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test_uav.py
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test_uav.py
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# Copyright (c) SenseTime. All Rights Reserved.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import sys
env_path = os.path.join(os.path.dirname(__file__), '..')
print(env_path)
if env_path not in sys.path:
sys.path.append(env_path)
import argparse
import cv2
import torch
import numpy as np
from pysot_toolkit.bbox import get_axis_aligned_bbox
from pysot_toolkit.toolkit.datasets import DatasetFactory
from pysot_toolkit.toolkit.utils.region import vot_overlap, vot_float2str
from pysot_toolkit.trackers.tracker import Tracker
from pysot_toolkit.trackers.net_wrappers import NetWithBackbone
parser = argparse.ArgumentParser(description='transt_m tracking')
parser.add_argument('--dataset', type=str,
help='datasets')
parser.add_argument('--video', default='', type=str,
help='eval one special video')
parser.add_argument('--vis', action='store_true',
help='whether visualzie result')
parser.add_argument('--name', default='', type=str,
help='name of results')
parser.add_argument('--mask', action='store_true',
help='whether predict mask')
args = parser.parse_args()
torch.set_num_threads(1)
def main():
# load config
dataset_root = '/home/cx/cx2/Downloads/UAV123/UAV123_fix/Dataset_UAV123/UAV123/data_seq/UAV123' # path to uav
net_path = '/home/cx/cx1/TransT_experiments/TransT-M/models/transtm.pth' # path to transtm model
# create model
net = NetWithBackbone(net_path=net_path, use_gpu=True)
tracker = Tracker(name='transt-m', net=net, mask=args.mask,
window_penalty=0.54, penalty_k=0,
update_threshold=0.95, exemplar_size=128, instance_size=256)
# create dataset
dataset = DatasetFactory.create_dataset(name=args.dataset,
dataset_root=dataset_root,
load_img=False)
model_name = tracker.name
total_lost = 0
if args.dataset in ['VOT2016', 'VOT2018', 'VOT2019']:
# restart tracking
for v_idx, video in enumerate(dataset):
if args.video != '':
# test one special video
if video.name != args.video:
continue
frame_counter = 0
lost_number = 0
toc = 0
pred_bboxes = []
for idx, (img, gt_bbox) in enumerate(video):
# convert bgr to rgb
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if len(gt_bbox) == 4:
gt_bbox = [gt_bbox[0], gt_bbox[1],
gt_bbox[0], gt_bbox[1] + gt_bbox[3] - 1,
gt_bbox[0] + gt_bbox[2] - 1, gt_bbox[1] + gt_bbox[3] - 1,
gt_bbox[0] + gt_bbox[2] - 1, gt_bbox[1]]
tic = cv2.getTickCount()
if idx == frame_counter:
cx, cy, w, h = get_axis_aligned_bbox(np.array(gt_bbox))
gt_bbox_ = [cx - w / 2, cy - h / 2, w, h]
init_info = {'init_bbox': gt_bbox_}
tracker.initialize(img, init_info)
pred_bbox = gt_bbox_
pred_bboxes.append(1)
elif idx > frame_counter:
info = {}
outputs = tracker.track(img, info)
pred_bbox = outputs['target_bbox']
if args.mask:
pred_mask = outputs['target_mask']
overlap = vot_overlap(pred_bbox, gt_bbox, (img.shape[1], img.shape[0]))
if overlap > 0:
# not lost
pred_bboxes.append(pred_bbox)
else:
# lost object
pred_bboxes.append(2)
frame_counter = idx + 5 # skip 5 frames
lost_number += 1
else:
pred_bboxes.append(0)
toc += cv2.getTickCount() - tic
if idx == 0:
cv2.destroyAllWindows()
if args.vis and idx > frame_counter:
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.polylines(img, [np.array(gt_bbox, np.int).reshape((-1, 1, 2))],
True, (0, 255, 0), 3)
# if args.mask:
# cv2.polylines(img, [np.array(pred_bbox, np.int).reshape((-1, 1, 2))],
# True, (0, 255, 255), 3)
bbox = list(map(int, pred_bbox))
cv2.rectangle(img, (bbox[0], bbox[1]),
(bbox[0] + bbox[2], bbox[1] + bbox[3]), (0, 255, 255), 3)
cv2.putText(img, str(idx), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
cv2.putText(img, str(lost_number), (40, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
cv2.imshow(video.name, img)
if cv2.waitKey() & 0xFF == ord('q'):
break
toc /= cv2.getTickFrequency()
# save results
video_path = os.path.join('results', args.dataset, model_name,
'baseline', video.name)
if not os.path.isdir(video_path):
os.makedirs(video_path)
