def init(self, img, box):
# set to evaluation mode
self.net.eval()
# convert box to 0-indexed and center based [y, x, h, w]
box = np.array([
box[1] - 1 + (box[3] - 1) / 2, box[0] - 1 +
(box[2] - 1) / 2, box[3], box[2]
],
dtype=np.float32)
self.center, self.target_sz = box[:2], box[2:]
# create hanning window
self.upscale_sz = self.cfg.response_up * self.cfg.response_sz
self.hann_window = np.outer(np.hanning(self.upscale_sz),
np.hanning(self.upscale_sz))
self.hann_window /= self.hann_window.sum()
# search scale factors
self.scale_factors = self.cfg.scale_step**np.linspace(
-(self.cfg.scale_num // 2), self.cfg.scale_num // 2,
self.cfg.scale_num)
# exemplar and search sizes
context = self.cfg.context * np.sum(self.target_sz)
self.z_sz = np.sqrt(np.prod(self.target_sz + context))
self.x_sz = self.z_sz * \
self.cfg.instance_sz / self.cfg.exemplar_sz
# exemplar image
self.avg_color = np.mean(img, axis=(0, 1))
z = ops.crop_and_resize(img,
self.center,
self.z_sz,
out_size=self.cfg.exemplar_sz,
border_value=self.avg_color)
# exemplar features
z = torch.from_numpy(z).to(self.device).permute(
2, 0, 1).unsqueeze(0).float()
self.kernel = self.net.backbone(z)
def update(self, img):
# set to evaluation mode
self.net.eval()
# search images
x = [
ops.crop_and_resize(img,
self.center,
self.x_sz * f,
out_size=self.cfg.instance_sz,
border_value=self.avg_color)
for f in self.scale_factors
]
x = np.stack(x, axis=0)
x = torch.from_numpy(x).to(self.device).permute(0, 3, 1, 2).float()
# responses
x = self.net.backbone(x)
responses = self.net.head(self.kernel, x)
responses = responses.squeeze(1).cpu().numpy()
# upsample responses and penalize scale changes
responses = np.stack([
cv2.resize(u, (self.upscale_sz, self.upscale_sz),
interpolation=cv2.INTER_CUBIC) for u in responses
])
responses[:self.cfg.scale_num // 2] *= self.cfg.scale_penalty
responses[self.cfg.scale_num // 2 + 1:] *= self.cfg.scale_penalty
# peak scale
scale_id = np.argmax(np.amax(responses, axis=(1, 2)))
# peak location
response = responses[scale_id]
response -= response.min()
response /= response.sum() + 1e-16
response = (1 - self.cfg.window_influence) * response + \
self.cfg.window_influence * self.hann_window
loc = np.unravel_index(response.argmax(), response.shape)
# locate target center
disp_in_response = np.array(loc) - (self.upscale_sz - 1) / 2
disp_in_instance = disp_in_response * \
self.cfg.total_stride / self.cfg.response_up
disp_in_image = disp_in_instance * self.x_sz * \
self.scale_factors[scale_id] / self.cfg.instance_sz
self.center += disp_in_image
# update target size
scale = (1 - self.cfg.scale_lr) * 1.0 + \
self.cfg.scale_lr * self.scale_factors[scale_id]
self.target_sz *= scale
self.z_sz *= scale
self.x_sz *= scale
# return 1-indexed and left-top based bounding box
box = np.array([
self.center[1] + 1 - (self.target_sz[1] - 1) / 2,
self.center[0] + 1 - (self.target_sz[0] - 1) / 2,
self.target_sz[1], self.target_sz[0]
])
return box