Add a simplified mode for contours processing (#339)

* Add a simplified mode for contours processing

* Renaming

* Fix mode selection logic

Author: sovrasov

src/cpp/models/include/models/segmentation_model.h

@@ -37,7 +37,8 @@ class SegmentationModel : public BaseModel {
     virtual std::vector<std::unique_ptr<ImageResult>> inferBatch(const std::vector<ImageInputData>& inputImgs);
 
     static std::string ModelType;
-    std::vector<Contour> getContours(const ImageResultWithSoftPrediction& imageResult);
+    std::vector<Contour> getContours(const ImageResultWithSoftPrediction& imageResult,
+                                     bool include_nested_contours = true);
 
 protected:
     void prepareInputsOutputs(std::shared_ptr<ov::Model>& model) override;

src/cpp/models/src/segmentation_model.cpp

@@ -283,14 +283,16 @@ std::unique_ptr<ResultBase> SegmentationModel::postprocess(InferenceResult& infR
     return std::unique_ptr<ResultBase>(result);
 }
 
-std::vector<Contour> SegmentationModel::getContours(const ImageResultWithSoftPrediction& imageResult) {
+std::vector<Contour> SegmentationModel::getContours(const ImageResultWithSoftPrediction& imageResult,
+                                                    bool include_nested_contours) {
     if (imageResult.soft_prediction.channels() == 1) {
         throw std::runtime_error{"Cannot get contours from soft prediction with 1 layer"};
     }
 
     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_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,
@@ -299,7 +301,7 @@ std::vector<Contour> SegmentationModel::getContours(const ImageResultWithSoftPre
                     label_index_map);
         std::vector<std::vector<cv::Point>> contours;
         std::vector<cv::Vec4i> hierarchy;
-        cv::findContours(label_index_map, contours, hierarchy, cv::RETR_CCOMP, cv::CHAIN_APPROX_NONE);
+        cv::findContours(label_index_map, contours, hierarchy, find_contours_mode, cv::CHAIN_APPROX_NONE);
 
         std::string label = getLabelName(index - 1);
 

src/python/model_api/models/segmentation.py

@@ -186,12 +186,24 @@ def postprocess(self, outputs: dict, meta: dict) -> ImageResultWithSoftPredictio
     def get_contours(
         self,
         prediction: ImageResultWithSoftPrediction,
+        include_nested_contours: bool = True,
     ) -> list[Contour]:
+        """Represents existing masks with contours.
+
+        Args:
+            prediction (ImageResultWithSoftPrediction): Input segmentation prediction.
+            include_nested_contours (bool, optional): Enables searching for holes in masks. Defaults to True.
+
+        Returns:
+            list[Contour]: Contours found.
+        """
         n_layers = prediction.soft_prediction.shape[2]
 
         if n_layers == 1:
             msg = "Cannot get contours from soft prediction with 1 layer"
             raise RuntimeError(msg)
+
+        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)
@@ -209,7 +221,7 @@ def get_contours(
 
             contours, hierarchy = cv2.findContours(
                 label_index_map,
-                cv2.RETR_CCOMP,
+                find_contours_mode,
                 cv2.CHAIN_APPROX_NONE,
             )
             if len(contours):