diff --git a/__pycache__/event_time_specify.cpython-39.pyc b/__pycache__/event_time_specify.cpython-39.pyc
index f810d64..dd44679 100644
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diff --git a/__pycache__/imgs_inference.cpython-39.pyc b/__pycache__/imgs_inference.cpython-39.pyc
index b9f30b5..981322a 100644
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diff --git a/__pycache__/move_detect.cpython-39.pyc b/__pycache__/move_detect.cpython-39.pyc
index 2565888..02e5739 100644
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diff --git a/__pycache__/track_reid.cpython-39.pyc b/__pycache__/track_reid.cpython-39.pyc
index c565e61..47bda46 100644
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diff --git a/bclass.py b/bclass.py
new file mode 100644
index 0000000..24cb443
--- /dev/null
+++ b/bclass.py
@@ -0,0 +1,50 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov 15 16:23:03 2024
+
+@author: ym
+"""
+
+class CamEvent:
+    def __init__(self, datapath):
+        self.data_path = datapath
+        self.bboxes = None
+        self.bfeats = None
+        self.tboxes = None
+        self.tfeats = None
+        
+        
+
+class ShopEvent:
+    def __init__(self, eventpath, stdpath):
+        self.barcode = ""
+        self.event_path = eventpath
+        self.event_type = self.get_event_type(eventpath)
+        
+        self.FrontEvent = ""
+        self.BackEvent = ""
+        self.fusion_boxes = None
+        self.fusion_feats = None
+        self.stdfeats = self.get_stdfeats(stdpath)
+        self.weight = None
+        self.imu = None
+        
+    def get_event_type(self, eventpath):
+        pass
+    
+    
+    
+    def get_stdfeats(self, stdpath):
+        pass
+    
+    
+    
+    
+    
+    
+    
+    
+    
+        
+    
+    
\ No newline at end of file
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index e81b06c..79cd9dd 100644
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diff --git a/contrast/__pycache__/feat_inference.cpython-39.pyc b/contrast/__pycache__/feat_inference.cpython-39.pyc
index 725550e..07eb78f 100644
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diff --git a/contrast/__pycache__/one2n_contrast.cpython-39.pyc b/contrast/__pycache__/one2n_contrast.cpython-39.pyc
index 7e47383..2f9f7ce 100644
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diff --git a/contrast/feat_analysisi.py b/contrast/feat_analysisi.py
new file mode 100644
index 0000000..fc3ebe5
--- /dev/null
+++ b/contrast/feat_analysisi.py
@@ -0,0 +1,160 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Nov 20 11:17:29 2024
+
+@author: ym
+"""
+
+import os
+import cv2
+import pickle
+import numpy as np
+from scipy.spatial.distance import cdist
+import matplotlib.pyplot as plt
+
+def save_imgpairs(barcode, imgpaths, matrix, savepath, thresh=(0.4, 0.6), ctype="intra"):
+    if ctype=="intra":
+        rows, cols = np.triu_indices(matrix.shape[0], k=1)   # k=1 表示不包括对角线
+        mask = matrix[rows, cols] < thresh[1]
+        indices = list(zip(rows[mask], cols[mask]))
+    else:
+        rows, cols = np.where(matrix > thresh[0])
+        indices = list(zip(rows, cols))
+    
+    
+    if len(indices):
+        savepath = os.path.join(savepath, barcode)
+        if not os.path.exists(savepath):
+            os.makedirs (savepath)
+    
+    
+    for idx1, idx2 in indices:
+        if len(imgpaths) == 1:
+            img1 = cv2.imread(imgpaths[0][idx1])
+            img2 = cv2.imread(imgpaths[0][idx2])
+        elif len(imgpaths) == 2:
+            img1 = cv2.imread(imgpaths[0][idx1])
+            img2 = cv2.imread(imgpaths[1][idx2])
+            
+            
+        
+        simi = matrix[idx1, idx2]
+
+        H1, W1 = img1.shape[:2]
+        H2, W2 = img2.shape[:2]
+        H, W = max((H1, H2)), max((W1, W2))
+        img = np.ones((H, 2*W, 3), dtype=np.uint8) *np.array([255, 128, 128])
+        
+        img[0:H1, 0:W1, :] = img1
+        img[0:H2, (2*W-W2):, :] = img2
+        
+        text = f"sim: {simi:.2f}"
+        org = (10, H-10)
+        cv2.putText(img, text, org, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.75, 
+                    color=(0, 0, 255), thickness=2, lineType=cv2.LINE_AA)
+        imgpath = os.path.join(savepath, f"{simi:.2f}_{barcode}_{idx1}_{idx2}.png")
+        cv2.imwrite(imgpath, img)
+        
+
+
+def feat_analysis(featpath):    
+    savepath = r"D:\exhibition\result\stdfeat"
+
+    InterThresh = (0.4, 0.6)
+    
+    featDict, features= [], []
+    for filename in os.listdir(featpath):
+        file, ext = os.path.splitext(filename)
+        if ext != ".pickle": continue
+        
+        filepath = os.path.join(featpath, filename)
+        with open(filepath, 'rb') as f:  
+            bpDict = pickle.load(f)
+
+            feat = bpDict["feats_ft32"]
+
+            featDict.append(bpDict)
+            features.append(feat)
+    N = len(features)   
+   
+    simMatrix = []
+    intra_simi = np.empty(0)   
+    low_simi_index = {}
+    for i, feats in enumerate(features):
+        matrix = 1 - cdist(feats, feats, 'cosine')
+        
+        simMatrix.append(matrix)
+        
+        '''提取相似矩阵上三角元素'''
+        rows, cols = np.triu_indices(matrix.shape[0], k=1)  # k=1 表示不包括对角线
+        upper_tri= matrix[rows, cols]        
+        intra_simi = np.concatenate((intra_simi, upper_tri))
+        
+        '''保存相似度小于阈值的图像对'''
+        barcode = featDict[i]["barcode"]
+        imgpaths = featDict[i]["imgpaths"]
+        # save_imgpairs(barcode, [imgpaths], matrix, savepath, InterThresh, "intra")
+        print(f"{barcode} have done!")
+        
+    Matrix = np.zeros((N, N))
+    inter_bcds = []
+    inter_simi = np.empty(0)
+    for i, feati in enumerate(features):
+        bcdi = featDict[i]["barcode"]
+        imgpathi = featDict[i]["imgpaths"]
+        for j, featj in enumerate(features):
+            bcdj = featDict[j]["barcode"]
+            imgpathj = featDict[j]["imgpaths"]
+        
+            matrix = 1 - cdist(feati, featj, 'cosine')
+            
+            inter_bcds.append((i, j, bcdi, bcdj))
+            Matrix[i, j] = np.mean(matrix)
+            if j>i:
+                bcd_ij = bcdi+'_'+bcdj
+                # save_imgpairs(bcd_ij, [imgpathi, imgpathj], matrix, savepath, InterThresh, "inter")
+                inter_simi = np.concatenate((inter_simi, matrix.ravel()))
+                
+                print(f"{bcd_ij} have done!")
+            
+    fig, axs = plt.subplots(2, 1)
+    axs[0].hist(intra_simi, bins=100, color='blue', edgecolor='black', alpha=0.7)
+    axs[0].set_xlim(0, 1)
+    axs[0].set_xlabel('Performance')
+    axs[0].set_title("inter similarity")
+    
+    axs[1].hist(inter_simi, bins=100, color='green', edgecolor='black', alpha=0.7)
+    axs[1].set_xlim(0, 1)
+    axs[1].set_xlabel('Performance')
+    axs[1].set_title("inter similarity")
+    
+    
+    
+    
+    
+    print("Done！")
+            
+            
+            
+    
+
+
+
+
+def main():
+    stdpath = r"D:\exhibition\dataset\feats"
+    
+    feat_analysis(stdpath)
+    
+
+if __name__ == '__main__':
+    main()
+    
+    
+    
+    
+    
+    
+    
+    
+    
\ No newline at end of file
diff --git a/contrast/feat_extract/__pycache__/config.cpython-39.pyc b/contrast/feat_extract/__pycache__/config.cpython-39.pyc
new file mode 100644
index 0000000..dd9fad2
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diff --git a/contrast/feat_extract/__pycache__/inference.cpython-39.pyc b/contrast/feat_extract/__pycache__/inference.cpython-39.pyc
new file mode 100644
index 0000000..1f5c72f
Binary files /dev/null and b/contrast/feat_extract/__pycache__/inference.cpython-39.pyc differ
diff --git a/contrast/checkpoints/resnet18_0515/best.rknn b/contrast/feat_extract/checkpoints/resnet18_0515/best.rknn
similarity index 100%
rename from contrast/checkpoints/resnet18_0515/best.rknn
rename to contrast/feat_extract/checkpoints/resnet18_0515/best.rknn
diff --git a/contrast/config.py b/contrast/feat_extract/config.py
similarity index 94%
rename from contrast/config.py
rename to contrast/feat_extract/config.py
index 4864cdb..3bb15c9 100644
--- a/contrast/config.py
+++ b/contrast/feat_extract/config.py
@@ -10,6 +10,8 @@ class Config:
     embedding_size = 256
     drop_ratio = 0.5
     img_size = 224
+    
+    batch_size = 8
 
     # data preprocess
     # input_shape = [1, 128, 128]
@@ -58,7 +60,11 @@ class Config:
     testbackbone = 'resnet18'  # [resnet18, mobilevit_s, mobilenet_v2, mobilenetv3_small, mobilenetv3_large, mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5]
     test_val = "D:/比对/cl"
     # test_val = "./data/test_data_100"
-    test_model = "checkpoints/resnet18_0515/best.pth"
+    
+    # test_model = "checkpoints/best_resnet18_v11.pth"   
+    test_model = "checkpoints/zhanting_cls22_v11.pth"
+    
+    
 
     train_batch_size = 512  # 256
     test_batch_size = 256  # 256
diff --git a/contrast/feat_inference.py b/contrast/feat_extract/inference.py
similarity index 71%
rename from contrast/feat_inference.py
rename to contrast/feat_extract/inference.py
index 613a620..70ff84a 100644
--- a/contrast/feat_inference.py
+++ b/contrast/feat_extract/inference.py
@@ -3,30 +3,140 @@
 
 @author: LiChen
 """
-
-
+# import pdb
+# import shutil
+import torch.nn as nn
+# import statistics
 import os
-import os.path as osp
-import pdb
 import numpy as np 
-import shutil
 from scipy.spatial.distance import cdist
 import torch
-import torch.nn as nn
+import os.path as osp
 from PIL import Image
 import json
-from config import config as conf
-from model import resnet18
+import matplotlib.pyplot as plt
+from pathlib import Path
+# import sys
+# sys.path.append(r"D:\DetectTracking")
+# from contrast.config import config as conf
+# from contrast.model import resnet18
+
+from .config import config as conf
+from .model import resnet18
 
 # from model import (mobilevit_s, resnet14, resnet18, resnet34, resnet50, mobilenet_v2,
 #                    MobileNetV3_Small, mobilenet_v1, PPLCNET_x1_0, PPLCNET_x0_5, PPLCNET_x2_5)
 
