调整Python环境和代码以支持新功能

- 修改Python版本为3.8.10，更新相关依赖 - 添加新的数据处理脚本dataprocessing_sar.py - 调整网络结构描述，增加注释 - 修改测试脚本以支持新功能 -调整训练脚本中的损失计算和学习率策略
2024-10-06 14:42:13 +08:00 · 2024-10-06 14:42:13 +08:00 · fe47313277
commit fe47313277
parent 82acfa83dc
9 changed files with 187 additions and 47 deletions
--- a/.idea/CDDFuse.iml
+++ b/.idea/CDDFuse.iml
@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
  <component name="NewModuleRootManager">
    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Remote Python 3.12.4 (sftp://star@192.168.50.108:22/home/star/anaconda3/bin/python)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Remote Python 3.8.10 (sftp://star@192.168.50.108:22/home/star/anaconda3/envs/cddfuse/bin/python)" jdkType="Python SDK" />
    <orderEntry type="sourceFolder" forTests="false" />
  </component>
 </module>
--- a/.idea/deployment.xml
+++ b/.idea/deployment.xml
@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="PublishConfigData" autoUpload="Always" serverName="star@192.168.50.108:22 password (6)" remoteFilesAllowedToDisappearOnAutoupload="false">
+  <component name="PublishConfigData" autoUpload="Always" serverName="star@192.168.50.108:22 password (7)" remoteFilesAllowedToDisappearOnAutoupload="false">
    <serverData>
      <paths name="star@192.168.50.108:22 password">
        <serverdata>
@ -12,7 +12,7 @@
      <paths name="star@192.168.50.108:22 password (2)">
        <serverdata>
          <mappings>
-            <mapping local="$PROJECT_DIR$" web="/" />
+            <mapping deploy="/home/star/whaiDir/CDDFuse" local="$PROJECT_DIR$" web="" />
          </mappings>
        </serverdata>
      </paths>
@ -44,6 +44,20 @@
          </mappings>
        </serverdata>
      </paths>
+      <paths name="star@192.168.50.108:22 password (7)">
+        <serverdata>
+          <mappings>
+            <mapping deploy="/home/star/whaiDir/CDDFuse" local="$PROJECT_DIR$" />
+          </mappings>
+        </serverdata>
+      </paths>
+      <paths name="star@192.168.50.108:22 password (8)">
+        <serverdata>
+          <mappings>
+            <mapping local="$PROJECT_DIR$" web="/" />
+          </mappings>
+        </serverdata>
+      </paths>
      <paths name="v100">
        <serverdata>
          <mappings>
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@ -1,5 +1,8 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Remote Python 3.8.10 (sftp://star@192.168.50.108:22/home/star/anaconda3/envs/cddfuse/bin/python)" />
+  </component>
  <component name="MavenImportPreferences">
    <option name="generalSettings">
      <MavenGeneralSettings>
@ -9,5 +12,5 @@
      </MavenGeneralSettings>
    </option>
  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Remote Python 3.12.4 (sftp://star@192.168.50.108:22/home/star/anaconda3/bin/python)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Remote Python 3.8.10 (sftp://star@192.168.50.108:22/home/star/anaconda3/envs/cddfuse/bin/python)" project-jdk-type="Python SDK" />
 </project>
--- a/data/MSRS_train_imgsize_128_stride_200.h5
+++ b/data/MSRS_train_imgsize_128_stride_200.h5
--- a/dataprocessing.py
+++ b/dataprocessing.py
@ -43,11 +43,11 @@ data_name="MSRS_train"
 img_size=128   #patch size
 stride=200     #patch stride

-IR_files = sorted(get_img_file(r"MSRS_train/ir"))
-VIS_files   = sorted(get_img_file(r"MSRS_train/vi"))
+IR_files = sorted(get_img_file(r"/media/star/8TB/whaiDownload/MSRS-main/train/ir"))
+VIS_files   = sorted(get_img_file(r"/media/star/8TB/whaiDownload/MSRS-main/train/vi"))

