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