+curpath = Path(__file__).resolve().parents[0]
+
+class FeatsInterface:
+    def __init__(self, conf):
+        self.device = conf.device
+
+        # if conf.backbone == 'resnet18':
+        #     model = resnet18().to(conf.device)
+            
+        model = resnet18().to(conf.device)
+        self.transform = conf.test_transform
+        self.batch_size = conf.batch_size
+        self.embedding_size = conf.embedding_size
+        
+        if conf.test_model.find("zhanting") == -1:
+            model = nn.DataParallel(model).to(conf.device)
+        self.model = model
+        
+        modpath = os.path.join(curpath, conf.test_model)
+        self.model.load_state_dict(torch.load(modpath, map_location=conf.device))
+        self.model.eval()
+        print('load model {} '.format(conf.testbackbone))
+    
+    def inference(self, images, detections=None):
+        '''
+        如果是BGR，需要转变为RGB格式
+        '''
+        if isinstance(images, np.ndarray):
+            imgs, features = self.inference_image(images, detections)
+            return imgs, features
+                  
+        batch_patches = []
+        patches = []
+        for i, img in enumerate(images):
+            img = img.copy()   
+            patch = self.transform(img)            
+            if str(self.device) != "cpu":
+                patch = patch.to(device=self.device).half()
+            else:
+                patch = patch.to(device=self.device)
+            
+            patches.append(patch)
+            if (i + 1) % self.batch_size == 0:
+                patches = torch.stack(patches, dim=0)
+                batch_patches.append(patches)
+                patches = []
+        
+        if len(patches):
+            patches = torch.stack(patches, dim=0)
+            batch_patches.append(patches)
+        
+        features = np.zeros((0, self.embedding_size))
+        for patches in batch_patches:
+            pred=self.model(patches)
+            pred[torch.isinf(pred)] = 1.0            
+            feat = pred.cpu().data.numpy()
+            features = np.vstack((features, feat))        
+        return features
+    
+    def inference_image(self, image, detections):
+        H, W, _ = np.shape(image)
+
+        batch_patches = []
+        patches = []
+        imgs = []
+        for d in range(np.size(detections, 0)):
+            tlbr = detections[d, :4].astype(np.int_)
+            tlbr[0] = max(0, tlbr[0])
+            tlbr[1] = max(0, tlbr[1])
+            tlbr[2] = min(W - 1, tlbr[2])
+            tlbr[3] = min(H - 1, tlbr[3])
+            img = image[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2], :]
+            
+            imgs.append(img)
+            
+            
+            img1 = img[:, :, ::-1].copy()    # the model expects RGB inputs
+            patch = self.transform(img1)
+
+            # patch = patch.to(device=self.device).half()
+            if str(self.device) != "cpu":
+                patch = patch.to(device=self.device).half()
+            else:
+                patch = patch.to(device=self.device)
+            
+            patches.append(patch)
+            if (d + 1) % self.batch_size == 0:
+                patches = torch.stack(patches, dim=0)
+                batch_patches.append(patches)
+                patches = []
+
+        if len(patches):
+            patches = torch.stack(patches, dim=0)
+            batch_patches.append(patches)
+
+        features = np.zeros((0, self.embedding_size))
+        for patches in batch_patches:
+            pred = self.model(patches)
+            pred[torch.isinf(pred)] = 1.0
+            feat = pred.cpu().data.numpy()
+            features = np.vstack((features, feat))
+
+        return imgs, features 
+        
+        
+    
+    
+
+
 
-import matplotlib.pyplot as plt
-import statistics
-embedding_size = conf.embedding_size
-img_size = conf.img_size
-device = conf.device
 
 
 def unique_image(pair_list) -> set:
@@ -102,38 +212,38 @@ def featurize(images: list, transform, net, device, train=False) -> dict:
         res = {img: feature for (img, feature) in zip(images, features)}
     return res
 
-def inference_image(images: list, transform, net, device, bs=16, embedding_size=256) -> dict:
-    batch_patches = []
-    patches = []
-    for d, img in enumerate(images):
-        img = Image.open(img)
-        patch = transform(img)
+# def inference_image(images: list, transform, net, device, bs=16, embedding_size=256) -> dict:
+#     batch_patches = []
+#     patches = []
+#     for d, img in enumerate(images):
+#         img = Image.open(img)
+#         patch = transform(img)
         
-        if str(device) != "cpu":
-            patch = patch.to(device).half()
-        else:
-            patch = patch.to(device)
+#         if str(device) != "cpu":
+#             patch = patch.to(device).half()
+#         else:
+#             patch = patch.to(device)
         
-        patches.append(patch)
-        if (d + 1) % bs == 0:
-            patches = torch.stack(patches, dim=0)
-            batch_patches.append(patches)
-            patches = []
+#         patches.append(patch)
+#         if (d + 1) % bs == 0:
+#             patches = torch.stack(patches, dim=0)
+#             batch_patches.append(patches)
+#             patches = []
 
-    if len(patches):
-        patches = torch.stack(patches, dim=0)
-        batch_patches.append(patches)
+#     if len(patches):
+#         patches = torch.stack(patches, dim=0)
+#         batch_patches.append(patches)
 
-    features = np.zeros((0, embedding_size), dtype=np.float32)
-    for patches in batch_patches:
-        pred = net(patches)
-        pred[torch.isinf(pred)] = 1.0
-        feat = pred.cpu().data.numpy()
-        features = np.vstack((features, feat))
+#     features = np.zeros((0, embedding_size), dtype=np.float32)
+#     for patches in batch_patches:
+#         pred = net(patches)
+#         pred[torch.isinf(pred)] = 1.0
+#         feat = pred.cpu().data.numpy()
+#         features = np.vstack((features, feat))
         
     
 
-    return features
+#     return features
 
 
 
@@ -283,6 +393,7 @@ def compute_contrast_accuracy(content_list_read):
     
     npairs = min((len(same_folder_pairs), len(cross_folder_pairs)))
     
+    Encoder = FeatsInterface(conf)
     
     same_pairs = same_folder_pairs[:npairs]
     cross_pairs = cross_folder_pairs[:npairs]
@@ -292,8 +403,8 @@ def compute_contrast_accuracy(content_list_read):
         images_a = [osp.join(conf.test_val, img) for img in same_pairs[i][0]]
         images_b = [osp.join(conf.test_val, img) for img in same_pairs[i][1]]
         
-        feats_a = inference_image(images_a, conf.test_transform, model, conf.device)
-        feats_b = inference_image(images_b, conf.test_transform, model, conf.device)
+        feats_a = Encoder.inference(images_a)
+        feats_b = Encoder.inference(images_b)
         # matrix = 1- np.maximum(0.0, cdist(feats_a, feats_b, 'cosine'))
         matrix = 1 - cdist(feats_a, feats_b, 'cosine')
         
@@ -324,8 +435,8 @@ def compute_contrast_accuracy(content_list_read):
         images_a = [osp.join(conf.test_val, img) for img in cross_pairs[i][0]]
         images_b = [osp.join(conf.test_val, img) for img in cross_pairs[i][1]]
         
-        feats_a = inference_image(images_a, conf.test_transform, model, conf.device)
-        feats_b = inference_image(images_b, conf.test_transform, model, conf.device)
+        feats_a = Encoder.inference(images_a)
+        feats_b = Encoder.inference(images_b)
         # matrix = 1- np.maximum(0.0, cdist(feats_a, feats_b, 'cosine'))
         matrix = 1 - cdist(feats_a, feats_b, 'cosine')
         
@@ -407,28 +518,28 @@ if __name__ == '__main__':
 
     # Network Setup
     if conf.testbackbone == 'resnet18':
-        # model = ResIRSE(img_size, embedding_size, conf.drop_ratio).to(device)
-        model = resnet18().to(device)
+        # model = ResIRSE(conf.img_size, conf.embedding_size, conf.drop_ratio).to(conf.device)
+        model = resnet18().to(conf.device)
     # elif conf.testbackbone == 'resnet34':
-    #     model = resnet34().to(device)
+    #     model = resnet34().to(conf.device)
     # elif conf.testbackbone == 'resnet50':
-    #     model = resnet50().to(device)
+    #     model = resnet50().to(conf.device)
     # elif conf.testbackbone == 'mobilevit_s':
-    #     model = mobilevit_s().to(device)
+    #     model = mobilevit_s().to(conf.device)
     # elif conf.testbackbone == 'mobilenetv3':
-    #     model = MobileNetV3_Small().to(device)
+    #     model = MobileNetV3_Small().to(conf.device)
     # elif conf.testbackbone == 'mobilenet_v1':
-    #     model = mobilenet_v1().to(device)
+    #     model = mobilenet_v1().to(conf.device)
     # elif conf.testbackbone == 'PPLCNET_x1_0':
-    #     model = PPLCNET_x1_0().to(device)
+    #     model = PPLCNET_x1_0().to(conf.device)
     # elif conf.testbackbone == 'PPLCNET_x0_5':
-    #     model = PPLCNET_x0_5().to(device)
+    #     model = PPLCNET_x0_5().to(conf.device)
     # elif conf.backbone == 'PPLCNET_x2_5':
-    #     model = PPLCNET_x2_5().to(device)
+    #     model = PPLCNET_x2_5().to(conf.device)
     # elif conf.testbackbone == 'mobilenet_v2':
-    #     model = mobilenet_v2().to(device)
+    #     model = mobilenet_v2().to(conf.device)
     # elif conf.testbackbone == 'resnet14':
-    #     model = resnet14().to(device)
+    #     model = resnet14().to(conf.device)
     else:
         raise ValueError('Have not model {}'.format(conf.backbone))
 
diff --git a/contrast/model/BAM.py b/contrast/feat_extract/model/BAM.py
similarity index 100%
rename from contrast/model/BAM.py
rename to contrast/feat_extract/model/BAM.py
diff --git a/contrast/model/CBAM.py b/contrast/feat_extract/model/CBAM.py
similarity index 100%
rename from contrast/model/CBAM.py
rename to contrast/feat_extract/model/CBAM.py
diff --git a/contrast/model/Tool.py b/contrast/feat_extract/model/Tool.py
similarity index 100%
rename from contrast/model/Tool.py
rename to contrast/feat_extract/model/Tool.py
diff --git a/contrast/model/__init__.py b/contrast/feat_extract/model/__init__.py
similarity index 100%
rename from contrast/model/__init__.py
rename to contrast/feat_extract/model/__init__.py
diff --git a/contrast/model/__pycache__/BAM.cpython-38.pyc b/contrast/feat_extract/model/__pycache__/BAM.cpython-38.pyc
similarity index 100%
rename from contrast/model/__pycache__/BAM.cpython-38.pyc
rename to contrast/feat_extract/model/__pycache__/BAM.cpython-38.pyc
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similarity index 100%
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similarity index 100%
rename from contrast/model/__pycache__/Tool.cpython-38.pyc
rename to contrast/feat_extract/model/__pycache__/Tool.cpython-38.pyc
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similarity index 100%
rename from contrast/model/__pycache__/Tool.cpython-39.pyc
rename to contrast/feat_extract/model/__pycache__/Tool.cpython-39.pyc
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similarity index 100%
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similarity index 100%
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similarity index 100%
rename from contrast/model/__pycache__/fmobilenet.cpython-39.pyc
rename to contrast/feat_extract/model/__pycache__/fmobilenet.cpython-39.pyc
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similarity index 100%
rename from contrast/model/__pycache__/lcnet.cpython-38.pyc
rename to contrast/feat_extract/model/__pycache__/lcnet.cpython-38.pyc
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rename to contrast/feat_extract/model/__pycache__/lcnet.cpython-39.pyc
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similarity index 100%
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rename to contrast/feat_extract/model/__pycache__/loss.cpython-38.pyc
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similarity index 100%
rename from contrast/model/__pycache__/loss.cpython-39.pyc
rename to contrast/feat_extract/model/__pycache__/loss.cpython-39.pyc
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similarity index 100%
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rename to contrast/feat_extract/model/__pycache__/metric.cpython-38.pyc
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similarity index 100%
rename from contrast/model/__pycache__/metric.cpython-39.pyc
rename to contrast/feat_extract/model/__pycache__/metric.cpython-39.pyc
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similarity index 100%
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similarity index 100%
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similarity index 74%
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similarity index 100%
rename from contrast/model/__pycache__/mobilevit.cpython-310.pyc
rename to contrast/feat_extract/model/__pycache__/mobilevit.cpython-310.pyc
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similarity index 100%
rename from contrast/model/__pycache__/mobilevit.cpython-38.pyc
rename to contrast/feat_extract/model/__pycache__/mobilevit.cpython-38.pyc
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similarity index 100%
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rename to contrast/feat_extract/model/__pycache__/resbam.cpython-38.pyc
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similarity index 100%
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similarity index 100%
rename from contrast/model/__pycache__/resnet.cpython-38.pyc
rename to contrast/feat_extract/model/__pycache__/resnet.cpython-38.pyc
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similarity index 100%
rename from contrast/model/__pycache__/resnet_face.cpython-38.pyc
rename to contrast/feat_extract/model/__pycache__/resnet_face.cpython-38.pyc
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similarity index 100%
rename from contrast/model/__pycache__/resnet_face.cpython-39.pyc
rename to contrast/feat_extract/model/__pycache__/resnet_face.cpython-39.pyc
diff --git a/contrast/model/__pycache__/resnet_pre.cpython-38.pyc b/contrast/feat_extract/model/__pycache__/resnet_pre.cpython-38.pyc
similarity index 100%
rename from contrast/model/__pycache__/resnet_pre.cpython-38.pyc
rename to contrast/feat_extract/model/__pycache__/resnet_pre.cpython-38.pyc
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similarity index 53%
rename from contrast/model/__pycache__/resnet_pre.cpython-39.pyc
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index d99c1c8..e2b7242 100644
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diff --git a/contrast/model/__pycache__/utils.cpython-38.pyc b/contrast/feat_extract/model/__pycache__/utils.cpython-38.pyc
similarity index 100%
rename from contrast/model/__pycache__/utils.cpython-38.pyc
rename to contrast/feat_extract/model/__pycache__/utils.cpython-38.pyc
diff --git a/contrast/model/__pycache__/utils.cpython-39.pyc b/contrast/feat_extract/model/__pycache__/utils.cpython-39.pyc
similarity index 100%
rename from contrast/model/__pycache__/utils.cpython-39.pyc
rename to contrast/feat_extract/model/__pycache__/utils.cpython-39.pyc
diff --git a/contrast/model/fmobilenet.py b/contrast/feat_extract/model/fmobilenet.py
similarity index 100%
rename from contrast/model/fmobilenet.py
rename to contrast/feat_extract/model/fmobilenet.py
diff --git a/contrast/model/lcnet.py b/contrast/feat_extract/model/lcnet.py
similarity index 100%
rename from contrast/model/lcnet.py
rename to contrast/feat_extract/model/lcnet.py
diff --git a/contrast/model/loss.py b/contrast/feat_extract/model/loss.py
similarity index 100%
rename from contrast/model/loss.py
rename to contrast/feat_extract/model/loss.py
diff --git a/contrast/model/metric.py b/contrast/feat_extract/model/metric.py
similarity index 100%
rename from contrast/model/metric.py
rename to contrast/feat_extract/model/metric.py
diff --git a/contrast/model/mobilenet_v1.py b/contrast/feat_extract/model/mobilenet_v1.py
similarity index 100%
rename from contrast/model/mobilenet_v1.py
rename to contrast/feat_extract/model/mobilenet_v1.py
diff --git a/contrast/model/mobilenet_v2.py b/contrast/feat_extract/model/mobilenet_v2.py
similarity index 99%
rename from contrast/model/mobilenet_v2.py
rename to contrast/feat_extract/model/mobilenet_v2.py
index d62f0cd..eec44d4 100644
--- a/contrast/model/mobilenet_v2.py
+++ b/contrast/feat_extract/model/mobilenet_v2.py
@@ -1,6 +1,6 @@
 from torch import nn
 from .utils import load_state_dict_from_url
-from config import config as conf
+from ..config import config as conf
 