result_path = os.path.join(video_path, '{}_001.txt'.format(video.name))
with open(result_path, 'w') as f:
for x in pred_bboxes:
if isinstance(x, int):
f.write("{:d}\n".format(x))
else:
f.write(','.join([vot_float2str("%.4f", i) for i in x]) + '\n')
print('({:3d}) Video: {:12s} Time: {:4.1f}s Speed: {:3.1f}fps Lost: {:d}'.format(
v_idx + 1, video.name, toc, idx / toc, lost_number))
total_lost += lost_number
print("{:s} total lost: {:d}".format(model_name, total_lost))
else:
# OPE tracking
for v_idx, video in enumerate(dataset):
if args.video != '':
# test one special video
if video.name != args.video:
continue
toc = 0
pred_bboxes = []
scores = []
track_times = []
for idx, (img, gt_bbox) in enumerate(video):
# convert bgr to rgb
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
tic = cv2.getTickCount()
if idx == 0:
cx, cy, w, h = get_axis_aligned_bbox(np.array(gt_bbox))
gt_bbox_ = [cx - w / 2, cy - h / 2, w, h]
init_info = {'init_bbox': gt_bbox_}
tracker.initialize(img, init_info)
pred_bbox = gt_bbox_
scores.append(None)
if 'VOT2018-LT' == args.dataset:
pred_bboxes.append([1])
else:
pred_bboxes.append(pred_bbox)
else:
outputs = tracker.track(img)
pred_bbox = outputs['target_bbox']
pred_bboxes.append(pred_bbox)
scores.append(outputs['best_score'])
if args.mask:
pred_mask = outputs['target_mask']
toc += cv2.getTickCount() - tic
track_times.append((cv2.getTickCount() - tic) / cv2.getTickFrequency())
if idx == 0:
cv2.destroyAllWindows()
if args.vis and idx > 0:
img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
gt_bbox = list(map(int, gt_bbox))
pred_bbox = list(map(int, pred_bbox))
cv2.rectangle(img_bgr, (gt_bbox[0], gt_bbox[1]),
(gt_bbox[0] + gt_bbox[2], gt_bbox[1] + gt_bbox[3]), (0, 255, 0), 3)
cv2.rectangle(img_bgr, (pred_bbox[0], pred_bbox[1]),
(pred_bbox[0] + pred_bbox[2], pred_bbox[1] + pred_bbox[3]), (0, 255, 255), 3)
cv2.putText(img_bgr, str(idx), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
img_bgr_m = img_bgr
if args.mask:
img_bgr_m = img_bgr.astype(np.float32)
img_bgr_m[:, :, 1] += 127.0 * pred_mask
img_bgr_m[:, :, 2] += 127.0 * pred_mask
# contours, _ = cv2.findContours(img_bgr_m, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# img_bgr_m = cv2.drawContours(img_bgr_m, contours, -1, (0, 255, 255), 4)
img_bgr_m = img_bgr_m.clip(0, 255).astype(np.uint8)
cv2.imshow(video.name, img_bgr_m)
cv2.waitKey(1)
toc /= cv2.getTickFrequency()
# save results
if 'VOT2018-LT' == args.dataset:
video_path = os.path.join('results', args.dataset, model_name,
'longterm', video.name)
if not os.path.isdir(video_path):
os.makedirs(video_path)
result_path = os.path.join(video_path,
'{}_001.txt'.format(video.name))
with open(result_path, 'w') as f:
for x in pred_bboxes:
f.write(','.join([str(i) for i in x]) + '\n')
result_path = os.path.join(video_path,
'{}_001_confidence.value'.format(video.name))
with open(result_path, 'w') as f:
for x in scores:
f.write('\n') if x is None else f.write("{:.6f}\n".format(x))
result_path = os.path.join(video_path,
'{}_time.txt'.format(video.name))
with open(result_path, 'w') as f:
for x in track_times:
f.write("{:.6f}\n".format(x))
elif 'GOT-10k' == args.dataset:
video_path = os.path.join('results', args.dataset, model_name, video.name)
if not os.path.isdir(video_path):
os.makedirs(video_path)
result_path = os.path.join(video_path, '{}_001.txt'.format(video.name))
with open(result_path, 'w') as f:
for x in pred_bboxes:
f.write(','.join([str(i) for i in x]) + '\n')
result_path = os.path.join(video_path,
'{}_time.txt'.format(video.name))
with open(result_path, 'w') as f:
for x in track_times:
f.write("{:.6f}\n".format(x))
else:
model_path = os.path.join('results', args.dataset, model_name)
if not os.path.isdir(model_path):
os.makedirs(model_path)
result_path = os.path.join(model_path, '{}.txt'.format(video.name))
with open(result_path, 'w') as f:
for x in pred_bboxes:
f.write(','.join([str(i) for i in x]) + '\n')
print('({:3d}) Video: {:12s} Time: {:5.1f}s Speed: {:3.1f}fps'.format(
v_idx + 1, video.name, toc, idx / toc))
if __name__ == '__main__':
main()