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evaluate.py
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evaluate.py
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import torch.utils.data as data
import torchvision.transforms as transforms
from torch.autograd import Variable
from model.utils import Reconstruction3DDataLoader
from model.autoencoder import *
from utils import *
import glob
import matplotlib.image as mpimg
from matplotlib.patches import Rectangle
import argparse
import time
parser = argparse.ArgumentParser(description="STEAL Net")
parser.add_argument('--test_batch_size', type=int, default=1, help='batch size for test')
parser.add_argument('--h', type=int, default=256, help='height of input images')
parser.add_argument('--w', type=int, default=256, help='width of input images')
parser.add_argument('--num_workers_test', type=int, default=1, help='number of workers for the test loader')
parser.add_argument('--dataset_type', type=str, default='ped2', help='type of dataset: ped2, avenue, shanghai')
parser.add_argument('--dataset_path', type=str, default='dataset', help='directory of data')
parser.add_argument('--model_dir', type=str, help='directory of model')
parser.add_argument('--img_dir', type=str, default=None, help='save image file')
parser.add_argument('--print_score', action='store_true', help='print score')
parser.add_argument('--vid_dir', type=str, default=None, help='save video frames file')
parser.add_argument('--print_time', action='store_true', help='print forward time')
args = parser.parse_args()
if args.img_dir is not None:
if not os.path.exists(args.img_dir):
os.makedirs(args.img_dir)
if args.vid_dir is not None:
if not os.path.exists(args.vid_dir):
os.makedirs(args.vid_dir)
torch.backends.cudnn.enabled = True # make sure to use cudnn for computational performance
loss_func_mse = nn.MSELoss(reduction='none')
# Loading the trained model
model = convAE()
model = nn.DataParallel(model)
model_dict = torch.load(args.model_dir)
try:
model_weight = model_dict['model']
model.load_state_dict(model_weight.state_dict())
except KeyError:
model.load_state_dict(model_dict['model_statedict'])
model.cuda()
labels = np.load('./data/frame_labels_'+args.dataset_type+'.npy')
# Loading dataset
test_folder = os.path.join(args.dataset_path, args.dataset_type, 'testing', 'frames')
img_extension = '.tif' if args.dataset_type == 'ped1' else '.jpg'
test_dataset = Reconstruction3DDataLoader(test_folder, transforms.Compose([transforms.ToTensor(),]),
resize_height=args.h, resize_width=args.w, dataset=args.dataset_type, img_extension=img_extension)
test_size = len(test_dataset)
test_batch = data.DataLoader(test_dataset, batch_size = args.test_batch_size,
shuffle=False, num_workers=args.num_workers_test, drop_last=False)
videos = OrderedDict()
videos_list = sorted(glob.glob(os.path.join(test_folder, '*/')))
for video in videos_list:
video_name = video.split('/')[-2]
videos[video_name] = {}
videos[video_name]['path'] = video
videos[video_name]['frame'] = glob.glob(os.path.join(video, '*' + img_extension))
videos[video_name]['frame'].sort()
videos[video_name]['length'] = len(videos[video_name]['frame'])
labels_list = []
label_length = 0
psnr_list = {}
print('Evaluation of', args.dataset_type)
# Setting for video anomaly detection
for video in sorted(videos_list):
video_name = video.split('/')[-2]
labels_list = np.append(labels_list, labels[0][8+label_length:videos[video_name]['length']+label_length-7])
label_length += videos[video_name]['length']
psnr_list[video_name] = []
label_length = 0
video_num = 0
label_length += videos[videos_list[video_num].split('/')[-2]]['length']
model.eval()
tic = time.time()
for k,(imgs) in enumerate(test_batch):
if k == label_length-15*(video_num+1):
video_num += 1
label_length += videos[videos_list[video_num].split('/')[-2]]['length']
imgs = Variable(imgs).cuda()
with torch.no_grad():
outputs = model(imgs)
loss_mse = loss_func_mse(outputs[0, :, 8], imgs[0, :, 8])
loss_pixel = torch.mean(loss_mse)
mse_imgs = loss_pixel.item()