 __all__ = ['MobileNetV2', 'mobilenet_v2']
 
diff --git a/contrast/model/mobilenet_v3.py b/contrast/feat_extract/model/mobilenet_v3.py
similarity index 99%
rename from contrast/model/mobilenet_v3.py
rename to contrast/feat_extract/model/mobilenet_v3.py
index d69a5a0..953db51 100644
--- a/contrast/model/mobilenet_v3.py
+++ b/contrast/feat_extract/model/mobilenet_v3.py
@@ -7,7 +7,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn import init
-from config import config as conf
+from ..config import config as conf
 
 
 class hswish(nn.Module):
diff --git a/contrast/model/mobilevit.py b/contrast/feat_extract/model/mobilevit.py
similarity index 98%
rename from contrast/model/mobilevit.py
rename to contrast/feat_extract/model/mobilevit.py
index f371ee9..c4a0824 100644
--- a/contrast/model/mobilevit.py
+++ b/contrast/feat_extract/model/mobilevit.py
@@ -2,7 +2,10 @@ import torch
 import torch.nn as nn
 
 from einops import rearrange
-from config import config as conf
+
+# import sys
+# sys.path.append(r"D:\DetectTracking")
+from ..config import config as conf
 
 
 def conv_1x1_bn(inp, oup):
diff --git a/contrast/model/resbam.py b/contrast/feat_extract/model/resbam.py
similarity index 97%
rename from contrast/model/resbam.py
rename to contrast/feat_extract/model/resbam.py
index 21395c3..15a88e6 100644
--- a/contrast/model/resbam.py
+++ b/contrast/feat_extract/model/resbam.py
@@ -1,7 +1,10 @@
-from model.CBAM import CBAM
 import torch
 import torch.nn as nn
-from model.Tool import GeM as gem
+
+from .CBAM import CBAM
+from .Tool import GeM as gem
+# from model.CBAM import CBAM
+# from model.Tool import GeM as gem
 
 
 class Bottleneck(nn.Module):
diff --git a/contrast/model/resnet.py b/contrast/feat_extract/model/resnet.py
similarity index 100%
rename from contrast/model/resnet.py
rename to contrast/feat_extract/model/resnet.py
diff --git a/contrast/model/resnet_face.py b/contrast/feat_extract/model/resnet_face.py
similarity index 100%
rename from contrast/model/resnet_face.py
rename to contrast/feat_extract/model/resnet_face.py
diff --git a/contrast/resnet_vit/model/resnet_pre.py b/contrast/feat_extract/model/resnet_pre.py
similarity index 99%
rename from contrast/resnet_vit/model/resnet_pre.py
rename to contrast/feat_extract/model/resnet_pre.py
index 5e52ad9..3ea6441 100644
--- a/contrast/resnet_vit/model/resnet_pre.py
+++ b/contrast/feat_extract/model/resnet_pre.py
@@ -1,6 +1,6 @@
 import torch
 import torch.nn as nn
-from config import config as conf
+from ..config import config as conf
 
 try:
     from torch.hub import load_state_dict_from_url
diff --git a/contrast/model/utils.py b/contrast/feat_extract/model/utils.py
similarity index 100%
rename from contrast/model/utils.py
rename to contrast/feat_extract/model/utils.py
diff --git a/contrast/model/vit.py b/contrast/feat_extract/model/vit.py
similarity index 100%
rename from contrast/model/vit.py
rename to contrast/feat_extract/model/vit.py
diff --git a/contrast/resnet_vit/.idea/.gitignore b/contrast/feat_extract/resnet_vit/.idea/.gitignore
similarity index 100%
rename from contrast/resnet_vit/.idea/.gitignore
rename to contrast/feat_extract/resnet_vit/.idea/.gitignore
diff --git a/contrast/resnet_vit/.idea/contrastInference.iml b/contrast/feat_extract/resnet_vit/.idea/contrastInference.iml
similarity index 100%
rename from contrast/resnet_vit/.idea/contrastInference.iml
rename to contrast/feat_extract/resnet_vit/.idea/contrastInference.iml
diff --git a/contrast/resnet_vit/.idea/deployment.xml b/contrast/feat_extract/resnet_vit/.idea/deployment.xml
similarity index 100%
rename from contrast/resnet_vit/.idea/deployment.xml
rename to contrast/feat_extract/resnet_vit/.idea/deployment.xml
diff --git a/contrast/resnet_vit/.idea/inspectionProfiles/Project_Default.xml b/contrast/feat_extract/resnet_vit/.idea/inspectionProfiles/Project_Default.xml
similarity index 100%
rename from contrast/resnet_vit/.idea/inspectionProfiles/Project_Default.xml
rename to contrast/feat_extract/resnet_vit/.idea/inspectionProfiles/Project_Default.xml
diff --git a/contrast/resnet_vit/.idea/inspectionProfiles/profiles_settings.xml b/contrast/feat_extract/resnet_vit/.idea/inspectionProfiles/profiles_settings.xml
similarity index 100%
rename from contrast/resnet_vit/.idea/inspectionProfiles/profiles_settings.xml
rename to contrast/feat_extract/resnet_vit/.idea/inspectionProfiles/profiles_settings.xml
diff --git a/contrast/resnet_vit/.idea/misc.xml b/contrast/feat_extract/resnet_vit/.idea/misc.xml
similarity index 100%
rename from contrast/resnet_vit/.idea/misc.xml
rename to contrast/feat_extract/resnet_vit/.idea/misc.xml
diff --git a/contrast/resnet_vit/.idea/modules.xml b/contrast/feat_extract/resnet_vit/.idea/modules.xml
similarity index 100%
rename from contrast/resnet_vit/.idea/modules.xml
rename to contrast/feat_extract/resnet_vit/.idea/modules.xml
diff --git a/contrast/resnet_vit/__init__.py b/contrast/feat_extract/resnet_vit/__init__.py
similarity index 100%
rename from contrast/resnet_vit/__init__.py
rename to contrast/feat_extract/resnet_vit/__init__.py
diff --git a/contrast/resnet_vit/__pycache__/__init__.cpython-39.pyc b/contrast/feat_extract/resnet_vit/__pycache__/__init__.cpython-39.pyc
similarity index 100%
rename from contrast/resnet_vit/__pycache__/__init__.cpython-39.pyc
rename to contrast/feat_extract/resnet_vit/__pycache__/__init__.cpython-39.pyc
diff --git a/contrast/resnet_vit/__pycache__/config.cpython-38.pyc b/contrast/feat_extract/resnet_vit/__pycache__/config.cpython-38.pyc
similarity index 100%
rename from contrast/resnet_vit/__pycache__/config.cpython-38.pyc
rename to contrast/feat_extract/resnet_vit/__pycache__/config.cpython-38.pyc
diff --git a/contrast/resnet_vit/__pycache__/config.cpython-39.pyc b/contrast/feat_extract/resnet_vit/__pycache__/config.cpython-39.pyc
similarity index 100%
rename from contrast/resnet_vit/__pycache__/config.cpython-39.pyc
rename to contrast/feat_extract/resnet_vit/__pycache__/config.cpython-39.pyc
diff --git a/contrast/resnet_vit/__pycache__/inference.cpython-39.pyc b/contrast/feat_extract/resnet_vit/__pycache__/inference.cpython-39.pyc
similarity index 100%
rename from contrast/resnet_vit/__pycache__/inference.cpython-39.pyc
rename to contrast/feat_extract/resnet_vit/__pycache__/inference.cpython-39.pyc
diff --git a/contrast/resnet_vit/config.py b/contrast/feat_extract/resnet_vit/config.py
similarity index 100%
rename from contrast/resnet_vit/config.py
rename to contrast/feat_extract/resnet_vit/config.py
diff --git a/contrast/resnet_vit/inference.py b/contrast/feat_extract/resnet_vit/inference.py
similarity index 100%
rename from contrast/resnet_vit/inference.py
rename to contrast/feat_extract/resnet_vit/inference.py
diff --git a/contrast/resnet_vit/model/__init__.py b/contrast/feat_extract/resnet_vit/model/__init__.py
similarity index 100%
rename from contrast/resnet_vit/model/__init__.py
rename to contrast/feat_extract/resnet_vit/model/__init__.py
diff --git a/contrast/resnet_vit/model/__pycache__/__init__.cpython-38.pyc b/contrast/feat_extract/resnet_vit/model/__pycache__/__init__.cpython-38.pyc
similarity index 100%
rename from contrast/resnet_vit/model/__pycache__/__init__.cpython-38.pyc
rename to contrast/feat_extract/resnet_vit/model/__pycache__/__init__.cpython-38.pyc
diff --git a/contrast/resnet_vit/model/__pycache__/__init__.cpython-39.pyc b/contrast/feat_extract/resnet_vit/model/__pycache__/__init__.cpython-39.pyc
similarity index 100%
rename from contrast/resnet_vit/model/__pycache__/__init__.cpython-39.pyc
rename to contrast/feat_extract/resnet_vit/model/__pycache__/__init__.cpython-39.pyc
diff --git a/contrast/resnet_vit/model/__pycache__/resnet_pre.cpython-38.pyc b/contrast/feat_extract/resnet_vit/model/__pycache__/resnet_pre.cpython-38.pyc
similarity index 100%
rename from contrast/resnet_vit/model/__pycache__/resnet_pre.cpython-38.pyc
rename to contrast/feat_extract/resnet_vit/model/__pycache__/resnet_pre.cpython-38.pyc
diff --git a/contrast/resnet_vit/model/__pycache__/resnet_pre.cpython-39.pyc b/contrast/feat_extract/resnet_vit/model/__pycache__/resnet_pre.cpython-39.pyc
similarity index 100%
rename from contrast/resnet_vit/model/__pycache__/resnet_pre.cpython-39.pyc
rename to contrast/feat_extract/resnet_vit/model/__pycache__/resnet_pre.cpython-39.pyc
diff --git a/contrast/model/resnet_pre.py b/contrast/feat_extract/resnet_vit/model/resnet_pre.py
similarity index 100%
rename from contrast/model/resnet_pre.py
rename to contrast/feat_extract/resnet_vit/model/resnet_pre.py
diff --git a/contrast/genfeats.py b/contrast/genfeats.py
index 2e889ee..ddaa509 100644
--- a/contrast/genfeats.py
+++ b/contrast/genfeats.py
@@ -6,26 +6,33 @@ Created on Sun Nov  3 12:05:19 2024
 """
 import os
 import time
-import torch
+# import torch
 import pickle
+# import json
 import numpy as np
-from config import config as conf
-from model import resnet18 as resnet18
-from feat_inference import inference_image
+from PIL import Image
+from feat_extract.config import config as conf
+# from model import resnet18 as resnet18
+from feat_extract.inference import FeatsInterface #, inference_image
 
