update data iter

data_iter/datasets.py

@@ -97,7 +97,7 @@ def M_rotate_image(image , angle , cx , cy):
     return cv2.warpAffine(image , M , (nW , nH)) , M
 
 class LoadImagesAndLabels(Dataset):  # for training/testing
-    def __init__(self, path, img_size=(224,224), flag_agu = False,fix_res = True,val_split = []):
+    def __init__(self, path, img_size=(224,224), flag_agu = False,fix_res = True,val_split = [],have_label_file = False):
         print('img_size (height,width) : ',img_size[0],img_size[1])
         labels_ = []
         files_ = []
@@ -117,6 +117,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         self.img_size = img_size
         self.flag_agu = flag_agu
         self.fix_res = fix_res
+        self.have_label_file = have_label_file
 
     def __len__(self):
         return len(self.files)
@@ -129,22 +130,23 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
 
         img = cv2.imread(img_path)  # BGR
         #--------------------------------------------
-        xml_ = img_path.replace(".jpg",".xml")
+        if self.have_label_file:
+            xml_ = img_path.replace(".jpg",".xml")
 
-        list_x = get_xml_msg(xml_)# 获取 xml 文件 的 object
+            list_x = get_xml_msg(xml_)# 获取 xml 文件 的 object
 
-        # 绘制 bbox
-        choose_idx = random.randint(0,int(len(list_x)-1))
-        for j in range(len(list_x)):
-            if j ==choose_idx:
-                _,bbox_ = list_x[j]
-                x1,y1,x2,y2 = bbox_
-                x1 = int(np.clip(x1,0,img.shape[1]-1))
-                y1 = int(np.clip(y1,0,img.shape[0]-1))
-                x2 = int(np.clip(x2,0,img.shape[1]-1))
-                y2 = int(np.clip(y2,0,img.shape[0]-1))
-                img = img[y1:y2,x1:x2,:]
-                break
+            # 绘制 bbox
+            choose_idx = random.randint(0,int(len(list_x)-1))
+            for j in range(len(list_x)):
+                if j ==choose_idx:
+                    _,bbox_ = list_x[j]
+                    x1,y1,x2,y2 = bbox_
+                    x1 = int(np.clip(x1,0,img.shape[1]-1))
+                    y1 = int(np.clip(y1,0,img.shape[0]-1))
+                    x2 = int(np.clip(x2,0,img.shape[1]-1))
+                    y2 = int(np.clip(y2,0,img.shape[0]-1))
+                    img = img[y1:y2,x1:x2,:]
+                    break
 
 
 
@@ -158,9 +160,12 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         if self.flag_agu == True and random.random()>0.6:
             cx = int(img.shape[1]/2)
             cy = int(img.shape[0]/2)
-            angle = random.randint(-45,45)
-            offset_x = random.randint(-3,3)
-            offset_y = random.randint(-3,3)
+            # 手势(gesture)分类建议是全角度旋转, 对于 Stanford dogs 数据集适当角度旋转扰动，目的是为了符合真实样本旋转角度样本分布情况。
+            angle = random.randint(-180,180)
+            range_limit_x = int(min(6,img.shape[1]/16))
+            range_limit_y = int(min(6,img.shape[0]/16))
+            offset_x = random.randint(-range_limit_x,range_limit_x)
+            offset_y = random.randint(-range_limit_y,range_limit_y)
             if not(angle==0 and offset_x==0 and offset_y==0):
                 img,_  = M_rotate_image(img , angle , cx+offset_x , cy+offset_y)