diff --git a/.idea/CopilotChatHistory.xml b/.idea/CopilotChatHistory.xml
index 6864f60..064fd76 100644
--- a/.idea/CopilotChatHistory.xml
+++ b/.idea/CopilotChatHistory.xml
@@ -3,6 +3,42 @@
   <component name="CopilotChatHistory">
     <option name="conversations">
       <list>
+        <Conversation>
+          <option name="createTime" value="1754286137102" />
+          <option name="id" value="0198739a1f0e75c38b0579ade7b34050" />
+          <option name="title" value="新对话 2025年8月04日 13:42:17" />
+          <option name="updateTime" value="1754286137102" />
+        </Conversation>
+        <Conversation>
+          <option name="createTime" value="1753932970546" />
+          <option name="id" value="01985e8d3a3170bf871ba640afdf246d" />
+          <option name="title" value="新对话 2025年7月31日 11:36:10" />
+          <option name="updateTime" value="1753932970546" />
+        </Conversation>
+        <Conversation>
+          <option name="createTime" value="1753932554257" />
+          <option name="id" value="01985e86e01170d6a09dca496e3dad46" />
+          <option name="title" value="新对话 2025年7月31日 11:29:14" />
+          <option name="updateTime" value="1753932554257" />
+        </Conversation>
+        <Conversation>
+          <option name="createTime" value="1753680371881" />
+          <option name="id" value="01984f7ee0a9779aabcd3f1671b815b3" />
+          <option name="title" value="新对话 2025年7月28日 13:26:11" />
+          <option name="updateTime" value="1753680371881" />
+        </Conversation>
+        <Conversation>
+          <option name="createTime" value="1753405176017" />
+          <option name="id" value="01983f17b8d173dda926d0ffa5422bbf" />
+          <option name="title" value="新对话 2025年7月25日 08:59:36" />
+          <option name="updateTime" value="1753405176017" />
+        </Conversation>
+        <Conversation>
+          <option name="createTime" value="1753065086744" />
+          <option name="id" value="01982ad25f18712f862c5c18b627f40d" />
+          <option name="title" value="新对话 2025年7月21日 10:31:26" />
+          <option name="updateTime" value="1753065086744" />
+        </Conversation>
         <Conversation>
           <option name="createTime" value="1752195523240" />
           <option name="id" value="0197f6fde2a87e68b893b3a36dfc838f" />
diff --git a/configs/compare.yml b/configs/compare.yml
index a09474a..c547a26 100644
--- a/configs/compare.yml
+++ b/configs/compare.yml
@@ -16,24 +16,24 @@ base:
 # 模型配置
 models:
   backbone: 'resnet18'
-  channel_ratio: 0.75
+  channel_ratio: 1.0
 
 # 训练参数
 training:
-  epochs: 600                         # 总训练轮次
+  epochs: 400                         # 总训练轮次
   batch_size: 128                     # 批次大小
-  lr: 0.007                           # 初始学习率
+  lr: 0.01                           # 初始学习率
   optimizer: "sgd"                    # 优化器类型
   metric: 'arcface'                   # 损失函数类型（可选：arcface/cosface/sphereface/softmax）
   loss: "cross_entropy"               # 损失函数类型（可选：cross_entropy/cross_entropy_smooth/center_loss/center_loss_smooth/arcface/cosface/sphereface/softmax）
-  lr_step: 10                         # 学习率调整间隔（epoch）
+  lr_step: 5                         # 学习率调整间隔（epoch）
   lr_decay: 0.95                      # 学习率衰减率
   weight_decay: 0.0005                  # 权重衰减
-  scheduler: "cosine"                 # 学习率调度器（可选：cosine/cosine_warm/step/None）
+  scheduler: "step"                 # 学习率调度器（可选：cosine/cosine_warm/step/None）
   num_workers: 32                     # 数据加载线程数
-  checkpoints: "./checkpoints/resnet18_20250717_scale=0.75_nosub/" # 模型保存目录
-  restore: true
-  restore_model: "./checkpoints/resnet18_20250716_scale=0.75_nosub/best.pth" # 模型恢复路径
+  checkpoints: "./checkpoints/resnet18_electornic_20250806/" # 模型保存目录
+  restore: false
+  restore_model: "./checkpoints/resnet18_20250717_scale=0.75_nosub/best.pth" # 模型恢复路径
   cosine_t_0: 10                       # 初始周期长度
   cosine_t_mult: 1                      # 周期长度倍率
   cosine_eta_min: 0.00001               # 最小学习率
@@ -49,8 +49,8 @@ data:
   train_batch_size: 128               # 训练批次大小
   val_batch_size: 128                 # 验证批次大小
   num_workers: 32                      # 数据加载线程数
-  data_train_dir: "../data_center/contrast_data/v2/train"   # 训练数据集根目录
-  data_val_dir: "../data_center/contrast_data/v2/val"   # 验证数据集根目录
+  data_train_dir: "../data_center/electornic/v1/train"   # 训练数据集根目录
+  data_val_dir: "../data_center/electornic/v1/val"   # 验证数据集根目录
 
