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Update test_IVF.py

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HXY13 2024-06-09 19:06:32 +08:00 committed by GitHub
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commit d65393b3c4
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1 changed files with 1 additions and 9 deletions
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test_IVF.py
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@ -8,7 +8,6 @@ import torch.nn as nn
from utils.img_read_save import img_save,image_read_cv2 from utils.img_read_save import img_save,image_read_cv2
import warnings import warnings
import logging import logging
# 增加
warnings.filterwarnings("ignore") warnings.filterwarnings("ignore")
logging.basicConfig(level=logging.CRITICAL) logging.basicConfig(level=logging.CRITICAL)
@ -26,7 +25,6 @@ for dataset_name in ["MSRS","TNO","RoadScene"]:
device = 'cuda' if torch.cuda.is_available() else 'cpu' device = 'cuda' if torch.cuda.is_available() else 'cpu'
Encoder = nn.DataParallel(Restormer_Encoder()).to(device) Encoder = nn.DataParallel(Restormer_Encoder()).to(device)
Decoder = nn.DataParallel(Restormer_Decoder()).to(device) Decoder = nn.DataParallel(Restormer_Decoder()).to(device)
# BaseFuseLayer = nn.DataParallel(BaseFeatureExtraction(dim=64, num_heads=8)).to(device)
BaseFuseLayer = nn.DataParallel(BaseFeatureExtraction(dim=64)).to(device) BaseFuseLayer = nn.DataParallel(BaseFeatureExtraction(dim=64)).to(device)
DetailFuseLayer = nn.DataParallel(DetailFeatureExtraction(num_layers=1)).to(device) DetailFuseLayer = nn.DataParallel(DetailFeatureExtraction(num_layers=1)).to(device)
@ -43,12 +41,9 @@ for dataset_name in ["MSRS","TNO","RoadScene"]:
for img_name in os.listdir(os.path.join(test_folder,"ir")): for img_name in os.listdir(os.path.join(test_folder,"ir")):
data_IR=image_read_cv2(os.path.join(test_folder,"ir",img_name),mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0 data_IR=image_read_cv2(os.path.join(test_folder,"ir",img_name),mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0
# 改
data_VIS = cv2.split(image_read_cv2(os.path.join(test_folder, "vi", img_name), mode='YCrCb'))[0][np.newaxis, np.newaxis, ...] / 255.0 data_VIS = cv2.split(image_read_cv2(os.path.join(test_folder, "vi", img_name), mode='YCrCb'))[0][np.newaxis, np.newaxis, ...] / 255.0
# ycrcb, uint8
data_VIS_BGR = cv2.imread(os.path.join(test_folder, "vi", img_name)) data_VIS_BGR = cv2.imread(os.path.join(test_folder, "vi", img_name))
_, data_VIS_Cr, data_VIS_Cb = cv2.split(cv2.cvtColor(data_VIS_BGR, cv2.COLOR_BGR2YCrCb)) _, data_VIS_Cr, data_VIS_Cb = cv2.split(cv2.cvtColor(data_VIS_BGR, cv2.COLOR_BGR2YCrCb))
# 改
data_IR,data_VIS = torch.FloatTensor(data_IR),torch.FloatTensor(data_VIS) data_IR,data_VIS = torch.FloatTensor(data_IR),torch.FloatTensor(data_VIS)
data_VIS, data_IR = data_VIS.cuda(), data_IR.cuda() data_VIS, data_IR = data_VIS.cuda(), data_IR.cuda()
@ -60,13 +55,10 @@ for dataset_name in ["MSRS","TNO","RoadScene"]:
data_Fuse, _ = Decoder(data_VIS, feature_F_B, feature_F_D) data_Fuse, _ = Decoder(data_VIS, feature_F_B, feature_F_D)
data_Fuse=(data_Fuse-torch.min(data_Fuse))/(torch.max(data_Fuse)-torch.min(data_Fuse)) data_Fuse=(data_Fuse-torch.min(data_Fuse))/(torch.max(data_Fuse)-torch.min(data_Fuse))
fi = np.squeeze((data_Fuse * 255).cpu().numpy()) fi = np.squeeze((data_Fuse * 255).cpu().numpy())
# 改
# float32 to uint8
fi = fi.astype(np.uint8) fi = fi.astype(np.uint8)
ycrcb_fi = np.dstack((fi, data_VIS_Cr, data_VIS_Cb)) ycrcb_fi = np.dstack((fi, data_VIS_Cr, data_VIS_Cb))
rgb_fi = cv2.cvtColor(ycrcb_fi, cv2.COLOR_YCrCb2RGB) rgb_fi = cv2.cvtColor(ycrcb_fi, cv2.COLOR_YCrCb2RGB)
img_save(rgb_fi, img_name.split(sep='.')[0], test_out_folder) img_save(rgb_fi, img_name.split(sep='.')[0], test_out_folder)
# 改
eval_folder=test_out_folder eval_folder=test_out_folder
ori_img_folder=test_folder ori_img_folder=test_folder