first commit

nodes/infers/vp_trt_vehicle_feature_encoder.cpp

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+#ifdef VP_WITH_TRT
+#include "vp_trt_vehicle_feature_encoder.h"
+
+namespace vp_nodes {
+    vp_trt_vehicle_feature_encoder::vp_trt_vehicle_feature_encoder(std::string node_name, 
+                                                                    std::string vehicle_feature_model_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, "", "", "", 1, 1, 1, p_class_ids_applied_to, min_width_applied_to, min_height_applied_to) {
+        vehicle_feature_encoder = std::make_shared<trt_vehicle::VehicleFeatureEncoder>(vehicle_feature_model_path);
+        this->initialized();
+    }
+    
+    vp_trt_vehicle_feature_encoder::~vp_trt_vehicle_feature_encoder() {
+        deinitialized();
+    }
+
+    void vp_trt_vehicle_feature_encoder::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<float>> vehicles_feature;
+        vehicle_feature_encoder->encode(mats_to_infer, vehicles_feature);
+
+        auto& frame_meta = frame_meta_with_batch[0];
+        auto index = 0;
+        for (int i = 0; i < vehicles_feature.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
+                auto& features = vehicles_feature[i];
+                for(int k = 0; k < features.size(); k++) {
+                    frame_meta->targets[j]->embeddings.push_back(features[k]);
+                }
+
+                // 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_vehicle_feature_encoder::postprocess(const std::vector<cv::Mat>& raw_outputs, const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch) {
+
+    }
+}
+#endif