 transform:
   img_size: 224                     # 图像尺寸
diff --git a/configs/pic_pic_similar.yml b/configs/pic_pic_similar.yml
new file mode 100644
index 0000000..fabb385
--- /dev/null
+++ b/configs/pic_pic_similar.yml
@@ -0,0 +1,53 @@
+# configs/similar_analysis.yml
+# 专为模型训练对比设计的配置文件
+# 支持对比不同训练策略（如蒸馏vs独立训练）
+
+# 基础配置
+base:
+  experiment_name: "model_comparison"  # 实验名称（用于结果保存目录）
+  device: "cuda"                      # 训练设备（cuda/cpu）
+  embedding_size: 256                 # 特征维度
+  pin_memory: true                    # 是否启用pin_memory
+  distributed: true                  # 是否启用分布式训练
+
+
+# 模型配置
+models:
+  backbone: 'resnet18'
+  channel_ratio: 0.75
+  model_path: "../checkpoints/resnet18_1009/best.pth"
+
+heatmap:
+  feature_layer: "layer4"
+  show_heatmap:  true
+# 数据配置
+data:
+  dataset: "imagenet"                 # 数据集名称（示例用，可替换为实际数据集）
+  train_batch_size: 128               # 训练批次大小
+  val_batch_size: 8                 # 验证批次大小
+  num_workers: 32                      # 数据加载线程数
+  data_dir: "/home/lc/data_center/image_analysis/pic_pic_similar_maxtrix"
+  image_joint_pth: "/home/lc/data_center/image_analysis/error_compare_result"
+  total_pkl: "/home/lc/data_center/image_analysis/pic_pic_similar_maxtrix/total.pkl"
+  result_txt: "/home/lc/data_center/image_analysis/pic_pic_similar_maxtrix/result.txt"
+
+transform:
+  img_size: 224                     # 图像尺寸
+  img_mean: 0.5                     # 图像均值
+  img_std: 0.5                      # 图像方差
+  RandomHorizontalFlip: 0.5         # 随机水平翻转概率
+  RandomRotation: 180               # 随机旋转角度
+  ColorJitter: 0.5                   # 随机颜色抖动强度
+
+# 日志与监控
+logging:
+  logging_dir: "./logs/resnet18_scale=0.75_nosub_log"                  # 日志保存目录
+  tensorboard: true                   # 是否启用TensorBoard
+  checkpoint_interval: 30             # 检查点保存间隔（epoch）
+
+event:
+  oneToOne_max_th: 0.9
+  oneToSn_min_th: 0.6
+  event_save_dir: "/home/lc/works/realtime_yolov10s/online_yolov10s_resnetv11_20250702/yolos_tracking"
+  stdlib_image_path: "/testDataAndLogs/module_test_record/comparison/标准图测试数据/pic/stlib_base"
+  pickle_path: "event.pickle"
diff --git a/configs/scatter_data.yml b/configs/scatter_data.yml
index 2fe7e14..1f42b13 100644
--- a/configs/scatter_data.yml
+++ b/configs/scatter_data.yml
@@ -4,10 +4,10 @@
 # 数据配置
 data:
   dataset: "imagenet"                 # 数据集名称（示例用，可替换为实际数据集）
-  source_dir: "../../data_center/scatter/v5/source"                      # 原始数据
-  train_dir: "../../data_center/scatter/v5/train"   # 训练数据集根目录
-  val_dir: "../../data_center/scatter/v5/val"   # 验证数据集根目录
-  extra_dir: "../../data_center/scatter/v5/extra"   # 验证数据集根目录
+  source_dir: "../../data_center/electornic/source"                      # 原始数据
+  train_dir: "../../data_center/electornic/v1/train"   # 训练数据集根目录
+  val_dir: "../../data_center/electornic/v1/val"   # 验证数据集根目录
+  extra_dir: "../../data_center/electornic/v1/extra"   # 验证数据集根目录
   split_ratio: 0.9
   max_files: 10                     # 数据集小于该阈值则归纳至extra
 
diff --git a/configs/similar_analysis.yml b/configs/similar_analysis.yml
index 03ecc9a..feb0076 100644
--- a/configs/similar_analysis.yml
+++ b/configs/similar_analysis.yml
@@ -43,7 +43,11 @@ logging:
   tensorboard: true                   # 是否启用TensorBoard
   checkpoint_interval: 30             # 检查点保存间隔（epoch）
 
-# 分布式训练（可选）
-distributed:
-  enabled: false                      # 是否启用分布式训练
-  backend: "nccl"                     # 分布式后端（nccl/gloo）
\ No newline at end of file
+event:
+  oneToOneTxt: "/home/lc/detecttracking/oneToOne.txt"
+  oneToSnTxt: "/home/lc/detecttracking/oneToSn.txt"
+  oneToOne_max_th: 0.9
+  oneToSn_min_th: 0.6
+  event_save_dir: "/home/lc/works/realtime_yolov10s/online_yolov10s_resnetv11_20250702/yolos_tracking"
+  stdlib_image_path: "/testDataAndLogs/module_test_record/comparison/标准图测试数据/pic/stlib_base"
+  pickle_path: "event.pickle"
diff --git a/configs/test.yml b/configs/test.yml
index 41fc129..52afa2f 100644
--- a/configs/test.yml
+++ b/configs/test.yml
@@ -13,8 +13,10 @@ base:
 # 模型配置
 models:
   backbone: 'resnet18'
-  channel_ratio: 1.0
-  model_path: "checkpoints/resnet18_scatter_7.3/best.pth"
+  channel_ratio: 0.75
+  model_path: "checkpoints/resnet18_1009/best.pth"
+  #resnet18_20250715_scale=0.75_sub
+  #resnet18_20250718_scale=0.75_nosub
   half: false                        # 是否启用半精度测试（fp16）
   contrast_learning: false
 