 
 IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
 
-'''======= 0. 配置特征提取模型地址 ======='''
-model_path = conf.test_model
-model_path = r"D:\exhibition\ckpt\zhanting.pth"
+# def model_init(conf, mpath=None):
+#     '''======= 0. 配置特征提取模型地址 ======='''
+#     if mpath is None:
+#         model_path = conf.test_model
+#     else:
+#         model_path = mpath
 
-##============ load resnet mdoel 
-model = resnet18().to(conf.device)
-# model = nn.DataParallel(model).to(conf.device)
-model.load_state_dict(torch.load(model_path, map_location=conf.device))
-model.eval()
-print('load model {} '.format(conf.testbackbone))
+#     ##============ load resnet mdoel 
+#     model = resnet18().to(conf.device)
+#     # model = nn.DataParallel(model).to(conf.device)
+#     model.load_state_dict(torch.load(model_path, map_location=conf.device))
+#     model.eval()
+#     print('load model {} '.format(conf.testbackbone))
+
+#     return model
 
 def get_std_barcodeDict(bcdpath, savepath, bcdSet):
     '''
@@ -42,9 +49,9 @@ def get_std_barcodeDict(bcdpath, savepath, bcdSet):
     '''读取数据集中 barcode 列表'''
     stdBarcodeList = []
     for filename in os.listdir(bcdpath):
-        # filepath = os.path.join(bcdpath, filename)
-        # if not os.path.isdir(filepath) or not filename.isdigit() or len(filename)<8: 
-        #     continue
+        filepath = os.path.join(bcdpath, filename)
+        if not os.path.isdir(filepath) or not filename.isdigit() or len(filename)<8: 
+            continue
         if bcdSet is None:
             stdBarcodeList.append(filename)
         elif filename in bcdSet:
@@ -59,7 +66,7 @@ def get_std_barcodeDict(bcdpath, savepath, bcdSet):
     for barcode, bpath in bcdPaths:  
         pickpath = os.path.join(savepath, f"{barcode}.pickle")
         if os.path.isfile(pickpath):
-            continue
+            continue 
         
         stdBarcodeDict = {}
         stdBarcodeDict[barcode] = []
@@ -89,6 +96,7 @@ def get_std_barcodeDict(bcdpath, savepath, bcdSet):
         pickpath = os.path.join(savepath, f"{barcode}.pickle")
         with open(pickpath, 'wb') as f:
             pickle.dump(stdBarcodeDict, f)
+
         print(f"Barcode: {barcode}")
 
         # k += 1
@@ -115,32 +123,37 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
     stdBarcodeDict = {}
     stdBarcodeDict_ft16 = {}
     
+    Encoder = FeatsInterface(conf)
     
-    '''4处同名: (1)barcode原始图像文件夹; (2)imgPath中的 .pickle 文件名、该pickle文件中字典的key值'''
-    
+    '''4处同名: (1)barcode原始图像文件夹; (2)imgPath中的 .pickle 文件名; 
+               (3)该pickle文件中字典的key值; (4)特征向量字典中的一个key值'''
     k = 0
     for filename in os.listdir(imgPath):
         bcd, ext = os.path.splitext(filename)
-        pkpath = os.path.join(featPath, f"{bcd}.pickle")
-        
-        if os.path.isfile(pkpath): continue
+        filepath = os.path.join(imgPath, filename)
+        if ext != ".pickle": continue
         if bcdSet is not None and bcd not in bcdSet:
             continue
 
-        filepath = os.path.join(imgPath, filename)
+        featpath = os.path.join(featPath, f"{bcd}.pickle")
 
         stdbDict = {}
-        stdbDict_ft16 = {}
-        stdbDict_uint8 = {}
-        
         t1 = time.time()
-        
         try:
             with open(filepath, 'rb') as f:  
-                bpDict = pickle.load(f)    
+                bpDict = pickle.load(f)   
+ 
             for barcode, imgpaths in bpDict.items():
                 # feature = batch_inference(imgpaths, 8) #from vit distilled model of LiChen
-                feature = inference_image(imgpaths, conf.test_transform, model, conf.device)
+                # feature = inference_image(imgpaths, conf.test_transform, model, conf.device)
+                
+                imgs = []
+                for d, imgpath in enumerate(imgpaths):
+                    img = Image.open(imgpath)
+                    imgs.append(img)
+                
+                feature = Encoder.inference(imgs)
+                
                 feature /= np.linalg.norm(feature, axis=1)[:, None]
                 
                 # float16
@@ -162,7 +175,7 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
         stdbDict["feats_ft16"] = feature_ft16
         stdbDict["feats_uint8"] = feature_uint8
         
-        with open(pkpath, 'wb') as f:
+        with open(featpath, 'wb') as f:
             pickle.dump(stdbDict, f)
         
         stdBarcodeDict[barcode] = feature
@@ -174,21 +187,10 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
         # if k == 10:
         #     break
     
-    ##================== float32 
-    # pickpath = os.path.join(featPath, f"barcode_features_{k}.pickle")
-    # with open(pickpath, 'wb') as f:
-    #     pickle.dump(stdBarcodeDict, f)  
-       
-    ##================== float16   
-    # pickpath_ft16 = os.path.join(featPath, f"barcode_features_ft16_{k}.pickle")
-    # with open(pickpath_ft16, 'wb') as f:
-    #     pickle.dump(stdBarcodeDict_ft16, f)  
-    
     return
 
 
-
-def genfeatures(imgpath, bcdpath, featpath, bcdSet=None):
+def gen_bcd_features(imgpath, bcdpath, featpath, bcdSet=None):
     ''' 生成标准特征集 '''
     '''1. 提取 imgpath 中样本地址，生成字典{barcode: [imgpath1, imgpath1, ...]}
           并存储于: bcdpath, 格式为 barcode.pickle'''
@@ -198,11 +200,12 @@ def genfeatures(imgpath, bcdpath, featpath, bcdSet=None):
     stdfeat_infer(bcdpath, featpath, bcdSet)
     
 def main():
-    imgpath = r"\\192.168.1.28\share\展厅barcode数据\整理\zhantingBase"
+    imgpath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v1.0\比对数据\整理\zhantingBase"
     bcdpath = r"D:\exhibition\dataset\bcdpath"  
     featpath = r"D:\exhibition\dataset\feats"
-    
-    genfeatures(imgpath, bcdpath, featpath)
+
+
+    gen_bcd_features(imgpath, bcdpath, featpath)
     
 
     
diff --git a/contrast/model/__pycache__/mobilenet_v3.cpython-39.pyc b/contrast/model/__pycache__/mobilenet_v3.cpython-39.pyc
deleted file mode 100644
index e1fc34a..0000000
Binary files a/contrast/model/__pycache__/mobilenet_v3.cpython-39.pyc and /dev/null differ
diff --git a/contrast/model/__pycache__/mobilevit.cpython-39.pyc b/contrast/model/__pycache__/mobilevit.cpython-39.pyc
deleted file mode 100644
index aff5d93..0000000
Binary files a/contrast/model/__pycache__/mobilevit.cpython-39.pyc and /dev/null differ
diff --git a/contrast/model/__pycache__/resbam.cpython-39.pyc b/contrast/model/__pycache__/resbam.cpython-39.pyc
deleted file mode 100644
index 65a91de..0000000
Binary files a/contrast/model/__pycache__/resbam.cpython-39.pyc and /dev/null differ
diff --git a/contrast/one2n_contrast.py b/contrast/one2n_contrast.py
index 57feea7..7ad3cb3 100644
--- a/contrast/one2n_contrast.py
+++ b/contrast/one2n_contrast.py
@@ -325,7 +325,7 @@ def one2n_deleted(all_list):
 
 
 
-def one2n_return(all_list, basepath): 
+def one2n_return(all_list): 
     corrpairs, corr_similarity, errpairs, err_similarity = [], [], [], []
 
     for s_list in all_list:
@@ -410,7 +410,7 @@ def test_rpath_return():
     savepath = r'D:\DetectTracking\contrast\result' 
     
     all_list = read_returnGoods_file(return_bfile)
-    corrpairs, errpairs, _, _ = one2n_return(all_list, basepath)
+    corrpairs, errpairs, _, _ = one2n_return(all_list)
     for corrpair in corrpairs:
         GetoutPath = os.path.join(basepath, corrpair[0])
         InputPath = os.path.join(basepath, corrpair[1])
@@ -435,7 +435,7 @@ def test_one2n():
     savepath: pr曲线保存路径 
     '''
     # fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'        # deletedBarcode.txt
-    fpath = r'\\192.168.1.28\share\测试_202406\returnGoods\all'               # returnGoods.txt
+    fpath = r'\\192.168.1.28\share\测试_202406\1108_展厅模型v800测试'               # returnGoods.txt
     savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
 
     if os.path.isdir(fpath):
@@ -476,9 +476,9 @@ def test_one2n():
         plt1.savefig(os.path.join(savepath, file+'_pr.png'))
         # plt1.close()
         
-        # plt2 = showHist(err_similarity, correct_similarity)
-        # plt2.show()
-        # plt2.savefig(os.path.join(savepath, file+'_hist.png'))
+        plt2 = showHist(err_similarity, correct_similarity)
+        plt2.show()
+        plt2.savefig(os.path.join(savepath, file+'_hist.png'))
         # plt.close()
 
 
@@ -486,7 +486,7 @@ def test_one2n():
 if __name__ == '__main__':
     # test_one2n()
     test_rpath_return()     # returnGoods.txt
-    test_rpath_deleted()    # deleteBarcode.txt
+    # test_rpath_deleted()    # deleteBarcode.txt
     
     
     # try:
diff --git a/contrast/one2one_contrast.py b/contrast/one2one_contrast.py
index 289d4fd..1d61aed 100644
--- a/contrast/one2one_contrast.py
+++ b/contrast/one2one_contrast.py
@@ -49,7 +49,8 @@ from datetime import datetime
 sys.path.append(r"D:\DetectTracking")
 from tracking.utils.read_data import extract_data, read_tracking_output, read_one2one_simi, read_deletedBarcode_file
 
-from genfeats import genfeatures, stdfeat_infer
+from config import config as conf
+from genfeats import model_init, genfeatures, stdfeat_infer
 
 IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
 
@@ -546,10 +547,10 @@ def test_one2one():
         
     
     
