Add an iterator through detections

Author: sovrasov

examples/cpp/synchronous_api/main.cpp

@@ -35,17 +35,11 @@ int main(int argc, char* argv[]) try {
     auto result = model->infer(image);
 
     // Process detections
-    for (auto& obj : result->objects) {
+    for (auto obj : *result) {
         std::cout << " " << std::left << std::setw(9) << obj.label << " | " << std::setw(10) << obj.confidence << " | "
                   << std::setw(4) << int(obj.x) << " | " << std::setw(4) << int(obj.y) << " | " << std::setw(4)
                   << int(obj.x + obj.width) << " | " << std::setw(4) << int(obj.y + obj.height) << "\n";
     }
-
-    // could be improved with an iterator
-    for (size_t i = 0; i < result->size(); ++i) {
-        std::cout << result->getDetection(i) << "\n";
-    }
-
 } catch (const std::exception& error) {
     std::cerr << error.what() << '\n';
     return 1;

src/cpp/models/include/models/results.h

@@ -165,20 +165,45 @@ struct DetectionResult : public ResultBase {
     DetectionResult(int64_t frameId = -1, const std::shared_ptr<MetaData>& metaData = nullptr)
         : ResultBase(frameId, metaData) {}
 
-    // memory to forward as np.array
     cv::Mat_<float> scores;
     cv::Mat_<float> bboxes;
     cv::Mat_<int> labels;
     std::vector<std::string> label_names;
-
-    std::vector<DetectedObject> objects; // to remove
     ov::Tensor saliency_map, feature_vector;  // Contain "saliency_map" and "feature_vector" model outputs if such exist
 
+    struct DetectionIterator {
+        const DetectionResult& result;
+        size_t index;
+
+        DetectionIterator(const DetectionResult& result, size_t index) : result(result), index(index) {}
+
+        bool operator!=(const DetectionIterator& other) const {
+            return index != other.index;
+        }
+
+        DetectionIterator& operator++() {
+            ++index;
+            return *this;
+        }
+
+        DetectedObject operator*() const {
+            return result.getObject(index);
+        }
+    };
+
+    DetectionIterator begin() const {
+        return DetectionIterator(*this, 0);
+    }
+
+    DetectionIterator end() const {
+        return DetectionIterator(*this, size());
+    }
+
     size_t size() const {
         return label_names.size();
     }
 
-    DetectedObject getDetection(size_t idx) {
+    DetectedObject getObject(size_t idx) const {
         DetectedObject result(bboxes.at<cv::Rect2f>(idx));
         result.labelID = labels.at<size_t>(idx);
         result.label = label_names[idx];
@@ -187,7 +212,7 @@ struct DetectionResult : public ResultBase {
     }
 
     friend std::ostream& operator<<(std::ostream& os, const DetectionResult& prediction) {
-        for (const DetectedObject& obj : prediction.objects) {
+        for (const DetectedObject obj : prediction) {
             os << obj << "; ";
         }
         try {

src/cpp/models/src/detection_model_ssd.cpp

@@ -131,7 +131,18 @@ std::unique_ptr<ResultBase> ModelSSD::postprocessSingleOutput(InferenceResult& i
         }
     }
 
-    result->label_names.reserve(numAndStep.detectionsNum);
+    size_t detections_num = 0;
+    for (size_t i = 0; i < numAndStep.detectionsNum; i++) {
+        float confidence = detections[i * numAndStep.objectSize + 2];
+        if (confidence > confidence_threshold) {
+            ++detections_num;
+        }
+    }
+    result->label_names.reserve(detections_num);
+    result->labels = cv::Mat(1, detections_num, CV_32S);
+    result->scores = cv::Mat(1, detections_num, CV_32F);
+    result->bboxes = cv::Mat(1, detections_num, CV_32FC4);
+
     for (size_t i = 0; i < numAndStep.detectionsNum; i++) {
         float image_id = detections[i * numAndStep.objectSize + 0];
         if (image_id < 0) {
@@ -142,11 +153,7 @@ std::unique_ptr<ResultBase> ModelSSD::postprocessSingleOutput(InferenceResult& i
 
         /** Filtering out objects with confidence < confidence_threshold probability **/
         if (confidence > confidence_threshold) {
-            DetectedObject desc;
-
-            desc.confidence = confidence;
-            desc.labelID = static_cast<size_t>(detections[i * numAndStep.objectSize + 1]);
-            desc.label = getLabelName(desc.labelID);
+            cv::Rect2f desc;
             desc.x =
                 clamp(round((detections[i * numAndStep.objectSize + 3] * netInputWidth - padLeft) * invertedScaleX),
                       0.f,
@@ -165,12 +172,12 @@ std::unique_ptr<ResultBase> ModelSSD::postprocessSingleOutput(InferenceResult& i
                       0.f,
                       floatInputImgHeight) -
                 desc.y;
-            result->objects.push_back(desc);
 
+            size_t det_idx = result->label_names.size();
             auto label_idx = static_cast<int>(detections[i * numAndStep.objectSize + 1]);
-            result->labels.at<int>(i) = label_idx;
-            result->scores.at<float>(i) = confidence;
-            result->bboxes.at<cv::Rect2f>(i) = cv::Rect2f(desc.x, desc.y, desc.width, desc.height);
+            result->labels.at<int>(det_idx) = label_idx;
+            result->scores.at<float>(det_idx) = confidence;
+            result->bboxes.at<cv::Rect2f>(det_idx) = desc;
             result->label_names.push_back(getLabelName(label_idx));
         }
     }
@@ -224,11 +231,8 @@ std::unique_ptr<ResultBase> ModelSSD::postprocessMultipleOutputs(InferenceResult
 
         /** Filtering out objects with confidence < confidence_threshold probability **/
         if (confidence > confidence_threshold) {
-            DetectedObject desc;
+            cv::Rect2f desc;
 
-            desc.confidence = confidence;
-            desc.labelID = labels[i];
-            desc.label = getLabelName(desc.labelID);
             desc.x = clamp_and_round((boxes[i * numAndStep.objectSize] * widthScale - padLeft) * invertedScaleX,
                                      0.f,
                                      floatInputImgWidth);
@@ -248,10 +252,12 @@ std::unique_ptr<ResultBase> ModelSSD::postprocessMultipleOutputs(InferenceResult
             if (desc.width * desc.height >= box_area_threshold) {
                 result->objects.push_back(desc);
             }
+
+            size_t det_idx = result->label_names.size();
             auto label_idx = static_cast<int>(labels[i]);
-            result->labels.at<int>(i) = label_idx;
-            result->scores.at<float>(i) = confidence;
-            result->bboxes.at<cv::Rect2f>(i) = cv::Rect2f(desc.x, desc.y, desc.width, desc.height);
+            result->labels.at<int>(det_idx) = label_idx;
+            result->scores.at<float>(det_idx) = confidence;
+            result->bboxes.at<cv::Rect2f>(det_idx) = desc;
             result->label_names.push_back(getLabelName(label_idx));
         }
     }