ',' + data[2] + ',' +
data[3] + '\n')
target_pos, target_sz = np.array([cx, cy]), np.array([w, h])
state = SiamRPN_init(image, target_pos, target_sz, net)
inited = True
cv2.rectangle(image,
(int(cx) - int(w) // 2, int(cy) - int(h) // 2),
(int(cx) + int(w) // 2, int(cy) + int(h) // 2),
(0, 255, 0), 3)
cv2.putText(image, sequence_name, (50, 50), 0, 5e-3 * 200,
(0, 255, 0), 2)
cv2.putText(image, 'initing...', (100, 100), 0, 5e-3 * 200,
(0, 255, 0), 2)
image2 = cv2.resize(image, (960, 540))
cv2.imshow('aa2', image2)
cv2.waitKey(1)
else:
data = fp.readline()
data = data.strip('\n')
data = data.split(',')
try:
truth = (int(data[0]),
int(data[1]), int(data[0]) + int(data[2]),
int(data[1]) + int(data[3]))
except:
truth = [0, 0, 0, 0]
#update the tracker
#print([cx, cy, w, h])
tic = cv2.getTickCount()
t1 = time.time()
state = SiamRPN_track(state, image) # track
#state['target_sz'] = np.array( [int(data[2]), int(data[3])] )
toc = (cv2.getTickCount() - tic) / cv2.getTickFrequency()
#print(1/toc)
#mytracker.target_sz = np.array([int(truth[2]),int(truth[3])])
res = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
res = [int(l) for l in res]
cv2.rectangle(image, (res[0], res[1]),
(res[0] + res[2], res[1] + res[3]),
(0, 255, 255), 2)
#visualize the result
cv2.rectangle(image, (int(truth[0]), int(truth[1])),
(int(truth[2]), int(truth[3])), (0, 255, 0), 2)
#mytracker.target_sz=np.array([int(data[2]),int(data[3])])
#cv2.putText(image, str(iou), (res[0] + res[2], res[1] + res[3]), 0, 5e-3*200, (0,255,0), 2)
cv2.putText(image, sequence_name, (50, 50), 0, 5e-3 * 200,
(0, 255, 0), 2)
image2 = cv2.resize(image, (960, 540))
cv2.imshow('aa2', image2)
if cv2.waitKey(1) == 97:
break
#if j>209:
#cv2.waitKey(0)
f.close()
# for i in range(10):
# net.temple(torch.autograd.Variable(torch.FloatTensor(1, 3, 127, 127)).cuda()) # selonsy:cuda()表示使用GPU进行计算,FloatTensor(1, 3, 127, 127)：浮点型四维张量
# net(torch.autograd.Variable(torch.FloatTensor(1, 3, 255, 255)).cuda())
# start to track
handle = vot.VOT("polygon")
Polygon = handle.region() # region：将配置消息发送到客户端并接收初始化区域和第一个图像的路径。其返回值为初始化区域。
cx, cy, w, h = get_axis_aligned_bbox(Polygon) # get_axis_aligned_bbox：将坐标数据转换成 RPN 的格式
image_file = handle.frame() # frame 函数从客户端获取帧（图像路径）
if not image_file:
sys.exit(0)
target_pos, target_sz = np.array([cx, cy]), np.array([w, h])
im = cv2.imread(image_file) # HxWxC
state = SiamRPN_init(im, target_pos, target_sz, net) # init tracker,SiamRPN_init:构造状态结构体并运行模板分支
# 从第一帧开始跟踪，表示很奇怪，难道直接给定的不准确么？ # selonsy：改进点
while True: # 进入跟踪循环
image_file = handle.frame()
if not image_file:
break
im = cv2.imread(image_file) # HxWxC
state = SiamRPN_track(state, im) # track,SiamRPN_track:运行检测分支并更新状态变量
res = cxy_wh_2_rect(state['target_pos'], state['target_sz']) # cxy_wh_2_rect:将坐标转换成矩形框的表示形式
handle.report(Rectangle(res[0], res[1], res[2], res[3])) # report:将跟踪结果报告给客户端
print(handle.result)
print(handle.frames)
del handle
# warm up
for i in range(10):
net.temple(torch.autograd.Variable(torch.ones(1, 3, 127, 127)).cuda(), \
torch.autograd.Variable(torch.ones(1, 3, 271, 271)).cuda())
net(torch.autograd.Variable(torch.ones(1, 3, 271, 271)).cuda())
for seq in OTB_seqs:
_data_provider.pick_seq(seq)
exemplar_path, exemplar_gt, cur_img_num = _data_provider.eval_pick_exemplar(
)
exemplar = cv2.imread(exemplar_path)
exemplar_pos, exemplar_sz = rect_2_cxy_wh(exemplar_gt)
state = SiamRPN_init(exemplar, exemplar_pos, exemplar_sz, net)
save_file = save_res_path + seq + '_ours.txt'
tracking_res = open(save_file, 'w')
for idx in range(cur_img_num):
instance_path = _data_provider.eval_pick_instance()
instance = cv2.imread(instance_path)
state = SiamRPN_track(state, instance)
print('seq:{}:{} , score:{}'.format(seq, idx, state['score']))
res = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
tracking_res.write('{},{},{},{}'.format(res[0], res[1], res[2],
res[3]))
tracking_res.write('\n')
tracking_res.close()