-    
+    model = model_init(conf)
     
     '''==== 1. 生成标准特征集, 只需运行一次 ==============='''    
-    genfeatures(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
+    genfeatures(model, stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
     print("stdFeats have generated and saved!")
     
     
diff --git a/contrast/one2one_onsite.py b/contrast/one2one_onsite.py
index a11769e..94c8c3e 100644
--- a/contrast/one2one_onsite.py
+++ b/contrast/one2one_onsite.py
@@ -7,7 +7,12 @@ Created on Wed Sep 11 11:57:30 2024
 """
 import os
 import numpy as np
+from pathlib import Path
 import matplotlib.pyplot as plt
+import sys
+
+sys.path.append(r"D:\DetectTracking")
+from tracking.utils.read_data import read_similar
 
 def read_one2one_data(filepath):
     simiList = []
@@ -85,7 +90,7 @@ def plot_pr_curve(matrix):
     pass
 
                 
-def main():
+def test_compare():
     filepaths = [r"\\192.168.1.28\share\测试_202406\0913_扫A放B\0913_1\OneToOneCompare.txt",
                  r"\\192.168.1.28\share\测试_202406\0913_扫A放B\0913_2\OneToOneCompare.txt",
                  r"\\192.168.1.28\share\测试_202406\0914_扫A放B\0914_1\OneToOneCompare.txt",
@@ -99,10 +104,251 @@ def main():
 
     plot_pr_curve(simiList)
     
+def one2one_pr(paths): 
+    paths = Path(paths)
+    evtpaths = [p for p in paths.iterdir() if p.is_dir() and len(p.name.split('_'))>=2]
+    
+    events, similars = [], []
+    
+    ##===================================== 扫A放A, 扫A放B场景
+    one2oneAA, one2oneAB = [], []
+    
+    ##===================================== 应用于展厅 1：N
+    tp_events, fn_events, fp_events, tn_events = [], [], [], []
+    tp_simi, fn_simi, tn_simi, fp_simi = [], [], [], []
+    
+    ##===================================== 应用于1:n
+    tpevents, fnevents, fpevents, tnevents = [], [], [], []
+    tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
+
+    for path in evtpaths:
+        barcode = path.stem.split('_')[-1]
+        datapath = path.joinpath('process.data')
+        
+        if not barcode.isdigit() or len(barcode)<10: continue
+        if not datapath.is_file(): continue
+    
+        try: 
+            SimiDict = read_similar(datapath)
+        except Exception as e:
+            print(f"{path.stem}, Error: {e}")
+        
+        one2one = SimiDict['one2one']
+        one2n = SimiDict['one2n']
+        
+        barcodes, similars = [], []
+        for dt in one2one:
+            barcodes.append(dt['barcode'])
+            similars.append(dt['similar'])
+        
+        if len(barcodes)!=len(similars) or len(barcodes)==0:
+            continue
+            
+        ##===================================== 扫A放A, 扫A放B场景    
+        simAA = [similars[i] for i in range(len(barcodes)) if barcodes[i]==barcode] 
+        simAB = [similars[i] for i in range(len(barcodes)) if barcodes[i]!=barcode]
+        
+        one2oneAA.extend(simAA)
+        one2oneAB.extend(simAB)
+        
+        
+        ##===================================== 以下应用适用于展厅 1：N
+        max_idx = similars.index(max(similars))
+        max_sim = similars[max_idx]
+        # max_bcd = barcodes[max_idx]
+        
+        for i in range(len(one2one)):
+            bcd, simi = barcodes[i], similars[i]
+            if bcd==barcode and simi==max_sim:
+                tp_simi.append(simi)
+                tp_events.append(path.stem)
+            elif bcd==barcode and simi!=max_sim:
+                fn_simi.append(simi)
+                fn_events.append(path.stem)
+            elif bcd!=barcode and simi!=max_sim: 
+                tn_simi.append(simi)
+                tn_events.append(path.stem)
+            else:
+                fp_simi.append(simi)
+                fp_events.append(path.stem)
+        
+                
+        ##===================================== 以下应用适用1:n 
+        events, evt_barcodes, evt_similars, evt_types = [], [], [], []        
+        for dt in one2n:
+            events.append(dt["event"])
+            evt_barcodes.append(dt["barcode"])
+            evt_similars.append(dt["similar"])
+            evt_types.append(dt["type"])
+        
+        if len(events)!=len(evt_barcodes) or len(evt_barcodes)!=len(evt_similars) \
+            or len(evt_barcodes)!=len(evt_similars) or len(events)==0: continue 
+  
+        maxsim = evt_similars[evt_similars.index(max(evt_similars))]
+        for i in range(len(one2n)):
+            bcd, simi = evt_barcodes[i], evt_similars[i]
+            
+            if bcd==barcode and simi==maxsim:
+                tpsimi.append(simi)
+                tpevents.append(path.stem)
+            elif bcd==barcode and simi!=maxsim:
+                fnsimi.append(simi)
+                fnevents.append(path.stem)
+            elif bcd!=barcode and simi!=maxsim: 
+                tnsimi.append(simi)
+                tnevents.append(path.stem)
+            else:
+                fpsimi.append(simi)
+                fpevents.append(path.stem)
+            
+    '''命名规则:
+          1:1              1:n                1:N
+          TP_              TP                 TPX
+        PPrecise_       PPrecise           PPreciseX
+                         tpsimi             tp_simi  
+    '''
+    
+    ''' 1:1 数据存储'''
+    PPrecise_, PRecall_ = [], []
+    NPrecise_, NRecall_ = [], []
+    
+    ''' 1:n 数据存储'''
+    PPrecise, PRecall = [], []
+    NPrecise, NRecall = [], []
+    
+    ''' 展厅 1:N 数据存储'''
+    PPreciseX, PRecallX = [], []
+    NPreciseX, NRecallX = [], []
+    
+    Thresh = np.linspace(-0.2, 1, 100)
+    for th in Thresh:
+        '''============================= 1:1'''
+        TP_ = sum(np.array(one2oneAA) >= th)
+        FP_ = sum(np.array(one2oneAB) >= th)
+        FN_ = sum(np.array(one2oneAA) < th)
+        TN_ = sum(np.array(one2oneAB) < th)
+        PPrecise_.append(TP_/(TP_+FP_+1e-6))
+        PRecall_.append(TP_/(TP_+FN_+1e-6))
+        NPrecise_.append(TN_/(TN_+FN_+1e-6))
+        NRecall_.append(TN_/(TN_+FP_+1e-6))
+        
+        '''============================= 1:n'''
+        TP = sum(np.array(tpsimi) >= th)
+        FP = sum(np.array(fpsimi) >= th)
+        FN = sum(np.array(fnsimi) < th)
+        TN = sum(np.array(tnsimi) < th)
+        PPrecise.append(TP/(TP+FP+1e-6))
+        PRecall.append(TP/(TP+FN+1e-6))
+        NPrecise.append(TN/(TN+FN+1e-6))
+        NRecall.append(TN/(TN+FP+1e-6))
+        
+        
+        '''============================= 1:N 展厅'''
+        TPX = sum(np.array(tp_simi) >= th)
+        FPX = sum(np.array(fp_simi) >= th)
+        FNX = sum(np.array(fn_simi) < th)
+        TNX = sum(np.array(tn_simi) < th)
+        PPreciseX.append(TPX/(TPX+FPX+1e-6))
+        PRecallX.append(TPX/(TPX+FNX+1e-6))
+        NPreciseX.append(TNX/(TNX+FNX+1e-6))
+        NRecallX.append(TNX/(TNX+FPX+1e-6))
+    
+    '''============================= 1:1 曲线'''
+    fig, ax = plt.subplots()
+    ax.plot(Thresh, PPrecise_, 'r', label='Precise_Pos: TP/TPFP')
+    ax.plot(Thresh, PRecall_, 'b', label='Recall_Pos: TP/TPFN')
+    ax.plot(Thresh, NPrecise_, 'g', label='Precise_Neg: TN/TNFP')
+    ax.plot(Thresh, NRecall_, 'c', label='Recall_Neg: TN/TNFN')
+    ax.set_xlim([0, 1])
+    ax.set_ylim([0, 1])
+    ax.grid(True)
+    ax.set_title('Precise & Recall')
+    ax.set_xlabel(f"Num: {len(evtpaths)}")     
+    ax.legend()
+    plt.show()
+    
+    '''============================= 1:1 直方图'''
+    fig, axes = plt.subplots(2, 1)
+    axes[0].hist(np.array(one2oneAA), bins=60, edgecolor='black')
+    axes[0].set_xlim([-0.2, 1])
+    axes[0].set_title('AA')
+    axes[1].hist(np.array(one2oneAB), bins=60, edgecolor='black')
+    axes[1].set_xlim([-0.2, 1])
+    axes[1].set_title('BB')
+    plt.show()
+        
+    '''============================= 1:n 曲线''' 
+    fig, ax = plt.subplots()
+    ax.plot(Thresh, PPrecise, 'r', label='Precise_Pos: TP/TPFP')
+    ax.plot(Thresh, PRecall, 'b', label='Recall_Pos: TP/TPFN')
+    ax.plot(Thresh, NPrecise, 'g', label='Precise_Neg: TN/TNFP')
+    ax.plot(Thresh, NRecall, 'c', label='Recall_Neg: TN/TNFN')
+    ax.set_xlim([0, 1])
+    ax.set_ylim([0, 1])
+    ax.grid(True)
+    ax.set_title('Precise & Recall')
+    ax.set_xlabel(f"Num: {len(evtpaths)}")     
+    ax.legend()
+    plt.show()
+    
+    '''============================= 1:n 直方图'''
+    fig, axes = plt.subplots(2, 2)
+    axes[0, 0].hist(tpsimi, bins=60, edgecolor='black')
+    axes[0, 0].set_xlim([-0.2, 1])
+    axes[0, 0].set_title('TP')
+    axes[0, 1].hist(fpsimi, bins=60, edgecolor='black')
+    axes[0, 1].set_xlim([-0.2, 1])
+    axes[0, 1].set_title('FP')
+    axes[1, 0].hist(tnsimi, bins=60, edgecolor='black')
+    axes[1, 0].set_xlim([-0.2, 1])
+    axes[1, 0].set_title('TN')
+    axes[1, 1].hist(fnsimi, bins=60, edgecolor='black')
+    axes[1, 1].set_xlim([-0.2, 1])
+    axes[1, 1].set_title('FN')
+    plt.show()
+    
+    
+    '''============================= 1:N 展厅 曲线''' 
+    fig, ax = plt.subplots()
+    ax.plot(Thresh, PPreciseX, 'r', label='Precise_Pos: TP/TPFP')
+    ax.plot(Thresh, PRecallX, 'b', label='Recall_Pos: TP/TPFN')
+    ax.plot(Thresh, NPreciseX, 'g', label='Precise_Neg: TN/TNFP')
+    ax.plot(Thresh, NRecallX, 'c', label='Recall_Neg: TN/TNFN')
+    ax.set_xlim([0, 1])
+    ax.set_ylim([0, 1])
+    ax.grid(True)
+    ax.set_title('Precise & Recall')
+    ax.set_xlabel(f"Num: {len(evtpaths)}")     
+    ax.legend()
+    plt.show()
+    
+    '''============================= 1:N 展厅 直方图'''
+    fig, axes = plt.subplots(2, 2)
+    axes[0, 0].hist(tp_simi, bins=60, edgecolor='black')
+    axes[0, 0].set_xlim([-0.2, 1])
+    axes[0, 0].set_title('TP')
+    axes[0, 1].hist(fp_simi, bins=60, edgecolor='black')
+    axes[0, 1].set_xlim([-0.2, 1])
+    axes[0, 1].set_title('FP')
+    axes[1, 0].hist(tn_simi, bins=60, edgecolor='black')
+    axes[1, 0].set_xlim([-0.2, 1])
+    axes[1, 0].set_title('TN')
+    axes[1, 1].hist(fn_simi, bins=60, edgecolor='black')
+    axes[1, 1].set_xlim([-0.2, 1])
+    axes[1, 1].set_title('FN')
+    plt.show()
+    
+    
+
+    
+    print('Done!')
+            
+
     
 
 if __name__ == "__main__":
-    main()
+    evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1120_展厅模型v801测试\扫A放A"
+    one2one_pr(evtpaths)
     