 assert len(IR_files) == len(VIS_files)
-h5f = h5py.File(os.path.join('.\\data',
+h5f = h5py.File(os.path.join('./data',
                                 data_name+'_imgsize_'+str(img_size)+"_stride_"+str(stride)+'.h5'),
                    'w')
 h5_ir = h5f.create_group('ir_patchs')
--- a/dataprocessing_sar.py
+++ b/dataprocessing_sar.py
@ -0,0 +1,93 @@
+import os
+import h5py
+import numpy as np
+from tqdm import tqdm
+from skimage.io import imread
+
+
+def get_img_file(file_name):
+    imagelist = []
+    for parent, dirnames, filenames in os.walk(file_name):
+        for filename in filenames:
+            if filename.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff', '.npy')):
+                imagelist.append(os.path.join(parent, filename))
+        return imagelist
+
+def rgb2y(img):
+    y = img[0:1, :, :] * 0.299000 + img[1:2, :, :] * 0.587000 + img[2:3, :, :] * 0.114000
+    return y
+
+def Im2Patch(img, win, stride=1):
+    k = 0
+    endc = img.shape[0]
+    endw = img.shape[1]
+    endh = img.shape[2]
+    patch = img[:, 0:endw-win+0+1:stride, 0:endh-win+0+1:stride]
+    TotalPatNum = patch.shape[1] * patch.shape[2]
+    Y = np.zeros([endc, win*win,TotalPatNum], np.float32)
+    for i in range(win):
+        for j in range(win):
+            patch = img[:,i:endw-win+i+1:stride,j:endh-win+j+1:stride]
+            Y[:,k,:] = np.array(patch[:]).reshape(endc, TotalPatNum)
+            k = k + 1
+    return Y.reshape([endc, win, win, TotalPatNum])
+
+def is_low_contrast(image, fraction_threshold=0.1, lower_percentile=10,
+                    upper_percentile=90):
+    """Determine if an image is low contrast."""
+    limits = np.percentile(image, [lower_percentile, upper_percentile])
+    ratio = (limits[1] - limits[0]) / limits[1]
+    return ratio < fraction_threshold
+
+data_name="MSRS_train"
+img_size=128   #patch size
+stride=200     #patch stride
+
+IR_files = sorted(get_img_file(r"/media/star/8TB/whaiDownload/MSRS-main/train/ir"))
+VIS_files   = sorted(get_img_file(r"/media/star/8TB/whaiDownload/MSRS-main/train/vi"))
+
+assert len(IR_files) == len(VIS_files)
+h5f = h5py.File(os.path.join('./data',
+                                 data_name+'_imgsize_'+str(img_size)+"_stride_"+str(stride)+'.h5'),
+                    'w')
+h5_ir = h5f.create_group('ir_patchs')
+h5_vis = h5f.create_group('vis_patchs')
+train_num=0
+for i in tqdm(range(len(IR_files))):
+        I_VIS = imread(VIS_files[i]).astype(np.float32).transpose(2,0,1)/255. # [3, H, W] Uint8->float32
+        I_VIS = rgb2y(I_VIS) # [1, H, W] Float32
+        I_IR = imread(IR_files[i]).astype(np.float32)[None, :, :]/255.  # [1, H, W] Float32
+
+        # crop
+        I_IR_Patch_Group = Im2Patch(I_IR,img_size,stride)
+        I_VIS_Patch_Group = Im2Patch(I_VIS, img_size, stride)  # (3, 256, 256, 12)
+
+        for ii in range(I_IR_Patch_Group.shape[-1]):
+            bad_IR = is_low_contrast(I_IR_Patch_Group[0,:,:,ii])
+            bad_VIS = is_low_contrast(I_VIS_Patch_Group[0,:,:,ii])
+            # Determine if the contrast is low
+            if not (bad_IR or bad_VIS):
+                avl_IR= I_IR_Patch_Group[0,:,:,ii]  #  available IR
+                avl_VIS= I_VIS_Patch_Group[0,:,:,ii]
+                avl_IR=avl_IR[None,...]
+                avl_VIS=avl_VIS[None,...]
+
+                h5_ir.create_dataset(str(train_num),     data=avl_IR,
+	                            dtype=avl_IR.dtype,   shape=avl_IR.shape)
+                h5_vis.create_dataset(str(train_num),    data=avl_VIS,
+	                            dtype=avl_VIS.dtype,  shape=avl_VIS.shape)
+                train_num += 1
+
+h5f.close()
+
+with h5py.File(os.path.join('data',
+                                 data_name+'_imgsize_'+str(img_size)+"_stride_"+str(stride)+'.h5'),"r") as f:
+    for key in f.keys():
+        print(f[key], key, f[key].name)
+
+
+
+
+
+
+
--- a/net.py
+++ b/net.py
@ -41,6 +41,14 @@ class DropPath(nn.Module):


