def main():
# load net
net_file = join(realpath(dirname(__file__)), 'SiamRPNBIG.model')
net = SiamRPNBIG()
net.load_state_dict(torch.load(net_file))
net.eval().cuda()
# warm up
for i in range(10):
net.temple(
torch.autograd.Variable(torch.FloatTensor(1, 3, 127, 127)).cuda())
net(torch.autograd.Variable(torch.FloatTensor(1, 3, 255, 255)).cuda())
# start to track
handle = vot.VOT("polygon")
Polygon = handle.region()
cx, cy, w, h = get_axis_aligned_bbox(Polygon)
image_file = handle.frame()
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
while True:
image_file = handle.frame()
if not image_file:
break
im = cv2.imread(image_file) # HxWxC
state = SiamRPN_track(state, im) # track
res = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
handle.report(Rectangle(res[0], res[1], res[2], res[3]))
def track_vot2(self, imgtype):
"""Run tracker on a sequence."""
handle = vot.VOT("rectangle", "rgbt")
rect = list(handle.region())
colorimage, thermalimage = handle.frame()
startnum = 20
image_rgb = self._read_image(colorimage[startnum:len(colorimage)-2])
image_ir = self._read_image(thermalimage[startnum:len(thermalimage)-2])
if imgtype == 'rgb':
self.initialize(image_ir, image_rgb, rect)
elif imgtype == 'ir':
self.initialize(image_rgb, image_ir, rect)
while True:
colorimage, thermalimage = handle.frame()
if not colorimage:
break
image_rgb = self._read_image(colorimage[startnum:len(colorimage)-2])
image_ir = self._read_image(thermalimage[startnum:len(thermalimage)-2])
if imgtype == 'rgb':
state = self.track(image_ir, image_rgb)
elif imgtype == 'ir':
state = self.track(image_rgb, image_ir)
region = np.array(state).astype(int)
handle.report(Rectangle(region[0], region[1], region[2], region[3]))
def run_vot_exp(tracker_name, para_name, vis=False):
torch.set_num_threads(1)
save_root = os.path.join(
'/data/sda/v-yanbi/iccv21/LittleBoy/vot20_lt_debug', para_name)
if vis and (not os.path.exists(save_root)):
os.makedirs(save_root)
tracker = stark_vot20_lt(tracker_name=tracker_name, para_name=para_name)
handle = vot.VOT("rectangle")
selection = handle.region()
imagefile = handle.frame()
init_box = [selection.x, selection.y, selection.width, selection.height]
if not imagefile:
sys.exit(0)
if vis:
'''for vis'''
seq_name = imagefile.split('/')[-3]
save_v_dir = os.path.join(save_root, seq_name)
if not os.path.exists(save_v_dir):
os.mkdir(save_v_dir)
cur_time = int(time.time() % 10000)
save_dir = os.path.join(save_v_dir, str(cur_time))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right
tracker.initialize(image, init_box)
while True:
imagefile = handle.frame()
if not imagefile:
break
image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right
b1, conf = tracker.track(image)
x1, y1, w, h = b1
handle.report(vot.Rectangle(x1, y1, w, h), conf)
if vis:
'''Visualization'''
# original image
image_ori = image[:, :, ::-1].copy() # RGB --> BGR
image_name = imagefile.split('/')[-1]
save_path = os.path.join(save_dir, image_name)
cv2.imwrite(save_path, image_ori)
# tracker box
image_b = image_ori.copy()
cv2.rectangle(image_b, (int(b1[0]), int(b1[1])),
(int(b1[0] + b1[2]), int(b1[1] + b1[3])),
(0, 0, 255), 2)
image_b_name = image_name.replace('.jpg', '_bbox.jpg')
save_path = os.path.join(save_dir, image_b_name)
cv2.imwrite(save_path, image_b)
def run_vot_exp(tracker_name,
para_name,
refine_model_name,
threshold,
VIS=False):
torch.set_num_threads(1)
save_root = os.path.join(
'/home/alphabin/Desktop/AlphaRefine_submit/vot20_debug', para_name)
if VIS and (not os.path.exists(save_root)):
os.mkdir(save_root)
tracker = build_tracker(tracker_name, para_name, refine_model_name,
threshold)
handle = vot.VOT("mask")
selection = handle.region()