     
     
diff --git a/contrast/说明文档.txt b/contrast/说明文档.txt
deleted file mode 100644
index e69de29..0000000
diff --git a/event_time_specify.py b/event_time_specify.py
index 8a421da..b0f1568 100644
--- a/event_time_specify.py
+++ b/event_time_specify.py
@@ -11,13 +11,10 @@ import numpy as np
 import matplotlib.pyplot as plt
 from move_detect import MoveDetect
 
-
 import sys
 sys.path.append(r"D:\DetectTracking")
 
 # from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_weight_timeConsuming
-
-
 from tracking.utils.read_data import read_weight_timeConsuming
 
 def str_to_float_arr(s):
@@ -118,7 +115,14 @@ def extract_data_1(datapath):
 
         
 def devide_motion_state(tboxes, width):
-    '''frameTstamp: 用于标记当前相机视野内用购物车运动状态变化'''
+    '''frameTstamp: 用于标记当前相机视野内用购物车运动状态变化
+    
+    Hand状态：
+        0: 不存在
+        1: 手部存在
+        2: 手部存在且处于某种状态（静止）
+    
+    '''
 
     periods = []
     if len(tboxes) < width: 
@@ -131,28 +135,22 @@ def devide_motion_state(tboxes, width):
 
     state = np.zeros((fnum, 2), dtype=np.int64)
     frameState = np.concatenate((frameTstamp, state), axis = 1).astype(np.int64)
+    handState = np.concatenate((frameTstamp, state), axis = 1).astype(np.int64)
     
     mtrackFid = {}
+    handFid = {}
     '''frameState 标记由图像判断的购物车状态：0: 静止，1: 运动'''
     for idx in range(width, fnum+1):
-        lboxes = np.concatenate(fboxes[idx-width:idx], axis = 0)
-
+        idx0 = idx-width
+        
+        lboxes = np.concatenate(fboxes[idx0:idx], axis = 0)
         md = MoveDetect(lboxes)
         md.classify()
         
-        # if idx==60:
-        #     print('a')
-        
         ## track.during 二元素组, 表征在该时间片段内，轨迹 track 的起止时间，数值用 boxes[:, 7]      
         for track in md.track_motion:
-            if track.cls == 0: continue
-            
-            
-        
             f1, f2 = track.during
-            
-            
-            
+            # if track.cls == 0: continue
             idx1 = set(np.where(frameState[:,0] >= f1)[0])
             idx2 = set(np.where(frameState[:,0] <= f2)[0])
             idx3 = list(idx1.intersection(idx2))
@@ -164,7 +162,25 @@ def devide_motion_state(tboxes, width):
 
             frameState[idx-1, 3] = 1
             frameState[idx3, 2] = 1
+
+        for track in md.hand_tracks:
+            f11, f22 = track.during
+            idx11 = set(np.where(handState[:,0] >= f11)[0])
+            idx22 = set(np.where(handState[:,0] <= f22)[0])
+            idx33 = list(idx11.intersection(idx22))
             
+            '''手部存在标记'''
+            handState[idx33, 2] = 1
+            '''未来改进方向：is_static 可以用手部状态判断的函数代替'''
+            if track.is_static(70) and len(idx33)>1:
+                idx11 = set(np.where(handState[:,0] >= f11)[0])
+                idx22 = set(np.where(handState[:,0] <= f22)[0])
+                idx33 = list(idx11.intersection(idx22))
+                
+                '''手部静止标记'''
+                handState[idx33, 2] = 2
+                
+    
         '''状态变化输出'''
     
     for tid, fid in mtrackFid.items():
@@ -172,16 +188,11 @@ def devide_motion_state(tboxes, width):
         fstate[list(fid), 0] = tid
         
         frameState = np.concatenate((frameState, fstate), axis = 1).astype(np.int64)
-        
-        
-        
-    
-    
-    
-    return frameState
+
+    return frameState, handState
 
 
-def state_measure(periods, weights, spath=None):
+def state_measure(periods, weights, hands, spath=None):
     '''两种状态：static、motion, 
     (t0, t1)
         t0: static ----> motion
@@ -269,15 +280,16 @@ def main():
     
     '''====================图像运动分析===================='''
     win_width = 12
-    periods = []
+    periods, hands = [], []
     for ctype, tboxes, _ in tracker_boxes: 
-        period = devide_motion_state(tboxes, win_width)
+        period, handState = devide_motion_state(tboxes, win_width)
         
         periods.append((ctype, period))
+        hands.append((ctype, handState))
         print('done!')
     
     '''===============重力、图像信息融合==================='''
-    state_measure(periods, weights)
+    state_measure(periods, weights, hands)
     
     
 if __name__ == "__main__":
diff --git a/imgs_inference.py b/imgs_inference.py
index ed314f3..69d903f 100644
--- a/imgs_inference.py
+++ b/imgs_inference.py
@@ -39,13 +39,20 @@ from tracking.trackers import BOTSORT, BYTETracker
 from tracking.utils.showtrack import drawtracks
 from hands.hand_inference import hand_pose
 
-from tracking.trackers.reid.reid_interface import ReIDInterface 
-from tracking.trackers.reid.config import config as ReIDConfig
+# from tracking.trackers.reid.reid_interface import ReIDInterface 
+# from tracking.trackers.reid.config import config as ReIDConfig
+# ReIDEncoder = ReIDInterface(ReIDConfig)
+
+from contrast.feat_extract.config import config as conf
+from contrast.feat_extract.inference import FeatsInterface
+ReIDEncoder = FeatsInterface(conf)
+
 
-ReIDEncoder = ReIDInterface(ReIDConfig)
 IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # include image suffixes
 VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes
 
+
+'''================== 对图像进行旋转 ================== '''
 class LoadImages:
     # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
     def __init__(self, files, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
diff --git a/move_detect.py b/move_detect.py
index 4701fdb..7103ed6 100644
--- a/move_detect.py
+++ b/move_detect.py
@@ -138,7 +138,7 @@ class TrackFrag:
         return False
 
     
-    def is_static(self):
+    def is_static(self, THRESH=50):
         box1 = self.boxes[0, :4]
         box2 = self.boxes[-1, :4]
         
@@ -148,7 +148,7 @@ class TrackFrag:
         ptd2 = np.linalg.norm((ptd[2], ptd[1]))
         ptd3 = np.linalg.norm((ptd[0], ptd[3]))
         ptd4 = np.linalg.norm((ptd[2], ptd[3]))
-        condt1 = ptd1<50 and ptd2<50 and ptd3<50 and ptd4<50
+        condt1 = ptd1<THRESH and ptd2<THRESH and ptd3<THRESH and ptd4<THRESH
         
         if not self.isCornpoint:
             self.trajdist_min < 120
@@ -179,16 +179,22 @@ class MoveDetect:
 
         tracks = self.tracks
         
-        '''减去静止轨迹'''
+        '''1. 提取手部轨迹轨迹'''
+        hand_tracks = [t for t in tracks if t.cls==0]
+        tracks = self.sub_tracks(tracks, hand_tracks)
+        
+        '''2. 提取静止轨迹'''
         tracks_static = [t for t in tracks if t.is_static()]
         tracks = self.sub_tracks(tracks, tracks_static)
+
         
-        '''更新轨迹点聚类'''
+        '''3. 更新轨迹点聚类'''
         for track in tracks:
             track.update_groups(18)
             
-            
+        self.hand_tracks = hand_tracks    
         self.track_motion = [t for t in tracks if len(t.groups)>=3]
+        
 
     
     def draw(self):
diff --git a/pipeline.py b/pipeline.py
index e013fd6..354563c 100644
--- a/pipeline.py
+++ b/pipeline.py
@@ -16,12 +16,6 @@ from tracking.dotrack.dotracks_front import doFrontTracks
 from tracking.utils.drawtracks import plot_frameID_y2, draw_all_trajectories
 from utils.getsource import get_image_pairs, get_video_pairs
 
-
-
-
-
-std_feature_path = r"\\192.168.1.28\share\测试_202406\contrast\std_features_2192_ft32vsft16"
-
 def get_interbcd_inputenents():
     bcdpath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
     eventpath = r"\\192.168.1.28\share\测试_202406\0918"
@@ -49,8 +43,7 @@ def pipeline(eventpath, stdfeat_path=None, SourceType = "image"):
     outputs:
         
     '''
-    SourceType = "image"   # image
-    
+    # SourceType = "image"   # image
     # eventpath = r"\\192.168.1.28\share\测试_202406\0918\images1\20240918-110822-1bc3902e-5a8e-4e23-8eca-fb3f02738551_6938314601726"
     
     savepath = r"D:\contrast\detect"
@@ -71,16 +64,14 @@ def pipeline(eventpath, stdfeat_path=None, SourceType = "image"):
 
 
     event_tracks = []
-    for vpath in vpaths:  
-        
+    for vpath in vpaths:
         '''事件结果文件夹'''
         save_dir_event = Path(savepath) / Path(eventname)
         if isinstance(vpath, list):
             save_dir_video = save_dir_event / Path("images")
         else:
             save_dir_video = save_dir_event / Path(str(Path(vpath).stem))
-            
-
+        
         if not save_dir_video.exists():
             save_dir_video.mkdir(parents=True, exist_ok=True)
         
@@ -112,6 +103,11 @@ def pipeline(eventpath, stdfeat_path=None, SourceType = "image"):
     for CamerType, vts in event_tracks:
         if CamerType == 'front':
             edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png")
+            
+            h, w = edgeline.shape[:2]
+            nh, nw = h//2, w//2
+            edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
+            
             img_tracking = draw_all_trajectories(vts, edgeline, save_dir_event, CamerType, draw5p=True)
             illus[0] = img_tracking
             
@@ -121,6 +117,11 @@ def pipeline(eventpath, stdfeat_path=None, SourceType = "image"):
         
         if CamerType == 'back':
             edgeline = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png")
+            
+            h, w = edgeline.shape[:2]
+            nh, nw = h//2, w//2
+            edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
+            
             img_tracking = draw_all_trajectories(vts, edgeline, save_dir_event, CamerType, draw5p=True)
             illus[1] = img_tracking
     
@@ -177,18 +178,14 @@ def main_loop():
             
 def main():
     eventpath = r"D:\datasets\ym\exhibition\175836"
+    
+    eventpath = r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1120_展厅模型v801测试\扫A放A\20241121-144855-dce94b09-1100-43f1-92e8-33a1b538b159_6924743915848_6924743915848"
+    
     SourceType = 'image'
     stdfeat_path = None
    
     pipeline(eventpath, stdfeat_path, SourceType)
-    
-       
-        
-        
-
-        
-        
-        
+  
 
 
 if __name__ == "__main__": 
diff --git a/time_devide.py b/time_devide.py
index 28d4d65..c035bd6 100644
--- a/time_devide.py
+++ b/time_devide.py
@@ -14,18 +14,14 @@ import glob
 import numpy as np
 import copy
 
-
 import matplotlib.pyplot as plt
 from imgs_inference import run_yolo
 from event_time_specify import devide_motion_state#, state_measure
 from tracking.utils.read_data import read_seneor
 
-
-
 # IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # include image suffixes
 # VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes
 
-
 def filesort(p):
     '''
     需将图像文件名标准化
@@ -104,7 +100,7 @@ def rerename(filePath=None):
                 os.rename(os.path.join(filePath, file), os.path.join(filePath, newname))
 
 
-def state_measure(periods, weights, spath=None):
+def state_measure(periods, weights, hands, spath=None):
     '''
     数据类型
         后摄: 0, 前摄: 1, CV综合: 2, 重力: 9
@@ -120,7 +116,7 @@ def state_measure(periods, weights, spath=None):
             0        1        2         3                 4                      5                     6         7
         
