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nodes/infers/vp_yolo5_seg_node.h

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+
+#pragma once
+
+#include "../vp_primary_infer_node.h"
+#include "../../objects/vp_frame_target.h"
+
+
+
+namespace vp_nodes {
+    // driving area segmentation based on yolov5s-seg-v7.0
+    // https://github.com/ultralytics/yolov5/releases/tag/v7.0
+    class vp_yolo5_seg_node: public vp_primary_infer_node
+    {
+    private:
+        /* data */
+        float conf_threshold = 0.5;
+        float iou_threshold = 0.7;
+        int MASK_CHANNELS = 32;
+        cv::Mat scale_ratio;
+        cv::Point padding;
+        cv::Mat letterbox(const cv::Mat& src, cv::Size target_size, cv::Mat& scale_ratio, cv::Point& padding);
+        cv::Mat sigmoid(const cv::Mat& x);
+        cv::Mat process_mask(const cv::Mat& proto,  // [1, 32, H/4, W/4]
+                    const cv::Mat& coeffs,          // [1, 32]
+                    const cv::Rect& bbox,           
+                    const cv::Size& input_size,     // e.g., 640x384
+                    const cv::Point& padding,
+                    const float x_scale,
+                    const float y_scale);
+        std::vector<int> nms(const std::vector<cv::Rect>& boxes, const std::vector<float>& scores, float iou_threshold);
+    protected:
+        virtual void infer(const cv::Mat& blob_to_infer, std::vector<cv::Mat>& raw_outputs) override;
+        virtual void preprocess(const std::vector<cv::Mat>& mats_to_infer, cv::Mat& blob_to_infer) override;
+        virtual void postprocess(const std::vector<cv::Mat>& raw_outputs, const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch) override;
+        virtual void prepare(const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch, std::vector<cv::Mat>& mats_to_infer) override;
+    public:
+        vp_yolo5_seg_node(std::string node_name, 
+                            std::string model_path, 
+                            std::string labels_path = "", 
+                            int input_width = 640, 
+                            int input_height = 384, 
+                            int batch_size = 1,
+                            int class_id_offset = 0,
+                            float conf_threshold = 0.5,
+                            float iou_threshold = 0.7);
+        ~vp_yolo5_seg_node();
+    };    
+}