def gen_samples(generator,
bbox,
n,
img_size,
overlap_range=None,
scale_range=None):
if overlap_range is None and scale_range is None:
return generator(bbox, n, img_size)
else:
samples = None
remain = n
factor = 2
while remain > 0 and factor < 32: #change to 32, before is 16
samples_ = generator(bbox, remain * factor, img_size)
#print(samples_)
idx = np.ones(len(samples_), dtype=bool)
if overlap_range is not None:
r = overlap_ratio(samples_, bbox)
idx *= (r >= overlap_range[0]) * (r <= overlap_range[1])
if scale_range is not None:
s = np.prod(samples_[:, 2:], axis=1) / np.prod(bbox[2:])
idx *= (s >= scale_range[0]) * (s <= scale_range[1])
samples_ = samples_[idx, :]
samples_ = samples_[:min(remain, len(samples_))]
if samples is None:
samples = samples_
else:
samples = np.concatenate([samples, samples_])
remain = n - len(samples)
factor = factor * 2
return samples
def train(self, X, bbox, gt):
X = X.cpu().numpy()
bbox = np.copy(bbox)
gt = np.copy(gt)
if gt.ndim == 1:
gt = gt[None, :]
r = overlap_ratio(bbox, gt)
s = np.prod(bbox[:, 2:], axis=1) / np.prod(gt[0, 2:])
idx = (r >= self.overlap_range[0]) * (r <= self.overlap_range[1]) * \
(s >= self.scale_range[0]) * (s <= self.scale_range[1])
X = X[idx]
bbox = bbox[idx]
Y = self.get_examples(bbox, gt)
self.model.fit(X, Y)
def predict(self, X, bbox):
X = X.cpu().numpy()
bbox_ = np.copy(bbox)
Y = self.model.predict(X)
bbox_[:, :2] = bbox_[:, :2] + bbox_[:, 2:] / 2
bbox_[:, :2] = Y[:, :2] * bbox_[:, 2:] + bbox_[:, :2]
bbox_[:, 2:] = np.exp(Y[:, 2:]) * bbox_[:, 2:]
bbox_[:, :2] = bbox_[:, :2] - bbox_[:, 2:] / 2
r = overlap_ratio(bbox, bbox_)
s = np.prod(bbox[:, 2:], axis=1) / np.prod(bbox_[:, 2:], axis=1)
idx = (r >= self.overlap_range[0]) * (r <= self.overlap_range[1]) * \
(s >= self.scale_range[0]) * (s <= self.scale_range[1])
idx = np.logical_not(idx)
bbox_[idx] = bbox[idx]
bbox_[:, :2] = np.maximum(bbox_[:, :2], 0)
bbox_[:, 2:] = np.minimum(bbox_[:, 2:], self.img_size - bbox[:, :2])
return bbox_
pre.append(1)
if frame == (num_frames - 1): #last frame
gt.append(0)
else:
gt.append(1)
if reset:
gt_Polygon = ground_truth[frame]
if gt_Polygon[2] * gt_Polygon[3] != 0:
tracker0.init(im, tuple(gt_Polygon))
template_gt = np.concatenate(
(template_gt,
tracker0.model.zf.cpu().data.numpy()))
else:
template_gt = np.concatenate(
(template_gt, np.zeros([1, 256, 7, 7])))
iou = overlap_ratio(np.array(gt_Polygon),
np.array(outputs['bbox']))
if iou <= 0:
break
else:
template_gt = np.concatenate(
(template_gt, np.zeros([1, 256, 7, 7])))
template_acc = np.concatenate(
(template_acc, np.zeros([1, 256, 7, 7])))
template_cur = np.concatenate(
(template_cur, np.zeros([1, 256, 7, 7])))
init0.append(0)
init.append(frame)
pre.append(1)
if frame == (num_frames - 1): #last frame
gt.append(0)
else:
if template_acc is None:
template_acc = tracker.model.zf.cpu().data.numpy()
else:
template_acc = np.concatenate(
(template_acc, tracker.model.zf.cpu().data.numpy()))
if template_cur is None:
template_cur = tracker.model.zf.cpu().data.numpy()
else:
template_cur = np.concatenate(
(template_cur, tracker.model.zf.cpu().data.numpy()))
init.append(num)
