fix:修复百香果模型在处理空果拖时gp计算分母可能为0的问题

20240529RGBtest3/classifer.py

@@ -373,6 +373,7 @@ class Spec_predict(object):
             self.model = model_dic
             self.log.log(f'Model loaded successfully from {path}')
 
+
     def predict(self, data_x):
         '''
         预测数据
@@ -381,7 +382,7 @@ class Spec_predict(object):
         '''
         # 对数据进行切片，筛选谱段
         #qt_test进行测试时如果读取的是（30，30，224）需要解开注释进行数据切片，筛选谱段
-        # data_x = data_x[ :25, :, setting.selected_bands ]
+        data_x = data_x[ :25, :, setting.selected_bands ]
         # 将筛选后的数据重塑为二维数组，每行代表一个样本
         data_x = data_x.reshape(-1, setting.n_spec_rows * setting.n_spec_cols * setting.n_spec_bands)
         data_y = self.model.predict(data_x)
@@ -574,8 +575,11 @@ class Data_processing:
         # print(np.sum(fore_g_r_t == 255))
         # print(np.sum(mask == 255))
         # print(np.sum(fore_g_r_t == 255) / np.sum(mask == 255))
-        green_percentage = np.sum(fore_g_r_t == 255) / np.sum(mask == 255)
+        if np.sum(mask == 255) == 0:
-        green_percentage = round(green_percentage, 2)
+            green_percentage = 0
+        else:
+            green_percentage = np.sum(fore_g_r_t == 255) / np.sum(mask == 255)
+            green_percentage = round(green_percentage, 2)
         # 获取西红柿的尺寸信息
         long_axis, short_axis = self.analyze_ellipse(mask)
         # 获取缺陷信息
@@ -616,8 +620,11 @@ class Data_processing:
         fore = pf.bitwise_and_rgb_with_binary(img, contour_mask)
         mask = pf.extract_green_pixels_cv(fore)
         green_img = pf.pixel_comparison(defect, mask)
-        green_percentage = np.sum(green_img == 255) / np.sum(contour_mask == 255)
+        if np.sum(contour_mask == 255) == 0:
-        green_percentage = round(green_percentage, 2)
+            green_percentage = 0
+        else:
+            green_percentage = np.sum(green_img == 255) / np.sum(contour_mask == 255)
+            green_percentage = round(green_percentage, 2)
         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)

20240529RGBtest3/main.py

@@ -31,8 +31,8 @@ def main(is_debug=False):
     dp = Data_processing()
     print('系统初始化中...')
     #模型预热
-    #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352行）
+    #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385行）
-    _ = detector.predict(np.ones((setting.n_spec_rows, setting.n_spec_cols, setting.n_spec_bands), dtype=np.uint16))
+    # _ = detector.predict(np.ones((setting.n_spec_rows, setting.n_spec_cols, setting.n_spec_bands), dtype=np.uint16))
     # _ = classifier.predict(np.ones((setting.n_rgb_rows, setting.n_rgb_cols, setting.n_rgb_bands), dtype=np.uint8))
     # _, _, _, _, _ =dp.analyze_tomato(cv2.imread(str(setting.tomato_img_dir)))
     # _, _, _, _, _ = dp.analyze_passion_fruit(cv2.imread(str(setting.passion_fruit_img_dir))

20240529RGBtest3/qt_test.py

@@ -89,7 +89,7 @@ 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 = 'TO'
+                    cmd = 'PF'
                     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)}')
@@ -112,7 +112,7 @@ 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 = 'TO'
+                    cmd = 'PF'
                     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)}')