diff --git a/.gitignore b/.gitignore
index 6bc7e2b..f42bd93 100644
--- a/.gitignore
+++ b/.gitignore
@@ -96,3 +96,6 @@ fabric.properties
 /20240627Actual_deployed/qt_test/
 /20240627Actual_deployed/qt_test/PF/
 /20240627Actual_deployed/image/
+/20240627Actual_deployed/pfzc/
+/20240627Actual_deployed/pfzz/
+/20240529RGBtest3/原图108测试过程图/
diff --git a/.idea/.name b/.idea/.name
index 11a5d8e..4c5bcef 100644
--- a/.idea/.name
+++ b/.idea/.name
@@ -1 +1 @@
-main.py
\ No newline at end of file
+pic.py
\ No newline at end of file
diff --git a/20240529RGBtest3/classifer.py b/20240529RGBtest3/classifer.py
index 70abbdd..7a6974c 100644
--- a/20240529RGBtest3/classifer.py
+++ b/20240529RGBtest3/classifer.py
@@ -569,6 +569,7 @@ class Data_processing:
         org_defect = tomato.bitwise_and_rgb_with_binary(edge, new_bin_img)
         fore = tomato.bitwise_and_rgb_with_binary(img, mask)
         fore_g_r_t = tomato.threshold_segmentation(tomato.extract_g_r(fore), threshold=setting.threshold_fore_g_r_t)
+        filled_img_nogreen = cv2.bitwise_or(filled_img, fore_g_r_t)
         res = cv2.bitwise_or(new_bin_img, fore_g_r_t)
         nogreen = tomato.bitwise_and_rgb_with_binary(edge, res)
         # 统计白色像素点个数
@@ -583,7 +584,7 @@ class Data_processing:
         # 获取西红柿的尺寸信息
         long_axis, short_axis = self.analyze_ellipse(mask)
         # 获取缺陷信息
-        number_defects, total_pixels = self.analyze_defect(filled_img)
+        number_defects, total_pixels = self.analyze_defect(filled_img_nogreen)
         # print(filled_img.shape)
         # print(f'缺陷数量：{number_defects}; 缺陷总面积：{total_pixels}')
         # cv2.imwrite('filled_img.jpg',filled_img)
diff --git a/20240529RGBtest3/tg/pic.py b/20240529RGBtest3/tg/pic.py
new file mode 100644
index 0000000..ce09650
--- /dev/null
+++ b/20240529RGBtest3/tg/pic.py
@@ -0,0 +1,31 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2024/7/4 下午10:43
+# @Author  : TG
+# @File    : pic.py
+# @Software: PyCharm
+import os
+
+
+def rename_bmp_images(folder_path, new_name_format):
+    # 获取文件夹中的所有文件
+    files = os.listdir(folder_path)
+    # 过滤出BMP图像文件
+    bmp_files = [f for f in files if f.lower().endswith('.bmp')]
+
+    # 对每个BMP图像文件进行重命名
+    for index, bmp_file in enumerate(bmp_files):
+        old_path = os.path.join(folder_path, bmp_file)
+        new_name = new_name_format.format(index + 1)
+        new_path = os.path.join(folder_path, new_name)
+
+        # 重命名文件
+        os.rename(old_path, new_path)
+        print(f'Renamed {old_path} to {new_path}')
+
+
+# 指定文件夹路径和新的命名格式
+folder_path = r'D:\桌面文件\裂口数据集扩充（4月份数据补充）\scar'
+new_name_format = 'scar_{:03d}.bmp'  # 例如，image_001.bmp, image_002.bmp, ...
+
+# 调用函数进行重命名
+rename_bmp_images(folder_path, new_name_format)
diff --git a/20240529RGBtest3/tg/split_data.py b/20240529RGBtest3/tg/split_data.py
new file mode 100644
index 0000000..dbba42d
--- /dev/null
+++ b/20240529RGBtest3/tg/split_data.py
@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2024/7/11 下午3:08
+# @Author  : TG
+# @File    : split_data.py
+# @Software: PyCharm
+import os
+import random
+import shutil
+from pathlib import Path
+
+# 设置数据集目录
+dataset_path = Path(r'F:\0711_lk')
+images = list(dataset_path.glob('*.bmp'))  # 假设图像文件是jpg格式
+
+# 设置随机种子以保证结果可复现
+random.seed(42)
+
+# 打乱数据集
+random.shuffle(images)
+
+# 计算划分点
+num_images = len(images)
+train_split = int(num_images * 0.6)
+val_split = int(num_images * 0.8)
+
+# 分割数据集
+train_images = images[:train_split]
+val_images = images[train_split:val_split]
+test_images = images[val_split:]
+
+# 创建保存分割后数据集的文件夹
+(train_path, val_path, test_path) = [dataset_path.parent / x for x in ['train', 'val', 'test']]
+for path in [train_path, val_path, test_path]:
+    path.mkdir(exist_ok=True)
+
+# 定义一个函数来复制图像和标签文件
+def copy_files(files, dest_folder):
+    for file in files:
+        shutil.copy(file, dest_folder)
+        label_file = file.with_suffix('.txt')
+        if label_file.exists():
+            shutil.copy(label_file, dest_folder)
+
+# 复制文件到新的文件夹
+copy_files(train_images, train_path)
+copy_files(val_images, val_path)
+copy_files(test_images, test_path)
+
+print("数据集划分完成。训练集、验证集和测试集已经被保存到对应的文件夹。")
diff --git a/20240529RGBtest3/xs/image_preprocessing20240615.py b/20240529RGBtest3/xs/image_preprocessing20240615.py
index d3f96fc..6e70d26 100644
--- a/20240529RGBtest3/xs/image_preprocessing20240615.py
+++ b/20240529RGBtest3/xs/image_preprocessing20240615.py
@@ -211,7 +211,8 @@ def extract_max_connected_area(image_path, lower_hsv, upper_hsv):
 
 def main():
     parser = argparse.ArgumentParser(description='Process some integers.')
-    parser.add_argument('--dir_path', type=str, default=r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\qt_test\TO\TOzhengchangyoudeng',
+    parser.add_argument('--dir_path', type=str,
+                        default=r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\20270708\T2\middle',
                         help='the directory path of images')
     parser.add_argument('--threshold_s_l', type=int, default=180,
                         help='the threshold for s_l')
@@ -247,8 +248,8 @@ def main():
 
 
             fore_g_r_t = threshold_segmentation(extract_g_r(fore), 20)
-            filled_img1 = cv2.bitwise_or(filled_img, fore_g_r_t)
-            cv2.imshow('filled_img1', filled_img1)
+            filled_img_nogreen = cv2.bitwise_or(filled_img, fore_g_r_t)
+            cv2.imshow('filled_img_nogreen', filled_img_nogreen)
             #统计白色像素点个数
             print(np.sum(fore_g_r_t == 255))
             print(np.sum(mask == 255))
diff --git a/20240627Actual_deployed/classifer.py b/20240627Actual_deployed/classifer.py
index e8754d4..2bceeb1 100644
--- a/20240627Actual_deployed/classifer.py
+++ b/20240627Actual_deployed/classifer.py
@@ -5,6 +5,8 @@
 # @Software: PyCharm
 
 import os
+import time
+
 import cv2
 import pipe_utils
 import joblib
@@ -14,7 +16,7 @@ import numpy as np
 from pipe_utils import Pipe
 from config import Config as setting
 from sklearn.ensemble import RandomForestRegressor
-from detector import run
+from detector import Detector_to
 
 import torch
 import torch.nn as nn
@@ -389,6 +391,174 @@ class Spec_predict(object):
         data_y = self.model.predict(data_x)
         return data_y[0]
 
+
+class BasicBlock(nn.Module):
+    '''
+    BasicBlock for ResNet18 and ResNet34
+
+    '''
+    expansion = 1
+
+    def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):
+        super(BasicBlock, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
+                               kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_channel)
+        self.relu = nn.ReLU()
+        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
+                               kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_channel)
+        self.downsample = downsample
+
+    def forward(self, x):
+        identity = x
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+class ResNet(nn.Module):
+    '''
+    ResNet18 and ResNet34
+    '''
+    def __init__(self,
+                 block,
+                 blocks_num,
+                 num_classes=1000,
+                 include_top=True,
+                 groups=1,
+                 width_per_group=64):
+        super(ResNet, self).__init__()
+        self.include_top = include_top
+        self.in_channel = 64
+
+        self.groups = groups
+        self.width_per_group = width_per_group
+
+        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
+                               padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(self.in_channel)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, blocks_num[0])
+        self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)
+        if self.include_top:
+            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (1, 1)
+            self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+
+    def _make_layer(self, block, channel, block_num, stride=1):
+        downsample = None
+        if stride != 1 or self.in_channel != channel * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(channel * block.expansion))
+
+        layers = []
+        layers.append(block(self.in_channel,
+                            channel,
+                            downsample=downsample,
+                            stride=stride,
+                            groups=self.groups,
+                            width_per_group=self.width_per_group))
+        self.in_channel = channel * block.expansion
+
+        for _ in range(1, block_num):
+            layers.append(block(self.in_channel,
+                                channel,
+                                groups=self.groups,
+                                width_per_group=self.width_per_group))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        if self.include_top:
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+            x = self.fc(x)
+
+        return x
+
+def resnet18(num_classes=1000, include_top=True):
+    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, include_top=include_top)
+
+def resnet34(num_classes=1000, include_top=True):
+    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)
+
+#百香果褶皱判别模型
+class ImageClassifier:
+    '''
+    图像分类器，用于加载预训练的 ResNet 模型并进行图像分类。
+    '''
+    def __init__(self, model_path, class_indices_path, device=None):
+        if device is None:
+            self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        else:
+            self.device = device
+
+        # 加载类别索引
+        assert os.path.exists(class_indices_path), f"File: '{class_indices_path}' does not exist."
+        with open(class_indices_path, "r") as json_file:
+            self.class_indict = json.load(json_file)
+
+        # 创建模型并加载权重
+        self.model = resnet18(num_classes=len(self.class_indict)).to(self.device)
+        assert os. path.exists(model_path), f"File: '{model_path}' does not exist."
+        self.model.load_state_dict(torch.load(model_path, map_location=self.device))
+        self.model.eval()
+
+        # 设置图像转换
+        self.transform = transforms.Compose([
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+        ])
+
+    def predict(self, image_np):
+        '''
+        对图像进行分类预测。
+        :param image_np:
+        :return:
+        '''
+        # 将numpy数组转换为图像
+        image = Image.fromarray(image_np.astype('uint8'), 'RGB')
+        image = self.transform(image).unsqueeze(0).to(self.device)
+
+        with torch.no_grad():
+            output = self.model(image).cpu()
+            predict = torch.softmax(output, dim=1)
+            predict_cla = torch.argmax(predict, dim=1).numpy()
+
+        # return self.class_indict[str(predict_cla[0])]
+        return predict_cla[0]
+
+
 #数据处理模型
 class Data_processing:
     def __init__(self, area_threshold=20000, density = 0.652228972, area_ratio=0.00021973702422145334):
@@ -534,7 +704,7 @@ class Data_processing:
         # 获取西红柿的尺寸信息
         long_axis, short_axis = self.analyze_ellipse(mask)
         # 获取缺陷信息
-        number_defects, total_pixels = self.analyze_defect(filled_img_nogreen)
+        _, total_pixels = self.analyze_defect(filled_img_nogreen)
         # print(filled_img.shape)
         # print(f'缺陷数量：{number_defects}; 缺陷总面积：{total_pixels}')
         # cv2.imwrite('filled_img.jpg',filled_img)
@@ -552,7 +722,7 @@ class Data_processing:
         #     total_pixels = 0
         #     rp = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
         #                               dtype=np.uint8), cv2.COLOR_BGR2RGB)
-        return diameter, green_percentage, number_defects, total_pixels, rp
+        return diameter, green_percentage, total_pixels, rp
 
     def analyze_passion_fruit(self, img):
         if img is None:
@@ -586,7 +756,7 @@ class Data_processing:
         if weight > 255:
             weight = random.randint(30, 65)
 