imagefile = handle.frame()
# if not imagefile:
# sys.exit(0)
# if VIS:
# '''for vis'''
# seq_name = imagefile.split('/')[-3]
# save_v_dir = os.path.join(save_root,seq_name)
# if not os.path.exists(save_v_dir):
# os.mkdir(save_v_dir)
# cur_time = int(time.time() % 10000)
# save_dir = os.path.join(save_v_dir, str(cur_time))
# if not os.path.exists(save_dir):
# os.makedirs(save_dir)
image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right
# mask given by the toolkit ends with the target (zero-padding to the right and down is needed)
mask = make_full_size(selection, (image.shape[1], image.shape[0]))
tracker.initialize(image, mask)
while True:
imagefile = handle.frame()
if not imagefile:
break
image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right
b1, m, search, search_m = tracker.track(image)
handle.report(m)
def run_vot_exp(tracker_name,
para_name,
refine_model_name,
threshold,
VIS=False):
torch.set_num_threads(1)
# torch.cuda.set_device(CUDA_ID) # set GPU id
save_root = os.path.join('<SAVE_DIR>', para_name)
if VIS and (not os.path.exists(save_root)):
os.makedirs(save_root)
tracker = STARK_ALPHA_SEG(tracker_name=tracker_name,
para_name=para_name,
refine_model_name=refine_model_name,
threshold=threshold)
handle = vot.VOT("mask")
selection = handle.region()
imagefile = handle.frame()
if not imagefile:
sys.exit(0)
if VIS:
'''for vis'''
seq_name = imagefile.split('/')[-3]
save_v_dir = os.path.join(save_root, seq_name)
if not os.path.exists(save_v_dir):
os.mkdir(save_v_dir)
cur_time = int(time.time() % 10000)
save_dir = os.path.join(save_v_dir, str(cur_time))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right
# mask given by the toolkit ends with the target (zero-padding to the right and down is needed)
mask = make_full_size(selection, (image.shape[1], image.shape[0]))
tracker.initialize(image, mask)
while True:
imagefile = handle.frame()
if not imagefile:
break
image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right
b1, m, search, search_m = tracker.track(image)
handle.report(m)
if VIS:
'''Visualization'''
# original image
image_ori = image[:, :, ::-1].copy() # RGB --> BGR
image_name = imagefile.split('/')[-1]
save_path = os.path.join(save_dir, image_name)
cv2.imwrite(save_path, image_ori)
# dimp box
image_b = image_ori.copy()
cv2.rectangle(image_b, (int(b1[0]), int(b1[1])),
(int(b1[0] + b1[2]), int(b1[1] + b1[3])),
(0, 0, 255), 2)
image_b_name = image_name.replace('.jpg', '_bbox.jpg')
save_path = os.path.join(save_dir, image_b_name)
cv2.imwrite(save_path, image_b)
# search region
search_bgr = search[:, :, ::-1].copy()
search_name = image_name.replace('.jpg', '_search.jpg')
save_path = os.path.join(save_dir, search_name)
cv2.imwrite(save_path, search_bgr)
# search region mask
search_bgr_m = search_bgr.astype(np.float32)
search_bgr_m[:, :, 1] += 127.0 * search_m
search_bgr_m[:, :, 2] += 127.0 * search_m
contours, _ = cv2.findContours(search_m, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
search_bgr_m = cv2.drawContours(search_bgr_m, contours, -1,
(0, 255, 255), 4)
search_bgr_m = search_bgr_m.clip(0, 255).astype(np.uint8)
search_name_m = image_name.replace('.jpg', '_search_mask.jpg')
save_path = os.path.join(save_dir, search_name_m)
cv2.imwrite(save_path, search_bgr_m)
# original image + mask
image_m = image_ori.copy().astype(np.float32)
image_m[:, :, 1] += 127.0 * m
image_m[:, :, 2] += 127.0 * m
contours, _ = cv2.findContours(m, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
image_m = cv2.drawContours(image_m, contours, -1, (0, 255, 255), 2)
image_m = image_m.clip(0, 255).astype(np.uint8)
image_mask_name_m = image_name.replace('.jpg', '_mask.jpg')
save_path = os.path.join(save_dir, image_mask_name_m)
