45 lines
2.1 KiB
C++
Executable File
45 lines
2.1 KiB
C++
Executable File
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#pragma once
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#include "vp_infer_node.h"
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namespace vp_nodes {
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// secondary infer node, it is the base class of infer node which MUST infer on small cropped image.
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// note: detector such as yolo can be also applied on small cropped images.
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class vp_secondary_infer_node: public vp_infer_node {
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private:
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protected:
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// define how to prepare data
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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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bool need_apply(int primary_class_id, int target_width, int target_height);
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public:
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vp_secondary_infer_node(std::string node_name,
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std::string model_path,
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std::string model_config_path = "",
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std::string labels_path = "",
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int input_width = 640,
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int input_height = 640,
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int batch_size = 1,
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std::vector<int> p_class_ids_applied_to = std::vector<int>(),
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int min_width_applied_to = 0,
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int min_height_applied_to = 0,
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int crop_padding = 10,
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float scale = 1.0,
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cv::Scalar mean = cv::Scalar(123.675, 116.28, 103.53), // imagenet dataset
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cv::Scalar std = cv::Scalar(1),
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bool swap_rb = true,
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bool swap_chn = false);
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~vp_secondary_infer_node();
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// we do secondary infer logic only if the primary class-id of target is in this vector.
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// not all targets in frame should be handled by vp_secondary_infer_node.
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std::vector<int> p_class_ids_applied_to;
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int crop_padding;
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// min height of target to be handled by vp_secondary_infer_node, 0 means no restriction
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int min_height_applied_to = 0;
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// min width of target to be handled by vp_secondary_infer_node，0 means no restriction
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int min_width_applied_to = 0;
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};
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} |