@@ -22,9 +24,9 @@ models:
 data:
   test_batch_size: 128               # 训练批次大小
   num_workers: 32                      # 数据加载线程数
-  test_dir: "../data_center/scatter/v4/val"   # 验证数据集根目录
+  test_dir: "../data_center/contrast_data/v1/extra"   # 验证数据集根目录
   test_group_json: "../data_center/contrast_learning/model_test_data/test/inner_group_pairs.json"
-  test_list: "../data_center/scatter/v4/standard_cross_same.txt"
+  test_list: "../data_center/contrast_data/v1/extra_cross_same.txt"
   group_test: false
   save_image_joint: true
   image_joint_pth: "./joint_images"
diff --git a/configs/write_feature.yml b/configs/write_feature.yml
index f726b64..e7b287b 100644
--- a/configs/write_feature.yml
+++ b/configs/write_feature.yml
@@ -14,7 +14,7 @@ base:
 models:
   backbone: 'resnet18'
   channel_ratio: 0.75
-  checkpoints: "../checkpoints/resnet18_1009/best.pth"
+  checkpoints: "../checkpoints/resnet18_20250715_scale=0.75_sub/best.pth"
 
 # 数据配置
 data:
@@ -42,7 +42,7 @@ logging:
 
 save:
   json_bin: "../search_library/yunhedian_05-09.json"       # 保存整个json文件
-  json_path: "/home/lc/data_center/baseStlib/feature_json/stlib_base"    # 保存单个json文件路径
+  json_path: "/home/lc/data_center/baseStlib/feature_json/stlib_base_resnet18_sub"    # 保存单个json文件路径
   error_barcodes: "error_barcodes.txt"
   barcodes_statistics: "../search_library/barcodes_statistics.txt"
   create_single_json: true                  # 是否保存单个json文件
\ No newline at end of file
diff --git a/data_preprocessing/data_split.py b/data_preprocessing/data_split.py
index d5979dc..6611d11 100644
--- a/data_preprocessing/data_split.py
+++ b/data_preprocessing/data_split.py
@@ -2,7 +2,7 @@ import os
 import shutil
 import random
 from pathlib import Path
-
+import yaml
 def is_image_file(filename):
     """检查文件是否为图像文件"""
     image_extensions = ('.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff')
@@ -61,10 +61,12 @@ def control_train_number():
     pass
 
 if __name__ == "__main__":
-    # 设置目录路径
-    TRAIN_DIR = "scatter_data/train"
-    VAL_DIR = "scatter_data/val"
+    # # 设置目录路径
+    # TRAIN_DIR = "/home/lc/data_center/electornic/v1/train"
+    # VAL_DIR = "/home/lc/data_center/electornic/v1/val"
 
+    with open('../configs/scatter_data.yml', 'r') as f:
+        conf = yaml.load(f, Loader=yaml.FullLoader)
     print("开始分割数据集...")
-    split_directory(TRAIN_DIR, VAL_DIR)
+    split_directory(conf)
     print("数据集分割完成")
diff --git a/data_preprocessing/extend.py b/data_preprocessing/extend.py
index cc15059..a168612 100644
--- a/data_preprocessing/extend.py
+++ b/data_preprocessing/extend.py
@@ -141,7 +141,7 @@ class ImageExtendProcessor:
                                                  same_directory,
                                                  dir_name)
 
-    def random_remove_image(self, subdir_path, max_count=200):
+    def random_remove_image(self, subdir_path, max_count=1000):
         """
         随机删除子目录中的图像文件，直到数量不超过max_count
         :param subdir_path: 子目录路径
diff --git a/test_ori.py b/test_ori.py
index b371029..554d62f 100644
--- a/test_ori.py
+++ b/test_ori.py
@@ -17,7 +17,7 @@ from configs import trainer_tools
 import yaml
 from datetime import datetime
 
-with open('../configs/test.yml', 'r') as f:
+with open('./configs/test.yml', 'r') as f:
     conf = yaml.load(f, Loader=yaml.FullLoader)
 
