def __call__(self, image, box, state, display_tracking_speed=False):
"""Track the object in an image using the pre-calculated internal state
and a new image. (init must be run before this function.)
Args:
image(A(H,W,3), uint8): Input image
box : Unused; only here for compatibility.
display_tracking_speed (bool, optional): Option to print trakcing fps
Returns:
box(A(4)): Returns a relative bounding box of type [center_x, center_y, width, height]
state(?): Returns meta-information required for maintaining tracking state.
Note:
A(shape[,dtype=float]) in the above documentation is a shorthand for numpy.array.
"""
# tracking and visualization
toc = 0
tic = cv2.getTickCount()
state = SiamRPN_track(state, image, use_gpu=self.use_gpu_) # track
toc += cv2.getTickCount() - tic
box = (
state["target_pos"][0],
state["target_pos"][1],
state["target_sz"][0],
state["target_sz"][1],
)
img_h, img_w = np.shape(image)[:2]
box = utils.convert_to_relative([img_w, img_h], box)
return box, state
def track(self, img):
self.count -= 1
# get siamrpn tracker's result
state = SiamRPN_track(self.state, img)
res = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
res = [int(l) for l in res]
if self.count <= 0:
# use detector to examine
human = self.yolo.detect_human(img)
if len(human) == 0:
self.human_detected = False
# if no detection, return sot result
return res
self.count = self.max_count
# compare iou between detect result and tracking result
bbox = [
human[0], human[1], human[2] - human[0], human[3] - human[1]
]
bbox = [int(l) for l in bbox]
if iou(bbox, res) < self.iou_threshold:
self.init_SOT(bbox, img)
return bbox
else:
return res
return res
def update(self, image):
self.state = SiamRPN_track(self.state, image)
target_pos = self.state['target_pos']
target_size = self.state['target_sz']
bbox = [target_pos[0], target_pos[1], target_size[0], target_size[1]]
net = self.state['net']
return bbox
def update(self, image):
image = np.asarray(image)
# track
self.state = SiamRPN_track(self.state, image)
# get box
box = cxy_wh_2_rect(self.state['target_pos'], self.state['target_sz'])
box = np.array([i for i in box])
return box
def main():
cap = cv2.VideoCapture(0)
net = SiamRPNvot()
net.load_state_dict(torch.load('./DaSiamRPN/SiamRPNVOT.model'))
net.eval().cuda()
_, im = cap.read()
init_box = cv2.selectROI('selectROI', im)
print(init_box)
target_pos, target_sz = rect_2_cxy_wh(init_box)
state = SiamRPN_init(im, target_pos, target_sz, net)
while True:
_, im = cap.read()
state = SiamRPN_track(state, im) # track
res = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
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)
cv2.waitKey(1)
def main():
# load model
net = SiamRPNvot()
net.load_state_dict(torch.load('./DaSiamRPN/SiamRPNVOT.model'))
net.eval().cuda()
# image and init box
dataset_folder = './test_video/Panda'
ground_truth = np.loadtxt(join(dataset_folder, 'groundtruth_rect.txt'),
delimiter=',')
init_box = ground_truth[0, :]
image_list = [
join(dataset_folder, 'img', x)
for x in list(listdir(join(dataset_folder, 'img')))
]
# tracker init
target_pos, target_sz = rect_2_cxy_wh(init_box)
im = cv2.imread(image_list[0])
state = SiamRPN_init(im, target_pos, target_sz, net)
toc = 0
# tracking and visualization
for image_name in image_list[1:]:
im = cv2.imread(image_name)
tic = cv2.getTickCount()
state = SiamRPN_track(state, im) # track
toc += cv2.getTickCount() - tic
res = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
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)
cv2.waitKey(1)
print('Tracking Speed {:.1f}fps'.format(
(len(image_list) - 1) / (toc / cv2.getTickFrequency())))
net.load_state_dict(torch.load('./DaSiamRPN/SiamRPNVOT.model'))
net.eval().cuda()
init_box = []
while not detect_human:
_, img = cap.read()
human = yolo.detect_human(img)
print(human)
if len(human) == 0:
continue
else:
detect_human = True
init_box = [
human[0], human[1], human[2] - human[0], human[3] - human[1]
]
print('human detected ! start initialize SiamRPN')
target_pos, target_sz = rect_2_cxy_wh(init_box)
state = SiamRPN_init(img, target_pos, target_sz, net)
while True:
_, img = cap.read()
state = SiamRPN_track(state, img) # track
res = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
res = [int(l) for l in res]
cv2.rectangle(img, (res[0], res[1]), (res[0] + res[2], res[1] + res[3]),
(0, 255, 255), 3)
cv2.imshow('SiamRPN', img)
cv2.waitKey(1)