-        number_defects, total_pixels = self.analyze_defect(filled_img)
+        _, total_pixels = self.analyze_defect(filled_img)
         # img1 = img.copy()
         edge = pf.draw_contours_on_image(img, contour_mask)
         org_defect = pf.bitwise_and_rgb_with_binary(edge, max_mask)
@@ -594,7 +764,7 @@ class Data_processing:
         #直径单位为cm
         # diameter = (long_axis + short_axis) * setting.pixel_length_ratio / 2
         diameter = long_axis * setting.pixel_length_ratio
-        print(f'长径：{long_axis}像素；短径：{short_axis}像素；直径：{diameter}cm')
+        # print(f'长径：{long_axis}像素；短径：{short_axis}像素；直径：{diameter}cm')
         # if diameter < 2.5:
         #     diameter = 0
         #     green_percentage = 0
@@ -603,9 +773,10 @@ class Data_processing:
         #     total_pixels = 0
         #     rp = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
         #                               dtype=np.uint8), cv2.COLOR_BGR2RGB)
-        return diameter, green_percentage, weight, number_defects, total_pixels, rp
+        return diameter, green_percentage, weight, total_pixels, rp
 
-    def process_data(seif, cmd: str, images: list, spec: any, pipe: Pipe, detector: Spec_predict) -> bool:
+    def process_data(seif, cmd: str, images: list, spec: any, pipe: Pipe,
+                     detector: Spec_predict, to: Detector_to, impf: ImageClassifier) -> bool:
         """
         处理指令
 
@@ -624,8 +795,11 @@ class Data_processing:
         for i, img in enumerate(images):
             if cmd == 'TO':
                 # 番茄
-                diameter, green_percentage, number_defects, total_pixels, rp = seif.analyze_tomato(img)
-                posun_num = run(img)
+                diameter, green_percentage, total_pixels, rp = seif.analyze_tomato(img)
+                ss = time.time()
+                posun_num = to.run(img)
+                es = time.time()
+                print(f'破损判断时间：{es-ss}')
                 print(f'破损判断：{posun_num}')
                 if i <= 2:
                     diameter_axis_list.append(diameter)
@@ -635,19 +809,20 @@ class Data_processing:
                 if i == 1:
                     rp_result = rp
                     gp = round(green_percentage, 2)
-                max_defect_num = sum(ps)
+                ps_sum = sum(ps)
 
             elif cmd == 'PF':
                 # 百香果
-                diameter, green_percentage, weight, number_defects, total_pixels, rp = seif.analyze_passion_fruit(img)
+                diameter, green_percentage, weight, total_pixels, rp = seif.analyze_passion_fruit(img)
                 if i <= 2:
                     diameter_axis_list.append(diameter)
-                    max_defect_num = max(max_defect_num, number_defects)
+                    # max_defect_num = max(max_defect_num, number_defects)
                     max_total_defect_area = max(max_total_defect_area, total_pixels)
                 if i == 1:
                     rp_result = rp
                     weight = weight
                     gp = round(green_percentage, 2)
+                    zz_sum = int(impf.predict(img))
 
             else:
                 logging.error(f'错误指令，指令为{cmd}')
@@ -662,14 +837,14 @@ class Data_processing:
             if diameter < 2.5:
                 diameter = 0
                 gp = 0
-                max_defect_num = 0
+                ps_sum = 0
                 max_total_defect_area = 0
                 rp_result = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
                                           dtype=np.uint8), cv2.COLOR_BGR2RGB)
             print(f'预测的brix值为：{brix}; 预测的直径为：{diameter}; 预测的重量为：{weight}; 预测的绿色比例为：{gp};'
-                  f' 预测的缺陷数量为：{max_defect_num}; 预测的总缺陷面积为：{max_total_defect_area};')
+                  f' 破损判别结果为：{ps_sum}; 预测的总缺陷面积为：{max_total_defect_area};')
             response = pipe.send_data(cmd=cmd, brix=brix, diameter=diameter, green_percentage=gp, weight=weight,
-                                      defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result)
+                                      defect_num=ps_sum, total_defect_area=max_total_defect_area, rp=rp_result)
             return response
         elif cmd == 'PF':
             brix = detector.predict(spec)
@@ -678,179 +853,14 @@ class Data_processing:
                 diameter = 0
                 gp = 0
                 weight = 0
-                max_defect_num = 0
+                zz_sum = 0
                 max_total_defect_area = 0
                 rp_result = cv2.cvtColor(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands),
                                           dtype=np.uint8), cv2.COLOR_BGR2RGB)
             print(f'预测的brix值为：{brix}; 预测的直径为：{diameter}; 预测的重量为：{weight}; 预测的绿色比例为：{green_percentage};'
-                  f' 预测的缺陷数量为：{max_defect_num}; 预测的总缺陷面积为：{max_total_defect_area};')
+                  f' 褶皱判别结果为：{zz_sum}; 预测的总缺陷面积为：{max_total_defect_area};')
             response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=gp, diameter=diameter, weight=weight,
-                                      defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result)
+                                      defect_num=zz_sum, total_defect_area=max_total_defect_area, rp=rp_result)
             return response
 
 
-class BasicBlock(nn.Module):
-    '''
-    BasicBlock for ResNet18 and ResNet34
-
-    '''
-    expansion = 1
-
-    def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):
-        super(BasicBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
-                               kernel_size=3, stride=stride, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(out_channel)
-        self.relu = nn.ReLU()
-        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
-                               kernel_size=3, stride=1, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(out_channel)
-        self.downsample = downsample
-
-    def forward(self, x):
-        identity = x
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-
-class ResNet(nn.Module):
-    '''
-    ResNet18 and ResNet34
-    '''
-    def __init__(self,
-                 block,
-                 blocks_num,
-                 num_classes=1000,
-                 include_top=True,
-                 groups=1,
-                 width_per_group=64):
-        super(ResNet, self).__init__()
-        self.include_top = include_top
-        self.in_channel = 64
-
-        self.groups = groups
-        self.width_per_group = width_per_group
-
-        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
-                               padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(self.in_channel)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, blocks_num[0])
-        self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)
-        if self.include_top:
-            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (1, 1)
-            self.fc = nn.Linear(512 * block.expansion, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-
-    def _make_layer(self, block, channel, block_num, stride=1):
-        downsample = None
-        if stride != 1 or self.in_channel != channel * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(channel * block.expansion))
-
-        layers = []
-        layers.append(block(self.in_channel,
-                            channel,
-                            downsample=downsample,
-                            stride=stride,
-                            groups=self.groups,
-                            width_per_group=self.width_per_group))
-        self.in_channel = channel * block.expansion
-
-        for _ in range(1, block_num):
-            layers.append(block(self.in_channel,
-                                channel,
-                                groups=self.groups,
-                                width_per_group=self.width_per_group))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        if self.include_top:
-            x = self.avgpool(x)
-            x = torch.flatten(x, 1)
-            x = self.fc(x)
-
-        return x
-
-def resnet18(num_classes=1000, include_top=True):
-    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes, include_top=include_top)
-
-def resnet34(num_classes=1000, include_top=True):
-    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)
-
-#图像有无果判别模型
-class ImageClassifier:
-    '''
-    图像分类器，用于加载预训练的 ResNet 模型并进行图像分类。
-    '''
-    def __init__(self, model_path, class_indices_path, device=None):
-        if device is None:
-            self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        else:
-            self.device = device
-
-        # 加载类别索引
-        assert os.path.exists(class_indices_path), f"File: '{class_indices_path}' does not exist."
-        with open(class_indices_path, "r") as json_file:
-            self.class_indict = json.load(json_file)
-
-        # 创建模型并加载权重
-        self.model = resnet34(num_classes=len(self.class_indict)).to(self.device)
-        assert os. path.exists(model_path), f"File: '{model_path}' does not exist."
-        self.model.load_state_dict(torch.load(model_path, map_location=self.device))
-        self.model.eval()
-
-        # 设置图像转换
-        self.transform = transforms.Compose([
-            transforms.Resize(256),
-            transforms.CenterCrop(224),
-            transforms.ToTensor(),
-            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-        ])
-
-    def predict(self, image_np):
-        '''
-        对图像进行分类预测。
-        :param image_np:
-        :return:
-        '''
-        # 将numpy数组转换为图像
-        image = Image.fromarray(image_np.astype('uint8'), 'RGB')
-        image = self.transform(image).unsqueeze(0).to(self.device)
-
-        with torch.no_grad():
-            output = self.model(image).cpu()
-            predict = torch.softmax(output, dim=1)
-            predict_cla = torch.argmax(predict, dim=1).numpy()
-
-        # return self.class_indict[str(predict_cla[0])]
-        return predict_cla[0]
\ No newline at end of file
diff --git a/20240627Actual_deployed/config.py b/20240627Actual_deployed/config.py
index 433dfb0..294e90b 100644
--- a/20240627Actual_deployed/config.py
+++ b/20240627Actual_deployed/config.py
@@ -17,9 +17,11 @@ class Config:
     #糖度模型
     brix_model_path = ROOT_DIR / 'models' / 'passion_fruit.joblib'
     #图像分类模型
-    imgclassifier_model_path = ROOT_DIR / 'models' / 'resnet18pf.pth'
+    imgclassifier_model_path = ROOT_DIR / 'models' / 'resnet18pf20240705.pth'
     imgclassifier_class_indices_path = ROOT_DIR / 'models' / 'class_indices.json'
 