 # Constants from config
@@ -141,7 +141,7 @@ def threshold_search(y_score, y_true):
 
 
 def showgrid(recall, recall_TN, PrecisePos, PreciseNeg, Correct):
-    x = np.linspace(start=0, stop=1.0, num=50, endpoint=True).tolist()
+    x = np.linspace(start=-1, stop=1.0, num=100, endpoint=True).tolist()
     plt.figure(figsize=(10, 6))
     plt.plot(x, recall, color='red', label='recall:TP/TPFN')
     plt.plot(x, recall_TN, color='black', label='recall_TN:TN/TNFP')
@@ -151,6 +151,7 @@ def showgrid(recall, recall_TN, PrecisePos, PreciseNeg, Correct):
     plt.legend()
     plt.xlabel('threshold')
     # plt.ylabel('Similarity')
+
     plt.grid(True, linestyle='--', alpha=0.5)
     plt.savefig('grid.png')
     plt.show()
@@ -162,19 +163,19 @@ def showHist(same, cross):
     Cross = np.array(cross)
 
     fig, axs = plt.subplots(2, 1)
-    axs[0].hist(Same, bins=50, edgecolor='black')
-    axs[0].set_xlim([-0.1, 1])
+    axs[0].hist(Same, bins=100, edgecolor='black')
+    axs[0].set_xlim([-1, 1])
     axs[0].set_title('Same Barcode')
 
-    axs[1].hist(Cross, bins=50, edgecolor='black')
-    axs[1].set_xlim([-0.1, 1])
+    axs[1].hist(Cross, bins=100, edgecolor='black')
+    axs[1].set_xlim([-1, 1])
     axs[1].set_title('Cross Barcode')
     plt.savefig('plot.png')
 
 
 def compute_accuracy_recall(score, labels):
     th = 0.1
-    squence = np.linspace(-1, 1, num=50)
+    squence = np.linspace(-1, 1, num=100)
     recall, PrecisePos, PreciseNeg, recall_TN, Correct = [], [], [], [], []
     Same = score[:len(score) // 2]
     Cross = score[len(score) // 2:]
diff --git a/tools/dataset.py b/tools/dataset.py
index 8e5b0d8..4060886 100644
--- a/tools/dataset.py
+++ b/tools/dataset.py
@@ -24,7 +24,7 @@ def get_transform(cfg):
         T.Normalize(mean=[cfg['transform']['img_mean']], std=[cfg['transform']['img_std']]),
     ])
     test_transform = T.Compose([
-        # T.Lambda(pad_to_square),  # 补边
+        T.Lambda(pad_to_square),  # 补边
         T.ToTensor(),
         T.Resize((cfg['transform']['img_size'], cfg['transform']['img_size']), antialias=True),
         T.ConvertImageDtype(torch.float32),
diff --git a/tools/event_similar_analysis.py b/tools/event_similar_analysis.py
new file mode 100644
index 0000000..89b1948
--- /dev/null
+++ b/tools/event_similar_analysis.py
@@ -0,0 +1,144 @@
+from similar_analysis import SimilarAnalysis
+import os
+import pickle
+from tools.image_joint import merge_imgs
+
+
+class EventSimilarAnalysis(SimilarAnalysis):
+    def __init__(self):
+        super(EventSimilarAnalysis, self).__init__()
+        self.fn_one2one_event, self.fp_one2one_event = self.One2one_similar_analysis()
+        self.fn_one2sn_event, self.fp_one2sn_event = self.One2Sn_similar_analysis()
+        if os.path.exists(self.conf['event']['pickle_path']):
+            print('pickle file exists')
+        else:
+            self.target_image = self.get_path()
+
+    def get_path(self):
+        events = [self.fn_one2one_event, self.fp_one2one_event,
+                  self.fn_one2sn_event, self.fp_one2sn_event]
+        event_image_path = []
+        barcode_image_path = []
+        for event in events:
+            for event_name, bcd in event:
+                event_sub_image = os.sep.join([self.conf['event']['event_save_dir'],
+                                               event_name,
+                                               'subimgs'])
+                barcode_images = os.sep.join([self.conf['event']['stdlib_image_path'],
+                                              bcd])
+                for image_name in os.listdir(event_sub_image):
+                    event_image_path.append(os.sep.join([event_sub_image, image_name]))
+                for barcode in os.listdir(barcode_images):
+                    barcode_image_path.append(os.sep.join([barcode_images, barcode]))
+        return list(set(event_image_path + barcode_image_path))
+
+
+    def write_dict_to_pickle(self, data):
+        """将字典写入pickle文件."""
+        with open(self.conf['event']['pickle_path'], 'wb') as file:
+            pickle.dump(data, file)
+
+    def get_dict_to_pickle(self):
+        with open(self.conf['event']['pickle_path'], 'rb') as f:
+            data = pickle.load(f)
+        return data
+
+    def create_total_feature(self):
+        feature_dicts = self.get_feature_map(self.target_image)
+        self.write_dict_to_pickle(feature_dicts)
+        print(feature_dicts)
+
+    def One2one_similar_analysis(self):
+        fn_event, fp_event = [], []
+        with open(self.conf['event']['oneToOneTxt'], 'r') as f:
+            lines = f.readlines()
+        for line in lines:
+            print(line.strip().split(' '))
+            event_infor = line.strip().split(' ')
+            label = event_infor[0]
+            event_name = event_infor[1]
+            bcd = event_infor[2]
+            simi1 = event_infor[3]
+            simi2 = event_infor[4]
+            if label == 'same' and float(simi2) < self.conf['event']['oneToOne_max_th']:
+                print(event_name, bcd, simi1)
+                fn_event.append((event_name, bcd))
+            elif label == 'diff' and float(simi2) > self.conf['event']['oneToSn_min_th']:
+                fp_event.append((event_name, bcd))
+        return fn_event, fp_event
+
+    def One2Sn_similar_analysis(self):
+        fn_event, fp_event = [], []
+        with open(self.conf['event']['oneToOneTxt'], 'r') as f:
+            lines = f.readlines()
+        for line in lines:
+            print(line.strip().split(' '))
+            event_infor = line.strip().split(' ')
+            label = event_infor[0]
+            event_name = event_infor[1]
+            bcd = event_infor[2]
+            simi = event_infor[3]
+            if label == 'fn':
+                print(event_name, bcd, simi)
+                fn_event.append((event_name, bcd))
+            elif label == 'fp':
+                fp_event.append((event_name, bcd))
+        return fn_event, fp_event
+
+    def save_joint_image(self, img_pth1, img_pth2, feature_dicts, record):
+        feature_dict1 = feature_dicts[img_pth1]
+        feature_dict2 = feature_dicts[img_pth2]
+        similarity = self.get_similarity(feature_dict1.cpu().numpy(),
+                                         feature_dict2.cpu().numpy())
+        dir_name = img_pth1.split('/')[-3]
+        save_path = os.sep.join([self.conf['data']['image_joint_pth'], dir_name, record])
+        if "fp" in record:
+            if similarity > 0.8:
+                merge_imgs(img_pth1,
+                           img_pth2,
+                           self.conf,
+                           similarity,
+                           label=None,
+                           cam=self.cam,
+                           save_path=save_path)
+        else:
+            if similarity < 0.8:
+                merge_imgs(img_pth1,
+                           img_pth2,
+                           self.conf,
+                           similarity,
+                           label=None,
+                           cam=self.cam,
+                           save_path=save_path)
+        print(similarity)
+
+    def get_contrast(self, feature_dicts):
+        events_compare = [self.fp_one2one_event, self.fn_one2one_event, self.fp_one2sn_event, self.fn_one2sn_event]
+        event_record = ['fp_one2one', 'fn_one2one', 'fp_one2sn', 'fn_one2sn']
+        for event_compare, record in zip(events_compare, event_record):
+            for img, img_std in event_compare:
+                imgs_pth1 = os.sep.join([self.conf['event']['event_save_dir'],
+                                         img,
+                                         'subimgs'])
+                imgs_pth2 = os.sep.join([self.conf['event']['stdlib_image_path'],
+                                         img_std])
+                for img1 in os.listdir(imgs_pth1):
+                    for img2 in os.listdir(imgs_pth2):
+                        img_pth1 = os.sep.join([imgs_pth1, img1])
+                        img_pth2 = os.sep.join([imgs_pth2, img2])
+                        try:
+                            self.save_joint_image(img_pth1, img_pth2, feature_dicts, record)
+                        except Exception as e:
+                            continue
+                            print(e)
+
+
+if __name__ == '__main__':
+    event_similar_analysis = EventSimilarAnalysis()
+    if os.path.exists(event_similar_analysis.conf['event']['pickle_path']):
+        print('pickle file exists')
+    else:
+        event_similar_analysis.create_total_feature()  # 生成pickle文件, 生成时间较长，生成一个文件即可
+    feature_dicts = event_similar_analysis.get_dict_to_pickle()
+    # all_compare_img = event_similar_analysis.get_image_map()
+    event_similar_analysis.get_contrast(feature_dicts)  # 获取比对结果
diff --git a/tools/getpairs.py b/tools/getpairs.py
index 1a94a64..7d6e5bc 100644
--- a/tools/getpairs.py
+++ b/tools/getpairs.py
@@ -9,8 +9,23 @@ import logging
 class PairGenerator:
     """Generate positive and negative image pairs for contrastive learning."""
 
