chenge to english version

evaluate.py

@@ -10,7 +10,7 @@ import config
 from models.rord import RoRD
 from utils.data_utils import get_transform
 from data.ic_dataset import ICLayoutDataset
-# (已修改) 导入新的匹配函数
+# (Modified) Import new matching function
 from match import match_template_multiscale
 
 def compute_iou(box1, box2):
@@ -22,48 +22,48 @@ def compute_iou(box1, box2):
     union_area = w1 * h1 + w2 * h2 - inter_area
     return inter_area / union_area if union_area > 0 else 0
 
-# --- (已修改) 评估函数 ---
+# --- (Modified) Evaluation function ---
 def evaluate(model, val_dataset_dir, val_annotations_dir, template_dir):
     model.eval()
     all_tp, all_fp, all_fn = 0, 0, 0
     
-    # 只需要一个统一的 transform 给匹配函数内部使用
+    # Only need a unified transform for internal use by matching function
     transform = get_transform()
     
     template_paths = [os.path.join(template_dir, f) for f in os.listdir(template_dir) if f.endswith('.png')]
     layout_image_names = [f for f in os.listdir(val_dataset_dir) if f.endswith('.png')]
 
-    # (已修改) 循环遍历验证集中的每个版图文件
+    # (Modified) Loop through each layout file in validation set
     for layout_name in layout_image_names:
-        print(f"\n正在评估版图: {layout_name}")
+        print(f"\nEvaluating layout: {layout_name}")
         layout_path = os.path.join(val_dataset_dir, layout_name)
         annotation_path = os.path.join(val_annotations_dir, layout_name.replace('.png', '.json'))
 
-        # 加载原始PIL图像，以支持滑动窗口
+        # Load original PIL image to support sliding window
         layout_image = Image.open(layout_path).convert('L')
 
-        # 加载标注信息
+        # Load annotation information
         if not os.path.exists(annotation_path):
             continue
         with open(annotation_path, 'r') as f:
             annotation = json.load(f)
         
-        # 按模板对真实标注进行分组
+        # Group ground truth annotations by template
         gt_by_template = {os.path.basename(box['template']): [] for box in annotation.get('boxes', [])}
         for box in annotation.get('boxes', []):
             gt_by_template[os.path.basename(box['template'])].append(box)
 
-        # 遍历每个模板，在当前版图上进行匹配
+        # Iterate through each template and perform matching on current layout
         for template_path in template_paths:
             template_name = os.path.basename(template_path)
             template_image = Image.open(template_path).convert('L')
             
-            # (已修改) 调用新的多尺度匹配函数
+            # (Modified) Call new multi-scale matching function
             detected = match_template_multiscale(model, layout_image, template_image, transform)
             
             gt_boxes = gt_by_template.get(template_name, [])
             
-            # 计算 TP, FP, FN (这部分逻辑不变)
+            # Calculate TP, FP, FN (this logic remains unchanged)
             matched_gt = [False] * len(gt_boxes)
             tp = 0
             if len(detected) > 0:
@@ -88,14 +88,14 @@ def evaluate(model, val_dataset_dir, val_annotations_dir, template_dir):
             all_fp += fp
             all_fn += fn
 
-    # 计算最终指标
+    # Calculate final metrics
     precision = all_tp / (all_tp + all_fp) if (all_tp + all_fp) > 0 else 0
     recall = all_tp / (all_tp + all_fn) if (all_tp + all_fn) > 0 else 0
     f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
     return {'precision': precision, 'recall': recall, 'f1': f1}
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="评估 RoRD 模型性能")
+    parser = argparse.ArgumentParser(description="Evaluate RoRD model performance")
     parser.add_argument('--model_path', type=str, default=config.MODEL_PATH)
     parser.add_argument('--val_dir', type=str, default=config.VAL_IMG_DIR)
     parser.add_argument('--annotations_dir', type=str, default=config.VAL_ANN_DIR)
@@ -105,10 +105,10 @@ if __name__ == "__main__":
     model = RoRD().cuda()
     model.load_state_dict(torch.load(args.model_path))
     
-    # (已修改) 不再需要预加载数据集，直接传入路径
+    # (Modified) No longer need to preload dataset, directly pass paths
     results = evaluate(model, args.val_dir, args.annotations_dir, args.templates_dir)
     
-    print("\n--- 评估结果 ---")
-    print(f"  精确率 (Precision): {results['precision']:.4f}")
-    print(f"  召回率 (Recall):    {results['recall']:.4f}")
-    print(f"  F1 分数 (F1 Score):  {results['f1']:.4f}")
+    print("\n--- Evaluation Results ---")
+    print(f"  Precision: {results['precision']:.4f}")
+    print(f"  Recall:    {results['recall']:.4f}")
+    print(f"  F1 Score:  {results['f1']:.4f}")