Fix mode selection logic

Author: sovrasov

src/cpp/models/src/segmentation_model.cpp

@@ -292,7 +292,7 @@ std::vector<Contour> SegmentationModel::getContours(const ImageResultWithSoftPre
     std::vector<Contour> combined_contours = {};
     cv::Mat label_index_map;
     cv::Mat current_label_soft_prediction;
-    int find_contours_mode = include_nested_contours ? cv::RETR_EXTERNAL : cv::RETR_CCOMP;
+    int find_contours_mode = include_nested_contours ? cv::RETR_CCOMP : cv::RETR_EXTERNAL;
     for (int index = 1; index < imageResult.soft_prediction.channels(); index++) {
         cv::extractChannel(imageResult.soft_prediction, current_label_soft_prediction, index);
         cv::inRange(imageResult.resultImage,

src/python/model_api/models/segmentation.py

@@ -203,7 +203,7 @@ def get_contours(
             msg = "Cannot get contours from soft prediction with 1 layer"
             raise RuntimeError(msg)
 
-        find_contours_mode = cv2.RETR_CCOMP if not include_nested_contours else cv2.RETR_EXTERNAL
+        find_contours_mode = cv2.RETR_CCOMP if include_nested_contours else cv2.RETR_EXTERNAL
         combined_contours = []
         for layer_index in range(1, n_layers):  # ignoring background
             label = self.get_label_name(layer_index - 1)