cv2.imwrite(save_path, image_m)
from run_SiamRPN import SiamRPN_init, SiamRPN_track
from utils import get_axis_aligned_bbox, cxy_wh_2_rect
# load net
net_file = join(realpath(dirname(__file__)), 'SiamRPNBIG.model')
net = SiamRPNBIG()
net.load_state_dict(torch.load(net_file))
net.eval().cuda()
# warm up
for i in range(10):
net.temple(torch.autograd.Variable(torch.FloatTensor(1, 3, 127, 127)).cuda())
net(torch.autograd.Variable(torch.FloatTensor(1, 3, 255, 255)).cuda())
# start to track
handle = vot.VOT("polygon")
Polygon = handle.region()
cx, cy, w, h = get_axis_aligned_bbox(Polygon)
image_file = handle.frame()
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
while True:
image_file = handle.frame()
if not image_file:
break
im = cv2.imread(image_file) # HxWxC
#!/usr/bin/python
#import torch
import vot
import sys
import time
from src.siamvggtracker import SiamVGGTracker
# *****************************************
# VOT: Create VOT handle at the beginning
# Then get the initializaton region
# and the first image
# *****************************************
handle = vot.VOT("rectangle")
selection = handle.region()
# Process the first frame
imagefile = handle.frame()
tracker = SiamVGGTracker(imagefile, selection)
if not imagefile:
sys.exit(0)
while True:
# *****************************************
# VOT: Call frame method to get path of the
# current image frame. If the result is
# null, the sequence is over.
# *****************************************
imagefile = handle.frame()
if not imagefile:
def run_vot_exp(tracker_name, para_name, vis=False):
torch.set_num_threads(1)
save_root = os.path.join('/data/sda/v-yanbi/iccv21/LittleBoy/vot20_debug',
para_name)
if vis and (not os.path.exists(save_root)):
os.mkdir(save_root)
tracker = stark_vot20(tracker_name=tracker_name, para_name=para_name)
handle = vot.VOT("mask")
selection = handle.region()
imagefile = handle.frame()
if not imagefile:
sys.exit(0)
if vis:
'''for vis'''
seq_name = imagefile.split('/')[-3]
save_v_dir = os.path.join(save_root, seq_name)
if not os.path.exists(save_v_dir):
os.mkdir(save_v_dir)
cur_time = int(time.time() % 10000)
save_dir = os.path.join(save_v_dir, str(cur_time))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right
# mask given by the toolkit ends with the target (zero-padding to the right and down is needed)
mask = make_full_size(selection, (image.shape[1], image.shape[0]))
tracker.initialize(image, mask)
while True:
imagefile = handle.frame()
if not imagefile:
break
image = cv2.cvtColor(cv2.imread(imagefile), cv2.COLOR_BGR2RGB) # Right
b1, m = tracker.track(image)
handle.report(m)
if vis:
'''Visualization'''
# original image
image_ori = image[:, :, ::-1].copy() # RGB --> BGR
image_name = imagefile.split('/')[-1]
save_path = os.path.join(save_dir, image_name)
cv2.imwrite(save_path, image_ori)
# tracker box
image_b = image_ori.copy()
cv2.rectangle(image_b, (int(b1[0]), int(b1[1])),
(int(b1[0] + b1[2]), int(b1[1] + b1[3])),
(0, 0, 255), 2)
image_b_name = image_name.replace('.jpg', '_bbox.jpg')
save_path = os.path.join(save_dir, image_b_name)
cv2.imwrite(save_path, image_b)
# original image + mask
image_m = image_ori.copy().astype(np.float32)
image_m[:, :, 1] += 127.0 * m
image_m[:, :, 2] += 127.0 * m
contours, _ = cv2.findContours(m, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
image_m = cv2.drawContours(image_m, contours, -1, (0, 255, 255), 2)
image_m = image_m.clip(0, 255).astype(np.uint8)
image_mask_name_m = image_name.replace('.jpg', '_mask.jpg')
save_path = os.path.join(save_dir, image_mask_name_m)
cv2.imwrite(save_path, image_m)
sys.path.append("/home/guo/zpy/Mypysot")
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