         单摄状态1：基于运动轨迹的起止点确定的运动区间
-        单摄状态2: 基于滑动窗口的起止点确定的运动区间
+        单摄状态2: 基于滑动窗口的起止点(窗口终点)确定的运动区间
         重力(t0, t1): 重力波动的精确时间区间，基于重力波动的起止点，而不是仅依赖重力稳定时间
         重力(t0', t1'): 根据加退购对重力波动窗口进行扩展，扩展应该涵盖购物事件的发生过程
     方案：
@@ -131,6 +127,7 @@ def state_measure(periods, weights, spath=None):
     # BackType = 0            # 后摄数据类型
     # FrontType = 1           # 前摄数据类型
     CameraType = 2            # CV数据综合类型
+    HandType = 3              # 手部类型
     WeightType = 9            # 重力数据类型
     WeightStableThresh = 7.5  # 单位：g，重力稳定状态下的最大波动范围
     WeightWinWidth = 10       # 单位：重力数据点数，该值和采样间隔关联，重力稳定时间设定为500ms = WeightWinWidth * 采样间隔
@@ -166,7 +163,8 @@ def state_measure(periods, weights, spath=None):
         '''对重力波动区间进行标记，并标记最新一次重力稳定值的索引和相应重力值'''
         if wmax - wmin > WeightStableThresh:
             weights[i2, 4] = w_max
-        elif i2==0:
+        
+        if i2==0:
             i0=0
             wi0 = weights[i0, 3]
         elif i2>0 and weights[i2-1, 4]==0:
@@ -207,6 +205,10 @@ def state_measure(periods, weights, spath=None):
     state1 = frstate_1[:,2][:, None]
     state11 = frstate_1[:,3][:, None]
     
+    
+    
+    
+    
 
 
     
@@ -268,10 +270,7 @@ def state_measure(periods, weights, spath=None):
             if ctype != ctype0 and  state !=0 and state0 !=0:
                 time_stream[i, 7] = 1
 
-    MotionSlice = []
-    
-    motion_slice = []
-    
+    MotionSlice, motion_slice = [], []
     t0 = time_stream[0, 7]
     for i in range(1, len(time_stream)):
         f0 = time_stream[i-1, 7]
@@ -285,14 +284,65 @@ def state_measure(periods, weights, spath=None):
                 motion_slice.append((t0, t1))
             else:
                 print(f"T0: {t0}, T1: {t1}")
+                
+    
+    
+    
+    '''===================== 4. Hands数据综合并排序 =========================='''
+    BackType, hdstate_0 =  hands[0]
+    FrontType, hdstate_1 =  hands[1]
+    n0, n1 = len(hdstate_0), len(hdstate_1)
+    idx0 = np.array([i for i in range(0, n0)], dtype=np.int64)[:, None]
+    idx1 = np.array([i for i in range(0, n1)], dtype=np.int64)[:, None]
+    ctype0 = BackType * np.ones((n0, 1), dtype=np.int64)
+    ctype1 = FrontType * np.ones((n1, 1), dtype=np.int64)
+    hstamp0 = hdstate_0[:,1][:, None]
+    hstamp1 = hdstate_1[:,1][:, None]
+    state0 = hdstate_0[:,2][:, None]
+    state1 = hdstate_1[:,2][:, None]
+    
+    '''序列索引号， 相机类型，时间戳, 单摄手部状态、手部综合状态、保留位2、综合数据类型、综合状态
+          0           1       2        3              4           5        6           7
+    '''
+    hstream0 = np.concatenate((idx0, ctype0, hstamp0, state0), axis=1)
+    hstream1 = np.concatenate((idx1, ctype1, hstamp1, state1), axis=1)
+    hstream = np.concatenate((hstream0, hstream1), axis=0)
+    hstream = np.concatenate((hstream, np.zeros((len(hstream), 4), dtype=np.int64)), axis=1)
+    hstream[:, 6] = HandType
+    hstream = hstream[np.argsort(hstream[:, 2]), :]
+    
+    for i in range(0, len(hstream)):
+        idx, ctype, stamp, state = hstream[i, :4]
+        if i==0: 
+            hstream[i, 4] = state
+        if i>0:
+            j = i-1
+            idx0, ctype0, stamp0, state0 = hstream[j, :4]
+            while stamp-stamp0 < CameraTimeInterval and ctype == ctype0 and j>0:
+                j -= 1
+                idx0, ctype0, stamp0, state0 = hstream[j, :4]
+            
+            '''两摄像头状态的或运算. 由于前后摄图像不同时，如何构造或运算，关键在于选择不同摄像头的对齐点
+                i时刻摄像头(ctype)状态state，另一摄像头(ctype0 != ctype)距 i 最近最近时刻 j 的状态state0
+            '''
+            if ctype != ctype0 and state0==2:
+                hstream[i, 4] = state0
+            elif ctype != ctype0 and state0==1:
+                hstream[i, 4] = state0
+            else:
+                hstream[i, 4] = state
+    
+
+                
+                
 
 
-    '''========================== 4 结果显示 ================================'''   
+    '''========================== 5 结果显示 ================================'''   
     frstate_0[:, 1] = frstate_0[:, 1]-tmin
     frstate_1[:, 1] = frstate_1[:, 1]-tmin
     tstream[:, 2] = tstream[:, 2]-tmin
 
-    fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, 1)
+    fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(6, 1)
     during = np.max(time_stream[:, 2])
     
     ax1.plot(weights[:, 2]-tmin, weights[:, 3], 'bo-', linewidth=1, markersize=4)
@@ -318,7 +368,12 @@ def state_measure(periods, weights, spath=None):
     ax5.plot(time_stream[:, 2], time_stream[:, 7], 'gx-', linewidth=1, markersize=4)
     ax5.set_xlim([0, during])
     ax5.set_title('Cart State')
-    
+
+    ax6.plot(hstream[:, 2]-tmin, hstream[:, 4], 'gx-', linewidth=1, markersize=4)
+    ax6.set_xlim([0, during])
+    ax6.set_title('Hand State')
+
+
     plt.show()
     if spath:        
         plt.savefig(spath)
@@ -352,12 +407,18 @@ def splitevent(imgpath, MotionSlice):
 def runyolo():
     eventdirs = r"\\192.168.1.28\share\realtime\eventdata"
     savedir = r"\\192.168.1.28\share\realtime\result"
- 
+    
+    k = 0
     for edir in os.listdir(eventdirs):
+        edir = "1731316835560"
         source = os.path.join(eventdirs, edir)
         files = filesort(source)
         for flist in files:
             run_yolo(flist, savedir)
+        
+        k += 1
+        if k==1:
+            break
 
 def run_tracking(trackboxes, MotionSlice):
     pass
@@ -367,8 +428,8 @@ def run_tracking(trackboxes, MotionSlice):
 
             
 def show_seri():    
-    datapath = r"\\192.168.1.28\share\realtime\eventdata\1728978106733"
-    savedir = r"\\192.168.1.28\share\realtime\result"
+    datapath = r"\\192.168.1.28\share\realtime\eventdata\1731316835560"
+    savedir = r"D:\DetectTracking\realtime"
     
     
     imgdir = datapath.split('\\')[-2] + "_" + datapath.split('\\')[-1]
@@ -378,7 +439,7 @@ def show_seri():
     
     datafiles = sorted(glob.glob(os.path.join(datapath, '*.npy')))
 
-    periods, trackboxes = [], []
+    periods, trackboxes, hands = [], [], []
     win_width = 12
     for npypath in datafiles:
         CameraType = Path(npypath).stem.split('_')[-1]
@@ -386,8 +447,9 @@ def show_seri():
         
         trackboxes.append((CameraType, tkboxes))
         
-        period = devide_motion_state(tkboxes, win_width)
+        period, handState = devide_motion_state(tkboxes, win_width)
         periods.append((int(CameraType), period))
+        hands.append((int(CameraType), handState))
         
     
         
@@ -401,23 +463,19 @@ def show_seri():
     
     '''===============重力、图像信息融合==================='''
     spath = os.path.join(savedir, f"{eventname}.png" )
-    tmin, MotionSlice = state_measure(periods, weights, spath)
-    
-    
-    
-    
+    tmin, MotionSlice = state_measure(periods, weights, hands, spath)
+
     # 第一次运行时用于更改图像文件名
     # rerename(imgpath)
     # rename(imgpath, tmin)
 
-
     # splitevent(imgpath, MotionSlice)
     
     
 
 
 def main():
-    # runyolo()
+    runyolo()
     
     show_seri()
 
diff --git a/track_reid.py b/track_reid.py
index 3e687be..9ccf665 100644
--- a/track_reid.py
+++ b/track_reid.py
@@ -60,53 +60,57 @@ from tracking.trackers import BOTSORT, BYTETracker
 from tracking.utils.showtrack import drawtracks
 from hands.hand_inference import hand_pose
 
-from tracking.trackers.reid.reid_interface import ReIDInterface 
-from tracking.trackers.reid.config import config as ReIDConfig
-ReIDEncoder = ReIDInterface(ReIDConfig)
+from contrast.feat_extract.config import config as conf
+from contrast.feat_extract.inference import FeatsInterface
+ReIDEncoder = FeatsInterface(conf)
+
+# from tracking.trackers.reid.reid_interface import ReIDInterface 
+# from tracking.trackers.reid.config import config as ReIDConfig
+# ReIDEncoder = ReIDInterface(ReIDConfig)
 
 # tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml"
 
-def inference_image(image, detections):
-    H, W, _ = np.shape(image)
-    imgs = []
-    batch_patches = []
-    patches = []
-    for d in range(np.size(detections, 0)):
-        tlbr = detections[d, :4].astype(np.int_)
-        tlbr[0] = max(0, tlbr[0])
-        tlbr[1] = max(0, tlbr[1])
-        tlbr[2] = min(W - 1, tlbr[2])
-        tlbr[3] = min(H - 1, tlbr[3])
-        img1 = image[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2], :]
+# def inference_image(image, detections):
+#     H, W, _ = np.shape(image)
+#     imgs = []
+#     batch_patches = []
+#     patches = []
+#     for d in range(np.size(detections, 0)):
+#         tlbr = detections[d, :4].astype(np.int_)
+#         tlbr[0] = max(0, tlbr[0])
+#         tlbr[1] = max(0, tlbr[1])
+#         tlbr[2] = min(W - 1, tlbr[2])
+#         tlbr[3] = min(H - 1, tlbr[3])
+#         img1 = image[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2], :]
 
-        img = img1[:, :, ::-1].copy()    # the model expects RGB inputs
-        patch = ReIDEncoder.transform(img)
+#         img = img1[:, :, ::-1].copy()    # the model expects RGB inputs
+#         patch = ReIDEncoder.transform(img)
         
-        imgs.append(img1)
-        # patch = patch.to(device=self.device).half()
-        if str(ReIDEncoder.device) != "cpu":
-            patch = patch.to(device=ReIDEncoder.device).half()
-        else:
-            patch = patch.to(device=ReIDEncoder.device)
+#         imgs.append(img1)
+#         # patch = patch.to(device=self.device).half()
+#         if str(ReIDEncoder.device) != "cpu":
+#             patch = patch.to(device=ReIDEncoder.device).half()
+#         else:
+#             patch = patch.to(device=ReIDEncoder.device)
         
-        patches.append(patch)
-        if (d + 1) % ReIDEncoder.batch_size == 0:
-            patches = torch.stack(patches, dim=0)
-            batch_patches.append(patches)
-            patches = []
+#         patches.append(patch)
+#         if (d + 1) % ReIDEncoder.batch_size == 0:
+#             patches = torch.stack(patches, dim=0)
+#             batch_patches.append(patches)
+#             patches = []
 
-    if len(patches):
-        patches = torch.stack(patches, dim=0)
-        batch_patches.append(patches)
+#     if len(patches):
+#         patches = torch.stack(patches, dim=0)
+#         batch_patches.append(patches)
 