+    #番茄破损模型
+    tomato_model_path = ROOT_DIR / 'weights' / 'best.pt'
 
     #classifer.py参数
     #tomato
diff --git a/20240627Actual_deployed/detector.py b/20240627Actual_deployed/detector.py
index eff47a0..0a65ed6 100644
--- a/20240627Actual_deployed/detector.py
+++ b/20240627Actual_deployed/detector.py
@@ -12,43 +12,38 @@ from utils.general import (
 )
 from utils.torch_utils import select_device, smart_inference_mode
 from utils.dataloaders import letterbox
+from config import Config as setting
 
-@smart_inference_mode()
-def run(
-    img,  # numpy array
-    weights=Path(r'D:\porject\PY\20240627Actual_deployed\weights\best.pt'),  # model path
-    imgsz=(640, 640),  # inference size (height, width)
-    conf_thres=0.25,  # confidence threshold
-    iou_thres=0.45,  # NMS IOU threshold
-    max_det=1000,  # maximum detections per image
-    device="0",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-    half=False,  # use FP16 half-precision inference
-):
-    """Runs YOLOv5 detection inference on a numpy array and returns the number of detections."""
-    # Load model
-    device = select_device(device)
-    model = DetectMultiBackend(weights, device=device, fp16=half)
-    stride = model.stride
-    imgsz = check_img_size(imgsz, s=stride)  # check image size
+class Detector_to:
+    def __init__(self, weights=Path(setting.tomato_model_path),
+                 device="", half=False):
+        self.device = select_device(device)
+        self.model = DetectMultiBackend(weights, device=self.device, fp16=half)
+        self.stride = int(self.model.stride)  # get stride from the model
+        self.fp16 = half
 
-    # Convert numpy array to tensor
-    img = letterbox(img, imgsz, stride=stride)[0]
-    img = img.transpose((2,0,1))
-    img = np.ascontiguousarray(img)
-    im = torch.from_numpy(img).to(model.device)
-    im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
-    im /= 255  # 0 - 255 to 0.0 - 1.0
-    if len(im.shape) == 3:
-        im = im[None]  # expand for batch dim
+    def run(self, img, imgsz=(640, 640), conf_thres=0.25, iou_thres=0.45, max_det=1000):
+        """Runs YOLOv5 detection inference on a numpy array and returns the number of detections."""
+        imgsz = check_img_size(imgsz, s=self.stride)  # check image size
 
-    # Inference
-    pred = model(im)
+        # Convert numpy array to tensor
+        img = letterbox(img, imgsz, stride=self.stride)[0]  # resize image to model expected size
+        img = img.transpose((2, 0, 1)) # HWC to CHW
+        img = np.ascontiguousarray(img) # make contiguous
+        im = torch.from_numpy(img).to(self.model.device)
+        im = im.half() if self.fp16 else im.float()  # uint8 to fp16/32
+        im /= 255  # 0 - 255 to 0.0 - 1.0
+        if len(im.shape) == 3:
+            im = im[None]  # expand for batch dim
 
-    # NMS
-    pred = non_max_suppression(pred, conf_thres, iou_thres, max_det=max_det)
+        # Inference
+        pred = self.model(im)
 
-    # Count detections
-    num_detections = sum([len(d) for d in pred if d is not None])
+        # NMS
+        pred = non_max_suppression(pred, conf_thres, iou_thres, max_det=max_det)
 
-    return num_detections
+        # Count detections
+        num_detections = sum([len(d) for d in pred if d is not None])
+
+        return num_detections
 
diff --git a/20240627Actual_deployed/main.py b/20240627Actual_deployed/main.py
index 787d77b..712d320 100644
--- a/20240627Actual_deployed/main.py
+++ b/20240627Actual_deployed/main.py
@@ -16,7 +16,7 @@ from pipe_utils import Pipe
 import numpy as np
 from config import Config
 import time
-from detector import run
+from detector import Detector_to
 # from clspredict import runcls
 
 def main(is_debug=False):
@@ -32,13 +32,24 @@ def main(is_debug=False):
     detector = Spec_predict()
     detector.load(path=setting.brix_model_path)
     dp = Data_processing()
+    to = Detector_to()
+    #impf为百香果褶皱判别模型，0为褶皱，1为正常
     impf = ImageClassifier(model_path=setting.imgclassifier_model_path,
-                                 class_indices_path=setting.imgclassifier_class_indices_path)
+                            class_indices_path=setting.imgclassifier_class_indices_path)
     print('系统初始化中...')
     #模型预热
+    hh = time.time()
     #与qt_test测试时需要注释掉预热，模型接收尺寸为（25，30，13），qt_test发送的数据为（30，30，224），需要对数据进行切片（classifer.py第379行）
     _ = detector.predict(np.ones((setting.n_spec_rows, setting.n_spec_cols, setting.n_spec_bands), dtype=np.uint16))
+    hk = time.time()
+    print(f'brix模型预热时间：{hk-hh}')
+    #run函数为番茄破损判别模型，返回0表示无破损，1、2、3即表示1、2、3处破损
+    _ = to.run(np.ones((800, 613, 3), dtype=np.uint8))
+    hi = time.time()
+    print(f'run模型预热时间：{hi-hk}')
     _ = impf.predict(np.ones((800, 613, 3), dtype=np.uint8))
+    gg = time.time()
+    print(f'impf模型预热时间：{gg-hi}')
 
     time.sleep(1)
     print('系统初始化完成')
@@ -59,20 +70,20 @@ def main(is_debug=False):
     #主循环
     q = 1
     while True:
-        # st = time.time()
+        st = time.time()
         #RGB图像部分
         images = []
         cmd = None
         for i in range(3):
-            # start_time = time.time()
+            start_time = time.time()
             data = pipe.receive_rgb_data(rgb_receive)
-            # end_time = time.time()
-            # print(f'接收第{q}个果子第{i+1}张图数据时间：{end_time-start_time}')
+            end_time = time.time()
+            print(f'接收第{q}个果子第{i+1}张图数据时间：{end_time-start_time}')
             print(f'接收第{q}个果子第{i+1}张图数据长度：{len(data)}')
             cmd, img = pipe.parse_img(data)
-            # end_time1 = time.time()
+            end_time1 = time.time()
             img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-            # print(f'解码第{q}个果子第{i + 1}张图数据时间：{end_time1 - end_time}')
+            print(f'解码第{q}个果子第{i + 1}张图数据时间：{end_time1 - end_time}')
             print(f'接收第{q}个果子第{i+1}张图：{img.shape}')
             # cv2.imwrite(f'./{q}_{i}.png', img)
             img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
@@ -94,13 +105,21 @@ def main(is_debug=False):
         #Spec数据部分
         spec = None
         if cmd == 'PF':
+            sp = time.time()
             spec_data = pipe.receive_spec_data(spec_receive)
+            ep = time.time()
+            print(f'接收到第{q}个果子的光谱数据时间：{ep-sp}')
             print(f'接收到第{q}个果子的光谱数据长度：{len(spec_data)}')
             _, spec = pipe.parse_spec(spec_data)
+            ep1 = time.time()
+            print(f'解码第{q}个果子的光谱数据时间：{ep1-ep}')
             print(f'接收到第{q}个果子的光谱数据尺寸：{spec.shape}')
         #数据处理部分
         if images:  # 确保images不为空
-            response = dp.process_data(cmd, images, spec, pipe, detector)
+            sg = time.time()
+            response = dp.process_data(cmd, images, spec, pipe, detector, to, impf)
+            eg = time.time()
+            print(f'第{q}个果子数据处理时间：{eg-sg}')
             if response:
                 logging.info(f'处理成功，响应为: {response}')
             else:
@@ -109,8 +128,8 @@ def main(is_debug=False):
             logging.error("没有有效的图像进行处理")
         print(f'第{q}个果子处理完成')
         q += 1
-        # end_time2 = time.time()
-        # print(f'第{q}个果子全流程时间：{end_time2-st}')
+        end_time2 = time.time()
+        print(f'第{q}个果子全流程时间：{end_time2-st}')
 
 
 if __name__ == '__main__':
diff --git a/20240627Actual_deployed/models/class_indices.json b/20240627Actual_deployed/models/class_indices.json
index 9ddf683..35e1dd7 100644
--- a/20240627Actual_deployed/models/class_indices.json
+++ b/20240627Actual_deployed/models/class_indices.json
@@ -1,4 +1,4 @@
 {
     "0": "De",
     "1": "N"
-}
\ No newline at end of file
+}
diff --git a/20240627Actual_deployed/models/yolo.py b/20240627Actual_deployed/models/yolo.py
index d89c5da..de9c301 100644
--- a/20240627Actual_deployed/models/yolo.py
+++ b/20240627Actual_deployed/models/yolo.py
@@ -186,7 +186,7 @@ class BaseModel(nn.Module):
 
     def fuse(self):
         """Fuses Conv2d() and BatchNorm2d() layers in the model to improve inference speed."""
-        LOGGER.info("Fusing layers... ")
+        # LOGGER.info("Fusing layers... ")
         for m in self.model.modules():
             if isinstance(m, (Conv, DWConv)) and hasattr(m, "bn"):
                 m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
diff --git a/20240627Actual_deployed/qt_test.py b/20240627Actual_deployed/qt_test.py
index 6b8208a..86b5edc 100644
--- a/20240627Actual_deployed/qt_test.py
+++ b/20240627Actual_deployed/qt_test.py
@@ -69,11 +69,11 @@ class MainWindow(QMainWindow):
         :param image_dir: bmp和raw文件所在文件夹
         :return:
         '''
-        rgb_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(('.bmp'))][:5]
-        # spec_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.raw')][:1]
+        rgb_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(('.bmp'))][:3]
+        spec_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.raw')][:1]
 
         self.send_YR()
-        for _ in range(5):
+        for _ in range(100):
             for image_path in rgb_files:
                 img = cv2.imread(image_path, cv2.IMREAD_COLOR)
                 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
@@ -96,29 +96,29 @@ class MainWindow(QMainWindow):
                 except Exception as e:
                     print(f"数据发送失败. 错误原因: {e}")
 
