first commit

nodes/infers/vp_trt_yolov8_classifier.cpp

@@ -0,0 +1,67 @@
+
+#ifdef VP_WITH_TRT
+#include "vp_trt_yolov8_classifier.h"
+
+namespace vp_nodes {
+    vp_trt_yolov8_classifier::vp_trt_yolov8_classifier(std::string node_name, 
+                                                        std::string model_path, 
+                                                        std::string labels_path,  
+                                                        std::vector<int> p_class_ids_applied_to,
+                                                        int min_width_applied_to, int min_height_applied_to):
+                                                        vp_secondary_infer_node(node_name, "", "", labels_path, 1, 1, 1, p_class_ids_applied_to, min_width_applied_to, min_height_applied_to) {
+        yolov8_classifier = std::make_shared<trt_yolov8::trt_yolov8_classifier>(model_path);
+        this->initialized();
+    }
+    
+    vp_trt_yolov8_classifier::~vp_trt_yolov8_classifier() {
+        deinitialized();
+    }
+
+    void vp_trt_yolov8_classifier::run_infer_combinations(const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch) {
+        assert(frame_meta_with_batch.size() == 1);
+        std::vector<cv::Mat> mats_to_infer;
+
+        // start
+        auto start_time = std::chrono::system_clock::now();
+
+        // prepare data, as same as base class
+        vp_secondary_infer_node::prepare(frame_meta_with_batch, mats_to_infer);
+        auto prepare_time = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::system_clock::now() - start_time);
+
+        // infer using trt_vehicle library
+        start_time = std::chrono::system_clock::now();
+        std::vector<std::vector<Classification>> classidications;
+        yolov8_classifier->classify(mats_to_infer, classidications);
+
+        auto& frame_meta = frame_meta_with_batch[0];
+        auto index = 0;
+        for (int i = 0; i < classidications.size(); i++) {
+            for (int j = index; j < frame_meta->targets.size(); j++) {
+                // need apply or not?
+                if (!need_apply(frame_meta->targets[j]->primary_class_id, frame_meta->targets[j]->width, frame_meta->targets[j]->height)) {
+                    // continue as its primary_class_id is not in p_class_ids_applied_to
+                    continue;
+                }
+
+                // update back to frame meta
+                frame_meta->targets[j]->secondary_class_ids.push_back(classidications[i][0].class_id);
+                frame_meta->targets[j]->secondary_scores.push_back(classidications[i][0].conf);
+                auto label = labels.size() == 0 ? "" : labels[classidications[i][0].class_id];
+                frame_meta->targets[j]->secondary_labels.push_back(label);
+
+                // break as we found the right target!
+                index = j + 1;
+                break;
+            }
+        }
+        auto infer_time = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::system_clock::now() - start_time);
+
+        // can not calculate preprocess time and postprocess time, set 0 by default.
+        vp_infer_node::infer_combinations_time_cost(mats_to_infer.size(), prepare_time.count(), 0, infer_time.count(), 0);
+    }
+
+    void vp_trt_yolov8_classifier::postprocess(const std::vector<cv::Mat>& raw_outputs, const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch) {
+
+    }
+}
+#endif