from os.path import realpath, dirname, join
del os.environ['MKL_NUM_THREADS']
import time
import cv2
import torch
import numpy as np
from mypysot.tracker.TSDMTrack import TSDMTracker
from mypysot.tools.bbox import get_axis_aligned_bbox
# start to track
handle = vot.VOT("rectangle", 'rgbd')
region = handle.region()
image_file1, image_file2 = handle.frame()
if not image_file1:
sys.exit(0)
image_rgb = cv2.imread(image_file1)
image_depth = cv2.imread(image_file2, -1)
SiamRes_dir = '/home/guo/zpy/Mypysot/mypysot/data_and_result/weight/modelMob.pth' #modelRes
SiamMask_dir = '/home/guo/zpy/Mypysot/mypysot/data_and_result/weight/Mob20.pth' #Res20.pth'
Dr_dir = '/home/guo/zpy/Mypysot/mypysot/data_and_result/weight/High-Low-two.pth.tar'
tracker = TSDMTracker(SiamRes_dir, SiamMask_dir, Dr_dir, image_rgb,
image_depth, region)
#track
while True:
net = net.cuda()
# warm up
print('==== warm up ====')
for i in range(10):
net.template(
torch.rand(1, 3, 127, 127).cuda(),
torch.rand(1, 127, 127).cuda())
net.track(torch.rand(1, 3, 255, 255).cuda())
tracker = OceanPlus(info)
# vot2020 settings
if mask_vot:
handle = vot.VOT("mask")
else:
handle = vot.VOT("rectangle")
image_file = handle.frame()
if not image_file:
sys.exit(0)
im = cv2.imread(image_file) # HxWxC
if mask_vot:
print('the input is a binary mask')
selection = handle.region()
mask = make_full_size(selection, (im.shape[1], im.shape[0]))
bbox = rect_from_mask(mask) # [cx,cy,w,h] TODO: use cv.minmaxRect here
if x.shape[0] == output_sz[1] and x.shape[1] == output_sz[0]:
return x
pad_x = output_sz[0] - x.shape[1]
if pad_x < 0:
x = x[:, :x.shape[1] + pad_x]
# padding has to be set to zero, otherwise pad function fails
pad_x = 0
pad_y = output_sz[1] - x.shape[0]
if pad_y < 0:
x = x[:x.shape[0] + pad_y, :]
# padding has to be set to zero, otherwise pad function fails
pad_y = 0
return np.pad(x, ((0, pad_y), (0, pad_x)), 'constant', constant_values=0)
handle = vot.VOT("mask")
selection = handle.region()
imagefile = handle.frame()
if not imagefile:
sys.exit(0)
image = cv2.imread(imagefile, cv2.IMREAD_GRAYSCALE)
# mask given by the toolkit ends with the target (zero-padding to the right and down is needed)
mask = make_full_size(selection, (image.shape[1], image.shape[0]))
tracker = NCCTracker(image, mask)
while True:
imagefile = handle.frame()
if not imagefile:
top_left, bottom_right = box[:2].tolist(), box[2:].tolist()
image = cv2.rectangle(image, tuple(top_left), tuple(bottom_right),
[0, 0, 255], 2)
save_path = os.path.join("/media/tangjiuqi097/ext", tracker_name)
os.makedirs(save_path, exist_ok=True)
split_image_file = image_file.split('/')
seq_name = split_image_file[-3]
im_name = split_image_file[-1]
os.makedirs(os.path.join(save_path, seq_name), exist_ok=True)
im_path = os.path.join(save_path, seq_name, im_name)
cv2.imwrite(im_path, image[:, :, ::-1])
# handle = vot.VOT("rectangle")
handle = vot.VOT("polygon", 'rgbd')
selection = handle.region()
if isinstance(selection, Polygon):
selection = np.array(selection).reshape(-1)
cx = np.mean(selection[0::2])
cy = np.mean(selection[1::2])
x1 = np.min(selection[0::2])
x2 = np.max(selection[0::2])
y1 = np.min(selection[1::2])
y2 = np.max(selection[1::2])
A1 = np.linalg.norm(selection[0:2] -
selection[2:4]) * np.linalg.norm(selection[2:4] -
selection[4:6])
A2 = (x2 - x1) * (y2 - y1)
s = np.sqrt(A1 / A2)
def run_meta_tracker():
handle = vot.VOT("rectangle")
selection = handle.region()
imagefile = handle.frame()
if not imagefile:
sys.exit(0)
# Init bbox
target_bbox = np.array([selection.x, selection.y, selection.width, selection.height])
prev_result = target_bbox
prev_result_bb = target_bbox
## Initialize Tracker Net ##
init_net = MetaSDNet(opts['init_model_path'])