-            # if spec_files:
-            #     spec_file = spec_files[0]
-            #     with open(spec_file, 'rb') as f:
-            #         spec_data = f.read()
-            #
-            #     try:
-            #         # win32file.WriteFile(self.spec_send, len(spec_data).to_bytes(4, byteorder='big'))
-            #         # print(f"发送的光谱数据长度: {len(spec_data)}")
-            #         heigth = 30
-            #         weight = 30
-            #         bands = 224
-            #         heigth = heigth.to_bytes(2, byteorder='big')
-            #         weight = weight.to_bytes(2, byteorder='big')
-            #         bands = bands.to_bytes(2, byteorder='big')
-            #         length = (len(spec_data)+8).to_bytes(4, byteorder='big')
-            #         # cmd = 'TO'：测试番茄数据；cmd = 'PF'：测试百香果数据
-            #         cmd = 'TO'
-            #         data_send = length + cmd.upper().encode('ascii') + heigth + weight + bands + spec_data
-            #         win32file.WriteFile(self.spec_send, data_send)
-            #         print(f'发送的光谱数据长度: {len(data_send)}')
-            #         print(f'spec长度: {len(spec_data)}')
-            #     except Exception as e:
-            #         print(f"数据发送失败. 错误原因: {e}")
+            if spec_files:
+                spec_file = spec_files[0]
+                with open(spec_file, 'rb') as f:
+                    spec_data = f.read()
+
+                try:
+                    # win32file.WriteFile(self.spec_send, len(spec_data).to_bytes(4, byteorder='big'))
+                    # print(f"发送的光谱数据长度: {len(spec_data)}")
+                    heigth = 30
+                    weight = 30
+                    bands = 224
+                    heigth = heigth.to_bytes(2, byteorder='big')
+                    weight = weight.to_bytes(2, byteorder='big')
+                    bands = bands.to_bytes(2, byteorder='big')
+                    length = (len(spec_data)+8).to_bytes(4, byteorder='big')
+                    # cmd = 'TO'：测试番茄数据；cmd = 'PF'：测试百香果数据
+                    cmd = 'TO'
+                    data_send = length + cmd.upper().encode('ascii') + heigth + weight + bands + spec_data
+                    win32file.WriteFile(self.spec_send, data_send)
+                    print(f'发送的光谱数据长度: {len(data_send)}')
+                    print(f'spec长度: {len(spec_data)}')
+                except Exception as e:
+                    print(f"数据发送失败. 错误原因: {e}")
 
             self.receive_result()
 
diff --git a/20240627Actual_deployed/tomato-passion_fruit.log b/20240627Actual_deployed/tomato-passion_fruit.log
index 31bd2bc..3935e3e 100644
--- a/20240627Actual_deployed/tomato-passion_fruit.log
+++ b/20240627Actual_deployed/tomato-passion_fruit.log
@@ -86,3 +86,81 @@
 2024-07-01 17:51:45,559 utils.py[line:230] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
 2024-07-01 17:51:45,559 main.py[line:101] - ERROR - 处理失败
 2024-07-01 17:51:45,560 utils.py[line:84] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-04 14:08:48,265 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 14:08:48,265 main.py[line:108] - ERROR - 处理失败
+2024-07-04 14:08:49,453 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 14:08:49,453 main.py[line:108] - ERROR - 处理失败
+2024-07-04 14:08:50,744 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 14:08:50,744 main.py[line:108] - ERROR - 处理失败
+2024-07-04 14:08:50,744 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-04 14:27:19,068 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-04 16:41:47,465 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-04 16:45:24,931 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-04 16:49:02,620 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-04 16:51:48,679 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-04 16:53:58,252 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-04 17:00:49,493 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-04 17:08:10,792 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:08:10,793 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:08:12,041 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:08:12,042 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:08:13,179 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:08:13,179 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:08:14,424 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:08:14,425 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:08:15,606 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:08:15,606 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:08:16,934 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:08:16,934 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:08:18,134 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:08:18,134 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:08:19,383 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:08:19,383 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:08:20,639 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:08:20,639 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:08:21,809 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:08:21,809 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:08:21,810 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-04 17:18:14,994 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-04 17:20:39,388 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:39,388 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:40,562 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:40,563 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:41,738 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:41,738 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:43,017 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:43,017 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:44,275 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:44,275 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:45,548 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:45,548 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:46,743 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:46,743 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:47,987 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:47,987 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:49,183 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:49,183 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:50,393 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:50,394 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:51,724 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:51,724 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:52,901 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:52,901 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:54,161 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:54,161 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:55,332 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:55,333 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:56,561 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-04 17:20:56,561 main.py[line:110] - ERROR - 处理失败
+2024-07-04 17:20:56,561 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-05 14:23:17,274 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-05 14:52:48,462 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-05 14:59:25,432 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-05 14:59:25,433 main.py[line:110] - ERROR - 处理失败
+2024-07-05 14:59:25,433 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-05 15:21:42,677 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-07 16:11:16,751 pipe_utils.py[line:231] - ERROR - 发送指令失败，错误类型：(232, 'WriteFile', '管道正在被关闭。')
+2024-07-07 16:11:16,752 main.py[line:125] - ERROR - 处理失败
+2024-07-07 16:11:16,752 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-07 16:25:38,533 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
+2024-07-07 16:34:38,764 pipe_utils.py[line:85] - ERROR - 数据接收失败，错误原因: (109, 'ReadFile', '管道已结束。')
diff --git a/20240627Actual_deployed/totest.py b/20240627Actual_deployed/totest.py
new file mode 100644
index 0000000..fba8382
--- /dev/null
+++ b/20240627Actual_deployed/totest.py
@@ -0,0 +1,45 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2024/7/7 下午4:33
+# @Author  : TG
+# @File    : totest.py
+# @Software: PyCharm
+import time
+
+from detector import Detector_to
+import numpy as np
+
+import os
+from PIL import Image
+
+
+
+
+def main():
+    s = []
+    path = r'D:\project\20240627Actual_deployed\to'
+    to = Detector_to()
+    i = 1
+    for filename in os.listdir(path):
+        if filename.endswith('.bmp'):
+            img_path = os.path.join(path, filename)
+            image = Image.open(img_path)
+            img = np.array(image)
+            t = time.time()
+            result = to.run(img)
+            e = time.time()
+            print(f'第{i}张图时间：{e-t}')
+            print(f'图片名：{filename},结果：{result}')
+            s.append(result)
+            i += 1
+    print(f'长度：{sum(s)}')
+
+
+#0为褶皱，1为正常
+
+
+if __name__ == '__main__':
+    '''
+    python与qt采用windows下的命名管道进行通信，数据流按照约定的通信协议进行
+    数据处理逻辑为：连续接收5张RGB图，然后根据解析出的指令部分决定是否接收一张光谱图，然后进行处理，最后将处理得到的指标结果进行编码回传
+    '''
+    main()
\ No newline at end of file
diff --git a/20240627Actual_deployed/utils/torch_utils.py b/20240627Actual_deployed/utils/torch_utils.py
index d15f1f7..242f638 100644
--- a/20240627Actual_deployed/utils/torch_utils.py
+++ b/20240627Actual_deployed/utils/torch_utils.py
@@ -144,7 +144,7 @@ def select_device(device="", batch_size=0, newline=True):
 
     if not newline:
         s = s.rstrip()
-    LOGGER.info(s)
+    # LOGGER.info(s)
     return torch.device(arg)
 
 
@@ -322,7 +322,7 @@ def model_info(model, verbose=False, imgsz=640):
         fs = ""
 
     name = Path(model.yaml_file).stem.replace("yolov5", "YOLOv5") if hasattr(model, "yaml_file") else "Model"
-    LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
+    # LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
 