-    features = np.zeros((0, ReIDEncoder.embedding_size))
-    for patches in batch_patches:
-        pred = ReIDEncoder.model(patches)
-        pred[torch.isinf(pred)] = 1.0
-        feat = pred.cpu().data.numpy()
-        features = np.vstack((features, feat))
+#     features = np.zeros((0, ReIDEncoder.embedding_size))
+#     for patches in batch_patches:
+#         pred = ReIDEncoder.model(patches)
+#         pred[torch.isinf(pred)] = 1.0
+#         feat = pred.cpu().data.numpy()
+#         features = np.vstack((features, feat))
 
-    return imgs, features
+#     return imgs, features
 
 
 
@@ -127,6 +131,7 @@ def init_trackers(tracker_yaml = None, bs=1):
     return trackers
 
 
+'''=============== used in pipeline.py =================='''
 @smart_inference_mode()
 def yolo_resnet_tracker(
         weights=ROOT / 'yolov5s.pt',  # model path or triton URL
@@ -237,7 +242,9 @@ def yolo_resnet_tracker(
                     
                 
                 '''================== 1. 存储 dets/subimgs/features Dict ============='''
-                imgs, features = inference_image(im0, tracks)
+                imgs, features = ReIDEncoder.inference(im0, tracks)
+                
+                # imgs, features = inference_image(im0, tracks)
 
                 # TrackerFeats = np.concatenate([TrackerFeats, features], axis=0)
 
@@ -499,7 +506,8 @@ def run(
                 tracks[:, 7] = frameId
                 
                 '''================== 1. 存储 dets/subimgs/features Dict ============='''
-                imgs, features = inference_image(im0, tracks)
+                # imgs, features = inference_image(im0, tracks)
+                imgs, features = ReIDEncoder.inference(im0, tracks)
 
                 TrackerFeats = np.concatenate([TrackerFeats, features], axis=0)
 
@@ -681,32 +689,17 @@ def main(opt):
     optdict = vars(opt)
     
     p = r"D:\datasets\ym"
-    p = r"D:\datasets\ym\exhibition\153112511_0_seek_105.mp4"
+    p = r"D:\exhibition\images\153112511_0_seek_105.mp4"
+    
+    optdict["project"] = r"D:\exhibition\result"
     
     files = []
-    k = 0
     if os.path.isdir(p):
         files.extend(sorted(glob.glob(os.path.join(p, '*.*'))))   
-        for file in files:
-            
-            optdict["source"] = file
-            run(**optdict)
-            
-            k += 1
-            if k == 1:
-                break         
+        optdict["source"] = files
     elif os.path.isfile(p):
         optdict["source"] = p
-        run(**vars(opt))
-
-def main_imgdir(opt):
-    check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
-    optdict = vars(opt)
     
-
-    optdict["project"] = r"\\192.168.1.28\share\realtime"
-    
-    optdict["source"] = r"\\192.168.1.28\share\realtime\addReturn\add\1728978052624"
     run(**optdict)
 
         
@@ -745,7 +738,7 @@ def main_loop(opt):
             #     break
     elif os.path.isfile(p):
         optdict["source"] = p
-        run(**vars(opt))
+        run(**optdict)
 
 
 
@@ -754,7 +747,6 @@ if __name__ == '__main__':
     opt = parse_opt()
     
     main(opt)
-    # main_imgdir(opt)
     # main_loop(opt)
     
     
diff --git a/tracking/module_analysis.py b/tracking/module_analysis.py
index 03a5579..4c653f2 100644
--- a/tracking/module_analysis.py
+++ b/tracking/module_analysis.py
@@ -24,7 +24,7 @@ from tracking.utils.drawtracks import plot_frameID_y2, draw_all_trajectories
 
 from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_returnGoods_file
 
-from contrast.one2n_contrast import get_relative_paths, one2n_new, read_returnGoods_file
+from contrast.one2n_contrast import get_contrast_paths, one2n_return
 from tracking.utils.annotator import TrackAnnotator
 
 W, H = 1024, 1280
@@ -362,10 +362,10 @@ def main_loop():
     saveimgs = True
     
     if os.path.basename(del_barcode_file).find('deletedBarcode'):
-        relative_paths = get_relative_paths(del_barcode_file, basepath, SavePath, saveimgs)
+        relative_paths = get_contrast_paths(del_barcode_file, basepath, SavePath, saveimgs)
     elif os.path.basename(del_barcode_file).find('returnGoods'):
         blist = read_returnGoods_file(del_barcode_file)
-        errpairs, corrpairs, err_similarity, correct_similarity = one2n_new(blist)
+        errpairs, corrpairs, err_similarity, correct_similarity = one2n_return(blist)
         relative_paths = []
         for getoutevent, inputevent, errevent in errpairs:
             relative_paths.append(os.path.join(basepath, getoutevent))
@@ -412,13 +412,13 @@ def main():
         SavePath: 包含二级目录，一级目录为轨迹图像;二级目录为与data文件对应的序列图像存储地址。
     '''
     # eventPaths = r'\\192.168.1.28\share\测试_202406\0723\0723_3'
-    eventPaths = r'D:\datasets\ym\exhibition\识别错'
-    savePath = r'D:\contrast\dataset\result'
+    eventPaths = r'\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1120_展厅模型v801测试\扫A放A'
+    savePath = r'D:\exhibition\result'
 
     k=0
     for pathname in os.listdir(eventPaths):
-        pathname = "放入薯片识别为辣条"
-                
+        pathname = "20241121-144901-fdba61c6-aefa-4b50-876d-5e05998befdc_6920459905012_6920459905012"
+       
         eventpath = os.path.join(eventPaths, pathname)
         savepath = os.path.join(savePath, pathname)
         if not os.path.exists(savepath):
diff --git a/tracking/trackers/__pycache__/bot_sort.cpython-39.pyc b/tracking/trackers/__pycache__/bot_sort.cpython-39.pyc
index 0c57999..2078695 100644
Binary files a/tracking/trackers/__pycache__/bot_sort.cpython-39.pyc and b/tracking/trackers/__pycache__/bot_sort.cpython-39.pyc differ
diff --git a/tracking/trackers/bot_sort.py b/tracking/trackers/bot_sort.py
index 7a96d90..40ad90d 100644
--- a/tracking/trackers/bot_sort.py
+++ b/tracking/trackers/bot_sort.py
@@ -10,8 +10,15 @@ from .utils import matching
 # from .utils.gmc import GMC
 from .utils.kalman_filter import KalmanFilterXYWH
 
-from .reid.reid_interface import ReIDInterface
-from .reid.config import config
+# from .reid.reid_interface import ReIDInterface
+# from .reid.config import config
+
+from contrast.feat_extract.inference import FeatsInterface
+from contrast.feat_extract.config import config as conf
+
+
+
+
 
 class BOTrack(STrack):
     shared_kalman = KalmanFilterXYWH()
@@ -111,7 +118,9 @@ class BOTSORT(BYTETracker):
 
         if args.with_reid:
             # Haven't supported BoT-SORT(reid) yet
-            self.encoder = ReIDInterface(config)
+            # self.encoder = ReIDInterface(config)
+            
+            self.encoder = FeatsInterface(conf)
         
         # self.gmc = GMC(method=args.gmc_method)     # commented by WQG
 
@@ -119,13 +128,13 @@ class BOTSORT(BYTETracker):
         """Returns an instance of KalmanFilterXYWH for object tracking."""
         return KalmanFilterXYWH()
 
-    def init_track(self, dets, scores, cls, imgs, features_keep):
+    def init_track(self, dets, scores, cls, image, features_keep):
         """Initialize track with detections, scores, and classes."""
         if len(dets) == 0:
             return []
         if self.args.with_reid and self.encoder is not None:
             if features_keep is None:
-                features_keep = self.encoder.inference(imgs, dets)
+                imgs, features_keep = self.encoder.inference(image, dets)
                 
             return [BOTrack(xyxy, s, c, f) for (xyxy, s, c, f) in zip(dets, scores, cls, features_keep)]  # detections
         else:
diff --git a/tracking/utils/__pycache__/plotting.cpython-39.pyc b/tracking/utils/__pycache__/plotting.cpython-39.pyc
index a0008c1..e0fd814 100644
Binary files a/tracking/utils/__pycache__/plotting.cpython-39.pyc and b/tracking/utils/__pycache__/plotting.cpython-39.pyc differ
diff --git a/tracking/utils/__pycache__/read_data.cpython-39.pyc b/tracking/utils/__pycache__/read_data.cpython-39.pyc
index 3fc17b0..5f04eaa 100644
Binary files a/tracking/utils/__pycache__/read_data.cpython-39.pyc and b/tracking/utils/__pycache__/read_data.cpython-39.pyc differ
diff --git a/tracking/utils/read_data.py b/tracking/utils/read_data.py
index a733fe6..1a12b73 100644
--- a/tracking/utils/read_data.py
+++ b/tracking/utils/read_data.py
@@ -1,9 +1,11 @@
 # -*- coding: utf-8 -*-
 """
 Created on Fri Jul  5 13:59:21 2024
-func: extract_data()
-  读取 Pipeline 各模块的数据，在 read_pipeline_data.py（马晓慧）的基础上完成接口改造
-
+       函数                           读取文件
+    extract_data()                 0/1_track.data
+  read_tracking_output()       0/1_tracking_output.data
+   read_similar()                  process.data
+   
 @author: ym
 """
 import numpy as np
@@ -275,6 +277,7 @@ def read_returnGoods_file(filePath):
                 continue
             if split_flag:
                 bcd = label.split('_')[-1]
+                if len(bcd)<8: continue
                 # event_list.append(label + '_' + bcd)
                 event_list.append(label)
                 barcode_list.append(bcd)
@@ -294,47 +297,78 @@ def read_returnGoods_file(filePath):
     
     
 
-# =============================================================================
-# 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_one2one_simi(filePath):
+def read_seneor(filepath):
+    WeightDict = OrderedDict()
+    with open(filepath, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+        clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]  
+        for i, line in enumerate(clean_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_similar(filePath):
     SimiDict = {}
+    SimiDict['one2one'] = []
+    SimiDict['one2n'] = []
+    
     with open(filePath, 'r', encoding='utf-8') as f:
         lines = f.readlines()
-        flag = False
-        for i, line in enumerate(lines):
+        clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]  
+        one2one_list, one2n_list = [], []
+        
+        Flag_1to1, Flag_1ton = False, False
+        for i, line in enumerate(clean_lines):
             line = line.strip()
-            if line.find('barcode:')<0 and not flag:
+            if line.endswith(','):  
+                line = line[:-1]
+            Dict = {}
+
+            
+            if not line:
+                if len(one2one_list): SimiDict['one2one'] = one2one_list
+                if len(one2n_list): SimiDict['one2n'] = one2n_list
+                one2one_list, one2n_list = [], []
+                Flag_1to1, Flag_1ton = False, False
                 continue
-            if line.find('barcode:')==0 :   
-                flag = True
+                
+            if line.find('oneToOne')>=0:
+                Flag_1to1, Flag_1ton = True, False
+                continue
+            if line.find('oneTon')>=0:
+                Flag_1to1, Flag_1ton = False, True
                 continue
             
-            # if line.endswith(','):  
-            #     line = line[:-1]   
-            if flag:
+            if Flag_1to1:
                 barcode = line.split(',')[0].strip()
                 value = line.split(',')[1].split(':')[1].strip()
-                SimiDict[barcode] = float(value)
+                Dict['barcode'] = barcode
+                Dict['similar'] = float(value)
+                one2one_list.append(Dict)
+                continue
 
-            if flag and not line:
-                flag = False
+            if Flag_1ton:
+                label = line.split(':')[0].strip()
+                value = line.split(':')[1].strip()
+                
+                bcd = label.split('_')[-1] 
+                if len(bcd)<8: continue
+            
+                Dict['event'] = label
+                Dict['barcode'] = bcd
+                Dict['similar'] = float(value.split(',')[0])
+                Dict['type'] = value.split('=')[-1]
+                one2n_list.append(Dict)
+
+        if len(one2one_list): SimiDict['one2one'] = one2one_list
+        if len(one2n_list): SimiDict['one2n'] = one2n_list
 
     return SimiDict
 
diff --git a/tracking/说明文档.txt b/tracking/说明文档.txt
deleted file mode 100644
index e69de29..0000000