if opts['use_gpu']:
init_net = init_net.cuda()
init_net.set_learnable_params(['features','fc'])
states = torch.load(opts['meta_tracker_path'])
meta_init = states['meta_init']
meta_alpha = states['meta_alpha']
init_net.copy_meta_weights(meta_init)
# Init criterion
criterion = BinaryLoss()
evaluator = Accuracy() # or Precision
# Load first image1
image1 = Image.open(imagefile).convert('RGB')
# Train bbox regressor
bbreg_examples = gen_samples(SampleGenerator('uniform', image1.size, 0.3, 1.5, 1.1),
target_bbox, opts['n_bbreg'], opts['overlap_bbreg'], opts['scale_bbreg'])
bbreg_feats = forward_samples(init_net, image1, bbreg_examples, out_layer='features')
bbreg = BBRegressor(image1.size)
bbreg.train(bbreg_feats, bbreg_examples, target_bbox)
# Initial training(for the first frame)
pos_feats, neg_feats, init_acc, first_acc = train_init(init_net, meta_alpha, criterion, image1, target_bbox, evaluator)
init_params = OrderedDict()
for k, p in init_net.named_parameters():
init_params[k] = p
online_net = MetaSDNetOnline()
online_net.copy_meta_weights(init_params)
online_net.set_learnable_params(['fc'])
if opts['use_gpu']:
online_net = online_net.cuda()
online_optimizer = set_optimizer(online_net, opts['lr_update'], opts['lr_mult'])
#print("\t[Init] init_acc:%.4f, acc:%.4f\n"%(init_acc, first_acc))
# Init sample generators
sample_generator = SampleGenerator('gaussian', image1.size, opts['trans_f'], opts['scale_f'], valid=True)
pos_generator = SampleGenerator('gaussian', image1.size, 0.1, 1.2)
neg_generator = SampleGenerator('uniform', image1.size, 1.5, 1.2)
# Init pos/neg features for update
pos_feats_all = [pos_feats]
neg_feats_all = [neg_feats]
# Main loop
iters = 1
while True:
# Load image
imagefile = handle.frame()
if not imagefile:
break
image = Image.open(imagefile).convert('RGB')
# Estimate target bbox,
# Using top-scored 5 bboxes, average bbox # regression
samples = gen_samples(sample_generator, target_bbox, opts['n_samples'])
sample_scores = forward_samples(online_net, image, samples)
top_scores, top_idx = sample_scores[:,1].topk(5)
top_idx = top_idx.cpu().numpy()
target_score = top_scores.mean()
target_bbox = samples[top_idx].mean(axis=0)
success = target_score > opts['success_thr']
# Expand search area at failure
if success:
sample_generator.set_trans_f(opts['trans_f'])
else:
sample_generator.set_trans_f(opts['trans_f_expand'])
# Bbox regression
if success:
bbreg_samples = samples[top_idx]
bbreg_feats = forward_samples(online_net, image, bbreg_samples, out_layer='features')
bbreg_samples = bbreg.predict(bbreg_feats, bbreg_samples)
bbreg_bbox = bbreg_samples.mean(axis=0)
else:
bbreg_bbox = target_bbox
# Copy previous result at failure
if not success:
target_bbox = prev_result
bbreg_bbox = prev_result_bb
# Save result
prev_result = target_bbox
prev_result_bb = bbreg_bbox
vot_result = vot.Rectangle(bbreg_bbox[0],bbreg_bbox[1],bbreg_bbox[2],bbreg_bbox[3])
handle.report(vot_result)
# Data collect
if success:
# Draw pos/neg samples
pos_examples = gen_samples(pos_generator, target_bbox,
opts['n_pos_update'],
opts['overlap_pos_update'])
neg_examples = gen_samples(neg_generator, target_bbox,
opts['n_neg_update'],
opts['overlap_neg_update'])
# Extract pos/neg features
pos_feats = forward_samples(online_net, image, pos_examples, out_layer='features')
neg_feats = forward_samples(online_net, image, neg_examples, out_layer='features')
pos_feats_all.append(pos_feats)
neg_feats_all.append(neg_feats)
if len(pos_feats_all) > opts['n_frames_long']:
del pos_feats_all[0]
if len(neg_feats_all) > opts['n_frames_short']:
del neg_feats_all[0]
# Short term update
if not success:
#print("Short term update!")
