update

tracking/utils/read_data.py

@@ -10,10 +10,10 @@ import numpy as np
 import re
 import os
 from collections import OrderedDict
+import warnings
 import matplotlib.pyplot as plt
 
 
-
 def str_to_float_arr(s):
     # 移除字符串末尾的逗号（如果存在）  
     if s.endswith(','):  
@@ -31,7 +31,9 @@ def find_samebox_in_array(arr, target):
             return i
     return -1
 
-import warnings
+
+
+
 
 
 def extract_data(datapath):
@@ -41,30 +43,26 @@ def extract_data(datapath):
     trackerfeats = np.empty((0, 256), dtype=np.float64)
     
     boxes, feats, tboxes, tfeats = [], [], [], []
+    
+    timestamps, frameIds = [], []
+
     with open(datapath, 'r', encoding='utf-8') as lines:
         for line in lines:  
             line = line.strip()  # 去除行尾的换行符和可能的空白字符  
             if not line:  # 跳过空行  
                 continue
-            
+             
             if line.find("CameraId")>=0:
                 if len(boxes): bboxes.append(np.array(boxes))
                 if len(feats): ffeats.append(np.array(feats))
-                
-                # with warnings.catch_warnings(record=True) as w:
-                #     if len(boxes): bboxes.append(np.array(boxes))
-                #     if len(feats): ffeats.append(np.array(feats))
-                # if w:
-                #     print(f"捕获到 {len(w)} 个警告:")
-                #     for warning in w:
-                #         print(f"警告类型: {warning.category}")
-                #         print(f"警告消息: {warning.message}")
-                #         print(f"警告发生的地方: {warning.filename}:{warning.lineno}")
-                
-                if len(tboxes):  
+                if len(tboxes): 
                     trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
                 if len(tfeats):
                     trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
+                    
+                timestamp, frameId = [int(ln.split(":")[1]) for ln in line.split(",")[1:]]    
+                timestamps.append(timestamp)
+                frameIds.append(frameId)
      
                 boxes, feats, tboxes, tfeats = [], [], [], []
             
@@ -103,6 +101,9 @@ def extract_data(datapath):
     assert(len(trackerboxes)==len(trackerfeats)), "Error at tracker output!"
     
     tracker_feat_dict = {}
+    tracker_feat_dict["timestamps"] = timestamps
+    tracker_feat_dict["frameIds"] = frameIds
+ 
     for i in range(len(trackerboxes)):
         tid, fid, bid = int(trackerboxes[i, 4]), int(trackerboxes[i, 7]), int(trackerboxes[i, 8])
         if f"frame_{fid}" not in tracker_feat_dict:
@@ -169,8 +170,8 @@ def read_tracking_output(filepath):
     return np.array(boxes), np.array(feats) 
     
 
-def read_deletedBarcode_file(filePth):
-    with open(filePth, 'r', encoding='utf-8') as f:
+def read_deletedBarcode_file(filePath):
+    with open(filePath, 'r', encoding='utf-8') as f:
         lines = f.readlines()
 
         split_flag, all_list = False, []
@@ -179,6 +180,9 @@ def read_deletedBarcode_file(filePth):
         clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
         
         for i, line in enumerate(clean_lines):
+            if line.endswith(','):  
+                line = line[:-1]
+                
             stripped_line = line.strip()
             if not stripped_line:
                 if len(barcode_list): dict['barcode'] = barcode_list
@@ -210,11 +214,106 @@ def read_deletedBarcode_file(filePth):
     return all_list
 
 
+def read_returnGoods_file(filePath):
+    '''
+    20241030开始，原 deletedBarcode.txt 中数据格式修改为 returnGoods.txt，读数方式随之变化
+    '''
+    
+    with open(filePath, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+        clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
+
+
+
+        all_list = []
+        split_flag, dict = False, {} 
+        barcode_list, similarity_list = [], []
+        event_list, type_list = [], []
+        
+        
+        for i, line in enumerate(clean_lines):
+            stripped_line = line.strip()
+            if line.endswith(','):  
+                line = line[:-1]
+            
+            if not stripped_line:
+                if len(barcode_list): dict['barcode'] = barcode_list
+                if len(similarity_list): dict['similarity'] = similarity_list
+                if len(event_list): dict['event'] = event_list
+                if len(type_list): dict['type'] = type_list
+                
+                if len(dict) and  dict['SeqDir'].find('*')<0: 
+                    all_list.append(dict)
+                
+                split_flag, dict = False, {} 
+                barcode_list, similarity_list = [], []
+                event_list, type_list = [], []
+                continue
+            
+            if line.find(':')<0: continue
+            if line.find('1:n')==0: continue
+        
+            label = line.split(':')[0].strip()
+            value = line.split(':')[1].strip()
+    
+            if label == 'SeqDir':
+                dict['SeqDir'] = value
+                dict['Deleted'] = value.split('_')[-1]
+            if label == 'List':
+                split_flag = True
+                continue
+            if split_flag:
+                event_list.append(label)
+                barcode_list.append(label.split('_')[-1])
+                similarity_list.append(value.split(',')[0])
+                type_list.append(value.split('=')[-1])
+        
+        if len(barcode_list): dict['barcode'] = barcode_list
+        if len(similarity_list): dict['similarity'] = similarity_list
+        if len(event_list): dict['event'] = event_list
+        if len(type_list): dict['type'] = type_list
+        if len(dict) and  dict['SeqDir'].find('*')<0: 
+            all_list.append(dict)
+    
+    return all_list
+        
+    
+    
+    
+    
+
+
+
+
+
+
+
+
+
+
+def read_seneor(filepath):
+    WeightDict = OrderedDict()
+    with open(filepath, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+        for i, line in enumerate(lines):
+            line = line.strip()
+            
+            keyword = line.split(':')[0]
+            value = line.split(':')[1]
+            
+            vdata = [float(s) for s in value.split(',') if len(s)]
+            
+            WeightDict[keyword] = vdata[-1]
+    
+    return WeightDict
+
+
 def read_weight_timeConsuming(filePth):
     WeightDict, SensorDict, ProcessTimeDict = OrderedDict(), OrderedDict(), OrderedDict()
     
     with open(filePth, 'r', encoding='utf-8') as f:
         lines = f.readlines()
+        # label = ''
         for i, line in enumerate(lines):
             line = line.strip()