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eva_MOT.py
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eva_MOT.py
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import torch
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from data import base_transform, MOT_CLASSES
from ssd import build_ssd
import os
import numpy as np
import cv2
import time
os.environ["CUDA_VISIBLE_DEVICES"] = '2'
class Timer(object):
"""A simple timer."""
def __init__(self):
self.total_time = 0.
self.calls = 0
self.start_time = 0.
self.diff = 0.
self.average_time = 0.
def tic(self):
# using time.time instead of time.clock because time time.clock
# does not normalize for multithreading
self.start_time = time.time()
def toc(self, average=True):
self.diff = time.time() - self.start_time
self.total_time += self.diff
self.calls += 1
self.average_time = self.total_time / self.calls
if average:
return self.average_time
else:
return self.diff
data_size = {'MOT17-02':600, 'MOT17-04':1050, 'MOT17-05':837, 'MOT17-09':525, 'MOT17-10':654, 'MOT17-11': 900,
'MOT17-13':750, 'MOT17-01':450, 'MOT17-03':1500, 'MOT17-06':1194, 'MOT17-07':500, 'MOT17-08':625, 'MOT17-12':900, 'MOT17-14':750,
'ADL-Rundle-6': 525, 'ADL-Rundle-8': 654, 'ETH-Bahnhof': 1000, 'ETH-Pedcross2':837, 'ETH-Sunnyday': 354,
'KITTI-13': 261, 'KITTI-17': 145, 'PETS09-S2L1':795, 'TUD-Campus':71,
'TUD-Stadtmitte':179, 'Venice-2':600, 'ADL-Rundle-1':500, 'ADL-Rundle-3':625, 'AVG-TownCentre':450,
'ETH-Crossing':219, 'ETH-Jelmoli':440, 'ETH-Linthescher':1194, 'KITTI-16':209, 'KITTI-19':1059,
'PETS09-S2L2':436, 'TUD-Crossing':201, 'Venice-1':450
}
model_dir='./weights/tssd300_MOT15_SAL222/ssd300_seqMOT15_4000.pth'
data_path = '/home/sean/data/MOT/MOT17Det/train/'
labelmap = MOT_CLASSES
num_classes = len(MOT_CLASSES) + 1
prior = 'v3'
model_name= 'ssd300'
confidence_threshold=0.3
nms_threshold =0.3
top_k=400
ssd_dim=300
vis = False
if model_dir.split('/')[2].split('_')[0][0]=='t':
tssd = 'tblstm'
attention = True
else:
tssd = 'ssd'
attention = False
refine = False
tub = 10
tub_thresh = 1
tub_generate_score = 0.3
tub_flag = '_t'+str(tub)+'s'+str(tub_thresh)+'g'+str(tub_generate_score)+'_nounique'
set_name = '2DMOT2015'
if set_name == '2DMOT2015':
val_list = ['TUD-Campus', 'ETH-Sunnyday', 'ETH-Pedcross2', 'ADL-Rundle-8', 'Venice-2', 'KITTI-17']
# val_list = ['ADL-Rundle-1' 'ADL-Rundle-3', 'AVG-TownCentre', 'ETH-Crossing', 'ETH-Jelmoli', 'ETH-Linthescher', 'KITTI-16', 'KITTI-19',
# 'PETS09-S2L2', 'TUD-Crossing', 'Venice-1']
elif set_name == 'MOT17Det':
val_list = ['MOT17-02', 'MOT17-04', 'MOT17-05', 'MOT17-09', 'MOT17-10', 'MOT17-11', 'MOT17-13']
# val_list = ['MOT17-01', 'MOT17-03', 'MOT17-06', 'MOT17-07', 'MOT17-08', 'MOT17-12', 'MOT17-14']
output_flag = True
output_dir = '/home/sean/data/MOT/motchallenge-devkit/motchallenge/res/%s/%s' % (set_name, model_dir.split('/')[2] + tub_flag)
if not os.path.exists(output_dir) and output_flag:
os.mkdir(output_dir)
torch.set_default_tensor_type('torch.cuda.FloatTensor')
def main():
mean = (104, 117, 123)
trained_model = model_dir
print('loading model!')