nframes = min(opts['n_frames_short'],len(pos_feats_all))
pos_feats_short = pos_feats_all[-nframes:]
pos_data = pos_feats_short[0]
for pos_feat in pos_feats_short[1:]:
pos_data = torch.cat([pos_data,pos_feat],0)
#neg_feats_short = neg_feats_all
neg_feats_short = neg_feats_all[-nframes:]
neg_data = neg_feats_short[0]
for neg_feat in neg_feats_short[1:]:
neg_data = torch.cat([neg_data,neg_feat],0)
train_online(online_net, online_optimizer, criterion, pos_data, neg_data, opts['n_tracker_updates'])
# Long term update
elif iters % opts['long_interval'] == 0:
#print("Long term update!")
pos_data = pos_feats_all[0]
for pos_feat in pos_feats_all[1:]:
pos_data = torch.cat([pos_data,pos_feat],0)
neg_data = neg_feats_all[0]
for neg_feat in neg_feats_all[1:]:
neg_data = torch.cat([neg_data,neg_feat],0)
train_online(online_net, online_optimizer, criterion, pos_data, neg_data, opts['n_tracker_updates'])
iters = iters+1
return prev_result, prev_result_bb
def run_crest():
handle = vot.VOT("rectangle")
selection = handle.region()
imagefile = handle.frame()
if not imagefile:
sys.exit(0)
# Generate sequence config
max_object_sz = 100
target_bbox1 = np.array([selection.x, selection.y, selection.width, selection.height])
## initialize feature extractor ##
feature_net = FeatureExtractor(feat_extractor_path)
if use_gpu:
feature_net = feature_net.cuda()
feature_net.eval()
## resize target object size - computational issue ##
scale = 1
if target_bbox1[2:].prod() > max_object_sz**2:
scale = max_object_sz/(target_bbox1[2:].max())
# load first image1
image1 = Image.open(imagefile).convert('RGB')
image_sz_resized = (int(image1.size[0]*scale), int(image1.size[1]*scale))
image1_resized = image1.resize(image_sz_resized,Image.BILINEAR)
# scale target bbox
resized_target_bbox1 = np.round(target_bbox1*scale)
target_sz1 = resized_target_bbox1[2:]
target_center1 = resized_target_bbox1[:2] + np.floor(target_sz1/2)
# get cosine window(square shape)
window_sz = get_search_size(target_sz1)
window_cell_sz = np.ceil(window_sz/cell_sz)
window_cell_sz = window_cell_sz - (window_cell_sz%2) + 1
cos_window = np.outer(np.hanning(window_cell_sz),np.hanning(window_cell_sz))
cos_window = cos_window[None,None,:,:].repeat(vgg_num_channels,axis=1)
cos_window = Variable(torch.from_numpy(cos_window).float(),requires_grad=False)
if use_gpu:
cos_window = cos_window.cuda()
# get search patch and extract the feature
patch = get_search_patch(image1_resized, target_center1, window_sz)
patch = Variable(torch.from_numpy(patch[None,:]))
if use_gpu:
patch = patch.cuda()
feat = feature_net.forward(patch)
# resizing features as same size as cos_window
feat = resize_feat_cos_window(feat, cos_window)
feat_sz=feat.size(3)
## get correlation filter label(gaussian shape) ##
target_cell_sz1 = np.ceil(target_sz1/cell_sz)
rw = int(np.ceil(target_cell_sz1[0]/2)) # padding for CF
rh = int(np.ceil(target_cell_sz1[1]/2)) # padding for CF
output_sigma = target_cell_sz1*output_sigma_factor
label1_np = gaussian_shaped_labels(output_sigma, feat_sz, target_sz1)
label1 = Variable(torch.from_numpy(label1_np).float())