 class AttentionBase(nn.Module):
+    """
+    一个基础的多头注意力机制类。
+
+    参数:
+        dim (int): 输入和输出的特征维度。
+        num_heads (int, 可选): 注意力头的数量，默认为8。
+        qkv_bias (bool, 可选): 是否为QKV投影层添加偏差，默认为False。
+    """
    def __init__(self,
                 dim,
                 num_heads=8,
@ -54,6 +62,15 @@ class AttentionBase(nn.Module):
        self.proj = nn.Conv2d(dim, dim, kernel_size=1, bias=qkv_bias)

    def forward(self, x):
+        """
+        定义了输入数据x通过多头注意力机制的前向传播过程。
+
+        参数:
+            x (Tensor): 输入的特征张量，形状为[batch_size, dim, height, width]。
+
+        返回:
+            Tensor: 输出的特征张量，形状为[batch_size, dim, height, width]。
+        """
        # [batch_size, num_patches + 1, total_embed_dim]
        b, c, h, w = x.shape
        qkv = self.qkv2(self.qkv1(x))
@ -79,6 +96,7 @@ class AttentionBase(nn.Module):
        out = self.proj(out)
        return out

+
 class Mlp(nn.Module):
    """
    MLP as used in Vision Transformer, MLP-Mixer and related networks
--- a/test_IVF.py
+++ b/test_IVF.py
@ -1,3 +1,5 @@
+import cv2
+
 from net import Restormer_Encoder, Restormer_Decoder, BaseFeatureExtraction, DetailFeatureExtraction
 import os
 import numpy as np
@ -12,7 +14,7 @@ logging.basicConfig(level=logging.CRITICAL)

 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 ckpt_path=r"models/CDDFuse_IVF.pth"
-for dataset_name in ["TNO","RoadScene"]:
+for dataset_name in ["TNO"]:
    print("\n"*2+"="*80)
    model_name="CDDFuse    "
    print("The test result of "+dataset_name+' :')
@ -36,9 +38,12 @@ for dataset_name in ["TNO","RoadScene"]:

    with torch.no_grad():
        for img_name in os.listdir(os.path.join(test_folder,"ir")):
+            print("Processing: "+img_name)

            data_IR=image_read_cv2(os.path.join(test_folder,"ir",img_name),mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0
            data_VIS = image_read_cv2(os.path.join(test_folder,"vi",img_name), mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0
+            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_IR,data_VIS = torch.FloatTensor(data_IR),torch.FloatTensor(data_VIS)
            data_VIS, data_IR = data_VIS.cuda(), data_IR.cuda()
@ -49,8 +54,15 @@ for dataset_name in ["TNO","RoadScene"]:
            feature_F_D = DetailFuseLayer(feature_V_D + feature_I_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))
+            # fi = np.squeeze((data_Fuse * 255).cpu().numpy())
+            # img_save(fi, img_name.split(sep='.')[0], test_out_folder)
+
            fi = np.squeeze((data_Fuse * 255).cpu().numpy())
-            img_save(fi, img_name.split(sep='.')[0], test_out_folder)
+            fi = fi.astype(np.uint8)
+            ycrcb_fi = np.dstack((fi, data_VIS_Cr, data_VIS_Cb))
+            rgb_fi = cv2.cvtColor(ycrcb_fi, cv2.COLOR_YCrCb2RGB)
+            img_save(rgb_fi, img_name.split(sep='.')[0], test_out_folder)
+            print("save path : "+os.path.join(test_out_folder,img_name.split(sep='.')[0]+".png"))


    eval_folder=test_out_folder
--- a/train.py
+++ b/train.py
@ -33,7 +33,7 @@ criteria_fusion = Fusionloss()
 model_str = 'CDDFuse'

 # . Set the hyper-parameters for training
-num_epochs = 120 # total epoch
+num_epochs = 10 # total epoch
 epoch_gap = 40  # epoches of Phase I

 lr = 1e-4