 
 def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
diff --git a/classfier.py b/classfier.py
deleted file mode 100644
index 2d476fc..0000000
--- a/classfier.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import logging
-import sys
-from typing import Optional
-
-import numpy as np
-import cv2
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.cluster import KMeans
-from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score, confusion_matrix
-from scipy import ndimage
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.feature_selection import SelectKBest
-from sklearn.neighbors import KNeighborsClassifier
-from scipy.stats import binom
-import matplotlib.pyplot as plt
-import time
-import pickle
-import os
-import utils
-from root_dir import ROOT_DIR
-
-
-class Astragalin(object):
-    def __init__(self, load_from=None, debug_mode=False, class_weight=None):
-        if load_from is None:
-            self.model = DecisionTreeClassifier(random_state=65, class_weight=class_weight)
-        else:
-            self.load(load_from)
-        self.log = utils.Logger(is_to_file=debug_mode)
-        self.debug_mode = debug_mode
-
-    def load(self, path=None):
-        if path is None:
-            path = os.path.join(ROOT_DIR, 'models')
-            model_files = os.listdir(path)
-            if len(model_files) == 0:
-                self.log.log("No model found!")
-                return 1
-            self.log.log("./ Models Found:")
-            _ = [self.log.log("├--" + str(model_file)) for model_file in model_files]
-            file_times = [model_file[6:-2] for model_file in model_files]
-            latest_model = model_files[int(np.argmax(file_times))]
-            self.log.log("└--Using the latest model: " + str(latest_model))
-            path = os.path.join(ROOT_DIR, "models", str(latest_model))
-        if not os.path.isabs(path):
-            logging.warning('给的是相对路径')
-            return -1
-        if not os.path.exists(path):
-            logging.warning('文件不存在')
-            return -1
-        with open(path, 'rb') as f:
-            model_dic = pickle.load(f)
-        self.model = model_dic['model']
-        return 0
-
-    def fit(self, data_x, data_y):
-        x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.3, random_state=65)
-        self.model.fit(x_train, y_train)
-        y_pred = self.model.predict(x_test)
-        print(confusion_matrix(y_test, y_pred))
-
-        pre_score = accuracy_score(y_test, y_pred)
-        self.log.log("Test accuracy is:" + str(pre_score * 100) + "%.")
-        y_pred = self.model.predict(x_train)
-
-        pre_score = accuracy_score(y_train, y_pred)
-        self.log.log("Train accuracy is:" + str(pre_score * 100) + "%.")
-        y_pred = self.model.predict(data_x)
-
-        pre_score = accuracy_score(data_y, y_pred)
-        self.log.log("Total accuracy is:" + str(pre_score * 100) + "%.")
-
-        return int(pre_score * 100)
-
-    def fit_value(self, file_name=None, data_path='data/1.txt', select_bands=[91, 92, 93, 94, 95, 96, 97, 98, 99, 100]):
-        data_x, data_y = self.data_construction(data_path, select_bands)
-        score = self.fit(data_x, data_y)
-        print('score:', score)
-        model_name = self.save(file_name=file_name)
-        return score, model_name
-
-    def save(self, file_name):
-        # 保存模型
-        if file_name is None:
-            file_name = "model_" + time.strftime("%Y-%m-%d_%H-%M") + ".p"
-        file_name = os.path.join(ROOT_DIR, "models", file_name)
-        model_dic = {'model': self.model}
-        with open(file_name, 'wb') as f:
-            pickle.dump(model_dic, f)
-        self.log.log("Model saved to '" + str(file_name) + "'.")
-        return file_name
-
-    # def data_construction(self, data_path, select_bands):
-    #     data = utils.read_envi_ascii(data_path)
-    #     beijing = data['beijing'][:, select_bands]
-    #     zazhi1 = data['zazhi1'][:, select_bands]
-    #     # zazhi2 = data['zazhi2'][:, select_bands]
-    #     huangqi = data['huangqi'][:, select_bands]
-    #     gancaopian = data['gancaopian'][:, select_bands]
-    #     # hongqi = data['hongqi'][:, select_bands]
-    #     beijing_y = np.zeros(beijing.shape[0])
-    #     zazhi1_y = np.ones(zazhi1.shape[0]) * 3
-    #     # zazhi2_y = np.ones(zazhi2.shape[0]) * 2
-    #     huangqi_y = np.ones(huangqi.shape[0]) * 1
-    #     gancaopian_y = np.ones(gancaopian.shape[0]) * 4
-    #     # hongqi_y = np.ones(hongqi.shape[0]) * 5
-    #     data_x = np.concatenate((beijing, zazhi1, huangqi, gancaopian), axis=0)
-    #     data_y = np.concatenate((beijing_y, zazhi1_y, huangqi_y, gancaopian_y), axis=0)
-    #     return data_x, data_y
-
-    def data_construction(self, data_path='data/1.txt', select_bands=[91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
-                          type=['beijing', 'zazhi1', 'huangqi', 'gancaopian']):
-        '''
-        :param data_path: 数据文件路径
-        :param select_bands: 选择的波段
-        :param type: 选择的类型
-        :return: data_x, data_y
-        '''
-        data = utils.read_envi_ascii(data_path)
-        # 判断读取的txt文件内是否有beijing和haungqi类型的数据
-        if 'beijing' not in data or 'huangqi' not in data:
-            logging.error("数据文件中缺少'beijing'或'huangqi'类型标签")
-            raise ValueError("数据文件中缺少'beijing'或'huangqi'类型标签")
-        data_x = np.concatenate([data[key][:, select_bands] for key in type], axis=0)
-        data_y = np.concatenate([np.zeros(data[key].shape[0]) if key == 'beijing' else np.ones(data[key].shape[0])
-            if key == 'huangqi' else np.ones(data[key].shape[0]) * (i + 2) for i, key in enumerate(type)], axis=0)
-        return data_x, data_y
-
-    def predict(self, data_x):
-        '''
-        对数据进行预测
-        :param data_x: 波段选择后的数据
-        :return: 预测结果二值化后的数据，0为背景，1为黄芪,2为杂质2，3为杂质1，4为甘草片，5为红芪
-        '''
-        data_x_shape = data_x.shape
-        data_x = data_x.reshape(-1, data_x.shape[2])
-        data_y = self.model.predict(data_x)
-        data_y = data_y.reshape(data_x_shape[0], data_x_shape[1]).astype(np.uint8)
-        data_y, centers, categories = self.connect_space(data_y)
-        result = {'data_y': data_y, 'centers': centers, 'categories': categories}
-        return result
-
-    def connect_space(self, data_y):
-        # 连通域处理离散点
-        labels, num_features = ndimage.label(data_y)
-        centers = []
-        categories = []
-        for i in range(1, num_features + 1):
-            mask = (labels == i)
-            counts = np.bincount(data_y[mask])
-            category = np.argmax(counts)
-            data_y[mask] = category
-            center = ndimage.measurements.center_of_mass(data_y, labels, [i])
-            center = list(center)
-            center = np.array(center).astype(int)
-            centers.append(center)
-            categories.append(category)
-        return data_y, centers, categories
-
-
-if __name__ == '__main__':
-    detector = Astragalin()
-    detector.fit_value(file_name="astragalin.p", data_path="data/1.txt")
\ No newline at end of file
diff --git a/main.py b/main.py
deleted file mode 100644
index 090ae06..0000000
--- a/main.py
+++ /dev/null
@@ -1,197 +0,0 @@
-
-import socket
-import time
-
-import numpy as np
-import logging
-import os
-import sys
-import cv2 as cv
-
-from classfier import Astragalin
-from utils import DualSock, try_connect, receive_sock, parse_protocol, ack_sock, done_sock
-from root_dir import ROOT_DIR
-from model import resnet34
-import torch
-from torchvision import transforms
-from PIL import Image
-import json
-import matplotlib.pyplot as plt
-from PIL import Image
-
-
-
-def process_cmd(cmd: str, data: any, connected_sock: socket.socket) -> tuple:
-    '''
-    处理指令
-    :param cmd: 指令类型
-    :param data: 指令内容
-    :param connected_sock: socket
-    :param detector: 模型
-    :return: 是否处理成功
-    '''
-    result = ''
-    if cmd == 'IM':
-        data = np.frombuffer(data, dtype=np.uint8)
-        data = cv.imdecode(data, cv.IMREAD_COLOR)
-
-        # 显示图片
-        cv.imshow('image', data)
-        cv.waitKey(0)
-        cv.destroyAllWindows()
-
-        # device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        #
-        # data_transform = transforms.Compose(
-        #     [transforms.Resize(256),
-        #      transforms.CenterCrop(224),
-        #      transforms.ToTensor(),
-        #      transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
-        #
-        # # load image
-        # # img_path = r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\1.jpg"
-        # # assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
-        # # img = Image.open(img_path)
-        # # plt.imshow(img)
-        # # [N, C, H, W]
-        # img = data_transform(data)
-        # # expand batch dimension
-        # img = torch.unsqueeze(img, dim=0)
-        #
-        # # read class_indict
-        # # json_path = './class_indices.json'
-        # # assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)
-        # #
-        # # with open(json_path, "r") as f:
-        # #     class_indict = json.load(f)
-        #
-        # # create model
-        # model = resnet34(num_classes=4).to(device)
-        #
-        # # load model weights
-        # weights_path = r"D:\project\deep-learning-for-image-processing-master\pytorch_classification\Test5_resnet\resNet34.pth"
-        # assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path)
-        # model.load_state_dict(torch.load(weights_path, map_location=device))
-        #
-        # # prediction
-        # model.eval()
-        # with torch.no_grad():
-        #     # predict class
-        #     output = torch.squeeze(model(img.to(device))).cpu()
-        #     predict = torch.softmax(output, dim=0)
-        #     predict_cla = torch.argmax(predict).numpy()
-        #     result = predict_cla
-            # print(predict_cla)
-        # print_res = "class: {}   prob: {:.3}".format(class_indict[str(predict_cla)],
-        #                                              predict[predict_cla].numpy())
-        # plt.title(print_res)
-        # for i in range(len(predict)):
-        #     print("class: {:10}   prob: {:.3}".format(class_indict[str(i)],
-        #                                               predict[i].numpy()))
-        # plt.show()
-
-
-        # result = detector.predict(data)
-        # # 取出result中的字典中的centers和categories
-        # centers = result['centers']
-        # categories = result['categories']
-        # # 将centers和categories转换为字符串，每一位之间用,隔开，centers是list,每个元素为np.array，categories是1维数组
-        # # centers_str = '|'.join([str(point[0][0]) + ',' + str(point[0][1]) for point in centers])
-        # # categories_str = ','.join([str(i) for i in categories])
-        # # # 将centers和categories的字符串拼接起来，中间用;隔开
-        # # result = centers_str + ';' + categories_str
-        # 给result直接赋值，用于测试
-        # result = 'HELLO WORLD'
-        # response = done_sock(connected_sock, cmd_type=cmd, result=result)
-        # print(result)
-    else:
-        logging.error(f'错误指令，指令为{cmd}')
-        # response = False
-    return result
-
-def bytes_to_img(data):
-    data1 = Image.frombytes('RGB', (1200, 4096), data, 'raw')
-
-
-
-
-
-# def main(is_debug=False):
-#     file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'report.log'))
-#     file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
-#     console_handler = logging.StreamHandler(sys.stdout)
