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