class SiamRPN(BaseTracker):
def __init__(self):
super(SiamRPN, self).__init__("SiamRPN")
self.net_file = path_config.SIAMRPN_MODEL
self.tracker = TrackerSiamRPN(net_path=self.net_file)
def initialize(self, image_file, box):
image = Image.open(image_file).convert("RGB")
self.tracker.init(image, box)
def track(self, image_file):
image = Image.open(image_file).convert("RGB")
return self.tracker.update(image)
from __future__ import absolute_import
from got10k.experiments import *
from siamrpn import TrackerSiamRPN
if __name__ == '__main__':
# setup tracker
net_path = 'pretrained/siamrpn/model.pth'
tracker = TrackerSiamRPN(net_path=net_path)
# setup experiments
# 7 datasets with different versions
experiments = [
ExperimentGOT10k('data/GOT-10k', subset='test'),
ExperimentOTB('data/OTB', version=2015),
ExperimentOTB('data/OTB', version=2013),
ExperimentVOT('data/vot2018', version=2018),
ExperimentUAV123('data/UAV123', version='UAV123'),
ExperimentUAV123('data/UAV123', version='UAV20L'),
ExperimentDTB70('data/DTB70'),
ExperimentTColor128('data/Temple-color-128'),
ExperimentNfS('data/nfs', fps=30),
ExperimentNfS('data/nfs', fps=240),
]
# run experiments
for e in experiments:
e.run(tracker, visualize=True)
e.report([tracker.name])
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from got10k.datasets import *
from got10k.experiments import *
from siamrpn import TrackerSiamRPN
# fitlog.commit(__file__ , fit_msg="这里是SiamRPN测试过程, 师兄改版(siamrpn_master)")
if __name__ == '__main__':
net_path = 'pretrained/siamrpn_e'
iters = 1
tracker = TrackerSiamRPN(net_path=net_path + str(iters) + ".pth",
fit_log=True)
experiments = [
ExperimentGOT10k('/data2/data_zyp/GOT-10k', subset='test'),
ExperimentOTB('/data2/data_zyp/OTB', version=2015),
ExperimentVOT('/data/zyp/data/VOT2019', version=2019),
# ExperimentGOT10k('data/GOT-10k', subset='test'),
# ExperimentOTB('data/OTB', version=2015),
# ExperimentOTB('data/OTB', version=2013),
# ExperimentVOT('data/vot2018', version=2018),
# ExperimentUAV123('data/UAV123', version='UAV123'),
# ExperimentUAV123('data/UAV123', version='UAV20L'),
# ExperimentDTB70('data/DTB70'),
# ExperimentTColor128('data/Temple-color-128'),
# ExperimentNfS('data/nfs', fps=30),
# ExperimentNfS('data/nfs', fps=240),
nw = np.arange(int(tw/f/2), w - int(tw/f/2), int(tw/f))
x = [crop_and_resize(img, np.array([p, q]), np.sqrt(th * tw),
out_size=271,
border_value=np.mean(img, axis=(0, 1))) for p in nh.flatten() for q in nw.flatten()]
for i, xx in enumerate(x):
cv2.imwrite('test2/%d.png' % (i), xx)
return x, (len(nh), len(nw))
if __name__ == '__main__':
# setup tracker
name = 5
name = str(name).zfill(2)
net_path = 'pretrained/siamrpn/model.pth'
tracker = TrackerSiamRPN(net_path=net_path)
ori_img = cv2.imread('/DATA2/wxr/pacman/data-png/%s.png'%(name))[:, :, :]
# ori_img = cv2.imread('/home/wxr/projects/siamrpn-pytorch/test2/17.png')[:, :, :]
h, w, c = ori_img.shape
template = cv2.imread('/DATA2/wxr/pacman/template.png')
template = cv2.resize(template, (127, 127),
interpolation=cv2.INTER_NEAREST)
template = cv2.copyMakeBorder(template, 72, 72, 72, 72, cv2.BORDER_CONSTANT)
cv2.imwrite('template.png', template)
imgs, (nh, nw) = crop_image(ori_img, f = 1)
print(len(imgs), (nh, nw))
tracker.init(template, None)
# box, response = tracker.update(ori_img)
# print(box, response)
def __init__(self):
super(SiamRPN, self).__init__("SiamRPN")
self.net_file = path_config.SIAMRPN_MODEL
self.tracker = TrackerSiamRPN(net_path=self.net_file)
from __future__ import absolute_import
# import fitlog
import os
import sys
sys.path.append('..')
from got10k.datasets import *
from siamrpn import TrackerSiamRPN
# fitlog.commit(__file__ , fit_msg="这里是SiamRPN训练过程, 师兄改版(siamrpn_master)")
root_dir = '/data2/data_zyp/GOT-10k'
# root_dir = os.path.expanduser('~/data/GOT-10k')
train_seqs = GOT10k(root_dir, subset='train', return_meta=True)
# using val
val_seqs = GOT10k(root_dir, subset='val', return_meta=True)
tracker = TrackerSiamRPN(fit_log=True)
tracker.train_over(seqs=train_seqs, val_seqs=val_seqs)
# fitlog.finish()
parser.add_argument('--region', nargs='*',action="store",help='set alarm region')
parser.add_argument('-v','--video',type = str, default = 'ARENA-W1-11_03_ENV_RGB_3.mp4', help='video name')
parser.add_argument('--type', type = int, default = 0, help = 'tracker type')
parser.add_argument('--boxnum', type = int, default = 1, help = 'box number')
args = parser.parse_args()
if __name__ == '__main__':
# Setup device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#torch.backends.cudnn.benchmark = True
# Setup Model
### init model ###
tracker = TrackerSiamRPN(net_path=args.net_path)
# Parse Image file
# cap = cv2.VideoCapture(args.video)
#if (cap.isOpened() == False):
# print('error opening video file')
# ret, frame = cap.read()
img_files = natural_sort(glob.glob(args.base_path + '*.jpg'))
ims = [cv2.imread(imf) for imf in img_files]
# Select ROI
cv2.namedWindow("SiamRPN_Tracking", cv2.WND_PROP_AUTOSIZE)
### init the region
region, object = SelectRoi(args.region, args.object, ims[0], number = args.boxnum)