-#     console_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
-#     logging.basicConfig(format='%(asctime)s %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s',
-#                         handlers=[file_handler, console_handler], level=logging.DEBUG)
-#     dual_sock = DualSock(connect_ip='127.0.0.1')
-#
-#     while not dual_sock.status:
-#         logging.error('连接被断开，正在重连')
-#         dual_sock.reconnect()
-#     detector = Astragalin(ROOT_DIR / 'models' / 'astragalin.p')
-#     # _ = detector.predict(np.ones((4096, 1200, 10), dtype=np.float32))
-#     while True:
-#         pack, next_pack = receive_sock(dual_sock) # 接收数据，如果没有数据则阻塞，如果返回的是空字符串则表示出现错误
-#         if pack == b"":  # 无数据表示出现错误
-#             time.sleep(5)
-#             dual_sock.reconnect()
-#             continue
-#
-#         cmd, data = parse_protocol(pack)
-#         print(cmd)
-#         # print(data)
-#
-#         process_cmd(cmd=cmd, data=data, connected_sock=dual_sock, detector=detector)
-
-
-def main(is_debug=False):
-    file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'report.log'))
-    file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
-    console_handler = logging.StreamHandler(sys.stdout)
-    console_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
-    logging.basicConfig(format='%(asctime)s %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s',
-                        handlers=[file_handler, console_handler], level=logging.DEBUG)
-    dual_sock = DualSock(connect_ip='127.0.0.1')
-
-    while not dual_sock.status:
-        logging.error('连接被断开，正在重连')
-        dual_sock.reconnect()
-
-
-
-    # detector = Astragalin(ROOT_DIR / 'models' / 'resNet34.pth')
-    result_buffer = []  # 存储处理结果的缓冲区
-
-    while True:
-        for _ in range(5):
-            pack, next_pack = receive_sock(dual_sock)  # 接收数据，如果没有数据则阻塞，如果返回的是空字符串则表示出现错误
-            if pack == b"":  # 无数据表示出现错误
-                time.sleep(5)
-                dual_sock.reconnect()
-                break
-
-            cmd, data = parse_protocol(pack)
-            print(cmd)
-            # print(data)
-
-            result = process_cmd(cmd=cmd, data=data, connected_sock=dual_sock)
-            result_buffer.append(result)  # 将处理结果添加到缓冲区
-
-        # 在这里进行对5次结果的处理，可以进行合并、比较等操作
-        final_result = combine_results(result_buffer)
-
-        # 发送最终结果
-        response = done_sock(dual_sock, cmd_type=cmd, result=final_result)
-        print(final_result)
-        result_buffer = []
-
-
-
-def combine_results(results):
-    # 在这里实现对5次结果的合并/比较等操作，根据实际需求进行修改
-    # 这里只是简单地将结果拼接成一个字符串，你可能需要根据实际情况进行更复杂的处理
-    return ';'.join(results)
-
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file
diff --git a/model.py b/model.py
deleted file mode 100644
index c2aa086..0000000
--- a/model.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import torch.nn as nn
-import torch
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):
-        super(BasicBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
-                               kernel_size=3, stride=stride, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(out_channel)
-        self.relu = nn.ReLU()
-        self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
-                               kernel_size=3, stride=1, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(out_channel)
-        self.downsample = downsample
-
-    def forward(self, x):
-        identity = x
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    """
-    注意：原论文中，在虚线残差结构的主分支上，第一个1x1卷积层的步距是2，第二个3x3卷积层步距是1。
-    但在pytorch官方实现过程中是第一个1x1卷积层的步距是1，第二个3x3卷积层步距是2，
-    这么做的好处是能够在top1上提升大概0.5%的准确率。
-    可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch
-    """
-    expansion = 4
-
-    def __init__(self, in_channel, out_channel, stride=1, downsample=None,
-                 groups=1, width_per_group=64):
-        super(Bottleneck, self).__init__()
-
-        width = int(out_channel * (width_per_group / 64.)) * groups
-
-        self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width,
-                               kernel_size=1, stride=1, bias=False)  # squeeze channels
-        self.bn1 = nn.BatchNorm2d(width)
-        # -----------------------------------------
-        self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups,
-                               kernel_size=3, stride=stride, bias=False, padding=1)
-        self.bn2 = nn.BatchNorm2d(width)
-        # -----------------------------------------
-        self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion,
-                               kernel_size=1, stride=1, bias=False)  # unsqueeze channels
-        self.bn3 = nn.BatchNorm2d(out_channel*self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-
-    def forward(self, x):
-        identity = x
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-
-
-class ResNet(nn.Module):
-
-    def __init__(self,
-                 block,
-                 blocks_num,
-                 num_classes=1000,
-                 include_top=True,
-                 groups=1,
-                 width_per_group=64):
-        super(ResNet, self).__init__()
-        self.include_top = include_top
-        self.in_channel = 64
-
-        self.groups = groups
-        self.width_per_group = width_per_group
-
-        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
-                               padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(self.in_channel)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, blocks_num[0])
-        self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)
-        if self.include_top:
-            self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (1, 1)
-            self.fc = nn.Linear(512 * block.expansion, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-
-    def _make_layer(self, block, channel, block_num, stride=1):
-        downsample = None
-        if stride != 1 or self.in_channel != channel * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(channel * block.expansion))
-
-        layers = []
-        layers.append(block(self.in_channel,
-                            channel,
-                            downsample=downsample,
-                            stride=stride,
-                            groups=self.groups,
-                            width_per_group=self.width_per_group))
-        self.in_channel = channel * block.expansion
-
-        for _ in range(1, block_num):
-            layers.append(block(self.in_channel,
-                                channel,
-                                groups=self.groups,
-                                width_per_group=self.width_per_group))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        if self.include_top:
-            x = self.avgpool(x)
-            x = torch.flatten(x, 1)
-            x = self.fc(x)
-
-        return x
-
-
-def resnet34(num_classes=1000, include_top=True):
-    # https://download.pytorch.org/models/resnet34-333f7ec4.pth
-    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)
-
-
-def resnet50(num_classes=1000, include_top=True):
-    # https://download.pytorch.org/models/resnet50-19c8e357.pth
-    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)
-
-
-def resnet101(num_classes=1000, include_top=True):
-    # https://download.pytorch.org/models/resnet101-5d3b4d8f.pth
-    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top)
-
-
-def resnext50_32x4d(num_classes=1000, include_top=True):
-    # https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
-    groups = 32
-    width_per_group = 4
-    return ResNet(Bottleneck, [3, 4, 6, 3],
-                  num_classes=num_classes,
-                  include_top=include_top,
-                  groups=groups,
-                  width_per_group=width_per_group)
-
-
-def resnext101_32x8d(num_classes=1000, include_top=True):
-    # https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth
-    groups = 32
-    width_per_group = 8
-    return ResNet(Bottleneck, [3, 4, 23, 3],
-                  num_classes=num_classes,
-                  include_top=include_top,
-                  groups=groups,
-                  width_per_group=width_per_group)
diff --git a/models/12.p b/models/12.p
deleted file mode 100644
index c876b0e..0000000
Binary files a/models/12.p and /dev/null differ
diff --git a/models/astragalin.p b/models/astragalin.p
deleted file mode 100644
index 4428ca7..0000000
Binary files a/models/astragalin.p and /dev/null differ
diff --git a/models/dt.p b/models/dt.p
deleted file mode 100644
index a2fd313..0000000
Binary files a/models/dt.p and /dev/null differ
diff --git a/models/resNet34.pth b/models/resNet34.pth
deleted file mode 100644
index 45154d6..0000000
Binary files a/models/resNet34.pth and /dev/null differ
diff --git a/qt_test.py b/qt_test.py
deleted file mode 100644
index b72f5a5..0000000
--- a/qt_test.py
+++ /dev/null
@@ -1,167 +0,0 @@
-
-import numpy as np
-import socket
-import logging
-import matplotlib.pyplot as plt
-import cv2
-
-def rec_socket(recv_sock: socket.socket, cmd_type: str, ack: bool) -> bool:
-    if ack:
-        cmd = 'A' + cmd_type
-    else:
-        cmd = 'D' + cmd_type
-    while True:
-        try:
-            temp = recv_sock.recv(1)
-        except ConnectionError as e:
-            logging.error(f'连接出错, 错误代码:\n{e}')
-            return False
-        except TimeoutError as e:
-            logging.error(f'超时了，错误代码: \n{e}')
-            return False
-        except Exception as e:
-            logging.error(f'遇见未知错误，错误代码: \n{e}')
-            return False
-        if temp == b'\xaa':
-            break
-
-    # 获取报文长度
-    temp = b''
-    while len(temp) < 4:
-        try:
-            temp += recv_sock.recv(1)
-        except Exception as e:
-            logging.error(f'接收报文长度失败, 错误代码: \n{e}')
-            return False
-    try:
-        data_len = int.from_bytes(temp, byteorder='big')
-    except Exception as e:
-        logging.error(f'转换失败,错误代码 \n{e}, \n报文内容\n{temp}')
-        return False
-
-    # 读取报文内容
-    temp = b''
-    while len(temp) < data_len:
-        try:
-            temp += recv_sock.recv(data_len)
-        except Exception as e:
-            logging.error(f'接收报文内容失败, 错误代码: \n{e}，\n报文内容\n{temp}')
-            return False
-    data = temp
-    if cmd.strip().upper() != data[:4].decode('ascii').strip().upper():
-        logging.error(f'客户端接收指令错误,\n指令内容\n{data}')
-        return False
-    else:
-        if cmd == 'DIM':
-            print(data)
-
-        # 进行数据校验
-        temp = b''
-        while len(temp) < 3:
-            try:
-                temp += recv_sock.recv(1)
-            except Exception as e:
-                logging.error(f'接收报文校验失败, 错误代码: \n{e}')
-                return False
-        if temp == b'\xff\xff\xbb':
-            return True
-        else:
-            logging.error(f"接收了一个完美的只错了校验位的报文，\n data: {data}")
-            return False
-
-
-# def main():
-#     socket_receive = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-#     socket_receive.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-#     socket_receive.bind(('127.0.0.1', 21123))
-#     socket_receive.listen(5)
-#     socket_send = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-#     socket_send.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-#     socket_send.bind(('127.0.0.1', 21122))
-#     socket_send.listen(5)
-#     print('等待连接')
-#     socket_send_1, receive_addr_1 = socket_send.accept()