label1 = label1.view(1,1,label1.size(0),label1.size(1))
if use_gpu:
label1 = label1.cuda()
## MetaCrest init ##
state = torch.load(tracker_path)
meta_init = state['meta_init']
meta_alpha = state['meta_alpha']
criterion = L2normLoss()
if use_gpu:
criterion = criterion.cuda()
## training first frame ##
tracker_net = train_init(meta_init, meta_alpha, target_cell_sz1, criterion, feat, label1)
online_optimizer = set_optimizer(tracker_net, online_lr_base, online_w_decay)
tracker_net.eval()
## online prediction ##
target_center = target_center1
target_sz = target_sz1
feat_update = []
label_update = []
iters = 0
while True:
imagefile = handle.frame()
if not imagefile:
break
image = Image.open(imagefile).convert('RGB')
image_resized = image.resize(image_sz_resized,Image.BILINEAR)
patch = get_search_patch(image_resized, target_center, window_sz)
#plt.imshow(patch[0].data.cpu().numpy().transpose(1,2,0)); plt.colorbar(); plt.show()
patch = Variable(torch.from_numpy(patch[None,:]))
if use_gpu:
patch = patch.cuda()
feat = feature_net.forward(patch)
feat = resize_feat_cos_window(feat, cos_window)
response = tracker_net.forward(feat)
score = response.max().data[0]
#plt.imshow(response.data.cpu().numpy()[0][0]); plt.colorbar(); plt.show()
motion_sigma = target_cell_sz1*motion_sigma_factor
motion_map = gaussian_shaped_labels(motion_sigma, window_cell_sz, target_sz)
#plt.imshow(motion_map); plt.colorbar(); plt.show()
motion_map = Variable(torch.from_numpy(motion_map).float())
motion_map = motion_map.view(1,1,motion_map.size(0),motion_map.size(1))
if use_gpu:
motion_map = motion_map.cuda()
response = response*motion_map
response_np = response.data[0][0].cpu().numpy()
#plt.imshow(response_np); plt.colorbar(); plt.show()
delta = np.argwhere(response_np.max()==response_np)[0]+1 #zero-index -> original-index
delta = delta-np.ceil(window_cell_sz/2)
target_center[0] = target_center[0] + delta[1]*cell_sz
target_center[1] = target_center[1] + delta[0]*cell_sz
target_center = valid_pos(target_center, image_sz_resized)
target_sz = scale_estimation(image_resized, feature_net, tracker_net, \
target_sz, target_center, window_sz, cos_window)
resized_target_bbox = np.zeros(4)
resized_target_bbox[:2] = target_center - target_sz/2
resized_target_bbox[2:] = target_sz
result = np.round(resized_target_bbox/scale)
vot_result = vot.Rectangle(result[0],result[1],result[2],result[3])
handle.report(vot_result)
if score > score_thres:
temp_label = np.roll(label1_np, int(delta[0]), axis=0) # circular shift y-axis
temp_label = np.roll(temp_label, int(delta[1]), axis=1) # x-axis
if len(label_update) == max_db_size:
label_update.pop(0)
feat_update.pop(0)
temp_label = torch.from_numpy(temp_label[None,:]).float()
if use_gpu:
temp_label = temp_label.cuda()
label_update.append(temp_label)
feat_update.append(feat[0].data.clone().float())
if (iters+1)%update_interval==0:
if len(label_update) >= update_interval:
tracker_net.train()
model_update(tracker_net, online_optimizer, criterion, torch.stack(feat_update), torch.stack(label_update))
iters = iters+1
return result, gt
