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nodes/infers/vp_trt_yolov8_seg_detector.cpp

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+
+#ifdef VP_WITH_TRT
+#include "vp_trt_yolov8_seg_detector.h"
+
+namespace vp_nodes {
+    
+    vp_trt_yolov8_seg_detector::vp_trt_yolov8_seg_detector(std::string node_name, 
+                                                    std::string model_path,
+                                                    std::string labels_path):
+                                                    vp_primary_infer_node(node_name, "", "", labels_path) {
+        yolov8_seg_detector = std::make_shared<trt_yolov8::trt_yolov8_seg_detector>(model_path);
+        this->initialized();
+    }
+    
+    vp_trt_yolov8_seg_detector::~vp_trt_yolov8_seg_detector() {
+        deinitialized();
+    }
+
+    // please refer to vp_infer_node::run_infer_combinations
+    void vp_trt_yolov8_seg_detector::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_primary_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);
+
+        start_time = std::chrono::system_clock::now();
+        std::vector<std::vector<Detection>> detections;
+        std::vector<std::vector<cv::Mat>> masks;
+        yolov8_seg_detector->detect(mats_to_infer, detections, masks);
+
+        assert(detections.size() == 1);
+        assert(masks.size() == 1);
+        auto& detection_list = detections[0];
+        auto& mask_list = masks[0];
+        assert(detection_list.size() == mask_list.size());
+        auto& frame_meta = frame_meta_with_batch[0];
+
+        for (int i = 0; i < detection_list.size(); i++) {
+            auto& objbox = detection_list[i];
+            auto& mask = mask_list[i];
+            auto scaled_mask = scale_mask(mask, frame_meta->frame);
+
+            // objbox.bbox: center_x center_y width height
+            auto rect = get_rect(frame_meta->frame, objbox.bbox);  // convert to: x, y, width,height
+            // check value range
+            rect.x = std::max(rect.x, 0);
+            rect.y = std::max(rect.y, 0);
+            rect.width = std::min(rect.width, frame_meta->frame.cols - rect.x);
+            rect.height = std::min(rect.height, frame_meta->frame.rows - rect.y);
+            if (rect.width <= 0 || rect.height <= 0) {
+                continue;
+            }
+
+            auto label = labels.size() == 0 ? "" : labels[objbox.class_id];
+            auto target = std::make_shared<vp_objects::vp_frame_target>(rect.x, rect.y, rect.width, rect.height, 
+                                                                        objbox.class_id, objbox.conf, frame_meta->frame_index, frame_meta->channel_index, label);
+            auto rect_mask = scaled_mask(rect);
+            target->mask = rect_mask;
+
+            // create target and update back into frame meta
+            frame_meta->targets.push_back(target);
+        }
+        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_seg_detector::postprocess(const std::vector<cv::Mat>& raw_outputs, const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch) {
+
+    }
+}
+#endif