-#     print("连接成功：", receive_addr_1)
-#     # socket_send_2 = socket_send_1
-#     socket_send_2, receive_addr_2 = socket_receive.accept()
-#     print("连接成功：", receive_addr_2)
-#     while True:
-#         cmd = input('请输入指令：').strip().upper()
-#         if cmd == 'IM':
-#             with open('data/newrawfile_ref.raw', 'rb') as f:
-#                 data = np.frombuffer(f.read(), dtype=np.float32).reshape(750, 288, 384)
-#             data = data[:, [91, 92, 93, 94, 95, 96, 97, 98, 99, 100], :]
-#             n_rows, n_bands, n_cols = data.shape[0], data.shape[1], data.shape[2]
-#             print(f'n_rows：{n_rows}, n_bands：{n_bands}, n_cols：{n_cols}')
-#             n_rows, n_cols, n_bands = [x.to_bytes(2, byteorder='big') for x in [n_rows, n_cols, n_bands]]
-#             data = data.tobytes()
-#             length = len(data) + 10
-#             print(f'length: {length}')
-#             length = length.to_bytes(4, byteorder='big')
-#             msg = b'\xaa' + length + ('  ' + cmd).upper().encode('ascii') + n_rows + n_cols + n_bands + data + b'\xff\xff\xbb'
-#             socket_send_1.send(msg)
-#             print('发送成功')
-#             result = socket_send_2.recv(5)
-#             length = int.from_bytes(result[1:5], byteorder='big')
-#             result = b''
-#             while len(result) < length:
-#                 result += socket_send_2.recv(length)
-#             print(result)
-#             data = result[4:length].decode()
-#             print(data)
-
-def main():
-    socket_receive = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-    socket_receive.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-    socket_receive.bind(('127.0.0.1', 21123))
-    socket_receive.listen(5)
-    socket_send = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-    socket_send.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-    socket_send.bind(('127.0.0.1', 21122))
-    socket_send.listen(5)
-    print('等待连接')
-    socket_send_1, receive_addr_1 = socket_send.accept()
-    print("连接成功：", receive_addr_1)
-    # socket_send_2 = socket_send_1
-    socket_send_2, receive_addr_2 = socket_receive.accept()
-    print("连接成功：", receive_addr_2)
-    while True:
-        cmd = input().strip().upper()
-        if cmd == 'IM':
-
-            image_paths = [
-                r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\1.jpg",
-                r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\2.jpg",
-                r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\3.jpg",
-                r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\4.jpg",
-                r"D:\project\deep-learning-for-image-processing-master\data_set\test_image\5.jpg",
-            ]
-
-        # 连续发送五张图片
-        for image_path in image_paths:
-                # 读取图片文件
-            img = cv2.imread(image_path)
-
-            # img = cv2.imread(r"/Users/zhouchao/Library/CloudStorage/OneDrive-macrosolid/PycharmProjects/wood_color/data/data20220919/dark/rgb60.png")
-            # img = cv2.imread(r"D:\project\Astragalin\data\02newrawfile_ref.png")
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-            img = np.asarray(img, dtype=np.uint8)
-            width = img.shape[0]
-            height = img.shape[1]
-            print(width, height)
-            img_bytes = img.tobytes()
-            length = len(img_bytes) + 8
-            print(length)
-            length = length.to_bytes(4, byteorder='big')
-            width = width.to_bytes(2, byteorder='big')
-            height = height.to_bytes(2, byteorder='big')
-            send_message = b'\xaa' + length + ('  ' + cmd).upper().encode('ascii') + width + height + img_bytes + b'\xff\xff\xbb'
-            socket_send_1.send(send_message)
-            print('发送成功')
-        result = socket_send_2.recv(10)
-        print(result)
-
-
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file
diff --git a/renovate.json b/renovate.json
deleted file mode 100644
index 5db72dd..0000000
--- a/renovate.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
-  "$schema": "https://docs.renovatebot.com/renovate-schema.json",
-  "extends": [
-    "config:recommended"
-  ]
-}
diff --git a/report.log b/report.log
deleted file mode 100644
index fbb5a0d..0000000
--- a/report.log
+++ /dev/null
@@ -1,840 +0,0 @@
-2024-01-15 14:32:13,594 utils.py[line:218] - WARNING - ���Է����1������...
-2024-01-15 14:32:18,595 utils.py[line:224] - ERROR - ��1������ʧ��... 5�����������...
- timed out
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diff --git a/root_dir.py b/root_dir.py
deleted file mode 100644
index 754b950..0000000
--- a/root_dir.py
+++ /dev/null
@@ -1,5 +0,0 @@
-
-import pathlib
-
-file_path = pathlib.Path(__file__)
-ROOT_DIR = file_path.parent
\ No newline at end of file
diff --git a/utils.py b/utils.py
deleted file mode 100644
index 4954707..0000000
--- a/utils.py
+++ /dev/null
@@ -1,516 +0,0 @@
-
-import cv2
-
-import numpy as np
-from genetic_selection import GeneticSelectionCV
-from sklearn.metrics import confusion_matrix, classification_report
-from sklearn.model_selection import train_test_split
-from sklearn.tree import DecisionTreeClassifier
-import logging
-import os
-import shutil
-import time
-import socket
-
-
-
-def read_envi_ascii(file_name, save_xy=False, hdr_file_name=None):
-    """
-    Read envi ascii file. Use ENVI ROI Tool -> File -> output ROIs to ASCII...
-
-    :param file_name: file name of ENVI ascii file
-    :param hdr_file_name: hdr file name for a "BANDS" vector in the output
-    :param save_xy: save the x, y position on the first two cols of the result vector
-    :return: dict {class_name: vector, ...}
-    """
-    number_line_start_with = "; Number of ROIs: "
-    roi_name_start_with, roi_npts_start_with = "; ROI name: ", "; ROI npts:"
-    data_start_with, data_start_with2, data_start_with3 = ";    ID", ";   ID", ";     ID"
-    class_num, class_names, class_nums, vectors = 0, [], [], []
-    with open(file_name, 'r') as f:
-        for line_text in f:
-            if line_text.startswith(number_line_start_with):
-                class_num = int(line_text[len(number_line_start_with):])
-            elif line_text.startswith(roi_name_start_with):
-                class_names.append(line_text[len(roi_name_start_with):-1])
-            elif line_text.startswith(roi_npts_start_with):
-                class_nums.append(int(line_text[len(roi_name_start_with):-1]))
-            elif line_text.startswith(data_start_with) or line_text.startswith(data_start_with2) or line_text.startswith(data_start_with3):
-                col_list = list(filter(None, line_text[1:].split(" ")))
-                assert (len(class_names) == class_num) and (len(class_names) == len(class_nums))
-                break
-            elif line_text.startswith(";"):
-                continue
-        for vector_rows in class_nums:
-            vector_str = ''
-            for i in range(vector_rows):
-                vector_str += f.readline()
-            vector = np.fromstring(vector_str, dtype=float, sep=" ").reshape(-1, len(col_list))
-            assert vector.shape[0] == vector_rows
-            vector = vector[:, 3:] if not save_xy else vector[:, 1:]
-            vectors.append(vector)
-            f.readline()  # suppose to read a blank line
-    if hdr_file_name is not None:
-        import re
-        with open(hdr_file_name, 'r') as f:
-            hdr_info = f.read()
-        bands = re.findall(r"wavelength = {[^{}]+}", hdr_info, flags=re.IGNORECASE | re.MULTILINE)
-        bands_num = re.findall(r"bands\s*=\s*(\d+)", hdr_info, flags=re.I)
-        if (len(bands) == 0) or len(bands_num) == 0:
-            Warning("The given hdr file is invalid, can't find bands = ? or wavelength = {?}.")
-        else:
-            bands = re.findall(r'{[^{}]+}', bands[0], flags=re.MULTILINE)[0][3:-2]
-            bands = bands.split(',\n')
-            bands = np.asarray(bands, dtype=float)
-            bands_num = int(bands_num[0])
-            if bands_num == bands.shape[0]:
-                bands = np.array(bands, dtype=float)
-                vectors.append(bands)
-                class_names.append("BANDS")
-            else:
-                Warning("The given hdr file is invalid, bands num is not equal to wavelength.")
-    return dict(zip(class_names, vectors))
-
-
-def ga_feature_extraction(data_x, data_y):
-    '''
-    使用遗传算法进行特征提取
-    :param data_x: 特征
-    :param data_y: 类别
-    '''
-    Xtrain, Xtest, Ytrain, Ytest = train_test_split(data_x, data_y, test_size=0.3)
-    clf = DecisionTreeClassifier(random_state=3)
-    selector = GeneticSelectionCV(clf, cv=30,
-                                  verbose=1,
-                                  scoring="accuracy",
-                                  max_features=10,
-                                  n_population=500,
-                                  crossover_proba=0.6,
-                                  mutation_proba=0.3,
-                                  n_generations=300,
-                                  crossover_independent_proba=0.6,
-                                  mutation_independent_proba=0.1,
-                                  tournament_size=10,
-                                  n_gen_no_change=10,
-                                  caching=True,
-                                  n_jobs=-1)
-    selector = selector.fit(Xtrain, Ytrain)
-    Xtrain_ga, Xtest_ga = Xtrain[:, selector.support_], Xtest[:, selector.support_]
-    clf = clf.fit(Xtrain_ga, Ytrain)
-    print(np.where(selector.support_ == True))
-    y_pred = clf.predict(Xtest_ga)
-    print(classification_report(Ytest, y_pred))
-    print(confusion_matrix(Ytest, y_pred))
-
-
-def read_raw(file_name, shape=None,  setect_bands=None, cut_shape=None):
-    '''
-    读取raw文件
-    :param file_name: 文件名
-    :param setect_bands: 选择的波段
-    :return: 波段数据
-    '''
-    if shape is None:
-        shape = (692, 272, 384)
-    with open(file_name, 'rb') as f:
-        data = np.frombuffer(f.read(), dtype=np.float32).reshape(shape).transpose(0, 2, 1)
-    if setect_bands is not None:
-        data = data[:, :, setect_bands]
-    if cut_shape is not None:
-        data = data[: cut_shape[0], : cut_shape[1], :]
-    return data
-
-
-def save_raw(file_name, data):
-    '''
-    保存raw文件
-    :param file_name: 文件名
-    :param data: 数据
-    '''
-    data = data.transpose(0, 2, 1)
-    # 将data转换为一维数组
-    data = data.reshape(-1)
-    with open(file_name, 'wb') as f:
-        f.write(data.astype(np.float32).tobytes())
-
-
-def read_rgb(file_name):
-    '''
-    读取rgb文件
-    :param file_name: 文件名
-    :return: rgb数据
-    '''
-    data = cv2.imread(file_name)
-    data = cv2.cvtColor(data, cv2.COLOR_BGR2RGB)
-    #给一个颜色对应的字典，用于将rgb转换为类别，白色对应0，黄色对应1，青色对应2，红色对应3，绿色对应4，蓝色对应5
-    color_dict = {(255, 255, 255): 0, (255, 255, 0): 1, (0, 255, 255): 2, (255, 0, 0): 3, (0, 255, 0): 4, (0, 0, 255): 5}
-    # 保存图片的形状，用于将一维数组转换为三维数组
-    shape = data.shape
-    # 将rgb转换为类别
-    data = data.reshape(-1, 3).tolist()
-    # 将rgb转换为类别
-    mapped_data = []
-
-    for i, color in enumerate(data):
-        mapped_value = color_dict.get(tuple(color))
-        if mapped_value is None:
-            print("No mapping found for color", color, "at index", i)
-        else:
-            mapped_data.append(mapped_value)
-    # 将一维数组转换为三维数组
-    data = np.array(mapped_data).reshape(shape[0], shape[1])
-    return data
-
-
