Refactor evaluation.py to improve model evaluation process and error handling

- Introduced a new function `load_and_process_single_model` to encapsulate the logic for evaluating a single model, enhancing code readability and maintainability.
- Updated `compute_all` to utilize the new function, streamlining the overall evaluation workflow.
- Improved error handling with logging for missing files and exceptions during processing.
- Enhanced caching mechanism for computed results to avoid redundant calculations.
- Added detailed comments and documentation for better understanding of the evaluation process.

code/evaluation/evaluation.py

@@ -129,92 +129,114 @@ def distance_fea(gt_pa, pred_pa):
 
     return dfg2p, dfp2g, fag2p, fap2g
 
-def compute_all():
-    gt_path = args.gt_path
-    pred_mesh_path = args.pred_path
-    namelst = args.name_list
-    output_path = 'eval_results.csv'
-
-    with open(os.path.join(project_dir, 'evaluation', namelst), 'r') as f:
-        lines = f.readlines()
-
-    d = {'name':[], 'CD':[], 'HD':[], 'HDgt2pred':[], 'HDpred2gt':[], 'AngleDiffMean':[], 'AngleDiffStd':[], 'FeaDfgt2pred':[], 'FeaDfpred2gt':[], 'FeaDf':[], 'FeaAnglegt2pred':[], 'FeaAnglepred2gt':[], 'FeaAngle':[]}
-
-    for line in lines:
+def load_and_process_single_model(line, gt_path, pred_mesh_path, args):
+    """处理单个模型的评估
+    Args:
+        line (str): 模型名称
+        gt_path (str): 真值路径
+        pred_mesh_path (str): 预测网格路径
+        args: 参数配置
+    Returns:
+        dict: 包含该模型所有评估指标的字典
+    """
+    try:
         line = line.strip()[:-4]
-        print(line)
+        result = {'name': line}
+        
+        # 加载点云数据
         test_xyz = os.path.join(gt_path, line+'_50k.xyz')
         ptnormal = np.loadtxt(test_xyz)
+        
+        # 加载预测网格
         meshfile = os.path.join(pred_mesh_path, '{}_50k.ply'.format(line))
-
         if not os.path.exists(meshfile):
-            print('file not exists: ', meshfile)
-            f = open(meshfile + 'noexists', 'w')
-            f.close()
-            continue
+            logger.warning(f'File not exists: {meshfile}')
+            return None
+            
+        # 检查缓存
         stat_file = meshfile + "_stat"
         if not args.regen and os.path.exists(stat_file) and os.path.getsize(stat_file) > 0:
-            #load compuated ones
-            f = open(stat_file, 'rb')
-            cur_dict = pickle.load(f)
-            for k in cur_dict:
-                d[k].append(cur_dict[k])
-            f.close()
-            continue
-
-        d['name'].append(line)
+            with open(stat_file, 'rb') as f:
+                return pickle.load(f)
 
+        # 计算网格距离指标
         mesh = trimesh.load(meshfile)
+        cd, hd, adm, ads, hd_pred2gt, hd_gt2pred = distance_p2mesh(
+            ptnormal[:,:3], ptnormal[:,3:], mesh)
+        
+        result.update({
+            'CD': cd, 'HD': hd, 'HDpred2gt': hd_pred2gt, 
+            'HDgt2pred': hd_gt2pred, 'AngleDiffMean': adm, 
+            'AngleDiffStd': ads
+        })
 
-        cd, hd, adm, ads, hd_pred2gt, hd_gt2pred = distance_p2mesh(ptnormal[:,:3], ptnormal[:,3:], mesh)
-
-        d['CD'].append(cd)
-        d['HD'].append(hd)
-        d['HDpred2gt'].append(hd_pred2gt)
-        d['HDgt2pred'].append(hd_gt2pred)
-        d['AngleDiffMean'].append(adm)
-        d['AngleDiffStd'].append(ads)
-
+        # 计算特征点指标
         gt_ptangle = np.loadtxt(os.path.join(gt_path, line + '_detectfea4e-3.ptangle'))
         pred_ptangle_path = meshfile[:-4]+'_4e-3.ptangle'
+        
         if not os.path.exists(pred_ptangle_path) or args.regen:
-            os.system('./evaluation/MeshFeatureSample/build/SimpleSample -i {} -o {} -s 4e-3'.format(meshfile, pred_ptangle_path))
+            os.system('./evaluation/MeshFeatureSample/build/SimpleSample -i {} -o {} -s 4e-3'.format(
+                meshfile, pred_ptangle_path))
+        
         pred_ptangle = np.loadtxt(pred_ptangle_path).reshape(-1,4)
         