init0.append(num_reset)
pre.append(0)
gt.append(1)
# ----------------
elif overlap_ratio(np.array(init_rect), np.array(img_rect)) > 0.05:
num_reset += 1
# execute tracker
outputs = tracker.track(img)
# ----------------
if template_acc is None:
template_acc = tracker.model.zf.cpu().data.numpy()
else:
template_acc = np.concatenate(
(template_acc, tracker.model.zf.cpu().data.numpy()))
if template_cur is None:
template_cur = tracker.model.zf.cpu().data.numpy()
else:
template_cur = np.concatenate(
(template_cur, outputs['zf_cur'].cpu().data.numpy()))
init.append(num)
gt_init = gt[0]
x, y, w, h = gt_init[0], gt_init[1], gt_init[2], gt_init[3]
cx, cy = x + w // 2, y + h // 2
# tracker init
target_pos, target_sz = np.array([cx, cy]), np.array([w, h])
image_file = os.path.join(img_dir, img_list[0])
im = cv2.imread(image_file) # HxWxC
tic = time.time()
state = SiamRPN_init(im, target_pos, target_sz, net, gt[0]) # init tracker
toc += time.time() - tic
# tracking
for i in range(len(img_list)):
image_file = os.path.join(img_dir, img_list[i])
if not image_file:
break
im = cv2.imread(image_file) # HxWxC
tic = time.time()
state = SiamRPN_track(state, im) # track
toc += time.time() - tic
res = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
rect_list.append(res.tolist())
overlap.append(overlap_ratio(res, gt[i]))
# store results
print("fps: {}".format(len(gt) / toc))
res_dict[seqname] = rect_list
res_dict[seqname + "time"] = [toc, len(gt), len(gt) / toc]
json.dump(res_dict, open("../results/DaSiamRPNAT.json", 'w'), indent=2)
def main(images, init_bbox, ground_truths, opts):
device = ('cuda' if opts.use_gpu else 'cpu')
model = MDNet(opts.model_path).to(device)
criterion = BCELoss()
# Set learnable parameters
for k, p in model.params.items():
p.requires_grad = any([k.startswith(l) for l in opts.ft_layers])
# Set optimizer states
def set_optimizer(lr_base, lr_mult, momentum=0.9, w_decay=0.0005):
param_list = []
for k, p in filter(lambda kp: kp[1].requires_grad, model.params.items()):
lr = lr_base
for l, m in lr_mult.items():
if k.startswith(l):
lr = lr_base * m
param_list.append({'params': [p], 'lr': lr})
return optim.SGD(param_list, lr=lr, momentum=momentum, weight_decay=w_decay)
init_optimizer = set_optimizer(opts.lr_init, opts.lr_mult)
update_optimizer = set_optimizer(opts.lr_update, opts.lr_mult)
# Load first image
image = Image.open(images[0]).convert('RGB')
# Draw pos/neg samples
pos_examples = SampleGenerator('gaussian', image.size, opts.trans_pos, opts.scale_pos)(
init_bbox, opts.n_pos_init, opts.overlap_pos_init)
neg_examples = np.concatenate([
SampleGenerator('uniform', image.size, opts.trans_neg_init, opts.scale_neg_init)(
init_bbox, int(opts.n_neg_init * 0.5), opts.overlap_neg_init),
SampleGenerator('whole', image.size)(
init_bbox, int(opts.n_neg_init * 0.5), opts.overlap_neg_init)])
neg_examples = np.random.permutation(neg_examples)
# Extract pos/neg features
pos_feats = forward_samples(model, image, pos_examples, opts)
neg_feats = forward_samples(model, image, neg_examples, opts)
# Initial training
train(model, criterion, init_optimizer, pos_feats, neg_feats, opts.maxiter_init, opts)