-    def __init__(self):
+    def __init__(self, original_path):
         self._setup_logging()
+        self.original_path = original_path
+        self._delete_space()
+
+    def _delete_space(self):  # 删除图片文件名中的空格
+        print(self.original_path)
+        for root, dirs, files in os.walk(self.original_path):
+            for file_name in files:
+                if file_name.endswith('.jpg' or '.png'):
+                    n_file_name = file_name.replace(' ', '')
+                    os.rename(os.path.join(root, file_name), os.path.join(root, n_file_name))
+                if 'rotate' in file_name:
+                    os.remove(os.path.join(root, file_name))
+            for dir_name in dirs:
+                n_dir_name = dir_name.replace(' ', '')
+                os.rename(os.path.join(root, dir_name), os.path.join(root, n_dir_name))
 
     def _setup_logging(self):
         """Configure logging settings."""
@@ -188,11 +203,11 @@ class PairGenerator:
 
 
 if __name__ == "__main__":
-    original_path = '/home/lc/data_center/scatter/v4/val'
+    original_path = '/home/lc/data_center/contrast_data/v1/extra'
     parent_dir = str(Path(original_path).parent)
-    generator = PairGenerator()
+    generator = PairGenerator(original_path)
 
     # Example usage:
     pairs = generator.get_pairs(original_path,
-                                output_txt=os.sep.join([parent_dir, 'cross_same.txt']))  # Individual pairs
+                                output_txt=os.sep.join([parent_dir, 'extra_cross_same.txt']))  # Individual pairs
     # groups = generator.get_group_pairs('val')  # Group pairs
diff --git a/tools/image_joint.py b/tools/image_joint.py
index 2995040..7944482 100644
--- a/tools/image_joint.py
+++ b/tools/image_joint.py
@@ -18,31 +18,49 @@ def merge_imgs(img1_path, img2_path, conf, similar=None, label=None, cam=None, s
         img2 = Image.open(img2_path)
         img1 = img1.resize((224, 224))
         img2 = img2.resize((224, 224))
+        new_img = Image.new('RGB', (img1.width + img2.width + 10, img1.height))
         # save_path = conf['data']['image_joint_pth']
     else:
         assert cam is not None, 'cam is None'
         img1 = cam.get_hot_map(img1_path)
         img2 = cam.get_hot_map(img2_path)
+        img1_ori = Image.open(img1_path)
+        img2_ori = Image.open(img2_path)
+        img1_ori = img1_ori.resize((224, 224))
+        img2_ori = img2_ori.resize((224, 224))
+        new_img = Image.new('RGB',
+                            (img1.width + img2.width + 10,
+                             img1.height + img2.width + 10))
         # save_path = conf['heatmap']['image_joint_pth']
     # print('img1_path', img1)
     # print('img2_path', img2)
-    if not os.path.exists(os.sep.join([save_path, str(label)])):
+    if not os.path.exists(os.sep.join([save_path, str(label)])) and (label is not None):
         os.makedirs(os.sep.join([save_path, str(label)]))
-    if save_path is None:
         save_path = os.sep.join([save_path, str(label)])
-    img_name = os.path.basename(img1_path).split('.')[0] + '_' + os.path.basename(img2_path).split('.')[0] + '.png'
+    if save_path is None:
+        # save_path = os.sep.join([save_path, str(label)])
+        pass
+    # img_name = os.path.basename(img1_path).split('.')[0] + '_' + os.path.basename(img2_path).split('.')[0] + '.png'
+    img_name = os.path.basename(img1_path).split('.')[0][:30] + '_' + os.path.basename(img2_path).split('.')[0][
+                                                                      :30] + '.png'
     assert img1.height == img2.height
 
-    new_img = Image.new('RGB', (img1.width + img2.width + 10, img1.height))
+
 
     # print('new_img', new_img)
-    new_img.paste(img1, (0, 0))
-    new_img.paste(img2, (img1.width + 10, 0))
+    if not conf['heatmap']['show_heatmap']:
+        new_img.paste(img1, (0, 0))
+        new_img.paste(img2, (img1.width + 10, 0))
+    else:
+        new_img.paste(img1_ori, (10, 10))
+        new_img.paste(img2_ori, (img2_ori.width + 20, 10))
+        new_img.paste(img1, (10, img1.height+20))
+        new_img.paste(img2, (img2.width+20, img2.height+20))
 
     if similar is not None:
-        if label == '1' and similar > 0.5:
+        if label == '1' and (similar > 0.5 or similar < 0.25):
             save = False
-        elif label == '0' and similar < 0.5:
+        elif label == '0' and similar > 0.25:
             save = False
         similar = str(similar) + '_' + str(label)
         draw = ImageDraw.Draw(new_img)
diff --git a/tools/model_rknn_transform.py b/tools/model_rknn_transform.py
index 421b645..959d79f 100644
--- a/tools/model_rknn_transform.py
+++ b/tools/model_rknn_transform.py
@@ -122,7 +122,7 @@ if __name__ == '__main__':
 