-        #for smooth case: if gt fea is empty, or pred fea is empty, then return 0
+        # 处理特征点结果
         if len(gt_ptangle) == 0 or len(pred_ptangle) == 0:
-            d['FeaDfgt2pred'].append(0.0)
-            d['FeaDfpred2gt'].append(0.0)
-            d['FeaAnglegt2pred'].append(0.0)
-            d['FeaAnglepred2gt'].append(0.0)
-            d['FeaDf'].append(0.0)
-            d['FeaAngle'].append(0.0)
+            result.update({
+                'FeaDfgt2pred': 0.0, 'FeaDfpred2gt': 0.0,
+                'FeaAnglegt2pred': 0.0, 'FeaAnglepred2gt': 0.0,
+                'FeaDf': 0.0, 'FeaAngle': 0.0
+            })
         else:
             dfg2p, dfp2g, fag2p, fap2g = distance_fea(gt_ptangle, pred_ptangle)
-            d['FeaDfgt2pred'].append(dfg2p)
-            d['FeaDfpred2gt'].append(dfp2g)
-            d['FeaAnglegt2pred'].append(fag2p)
-            d['FeaAnglepred2gt'].append(fap2g)
-            d['FeaDf'].append((dfg2p + dfp2g) / 2.0)
-            d['FeaAngle'].append((fag2p + fap2g) / 2.0)
-
-        cur_d = {}
-        for k in d:
-            cur_d[k] = d[k][-1]
-        
-        f = open(stat_file,"wb")
-        pickle.dump(cur_d, f)
-        f.close()
+            result.update({
+                'FeaDfgt2pred': dfg2p, 'FeaDfpred2gt': dfp2g,
+                'FeaAnglegt2pred': fag2p, 'FeaAnglepred2gt': fap2g,
+                'FeaDf': (dfg2p + dfp2g) / 2.0,
+                'FeaAngle': (fag2p + fap2g) / 2.0
+            })
         
+        # 保存缓存
+        with open(stat_file, "wb") as f:
+            pickle.dump(result, f)
+            
+        return result
+    except Exception as e:
+        logger.error(f"Error processing {line}: {str(e)}")
+        return None
 
-    d['name'].append('mean')
-    for key in d:
-        if key != 'name':
-            d[key].append(sum(d[key])/len(d[key]))
-
-    df = pd.DataFrame(d, columns=['name', 'CD', 'HD', 'HDpred2gt', 'HDgt2pred', 'AngleDiffMean', 'AngleDiffStd','FeaDfgt2pred', 'FeaDfpred2gt', 'FeaDf', 'FeaAnglegt2pred', 'FeaAnglepred2gt', 'FeaAngle'])
-
-    df.to_csv(output_path, index = False, header=True)
+def compute_all():
+    """计算所有模型的评估指标"""
+    try:
+        # 初始化结果字典
+        results = []
+        
+        # 读取模型列表
+        with open(os.path.join(project_dir, 'evaluation', args.name_list), 'r') as f:
+            lines = f.readlines()
+        
+        # 处理每个模型
+        for line in lines:
+            result = load_and_process_single_model(line, args.gt_path, args.pred_path, args)
+            if result:
+                results.append(result)
+        
+        # 计算平均值
+        mean_result = {'name': 'mean'}
+        for key in results[0].keys():
+            if key != 'name':
+                mean_result[key] = sum(r[key] for r in results) / len(results)
+        results.append(mean_result)
+        
+        # 保存结果
+        df = pd.DataFrame(results)
+        df.to_csv('eval_results.csv', index=False)
+        
+        logger.info(f"Evaluation completed. Results saved to {os.path.abspath('eval_results.csv')}")
+        
+    except Exception as e:
+        logger.error(f"Error in compute_all: {str(e)}")
+        raise
 
 if __name__ == '__main__':
     compute_all()