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test.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 argparse
import os
import cv2
import torch
import numpy as np
import sys
sys.path.append(os.getcwd())
from tqdm import tqdm
from nanotrack.core.config import cfg
from nanotrack.models.model_builder import ModelBuilder
from nanotrack.tracker.tracker_builder import build_tracker
from nanotrack.utils.bbox import get_axis_aligned_bbox
from nanotrack.utils.model_load import load_pretrain
from toolkit.datasets import DatasetFactory
from toolkit.utils.region import vot_overlap, vot_float2str
from bin.eval import eval
parser = argparse.ArgumentParser(description='nanotrack')
parser.add_argument('--dataset', default='GOT-10k', type=str,help='datasets')
parser.add_argument('--tracker_name', '-t', default='nanotrack',type=str,help='tracker name')
parser.add_argument('--config', default='./models/config/configv3.yaml', type=str,help='config file')
parser.add_argument('--snapshot', default='models/pretrained/nanotrackv3.pth', type=str,help='snapshot of models to eval')
parser.add_argument('--save_path', default='./results', type=str, help='snapshot of models to eval')
parser.add_argument('--video', default='', type=str, help='eval one special video')
parser.add_argument('--vis', action='store_true',help='whether v isualzie result')
parser.add_argument('--gpu_id', default='not_set', type=str, help="gpu id")
parser.add_argument('--tracker_path', '-p', default='./results', type=str,help='tracker result path')
parser.add_argument('--num', '-n', default=4, type=int,help='number of thread to eval')
parser.add_argument('--show_video_level', '-s', dest='show_video_level',action='store_true')
parser.set_defaults(show_video_level=False)
args = parser.parse_args()
if args.gpu_id != 'not_set':
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
torch.set_num_threads(1)
def main():
cfg.merge_from_file(args.config)
dataset_root = os.path.join('./datasets', args.dataset)
params = [0.0,0.0,0.0]
params[0] =cfg.TRACK.LR
params[1]=cfg.TRACK.PENALTY_K
params[2] =cfg.TRACK.WINDOW_INFLUENCE
params_name = args.snapshot.split('/')[-1] + ' '+ args.dataset + ' lr-' + str(params[0]) + ' pk-' + '_' + str(params[1]) + ' win-' + '_' + str(params[2])
# create model
model = ModelBuilder()
# load model
model = load_pretrain(model, args.snapshot).cuda().eval()
# build tracker
tracker = build_tracker(model)
# create dataset
dataset = DatasetFactory.create_dataset(name=args.dataset,
dataset_root=dataset_root,
load_img=False)
if args.dataset in ['VOT2016', 'VOT2018', 'VOT2019']:
total_lost=0
avg_speed =0
for v_idx, video in tqdm(enumerate(dataset)):
if args.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):
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-1)/2, cy-(h-1)/2, w, h] #[topx,topy,w,h]
tracker.init(img, gt_bbox_)
pred_bbox = gt_bbox_
pred_bboxes.append(1)
elif idx > frame_counter:
outputs = tracker.track(img)
pred_bbox = outputs['bbox']
if cfg.MASK.MASK:
pred_bbox = outputs['polygon']
overlap = vot_overlap(pred_bbox, gt_bbox, (img.shape[1], img.shape[0]))
if overlap > 0:
pred_bboxes.append(pred_bbox)
else:
pred_bboxes.append(2)
frame_counter = idx + 5
lost_number += 1
else:
pred_bboxes.append(0)
toc += cv2.getTickCount() - tic
if idx == 0:
cv2.destroyAllWindows()
if args.vis and idx > frame_counter:
cv2.polylines(img, [np.array(gt_bbox, np.int).reshape((-1, 1, 2))],
True, (0, 255, 0), 3)
if cfg.MASK.MASK:
cv2.polylines(img, [np.array(pred_bbox, np.int).reshape((-1, 1, 2))],
True, (0, 255, 255), 3)
else:
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)
cv2.waitKey(1)
toc /= cv2.getTickFrequency()
# save results
video_path = os.path.join(args.save_path, args.dataset, args.tracker_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')
total_lost += lost_number
avg_speed += idx / toc
print('Speed: {:3.1f}fps'.format(avg_speed/60))
print(params_name)
else:
# OPE tracking
for v_idx, video in tqdm(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):
tic = cv2.getTickCount()
if idx == 0:
cx, cy, w, h = get_axis_aligned_bbox(np.array(gt_bbox))
gt_bbox_ = [cx-(w-1)/2, cy-(h-1)/2, w, h] #[topx,topy,w,h]
tracker.init(img, gt_bbox_)
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['bbox']
pred_bboxes.append(pred_bbox)
#scores.append(outputs['best_score'])
toc += cv2.getTickCount() - tic
track_times.append((cv2.getTickCount() - tic)/cv2.getTickFrequency())
if idx == 0:
cv2.destroyAllWindows()
if args.vis and idx > 0:
gt_bbox = list(map(int, gt_bbox))
pred_bbox = list(map(int, pred_bbox))
cv2.rectangle(img, (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, (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, str(idx), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
cv2.imshow(video.name, img)
cv2.waitKey(1)
toc /= cv2.getTickFrequency()
# save results
if 'VOT2018-LT' == args.dataset:
video_path = os.path.join(args.save_path, args.dataset, args.tracker_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(args.save_path, args.dataset, args.tracker_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(args.save_path, args.dataset, args.tracker_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')
eval(args)
if __name__ == '__main__':
main()