增加”软件所的数据的预处理程序“

img_dataset.py

@@ -25,6 +25,8 @@ class Image_DataSet(torch.utils.data.Dataset):
         # image = np.expand_dims(source_image.transpose((2, 0, 1)), 0)
         if self.transform is not None:
             image = self.transform(source_image)
+        if image.shape[1]<image.shape[2]:
+            image = torch.transpose(image, 2, 1)
 
         img_file_only = imgs[index]
         tmpstrs = os.path.splitext(img_file_only)[0].split('-')
@@ -68,8 +70,11 @@ def check_label(xlsx_path, img_file, property='舌色'):
                                 if label in property_label_to_idx:
                                     label = property_label_to_idx[label]
                                 else:
-                                    raise ValueError(
-                                        'error label {}, which not found in property_label_to_idx'.format(label))
+                                    print('error label {}, which not found in property_label_to_idx'.format(label))
+                                    # raise ValueError(
+                                    #     'error label {}, which not found in property_label_to_idx'.format(label))
+                                    label = None
+                                    return False, tmp_name, label
                             print('I find [{0} → {1}] in [{2}].[{3}]'.format(tmp_name, label, sheet_name, col_nm2))
                             return True, tmp_name, label
     return False, patient_name, None
@@ -103,11 +108,53 @@ def preprocess_data():
                 img_file = os.path.join(img_path, img_files[k])
                 img_file_only = img_files[k]
                 islabeled, name, label = check_label(xlsx_path, img_file_only, property=property)
-                if islabeled:
+                if islabeled and label!=None:
+                    if label >= 8:
+                        continue
                     tmpstrs = os.path.splitext(img_file_only)
                     dst_img_file_only = tmpstrs[0] + '-' + property + '_' + str(label) + tmpstrs[1]
                     dst_img_file = os.path.join(processed_dir, dst_img_file_only)
                     copyfile(img_file, dst_img_file)
 
-# if __name__ == "__main__":
-#     preprocess_data()
+def crop_img():
+    from PIL import Image
+    import re
+    rootdir = 'E:\\workspace\\zhongyi\\软件所-第一批数据\\JDYT\\'
+    cropeddir = 'E:\\workspace\\zhongyi\\软件所-第一批数据\\croped\\'
+    list = os.listdir(rootdir)
+    for i in range(0, len(list)):
+        img_file = os.path.join(rootdir, list[i])
+        img_hold = Image.open(img_file).convert('RGB')
+        img_size = img_hold.size
+        if img_size[0]<img_size[1]:
+            w=img_size[0]
+            h=img_size[1]
+            left = int((0.8/3)*w)
+            right = w-left
+
+            bottom = int((9/10)*h)
+            top = bottom - 1200
+            box = (left, top, right, bottom)
+        else:
+            w = img_size[0]
+            h = img_size[1]
+            left = int((0.8 / 10) * w)
+            right = left + 1200*1.1
+
+            top = int((0.9 / 3) * h)
+            bottom = top+900*1.1
+            box = (left, top, right, bottom)
+        # 开始截取
+        region = img_hold.crop(box)
+
+        name = ''.join(re.findall('[\u4e00-\u9fa5]', list[i]))
+        name = name + '-' + 'croped' + '.jpg'
+
+        croped_img_file = os.path.join(cropeddir, name)
+        region.save(croped_img_file)
+
+
+
+if __name__ == "__main__":
+    preprocess_data()
+    # crop_img()

train_tongue_diagnosis.py

@@ -52,7 +52,7 @@ normalizer = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]
 transform_train = transforms.Compose([
     transforms.ToTensor(),
     transforms.ToPILImage(),
-    transforms.Resize((298, 224)),
+    transforms.Resize((224)),
     # transforms.RandomCrop((224, 224), padding=14),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),