     # Build model
     print('--> Building model')
-    ret = rknn.build(do_quantization=True,
+    ret = rknn.build(do_quantization=False,  # True
                      dataset='./dataset.txt',
                      rknn_batch_size=conf['models']['rknn_batch_size'])
     # ret = rknn.build(do_quantization=False, dataset='./dataset.txt')
diff --git a/tools/picdir_to_picdir_similar.py b/tools/picdir_to_picdir_similar.py
new file mode 100644
index 0000000..a12c420
--- /dev/null
+++ b/tools/picdir_to_picdir_similar.py
@@ -0,0 +1,242 @@
+from similar_analysis import SimilarAnalysis
+import os
+import pickle
+from tools.image_joint import merge_imgs
+import yaml
+from PIL import Image
+import torch
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import matplotlib.pyplot as plt
+
+'''
+轨迹图与标准库之间的相似度分析
+1.用于生成轨迹图与标准库中所有图片的相似度
+2.用于分析轨迹图与标准库比对选取策略的判断
+'''
+
+
+class picDirSimilarAnalysis(SimilarAnalysis):
+    def __init__(self):
+        super(picDirSimilarAnalysis, self).__init__()
+        with open('../configs/pic_pic_similar.yml', 'r') as f:
+            self.conf = yaml.load(f, Loader=yaml.FullLoader)
+        if not os.path.exists(self.conf['data']['total_pkl']):
+            # self.create_total_feature()
+            self.create_total_pkl()
+        if os.path.exists(self.conf['data']['total_pkl']):
+            self.all_dicts = self.load_dict_from_pkl()
+
+    def is_image_file(self, filename):
+        """
+        检查文件是否为图像文件
+        """
+        image_extensions = ('.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff')
+        return filename.lower().endswith(image_extensions)
+
+    def create_total_pkl(self):  # 将目录下所有的图片特征存入pkl文件
+        all_images_feature_dict = {}
+        for roots, dirs, files in os.walk(self.conf['data']['data_dir']):
+            for file_name in files:
+                if self.is_image_file(file_name):
+                    try:
+                        print(f"处理图像 {os.sep.join([roots, file_name])}")
+                        feature = self.extract_features(os.sep.join([roots, file_name]))
+                    except Exception as e:
+                        print(f"处理图像 {os.sep.join([roots, file_name])} 时出错: {e}")
+                        feature = None
+                    all_images_feature_dict[os.sep.join([roots, file_name])] = feature
+        if not os.path.exists(self.conf['data']['total_pkl']):
+            with open(self.conf['data']['total_pkl'], 'wb') as f:
+                pickle.dump(all_images_feature_dict, f)
+
+    def load_dict_from_pkl(self):
+        with open(self.conf['data']['total_pkl'], 'rb') as f:
+            data = pickle.load(f)
+        print(f"字典已从 {self.conf['data']['total_pkl']} 加载")
+        return data
+
+    def get_image_files(self, folder_path):
+        """
+        获取文件夹中的所有图像文件
+        """
+        image_files = []
+        for root, _, files in os.walk(folder_path):
+            for file in files:
+                if self.is_image_file(file):
+                    image_files.append(os.path.join(root, file))
+        return image_files
+
+    def extract_features(self, image_path):
+        feature_dict = self.get_feature(image_path)
+        return feature_dict[image_path]
+
+    def create_one_similarity_matrix(self, folder1_path, folder2_path):
+        images1 = self.get_image_files(folder1_path)
+        images2 = self.get_image_files(folder2_path)
+
+        print(f"文件夹1 ({folder1_path}) 包含 {len(images1)} 张图像")
+        print(f"文件夹2 ({folder2_path}) 包含 {len(images2)} 张图像")
+
+        if len(images1) == 0 or len(images2) == 0:
+            raise ValueError("至少有一个文件夹中没有图像文件")
+
+        # 提取文件夹1中的所有图像特征
+        features1 = []
+        print("正在提取文件夹1中的图像特征...")
+        for i, img_path in enumerate(images1):
+            try:
+                # feature = self.extract_features(img_path)
+                feature = self.all_dicts[img_path]
+                features1.append(feature.cpu().numpy())
+                # if (i + 1) % 10 == 0:
+                #     print(f"已处理 {i + 1}/{len(images1)} 张图像")
+            except Exception as e:
+                print(f"处理图像 {img_path} 时出错: {e}")
+                features1.append(None)
+
+        # 提取文件夹2中的所有图像特征
+        features2 = []
+        print("正在提取文件夹2中的图像特征...")
+        for i, img_path in enumerate(images2):
+            try:
+                # feature = self.extract_features(img_path)
+                feature = self.all_dicts[img_path]
+                features2.append(feature.cpu().numpy())
+                # if (i + 1) % 10 == 0:
+                #     print(f"已处理 {i + 1}/{len(images2)} 张图像")
+            except Exception as e:
+                print(f"处理图像 {img_path} 时出错: {e}")
+                features2.append(None)
+
+        # 移除处理失败的图像
+        valid_features1 = []
+        valid_images1 = []
+        for i, feature in enumerate(features1):
+            if feature is not None:
+                valid_features1.append(feature)
+                valid_images1.append(images1[i])
+
+        valid_features2 = []
+        valid_images2 = []
+        for i, feature in enumerate(features2):
+            if feature is not None:
+                valid_features2.append(feature)
+                valid_images2.append(images2[i])
+
+        # print(f"文件夹1中成功处理 {len(valid_features1)} 张图像")
+        # print(f"文件夹2中成功处理 {len(valid_features2)} 张图像")
+
+        if len(valid_features1) == 0 or len(valid_features2) == 0:
+            raise ValueError("没有成功处理任何图像")
+
+        # 计算相似度矩阵
+        print("正在计算相似度矩阵...")
+        similarity_matrix = cosine_similarity(valid_features1, valid_features2)
+
+        return similarity_matrix, valid_images1, valid_images2
+
+    def get_group_similarity_matrix(self, folder_path):
+        tracking_folder = os.sep.join([folder_path, 'tracking'])
+        standard_folder = os.sep.join([folder_path, 'standard_slim'])
+        for dir_name in os.listdir(tracking_folder):
+            tracking_dir = os.sep.join([tracking_folder, dir_name])
+            standard_dir = os.sep.join([standard_folder, dir_name])
+            similarity_matrix, valid_images1, valid_images2 = self.create_one_similarity_matrix(tracking_dir,
+                                                                                                standard_dir)
+            mean_similarity = np.mean(similarity_matrix)
+            std_similarity = np.std(similarity_matrix)
+            max_similarity = np.max(similarity_matrix)
+            min_similarity = np.min(similarity_matrix)
+            print(f"文件夹 {dir_name} 的相似度矩阵已计算完成 "
+                  f"均值：{mean_similarity}  标准差：{std_similarity}  最大值：{max_similarity} 最小值：{min_similarity}")
+            result = f"{os.path.basename(standard_folder)} {dir_name} {mean_similarity:.3f} {std_similarity:.3f} {max_similarity:.3f} {min_similarity:.3f}"
+            with open(self.conf['data']['result_txt'], 'a') as f:
+                f.write(result + '\n')
+
+
+def read_result_txt():
+    parts = []
+    value_num = 2
+    with open('../configs/pic_pic_similar.yml', 'r') as f:
+        conf = yaml.load(f, Loader=yaml.FullLoader)
+        f.close()
+    with open(conf['data']['result_txt'], 'r') as f:
+        lines = f.readlines()
+    for line in lines:
+        line = line.strip()
+        if line:
+            parts.append(line.split(' '))
+    parts = np.array(parts)
+    print(parts)
+    labels = ['Mean', 'Std', 'Max', 'Min']
+    while value_num < 6:
+        dicts = {}
+        for barcode, value in zip(parts[:, 1], parts[:, value_num]):
+            if barcode in dicts:
+                dicts[barcode].append(float(value))
+            else:
+                dicts[barcode] = [float(value)]
+        get_histogram(dicts, labels[value_num - 2])
+        value_num += 1
+    f.close()
+
+
+def get_histogram(data, label=None):
+    # 准备数据
+    categories = list(data.keys())
+    values1 = [data[cat][0] for cat in categories]  # 第一个值
+    values2 = [data[cat][1] for cat in categories]  # 第二个值
+
+    # 设置柱状图的位置
+    x = np.arange(len(categories))  # 标签位置
+    width = 0.35  # 柱状图的宽度
+
+    # 创建图形和轴
+    fig, ax = plt.subplots(figsize=(10, 6))
+
+    # 绘制柱状图
+    bars1 = ax.bar(x - width / 2, values1, width, label='standard', color='red', alpha=0.7)
+    bars2 = ax.bar(x + width / 2, values2, width, label='standard_slim', color='green', alpha=0.7)
+
+    # 在每个柱状图上显示数值
+    for bar in bars1:
+        height = bar.get_height()
+        ax.annotate(f'{height:.3f}',
+                    xy=(bar.get_x() + bar.get_width() / 2, height),
+                    xytext=(0, 3),  # 3点垂直偏移
+                    textcoords="offset points",
+                    ha='center', va='bottom',
+                    fontsize=12)
+
+    for bar in bars2:
+        height = bar.get_height()
+        ax.annotate(f'{height:.3f}',
+                    xy=(bar.get_x() + bar.get_width() / 2, height),
+                    xytext=(0, 3),  # 3点垂直偏移
+                    textcoords="offset points",
+                    ha='center', va='bottom',
+                    fontsize=12)
+
+    # 添加标签和标题
+    if label is None:
+        label = ''
+    ax.set_xlabel('barcode')
+    ax.set_ylabel('Values')
+    ax.set_title(label)
+    ax.set_xticks(x)
+    ax.set_xticklabels(categories)
+    ax.legend()
+
+    # 添加网格
+    ax.grid(True, alpha=0.3)
+
+    # 调整布局并显示
+    plt.tight_layout()
+    plt.show()
+
+
+if __name__ == '__main__':
+    # picTopic_matrix = picDirSimilarAnalysis()
+    # picTopic_matrix.get_group_similarity_matrix('/home/lc/data_center/image_analysis/pic_pic_similar_maxtrix')
+    read_result_txt()
diff --git a/tools/similar_analysis.py b/tools/similar_analysis.py
index 4547c14..565d534 100644
--- a/tools/similar_analysis.py
+++ b/tools/similar_analysis.py
@@ -10,7 +10,7 @@ import yaml
 import os
 