net = build_ssd('test', ssd_dim, num_classes, tssd=tssd,
top_k=top_k,
thresh=confidence_threshold,
nms_thresh=nms_threshold,
attention=attention,
prior=prior,
tub = tub,
tub_thresh = tub_thresh,
tub_generate_score=tub_generate_score,
bn=False)
net.load_state_dict(torch.load(trained_model))
net.eval()
print('Finished loading model!', model_dir)
net = net.cuda()
cudnn.benchmark = True
_t = {'im_detect': Timer(), 'misc': Timer()}
all_time = 0.
total_frame = 0
for val in val_list:
img_path = '/home/sean/data/MOT/%s/train/%s/img1' % (set_name, val)
output_path = os.path.join(output_dir, val + '.txt')
if output_flag:
wf = open(output_path, 'w')
frame_num = 0
pre_frame = cv2.imread(os.path.join(img_path, '000001.jpg'))
h, w, _ = pre_frame.shape
state = [None] * 6 if tssd in ['lstm', 'tblstm'] else None
init_tub = True
for i in range(1, data_size[val]+1):
frame = cv2.imread(os.path.join(img_path, str(i).zfill(6)+'.jpg'))
frame_draw = frame.copy()
frame_num += 1
im_trans = base_transform(frame, ssd_dim, mean)
x = Variable(torch.from_numpy(im_trans).unsqueeze(0).permute(0, 3, 1, 2), volatile=True)
x = x.cuda()
if tssd == 'ssd':
_t['im_detect'].tic()
detections, _ = net(x)
detect_time = _t['im_detect'].toc(average=False)
detections = detections.data
else:
_t['im_detect'].tic()
detections, state, _ = net(x, state, init_tub)
detect_time = _t['im_detect'].toc(average=False)
detections = detections.data
init_tub = False
all_time += detect_time
out = list()
for j in range(1, detections.size(1)):
for k in range(detections.size(2)):
dets = detections[0, j, k, :]
# mask = dets[:, 0].gt(0.).expand(5, dets.size(0)).t()
# dets = torch.masked_select(dets, mask).view(-1, 5)
if dets.dim() == 0:
continue
boxes = dets[1:-1] if dets.size(0) == 6 else dets[1:]
identity = dets[-1] if dets.size(0) == 6 else -1
x_min = int(boxes[0] * w)
x_max = int(boxes[2] * w)
y_min = int(boxes[1] * h)
y_max = int(boxes[3] * h)
score = dets[0]
if score > confidence_threshold:
out.append([x_min, y_min, x_max, y_max, j - 1, score, identity])
wf.write(str(frame_num)+','+str(int(identity))+','+str(x_min)+','+str(y_min)+','+str(x_max-x_min)+','+str(y_max-y_min)+','+str(np.around(score, decimals=2))+',-1,-1,-1\n')
print(val + ':' + str(frame_num))
if vis:
for object in out:
color = (0, 0, 255)
x_min, y_min, x_max, y_max, cls, score, identity = object
cv2.rectangle(frame_draw, (x_min, y_min), (x_max, y_max), color, thickness=2)
cv2.fillConvexPoly(frame_draw, np.array(
[[x_min - 1, y_min], [x_min - 1, y_min - 50], [x_max + 1, y_min - 50], [x_max + 1, y_min]], np.int32),
color)
put_str = str(int(identity))+':'+ str(np.around(score, decimals=2))
cv2.putText(frame_draw, put_str,
(x_min + 10, y_min - 10), cv2.FONT_HERSHEY_DUPLEX, 1, color=(255, 255, 255), thickness=1)
cv2.imshow('frame', cv2.resize(frame_draw, (640,360)))
ch = cv2.waitKey(1)
if ch == 32:
while 1:
in_ch = cv2.waitKey(10)
if in_ch == 32:
break
if output_flag:
wf.close()
total_frame += frame_num
fps = total_frame/all_time
print('frames:', total_frame, 'all time:', all_time, 'fps:', fps)
print(output_dir.split('/')[-1])
cv2.destroyAllWindows()
if __name__ == '__main__':
main()