del init_optimizer, neg_feats
torch.cuda.empty_cache()
# Train bbox Regressor
bbreg_examples = SampleGenerator('uniform', image.size, opts.trans_bbreg, opts.scale_bbreg, opts.aspect_bbreg)\
(init_bbox, opts.n_bbreg, opts.overlap_bbreg)
bbreg_feats = forward_samples(model, image, bbreg_examples, opts)
bbreg = BBRegressor(image.size)
bbreg.train(bbreg_feats, bbreg_examples, init_bbox)
del bbreg_feats
torch.cuda.empty_cache()
# Init sample generators for update
sample_generator = SampleGenerator('gaussian', image.size, opts.trans, opts.scale)
pos_generator = SampleGenerator('gaussian', image.size, opts.trans_pos, opts.scale_pos)
neg_generator = SampleGenerator('uniform', image.size, opts.trans_neg, opts.scale_neg)
# Init pos/neg features for update
neg_examples = neg_generator(init_bbox, opts.n_neg_update, opts.overlap_neg_init)
neg_feats = forward_samples(model, image, neg_examples, opts)
pos_feats_all = [pos_feats]
neg_feats_all = [neg_feats]
# Main loop
for i, image in enumerate(images[1:], 1):
image = Image.open(image).convert('RGB')
# Estimate target bbox
samples = sample_generator(init_bbox, opts.n_samples)
sample_scores = forward_samples(model, image, samples, opts, out_layer='fc6')
top_scores, top_idx = sample_scores[:, 1].topk(5)
top_idx = top_idx.cpu()
target_score = top_scores.mean()
init_bbox = samples[top_idx]
if top_idx.shape[0] > 1:
init_bbox = init_bbox.mean(axis=0)
success = target_score > 0
# Expand search area at failure
sample_generator.trans = opts.trans if success else min(sample_generator.trans * 1.1, opts.trans_limit)
# Bbox regression
if success:
bbreg_samples = samples[top_idx]
if top_idx.shape[0] == 1:
bbreg_samples = bbreg_samples[None, :]
bbreg_feats = forward_samples(model, image, bbreg_samples, opts)
bbreg_samples = bbreg.predict(bbreg_feats, bbreg_samples)
bbreg_bbox = bbreg_samples.mean(axis=0)
else:
bbreg_bbox = init_bbox
yield init_bbox, bbreg_bbox, overlap_ratio(ground_truths[i], bbreg_bbox)[0], target_score
# Data collect
if success:
pos_examples = pos_generator(init_bbox, opts.n_pos_update, opts.overlap_pos_update)
pos_feats = forward_samples(model, image, pos_examples, opts)
pos_feats_all.append(pos_feats)
if len(pos_feats_all) > opts.n_frames_long:
del pos_feats_all[0]
neg_examples = neg_generator(init_bbox, opts.n_neg_update, opts.overlap_neg_update)
neg_feats = forward_samples(model, image, neg_examples, opts)
neg_feats_all.append(neg_feats)
if len(neg_feats_all) > opts.n_frames_short:
del neg_feats_all[0]
# Short term update
# TODO: What if disable Short term upate?
if not success:
nframes = min(opts.n_frames_short, len(pos_feats_all))
pos_data = torch.cat(pos_feats_all[-nframes:], 0)
neg_data = torch.cat(neg_feats_all, 0)
train(model, criterion, update_optimizer, pos_data, neg_data, opts.maxiter_update, opts)
# Long term update
# TODO: What if disable Long term update?
elif i % opts.long_interval == 0:
pos_data = torch.cat(pos_feats_all, 0)
neg_data = torch.cat(neg_feats_all, 0)
train(model, criterion, update_optimizer, pos_data, neg_data, opts.maxiter_update, opts)
torch.cuda.empty_cache()