-def read_data(raw_path, rgb_path, shape=None, setect_bands=None, blk_size=4, cut_shape=None, dp=False):
-    '''
-    读取数据
-    :param raw_path: raw文件路径
-    :param rgb_path: rgb文件路径
-    :param setect_bands: 选择的波段
-    :return: 波段数据，rgb数据
-    '''
-    if shape is None:
-        shape = (692, 272, 384)
-    with open(raw_path, 'rb') as f:
-        raw = np.frombuffer(f.read(), dtype=np.float32).reshape(shape).transpose(0, 2, 1)
-    if setect_bands is not None:
-        raw = raw[:, :, setect_bands]
-    color_dict = {(255, 255, 255): 0, (255, 255, 0): 1, (0, 255, 255): 2, (255, 0, 0): 3, (0, 255, 0): 4,
-                  (0, 0, 255): 5}
-    rgb = cv2.imread(rgb_path)
-    rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB)
-    if cut_shape is not None:
-        raw = raw[ :cut_shape[0], :cut_shape[1], :]
-        rgb = rgb[ :cut_shape[0], :cut_shape[1], :]
-    data_x = []
-    data_y = []
-    for i in range(0, rgb.shape[0], blk_size):
-        for j in range(0, rgb.shape[1], blk_size):
-            x = raw[i:i + blk_size, j:j + blk_size, :]
-            y = rgb[i:i + blk_size, j:j + blk_size]
-            # # 取y的第三行第三列的像素值，判断该像素值是否在color_dict中，如果在则将x和y添加到data_x和data_y中
-            # y = tuple(y[2, 2, :])
-            # if y in color_dict.keys():
-            #     data_x.append(x)
-            #     data_y.append(color_dict[y])
-            # 取y的中心点像素值，判断该像素值是否在color_dict中，如果在则将x和y添加到data_x和data_y中
-            y = tuple(y[blk_size//2, blk_size//2, :])
-            if y in color_dict.keys():
-                data_x.append(x)
-                data_y.append(color_dict[y])
-    data_x = np.array(data_x)
-    data_y = np.array(data_y).astype(np.uint8)
-    return data_x, data_y
-
-
-def try_connect(connect_ip: str, port_number: int, is_repeat: bool = False, max_reconnect_times: int = 50) -> (
-                bool, socket.socket):
-    """
-    尝试连接.
-
-    :param is_repeat: 是否是重新连接
-    :param max_reconnect_times:最大重连次数
-    :return: (连接状态True为成功, Socket / None)
-    """
-    reconnect_time = 0
-    while reconnect_time < max_reconnect_times:
-        logging.warning(f'尝试{"重新" if is_repeat else ""}发起第{reconnect_time + 1}次连接...')
-        try:
-            connected_sock = PreSocket(socket.AF_INET, socket.SOCK_STREAM)
-            connected_sock.connect((connect_ip, port_number))
-        except Exception as e:
-            reconnect_time += 1
-            logging.error(f'第{reconnect_time}次连接失败... 5秒后重新连接...\n {e}')
-            time.sleep(5)
-            continue
-        logging.warning(f'{"重新" if is_repeat else ""}连接成功')
-        return True, connected_sock
-    return False, None
-
-
-class PreSocket(socket.socket):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.pre_pack = b''
-        self.settimeout(5)
-
-    def receive(self, *args, **kwargs):
-        if self.pre_pack == b'':
-            return self.recv(*args, **kwargs)
-        else:
-            data_len = args[0]
-            required, left = self.pre_pack[:data_len], self.pre_pack[data_len:]
-            self.pre_pack = left
-            return required
-
-    def set_prepack(self, pre_pack: bytes):
-        temp = self.pre_pack
-        self.pre_pack = temp + pre_pack
-
-
-class DualSock(PreSocket):
-    def __init__(self, connect_ip='127.0.0.1', recv_port: int = 21122, send_port: int = 21123):
-        super().__init__()
-        received_status, self.received_sock = try_connect(connect_ip=connect_ip, port_number=recv_port)
-        send_status, self.send_sock = try_connect(connect_ip=connect_ip, port_number=send_port)
-        self.status = received_status and send_status
-
-    def send(self, *args, **kwargs) -> int:
-        return self.send_sock.send(*args, **kwargs)
-
-    def receive(self, *args, **kwargs) -> bytes:
-        return self.received_sock.receive(*args, **kwargs)
-
-    def set_prepack(self, pre_pack: bytes):
-        self.received_sock.set_prepack(pre_pack)
-
-    def reconnect(self, connect_ip='127.0.0.1', recv_port:int = 21122, send_port: int = 21123):
-        received_status, self.received_sock = try_connect(connect_ip=connect_ip, port_number=recv_port)
-        send_status, self.send_sock = try_connect(connect_ip=connect_ip, port_number=send_port)
-        return received_status and send_status
-
-
-def receive_sock(recv_sock: PreSocket, pre_pack: bytes = b'', time_out: float = -1.0, time_out_single=5e20) -> (
-bytes, bytes):
-    """
-    从指定的socket中读取数据.自动阻塞，如果返回的数据为空则说明连接出现问题，需要重新连接。
-
-    :param recv_sock: 指定sock
-    :param pre_pack: 上一包的粘包内容
-    :param time_out: 每隔time_out至少要发来一次指令,否则认为出现问题进行重连，小于0则为一直等
-    :param time_out_single: 单次指令超时时间，单位是秒
-    :return: data, next_pack
-    """
-    recv_sock.set_prepack(pre_pack)
-    # 开头校验
-    time_start_recv = time.time()
-    while True:
-        if time_out > 0:
-            if (time.time() - time_start_recv) > time_out:
-                logging.error(f'指令接收超时')
-                return b'', b''
-        try:
-            temp = recv_sock.receive(1)
-        except ConnectionError as e:
-            logging.error(f'连接出错, 错误代码:\n{e}')
-            return b'', b''
-        except TimeoutError as e:
-            # logging.error(f'超时了，错误代码: \n{e}')
-            logging.info('运行中,等待指令..')
-            continue
-        except socket.timeout as e:
-            logging.info('运行中,等待指令..')
-            continue
-        except Exception as e:
-            logging.error(f'遇见未知错误，错误代码: \n{e}')
-            return b'', b''
-        if temp == b'\xaa':
-            break
-
-    # 接收开头后，开始进行时间记录
-    time_start_recv = time.time()
-
-    # 获取报文长度
-    temp = b''
-    while len(temp) < 4:
-        if (time.time() - time_start_recv) > time_out_single:
-            logging.error(f'单次指令接收超时')
-            return b'', b''
-        try:
-            temp += recv_sock.receive(1)
-        except Exception as e:
-            logging.error(f'接收报文的长度不正确, 错误代码: \n{e}')
-            return b'', b''
-    try:
-        data_len = int.from_bytes(temp, byteorder='big')
-    except Exception as e:
-        logging.error(f'转换失败,错误代码 \n{e}')
-        return b'', b''
-
-    # 读取报文内容
-    temp = b''
-    while len(temp) < data_len:
-        if (time.time() - time_start_recv) > time_out_single:
-            logging.error(f'单次指令接收超时')
-            return b'', b''
-        try:
-            temp += recv_sock.receive(data_len)
-        except Exception as e:
-            logging.error(f'接收报文内容失败, 错误代码: \n{e}')
-            return b'', b''
-    data, next_pack = temp[:data_len], temp[data_len:]
-    recv_sock.set_prepack(next_pack)
-    next_pack = b''
-
-    # 进行数据校验
-    temp = b''
-    while len(temp) < 3:
-        if (time.time() - time_start_recv) > time_out_single:
-            logging.error(f'单次指令接收超时')
-            return b'', b''
-        try:
-            temp += recv_sock.receive(1)
-        except Exception as e:
-            logging.error(f'接收报文校验失败, 错误代码: \n{e}')
-            return b'', b''
-    if temp == b'\xff\xff\xbb':
-        return data, next_pack
-    else:
-        logging.error(f"接收了一个完美的只错了校验位的报文")
-        return b'', b''
-
-
-def parse_protocol(data: bytes) -> (str, any):
-    '''
-    指令转换
-    :param data: 接收到的报文
-    :return: 指令类型，指令内容
-    '''
-    try:
-        assert len(data) > 4
-    except AssertionError:
-        logging.error('指令转换失败，长度不足5')
-        return '', None
-    cmd, data = data[:4], data[4:]
-    cmd = cmd.decode('ascii').strip().upper()
-    if cmd == 'IM':
-        n_rows, n_cols, img = data[:2], data[2:4], data[4:]
-        try:
-            n_rows, n_cols = [int.from_bytes(x, byteorder='big') for x in [n_rows, n_cols]]
-        except Exception as e:
-            logging.error(f'长宽转换失败, 错误代码{e}, 报文大小: n_rows:{n_rows}, n_cols: {n_cols}')
-            return '', None
-        try:
-            assert n_rows * n_cols * 3 == len(img)
-        except AssertionError:
-            logging.error('图像指令IM转换失败，数据长度错误')
-            return '', None
-        img = np.frombuffer(img, dtype=np.uint8).reshape((n_rows, n_cols, -1))
-        return cmd, img
-
-
-def ack_sock(send_sock: socket.socket, cmd_type: str) -> bool:
-    '''
-    发送应答
-    :param cmd_type:指令类型
-    :param send_sock:指定sock
-    :return:是否发送成功
-    '''
-    msg = b'\xaa\x00\x00\x00\x05' + (' A' + cmd_type).upper().encode('ascii') + b'\xff\xff\xff\xbb'
-    try:
-        send_sock.send(msg)
-    except Exception as e:
-        logging.error(f'发送应答失败，错误类型：{e}')
-        return False
-    return True
-
-
-def done_sock(send_sock: socket.socket, cmd_type: str, result = '') -> bool:
-    '''
-    发送任务完成指令
-    :param send_sock: 指定sock
-    :param cmd_type: 指令类型
-    :param result: 数据
-    :return: 是否发送成功
-    '''
-    cmd = cmd_type.strip().upper()
-    if cmd_type == 'IM':
-        result = result.encode()
-        # 指令4位
-        length = len(result) + 4
-        length = length.to_bytes(4, byteorder='big')
-        # msg = b'\xaa' + length + (' D' + cmd).upper().encode('ascii') + result + b'\xff\xff\xbb'
-        msg = result
-    try:
-        send_sock.send(msg)
-    except Exception as e:
-        logging.error(f'发送完成指令失败，错误类型：{e}')
-        return False
-    return True
-
-def simple_sock(send_sock: socket.socket, cmd_type: str, result) -> bool:
-    '''
-    发送任务完成指令
-    :param cmd_type:指令类型
-    :param send_sock:指定sock
-    :param result:数据
-    :return:是否发送成功
-    '''
-    cmd_type = cmd_type.strip().upper()
-    if cmd_type == 'IM':
-        if result == 0:
-            msg = b'S'
-        elif result == 1:
-            msg = b'Z'
-        elif result == 2:
-            msg = b'Q'
-    elif cmd_type == 'TR':
-        msg = b'A'
-    elif cmd_type == 'MD':
-        msg = b'D'
-    elif cmd_type == 'KM':
-        msg = b'K'
-        result = result.encode('ascii')
-        result = b',' + result
-        length = len(result)
-        msg = msg + length.to_bytes(4, 'big') + result
-    try:
-        send_sock.send(msg)
-    except Exception as e:
-        logging.error(f'发送完成指令失败，错误类型：{e}')
-        return False
-    return True
-
-def mkdir_if_not_exist(dir_name, is_delete=False):
-    """
-    创建文件夹
-    :param dir_name: 文件夹
-    :param is_delete: 是否删除
-    :return: 是否成功
-    """
-    try:
-        if is_delete:
-            if os.path.exists(dir_name):
-                shutil.rmtree(dir_name)
-                print('[Info] 文件夹 "%s" 存在, 删除文件夹.' % dir_name)
-
-        if not os.path.exists(dir_name):
-            os.makedirs(dir_name)
-            print('[Info] 文件夹 "%s" 不存在, 创建文件夹.' % dir_name)
-        return True
-    except Exception as e:
-        print('[Exception] %s' % e)
-        return False
-
-def create_file(file_name):
-    """
-    创建文件
-    :param file_name: 文件名
-    :return: None
-    """
-    if os.path.exists(file_name):
-        print("文件存在：%s" % file_name)
-        return False
-        # os.remove(file_name)  # 删除已有文件
-    if not os.path.exists(file_name):
-        print("文件不存在，创建文件：%s" % file_name)
-        open(file_name, 'a').close()
-        return True
-
-
-class Logger(object):
-    def __init__(self, is_to_file=False, path=None):
-        self.is_to_file = is_to_file
-        if path is None:
-            path = "Astragalins.log"
-        self.path = path
-        create_file(path)
-
-    def log(self, content):
-        if self.is_to_file:
-            with open(self.path, "a") as f:
-                print(time.strftime("[%Y-%m-%d_%H-%M-%S]:"), file=f)
-                print(content, file=f)
-        else:
-            print(content)
\ No newline at end of file