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test_TDV.py
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test_TDV.py
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import os
from skimage import io, transform
import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
# import torch.optim as optim
import json
import numpy as np
from PIL import Image
import glob
import cv2
from KL_loss import KLDLoss1vs1, cc_numeric, SIM_numeric,NSS,AUC_Judd_batch,AUC_Borji_batch,InfoGain
from data import SalObjDataset_test_CDNN
from model import MTSF # full size version 173.6 MB
# normalize the predicted SOD probability map
dev_name = 'cuda:0'
dev = torch.device(dev_name if torch.cuda.is_available() else "cpu")
print(dev)
kl_loss = KLDLoss1vs1(dev)
def muti_bce_loss_fusion(d0, labels_v,fixlabels_v,baselabels):
KL_loss0 = kl_loss(d0,labels_v)
CC_loss0 = cc_numeric(labels_v,d0)
SIM_loss = SIM_numeric(d0, labels_v)
NSS_loss = NSS(d0, fixlabels_v)
AUC_Judd_loss = AUC_Judd_batch(d0, fixlabels_v)
#IG_loss = InfoGain(d0, fixlabels_v, baselabels)
AUC_Borji_loss = AUC_Borji_batch(d0, fixlabels_v)
IG_loss = 0
print("KL_l0: %3f ,CC_l0: %3f, SIM_l0:%3f , NSS_l0:%3f, AUC_Judd_l0:%3f ,IG_l0:%3f,AUC_Borji_l0:%3f"%(KL_loss0.data.item(),CC_loss0,SIM_loss,NSS_loss,AUC_Judd_loss,IG_loss,AUC_Borji_loss))
return KL_loss0,CC_loss0,SIM_loss,NSS_loss,AUC_Judd_loss,IG_loss,AUC_Borji_loss
def normPRED(d):
batchsize = d.size(0)
for k in range(batchsize):
ma = torch.max(d[k])
mi = torch.min(d[k])
d[k] = (d[k]-mi)/(ma-mi)
return d
def save_output(image_name_list,pred,d_dir,i_test):
predict = pred
predict = predict.squeeze()
predict_np = predict.cpu().data.numpy()
batchsize = predict.size(0)
h = predict.size(1)
w = predict.size(2)
for k in range(batchsize):
im_k = np.zeros((h, w))
im_k = predict_np[k, :, :]
im = Image.fromarray(im_k*255).convert('RGB')
img_name = image_name_list[batchsize*i_test+k].split(os.sep)[-1]
image = io.imread('/home/ailvin/forlunwen/CDNN_code_data/traffic_dataset/traffic_videos/'+image_name_list[batchsize*i_test+k])
imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BILINEAR)
image = Image.fromarray(image)
pb_np = np.array(imo)
video_name = image_name_list[batchsize*i_test+k][0:2]
aaa = img_name.split(".")
bbb = aaa[0:-1]
imidx = bbb[0]
for i in range(1,len(bbb)):
imidx = imidx + "." + bbb[i]
print(imidx)
image.save(d_dir + video_name + '_' + imidx + '.jpg')
imo.save(d_dir+video_name+'_'+imidx+'.png')
def main():
# --------- 1. get image path and name ---------
model_name='MTSF'
prediction_dir = '/home/ailvin/桌面/实验记录/prediction/TDV/'
root = '/home/ailvin/forlunwen/CDNN_code_data/traffic_dataset/traffic_frames/'
#train_imgs = [json.loads(line) for line in open(root + 'train.json')]
#valid_imgs = [json.loads(line) for line in open(root + 'valid.json')]
test_imgs = [json.loads(line) for line in open(root + 'test.json')]
test_img_name_list = test_imgs
test_img_name_list = sorted(test_img_name_list)
test_img_name_list = test_img_name_list[7000:]
print("---")
a = len(test_img_name_list)
print("test images: ", a)
print("---")
salobj_dataset = SalObjDataset_test_CDNN(
img_name_list=test_img_name_list
)
test_salobj_dataloader = DataLoader(salobj_dataset, batch_size=1, shuffle=False, num_workers=40)
# --------- 3. model define ---------
if(model_name=='MTSF'):
print("...load MTSF---189.1 MB")
net = MTSF(pretrained=False)
if torch.cuda.is_available():
#model_dir = model_dir + '/u2net.pth'
model_dir = '/home/ailvin/forlunwen/MTSF/saved_models/' + 'MTSF2022-06-14_bce_itr_8000_train_0.358060_tar_0.067943.pth'
net.load_state_dict(torch.load(model_dir))
net.cuda()
print('cuda:0')
net.eval() #可以防止由于测试和训练的batchsize不同而导致的错误
KL_running_loss = 0.0
CC_running_loss = 0.0
SIM_running_loss = 0.0
NSS_running_loss = 0.0
IG_running_loss = 0.0