psnr_list[videos_list[video_num].split('/')[-2]].append(psnr(mse_imgs))
if args.img_dir is not None or args.vid_dir is not None:
output = (outputs[0,:,8].cpu().detach().numpy() + 1) * 127.5
output = output.transpose(1,2,0).astype(dtype=np.uint8)
if args.img_dir is not None:
cv2.imwrite(os.path.join(args.img_dir, '{:04d}.jpg').format(k), output)
if args.vid_dir is not None:
cv2.imwrite(os.path.join(args.vid_dir, 'out_{:04d}.png').format(k), output)
if args.vid_dir is not None:
saveimgs = (imgs[0,:,8].cpu().detach().numpy() + 1) * 127.5
saveimgs = saveimgs.transpose(1,2,0).astype(dtype=np.uint8)
cv2.imwrite(os.path.join(args.vid_dir, 'GT_{:04d}.png').format(k), saveimgs)
mseimgs = (loss_func_mse(outputs[0,:,8], imgs[0,:,8])[0].cpu().detach().numpy())
mseimgs = mseimgs[:,:,np.newaxis]
mseimgs = (mseimgs - np.min(mseimgs)) / (np.max(mseimgs)-np.min(mseimgs))
mseimgs = mseimgs * 255
mseimgs = mseimgs.astype(dtype=np.uint8)
color_mseimgs = cv2.applyColorMap(mseimgs, cv2.COLORMAP_JET)
if args.img_dir is not None:
cv2.imwrite(os.path.join(args.img_dir, 'MSE_{:04d}.jpg').format(k), color_mseimgs)
if args.vid_dir is not None:
cv2.imwrite(os.path.join(args.vid_dir, 'MSE_{:04d}.png').format(k), color_mseimgs)
toc = time.time()
if args.print_time:
time_elapsed = (toc-tic)/len(test_batch)
print('time: ', time_elapsed)
print('fps: ', 1/time_elapsed)
# Measuring the abnormality score (S) and the AUC
anomaly_score_total_list = []
vid_idx = []
for vi, video in enumerate(sorted(videos_list)):
video_name = video.split('/')[-2]
score = anomaly_score_list(psnr_list[video_name])
anomaly_score_total_list += score
vid_idx += [vi for _ in range(len(score))]
anomaly_score_total_list = np.asarray(anomaly_score_total_list)
accuracy = AUC(anomaly_score_total_list, np.expand_dims(1-labels_list, 0))
if args.print_score:
print('vididx,frame,anomaly_score,anomaly_label')
for a in range(len(anomaly_score_total_list)):
print(str(vid_idx[a]), ',', str(a), ',', 1-anomaly_score_total_list[a], ',', labels_list[a])
if args.vid_dir is not None:
a = 0
vids_len = []
while a < len(vid_idx):
start_a = a
cur_vid_idx = vid_idx[a]
num_frames = 0
while vid_idx[a] == cur_vid_idx:
num_frames += 1
a += 1
if a >= len(vid_idx):
break
vids_len.append(num_frames)
a = 0
while a < len(vid_idx):
start_a = a
atemp = a
cur_vid_idx = vid_idx[a]
vid_len = vids_len[cur_vid_idx]
# rectangle position
idx = 0
rect_start = []
rect_end = []
anom_status = False
while vid_idx[atemp] == cur_vid_idx:
if not anom_status:
if labels_list[atemp] == 1:
anom_status = True
rect_start.append(idx)
else:
if labels_list[atemp] == 0:
anom_status = False
rect_end.append(idx)
idx += 1
atemp += 1
if atemp >= len(vid_idx):
break
if anom_status:
rect_end.append(idx - 1)
while vid_idx[a] == cur_vid_idx:
# GT
imggt = cv2.imread(os.path.join(args.vid_dir, 'GT_{:04d}.png').format(a))[:,:,[2,1,0]]
plt.axis('off')
plt.subplot(231)
plt.title('Frame', fontsize='small')
plt.imshow(imggt)
# Recon
imgout = cv2.imread(os.path.join(args.vid_dir, 'out_{:04d}.png').format(a))[:,:,[2,1,0]]
plt.axis('off')
plt.subplot(232)
plt.title('Reconstruction', fontsize='small')
plt.axis('off')
plt.imshow(imgout)
# MSE
imgmse = mpimg.imread(os.path.join(args.vid_dir, 'MSE_{:04d}.png').format(a))
plt.subplot(233)
plt.title('Reconstruction Error', fontsize='small')
plt.axis('off')
plt.imshow(imgmse)
# anomaly score plot
plt.subplot(212)
plt.plot(range(a-start_a+1), 1-anomaly_score_total_list[start_a:a+1], label='prediction', color='blue')
plt.xlim(0, vid_len-1)
plt.xticks(fontsize='x-small')
plt.xlabel('Frames', fontsize='x-small')
plt.ylim(-0.01, 1.01)
plt.ylabel('Anomaly Score', fontsize='x-small')
plt.yticks(fontsize='x-small')
plt.title('Anomaly Score Over Time')
for rs, re in zip(rect_start, rect_end):
currentAxis = plt.gca()
currentAxis.add_patch(Rectangle((rs, -0.01), re-rs, 1.02, facecolor="pink"))
plt.savefig(os.path.join(args.vid_dir, 'frame_{:02d}_{:04d}.png').format(cur_vid_idx, a-start_a), dpi=300)
plt.close()
a += 1
if a >= len(vid_idx):
break
print('The result of ', args.dataset_type)
print('AUC: ', accuracy*100, '%')