refactor:修改代码结构，将process_data函数从main.py移动至classfier.py的Data_processing类下

20240529RGBtest3/classifer.py

@@ -13,6 +13,7 @@ import logging
 import random
 import numpy as np
 from PIL import Image
+from utils import Pipe
 from sklearn.ensemble import RandomForestRegressor
 #图像分类网络所需库，实际并未使用分类网络
 # import torch
@@ -585,9 +586,9 @@ class Data_processing:
         long_axis, short_axis = self.analyze_ellipse(contour_mask)
         #重量单位为g，加上了一点随机数
         weight_real = self.weight_estimates(long_axis, short_axis)
-        print(f'真实重量：{weight_real}')
+        # print(f'真实重量：{weight_real}')
         weight = (weight_real * 2) + random.randint(0, 30)
-        print(f'估算重量：{weight}')
+        # print(f'估算重量：{weight}')
         if weight > 255:
             weight = weight_real
 
@@ -606,6 +607,71 @@ class Data_processing:
             rp = cv2.cvtColor(np.ones((613, 800, 3), dtype=np.uint8), cv2.COLOR_BGR2RGB)
         return diameter, weight, number_defects, total_pixels, rp
 
+    def process_data(seif, cmd: str, images: list, spec: any, pipe: Pipe, detector: Spec_predict) -> bool:
+        """
+        处理指令
+
+        :param cmd: 指令类型
+        :param images: 图像数据列表
+        :param spec: 光谱数据
+        :param detector: 模型
+        :return: 是否处理成功
+        """
+        # pipe = Pipe()
+        diameter_axis_list = []
+        max_defect_num = 0  # 初始化最大缺陷数量为0
+        max_total_defect_area = 0  # 初始化最大总像素数为0
+
+        for i, img in enumerate(images):
+            if cmd == 'TO':
+                # 番茄
+                diameter, green_percentage, number_defects, total_pixels, rp = seif.analyze_tomato(img)
+                if i <= 2:
+                    diameter_axis_list.append(diameter)
+                    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
+                    gp = round(green_percentage, 2)
+
+            elif cmd == 'PF':
+                # 百香果
+                diameter, weight, number_defects, 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_total_defect_area = max(max_total_defect_area, total_pixels)
+                if i == 1:
+                    rp_result = rp
+                    weight = weight
+
+            else:
+                logging.error(f'错误指令，指令为{cmd}')
+                return False
+
+        diameter = round(sum(diameter_axis_list) / 3, 2)
+
+        if cmd == 'TO':
+            brix = 0
+            weight = 0
+            # 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};')
+            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)
+            return response
+        elif cmd == 'PF':
+            green_percentage = 0
+            brix = detector.predict(spec)
+            if diameter == 0:
+                brix = 0
+            # 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};')
+            response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=green_percentage, diameter=diameter,
+                                      weight=weight,
+                                      defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result)
+            return response
+
+
 
 # #下面封装的是ResNet18和ResNet34的网络模型构建
 # #原定用于构建RGB图像有果无果判断，后续发现存在纰漏，暂时搁置并未实际使用

20240529RGBtest3/main.py

@@ -18,68 +18,6 @@ import time
 
 
 
-def process_data(cmd: str, images: list, spec: any, dp: Data_processing, pipe: Pipe, detector: Spec_predict) -> bool:
-    """
-    处理指令
-
-    :param cmd: 指令类型
-    :param images: 图像数据列表
-    :param spec: 光谱数据
-    :param detector: 模型
-    :return: 是否处理成功
-    """
-    diameter_axis_list = []
-    max_defect_num = 0  # 初始化最大缺陷数量为0
-    max_total_defect_area = 0  # 初始化最大总像素数为0
-
-    for i, img in enumerate(images):
-        if cmd == 'TO':
-            # 番茄
-            diameter, green_percentage, number_defects, total_pixels, rp = dp.analyze_tomato(img)
-            if i <= 2:
-                diameter_axis_list.append(diameter)
-                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
-                gp = round(green_percentage, 2)
-
-        elif cmd == 'PF':
-            # 百香果
-            diameter, weight, number_defects, total_pixels, rp = dp.analyze_passion_fruit(img)
-            if i <= 2:
-                diameter_axis_list.append(diameter)
-                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
-
-        else:
-            logging.error(f'错误指令，指令为{cmd}')
-            return False
-
-    diameter = round(sum(diameter_axis_list) / 3, 2)
-
-    if cmd == 'TO':
-        brix = 0
-        weight = 0
-        # 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};')
-        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)
-        return response
-    elif cmd == 'PF':
-        green_percentage = 0
-        brix = detector.predict(spec)
-        if diameter == 0:
-            brix = 0
-        # 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};')
-        response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=green_percentage, diameter=diameter, weight=weight,
-                                  defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result)
-        return response
-
 def main(is_debug=False):
     file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'tomato.log'), encoding='utf-8')
     file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
@@ -165,8 +103,8 @@ def main(is_debug=False):
         #数据处理部分
         # start_time3 = time.time()
         if images:  # 确保images不为空
-            response = process_data(cmd, images, spec, dp, pipe, detector)
+            response = dp.process_data(cmd, images, spec, pipe, detector)
-            end_time3 = time.time()
+            # end_time3 = time.time()
             # print(f'第{q}组处理时间：{(end_time3 - start_time3) * 1000}毫秒')
             if response:
                 logging.info(f'处理成功，响应为: {response}')

20240529RGBtest3/utils.py

@@ -241,9 +241,6 @@ class Pipe:
         return True
 
 
-
-
-
 def create_file(file_name):
     """
     创建文件