Refactor evaluation.py for improved project structure and logging

- Added project root directory setup to ensure consistent file paths.
- Integrated a custom logger for enhanced logging capabilities.
- Updated argument parsing to use absolute paths for ground truth and prediction data.
- Improved documentation for distance functions and added a new function to compute feature distances and angle differences.
- Refactored file reading to use context management for better resource handling.

code/evaluation/evaluation.py

@@ -1,4 +1,14 @@
 import os
+import sys
+
+# 设置项目根目录
+project_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))
+sys.path.append(project_dir)
+os.chdir(project_dir)
+
+# 导入日志系统
+from utils.logger import logger
+
 import numpy as np
 from scipy.spatial import cKDTree
 from scipy.spatial.distance import directed_hausdorff
@@ -11,8 +21,12 @@ import pickle
 import argparse
 # parse args first and set gpu id
 parser = argparse.ArgumentParser()
-parser.add_argument('--gt_path', type=str, default='../../data/eval_data', help='ground truth data path')
+parser.add_argument('--gt_path', type=str, 
-parser.add_argument('--pred_path', type=str, default='../../data/output_data', help='converted data path')
+    default=os.path.join(project_dir, '../data/eval_data'), 
+    help='ground truth data path')
+parser.add_argument('--pred_path', type=str, 
+    default=os.path.join(project_dir, '../data/output_data'), 
+    help='converted data path')
 parser.add_argument('--name_list', type=str, default='broken_bullet_name.txt', help='names of models to be evaluated, if you want to evaluate the whole dataset, please set it as all_names.txt')
 parser.add_argument('--nsample', type=int, default=50000, help='point batch size')
 parser.add_argument('--regen', default = False, action="store_true", help = 'regenerate feature curves')
@@ -22,10 +36,13 @@ def distance_p2p(points_src, normals_src, points_tgt, normals_tgt):
     ''' Computes minimal distances of each point in points_src to points_tgt.
 
     Args:
-        points_src (numpy array): source points
+        points_src (numpy array [N, 3]): source points
-        normals_src (numpy array): source normals
+        normals_src (numpy array [N, 3]): source normals
-        points_tgt (numpy array): target points
+        points_tgt (numpy array [M, 3]): target points
-        normals_tgt (numpy array): target normals
+        normals_tgt (numpy array [M, 3]): target 
+    Returns:
+        dist (numpy array [N]): minimal distances of each point in points_src to points_tgt
+        normals_dot_product (numpy array [N]): dot product of normals of points_src and points_tgt
     '''
     kdtree = cKDTree(points_tgt)
     dist, idx = kdtree.query(points_src)
@@ -78,6 +95,16 @@ def distance_p2mesh(points_src, normals_src, mesh):
 
 
 def distance_fea(gt_pa, pred_pa):
+    """计算特征点之间的距离和角度差异
+    Args:
+        gt_pa: 真实特征点和角度 [N, 4]
+        pred_pa: 预测特征点和角度 [N, 4]
+    Returns:
+        dfg2p: 真实到预测的距离
+        dfp2g: 预测到真实的距离
+        fag2p: 真实到预测的角度差
+        fap2g: 预测到真实的角度差
+    """
     gt_points = gt_pa[:,:3]
     pred_points = pred_pa[:,:3]
     gt_angle = gt_pa[:,3]
@@ -108,9 +135,8 @@ def compute_all():
     namelst = args.name_list
     output_path = 'eval_results.csv'
 
-    f = open(namelst, 'r')
+    with open(os.path.join(project_dir, 'evaluation', namelst), 'r') as f:
-    lines = f.readlines()
+        lines = f.readlines()
-    f.close()
 
     d = {'name':[], 'CD':[], 'HD':[], 'HDgt2pred':[], 'HDpred2gt':[], 'AngleDiffMean':[], 'AngleDiffStd':[], 'FeaDfgt2pred':[], 'FeaDfpred2gt':[], 'FeaDf':[], 'FeaAnglegt2pred':[], 'FeaAnglepred2gt':[], 'FeaAngle':[]}