AUC_Judd_running_loss = 0.0
AUC_Borji_running_loss = 0.0
test_num = 0
# 创建对象，用于视频的写出.每次都要改名
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
videoWrite_frame = cv2.VideoWriter(r'/media/ailvin/TOSHIBA EXT/data/demo/New_RGB_Channel/test_CDNN_2.mp4', fourcc, 25, (1344, 768))
videoWrite_SOE = cv2.VideoWriter(r'/media/ailvin/TOSHIBA EXT/data/demo/New_RGB_Channel/MTSF_test_CDNN_2.mp4', fourcc, 25,(1344, 768))
# --------- 4. inference for each image ---------
for i_test, data_test in enumerate(test_salobj_dataloader):
test_num = i_test +1
img_name = test_img_name_list[i_test]
#print(img_name)
vid_index = int(img_name[0:2])
# print(vid_index)
print("inferencing:%3i/%3i"%( int(i_test*1),int(a)))
img_path = test_img_name_list[i_test]
data_path = '/home/ailvin/forlunwen/CDNN_code_data/traffic_dataset/traffic_videos/'
img = cv2.imread(data_path+img_path)
origin_img = cv2.resize(img, (224, 224))
#origin_img = cv2.cvtColor(origin_img, cv2.COLOR_BGR2RGB)
inputs_test,labels, fixlabels = data_test['image'], data_test['label'], data_test['fixlabel']
inputs_test = inputs_test.type(torch.FloatTensor)
labels = labels.type(torch.FloatTensor)
fixlabels = fixlabels.type(torch.FloatTensor)
if torch.cuda.is_available():
inputs_test,labels_v,fixlabels_v = Variable(inputs_test.cuda()),Variable(labels.cuda()),Variable(fixlabels.cuda())
else:
inputs_test,labels_v,fixlabels_v = Variable(inputs_test),Variable(labels),Variable(fixlabels)
d1,d2,d3,d4,d5 = net(inputs_test)
#d1 = net(inputs_test)
baseline_path = '/home/ailvin/forlunwen/CDNN_code_data/traffic_dataset/saliencydata/mean_saliency/' + str(vid_index) + '.png'
baselabels_3 = cv2.imread(baseline_path)
baselabels_3 = cv2.cvtColor(baselabels_3, cv2.COLOR_BGR2RGB)
baselabels_3 = cv2.resize(baselabels_3, (224, 224))
# baselabels = np.zeros((256,256,1))
# print(baselabels.shape)
baselabels = baselabels_3[:, :, 0]
baselabels = baselabels / 255
baselabels = baselabels[:, :, np.newaxis]
# print(baselabels.shape)
baselabels = baselabels.transpose((2, 0, 1))
KL_loss, CC_loss, SIM_loss, NSS_loss, AUC_Judd_loss, IG_loss, AUC_Borji_loss = muti_bce_loss_fusion(d1,
labels_v,
fixlabels_v,
baselabels)
if str(CC_loss) == 'nan':
CC_loss = 0.0
KL_running_loss += KL_loss.data.item()
CC_running_loss += CC_loss
SIM_running_loss += SIM_loss
NSS_running_loss += NSS_loss
AUC_Judd_running_loss += AUC_Judd_loss
IG_running_loss += IG_loss
AUC_Borji_running_loss += AUC_Borji_loss
else:
KL_running_loss += KL_loss.data.item()
CC_running_loss += CC_loss
SIM_running_loss += SIM_loss
NSS_running_loss += NSS_loss
AUC_Judd_running_loss += AUC_Judd_loss
IG_running_loss += IG_loss
AUC_Borji_running_loss += AUC_Borji_loss
print(
" KL_loss: %3f ,CC_loss: %3f, SIM_loss: %3f, NSS_loss: %3f, AUC_Judd_loss: %3f, IG_loss: %3f,AUC_Borji_loss: %3f" % (
KL_running_loss / test_num, CC_running_loss / test_num, SIM_running_loss / test_num,
NSS_running_loss / test_num, AUC_Judd_running_loss / test_num, IG_running_loss / test_num,
AUC_Borji_running_loss / test_num))
sal_map = d1.cpu().detach().numpy()[0]
sal_map = sal_map[0, :, :]
sal_map = np.clip(sal_map, 0, 1) * 255 # normalized
sal_map = sal_map.astype(np.uint8) # type
sal_heatmap = cv2.applyColorMap(sal_map, cv2.COLORMAP_JET) # 此处的三通道热力图是cv2使用GBR排列
origin_img = np.asarray(origin_img)
origin_img_1 = cv2.addWeighted(origin_img, 1.0, sal_heatmap, 0.6, 0) # img + sal
origin_img_1 = cv2.resize(origin_img_1, (1344, 768))
origin_img = cv2.resize(origin_img, (1344, 768))
cv2.imshow("origin_img", origin_img)
cv2.imshow("origin_img_1", origin_img_1)
# 保存视频
videoWrite_frame.write(origin_img)
videoWrite_SOE.write(origin_img_1)
k = cv2.waitKey(1) & 0xFF
if k == ord('q'): # 按下q(quit)键，程序退出
break
cv2.destroyAllWindows()
del d1,d2,d3,d4,d5
#del d1
if __name__ == "__main__":
main()