diff --git a/20240529RGBtest3/classifer.py b/20240529RGBtest3/classifer.py
index cf9a9ee..10e5cab 100644
--- a/20240529RGBtest3/classifer.py
+++ b/20240529RGBtest3/classifer.py
@@ -359,7 +359,7 @@ class Data_processing:
     def contour_process(self, image_array):
         # 检查图像是否为空或全黑
         if image_array is None or image_array.size == 0 or np.all(image_array == 0):
-            # print("输入的图像为空或全黑，返回一个全黑图像。")
+            logging.error("输入的图像为空或全黑，返回一个全黑图像。")
             return np.zeros_like(image_array) if image_array is not None else np.zeros((100, 100), dtype=np.uint8)
         # 应用中值滤波
         image_filtered = cv2.medianBlur(image_array, 5)
@@ -488,11 +488,18 @@ class Data_processing:
         # 将处理后的图像转换为 RGB 格式
         rp = cv2.cvtColor(org_defect, cv2.COLOR_BGR2RGB)
         diameter = (long_axis + short_axis) / 2
+        if diameter < 200:
+            diameter = 0
+            green_percentage = 0
+            number_defects = 0
+            total_pixels = 0
+            rp = cv2.cvtColor(np.ones((100, 100, 3), dtype=np.uint8), cv2.COLOR_BGR2RGB)
+            return diameter, green_percentage, number_defects, total_pixels, rp
         return diameter, green_percentage, number_defects, total_pixels, rp
 
     def analyze_passion_fruit(self, img, hue_value=37, hue_delta=10, value_target=25, value_delta=10):
         if img is None:
-            print("Error: 无图像数据.")
+            logging.error("Error: 无图像数据.")
             return None
 
         # 创建PassionFruit类的实例
diff --git a/20240529RGBtest3/main.py b/20240529RGBtest3/main.py
index abd9599..38e1e4b 100644
--- a/20240529RGBtest3/main.py
+++ b/20240529RGBtest3/main.py
@@ -99,14 +99,14 @@ def main(is_debug=False):
     pipe = Pipe(rgb_receive_name, rgb_send_name, spec_receive_name)
     rgb_receive, rgb_send, spec_receive = pipe.create_pipes(rgb_receive_name, rgb_send_name, spec_receive_name)
     # 预热循环，只处理cmd为'YR'的数据
-    while True:
-        start_time00 = time.time()
-        data = pipe.receive_rgb_data(rgb_receive)
-        cmd, _ = pipe.parse_img(data)
-        end_time00 = time.time()
-        print(f'接收预热数据时间：{end_time00 - start_time00}秒')
-        if cmd == 'YR':
-            break  # 当接收到的不是预热命令时，结束预热循环
+    # while True:
+    #     start_time00 = time.time()
+    #     data = pipe.receive_rgb_data(rgb_receive)
+    #     cmd, _ = pipe.parse_img(data)
+    #     end_time00 = time.time()
+    #     print(f'接收预热数据时间：{end_time00 - start_time00}秒')
+    #     if cmd == 'YR':
+    #         break  # 当接收到的不是预热命令时，结束预热循环
     while True:
         start_time = time.time()
         images = []
@@ -115,13 +115,13 @@ def main(is_debug=False):
             start_time1 = time.time()
             data = pipe.receive_rgb_data(rgb_receive)
             end_time10 = time.time()
-            print(f'接收一份数据时间：{end_time10 - start_time1}秒')
+            # print(f'接收一份数据时间：{end_time10 - start_time1}秒')
 
             start_time11 = time.time()
             cmd, img = pipe.parse_img(data)
             end_time1 = time.time()
-            print(f'处理一份数据时间：{end_time1 - start_time11}秒')
-            print(f'接收一张图时间：{end_time1 - start_time1}秒')
+            # print(f'处理一份数据时间：{end_time1 - start_time11}秒')
+            # print(f'接收一张图时间：{end_time1 - start_time1}秒')
 
             # 使用分类器进行预测
             # prediction = classifier.predict(img)
@@ -133,7 +133,7 @@ def main(is_debug=False):
             else:
                 response = pipe.send_data(cmd='KO', brix=0, diameter=0, green_percentage=0, weigth=0, defect_num=0,
                                            total_defect_area=0, rp=np.zeros((100, 100, 3), dtype=np.uint8))
-                print("图像中无果，跳过此图像")
+                logging.info("图像中无果，跳过此图像")
                 continue
 
         if cmd not in ['TO', 'PF', 'YR', 'KO']:
@@ -146,19 +146,19 @@ def main(is_debug=False):
             spec_data = pipe.receive_spec_data(spec_receive)
             _, spec = pipe.parse_spec(spec_data)
             end_time2 = time.time()
-            print(f'接收光谱数据时间：{end_time2 - start_time2}秒')
+            # print(f'接收光谱数据时间：{end_time2 - start_time2}秒')
 
         start_time3 = time.time()
         if images:  # 确保images不为空
             response = process_data(cmd, images, spec, dp, pipe, detector)
             end_time3 = time.time()
-            print(f'处理时间：{end_time3 - start_time3}秒')
+            # print(f'处理时间：{end_time3 - start_time3}秒')
             if response:
                 logging.info(f'处理成功，响应为: {response}')
             else:
                 logging.error('处理失败')
         else:
-            print("没有有效的图像进行处理")
+            logging.error("没有有效的图像进行处理")
 
         end_time = time.time()
         print(f'全流程时间：{end_time - start_time}秒')
diff --git a/20240529RGBtest3/qt_test.py b/20240529RGBtest3/qt_test.py
index b2207f3..3ec7e8f 100644
--- a/20240529RGBtest3/qt_test.py
+++ b/20240529RGBtest3/qt_test.py
@@ -90,7 +90,7 @@ class MainWindow(QMainWindow):
                 img_data = img.tobytes()
                 length = (len(img_data) + 6).to_bytes(4, byteorder='big')
                 # cmd = 'TO'：测试番茄数据；cmd = 'PF'：测试百香果数据
-                cmd = 'PF'
+                cmd = 'TO'
                 data_send = length + cmd.upper().encode('ascii') + height + width + img_data
                 win32file.WriteFile(self.rgb_send, data_send)
                 print(f'发送的图像数据长度: {len(data_send)}')
@@ -113,7 +113,7 @@ class MainWindow(QMainWindow):
                 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 = 'PF'
+                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)}')