 
-class analysis:
+class SimilarAnalysis:
     def __init__(self):
         with open('../configs/similar_analysis.yml', 'r') as f:
             self.conf = yaml.load(f, Loader=yaml.FullLoader)
@@ -53,6 +53,7 @@ class analysis:
     def get_feature_map(self, all_imgs):
         feature_dicts = {}
         for img_pth in all_imgs:
+            print(f"Processing {img_pth}")
             feature_dict = self.get_feature(img_pth)
             feature_dicts = dict(ChainMap(feature_dict, feature_dicts))
         return feature_dicts
@@ -85,7 +86,7 @@ class analysis:
             feature_dict2 = feature_dicts[img_pth2]
             similarity = self.get_similarity(feature_dict1.cpu().numpy(),
                                              feature_dict2.cpu().numpy())
-            dir_name = img_pth1.split(os.sep)[-3]
+            dir_name = img_pth1.split('/')[-3]
             save_path = os.sep.join([self.conf['data']['image_joint_pth'], dir_name])
             if similarity > 0.7:
                 merge_imgs(img_pth1,
@@ -99,7 +100,7 @@ class analysis:
 
 
 if __name__ == '__main__':
-    ana = analysis()
+    ana = SimilarAnalysis()
     all_imgs = ana.create_total_feature()
     feature_dicts = ana.get_feature_map(all_imgs)
     all_compare_img = ana.get_image_map()