#pragma once
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#include <string>
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#include <vector>
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#include <memory>
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#include <opencv2/core.hpp>
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#include "shapes/vp_rect.h"
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#include "vp_sub_target.h"
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/*
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* ##################################################
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* what is frame target?
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* ##################################################
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* frame target are those detected by deep learning models(detectors) and then updated by other classifiers.
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* we can detect vehicles, pedestrain, traffic lights, firesmoke and so on using vp_primary_infer_node, and then figure out what color the vehicles are, if the pedstrain wear a hat or not using vp_secondary_infer_node.
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* vehicles, pedstrain are frame targets detected in current frame.
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*
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* note:
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* frame target is an important concept and it contains a lot of data which would be updated/filled by vp_node when flowing through the piepline.
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* see vp_frame_meta also.
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* ##################################################
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*/
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namespace vp_objects {
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// target in frame, detected by detectors(such as yolo/ssd).
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class vp_frame_target {
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private:
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/* data */
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public:
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// default person label
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std::string class_label_of_person = "person";
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// default car label
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std::string class_label_of_car = "car";
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// default trush label
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std::string class_label_of_trush = "trush";
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// x of top left
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int x;
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// y of top left
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int y;
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// width of rect
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int width;
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// height of rect
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int height;
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// class id created by primary infer nodes.
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// allow multi primary infer nodes to exist in a pipeline, the class id is unique because we apply class_id_offset in each primary infer node.
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int primary_class_id;
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// score created by primary infer nodes
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float primary_score;
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// label created by primary infer nodes
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std::string primary_label;
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// frame the target belongs to
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int frame_index;
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// channel the target belongs to
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int channel_index;
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// track id created by track node if it exists
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int track_id = -1;
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// cache of track rects in the previous frames, filled by track node if it exists.
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// we can draw / analyse depend on these track rects later.
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std::vector<vp_objects::vp_rect> tracks;
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// mask of the target, used for Instance Segmentation like mask rcnn network (ignore for other situations).
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cv::Mat mask;
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// class ids filled/appended by multi secondary infer nodes.
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std::vector<int> secondary_class_ids;
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// scores filled/appended by multi secondary infer nodes.
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std::vector<float> secondary_scores;
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// labels filled/appended by multi secondary infer nodes.
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std::vector<std::string> secondary_labels;
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// sub targets inside current target.
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// in case detectors applied on small cropped image.
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std::vector<std::shared_ptr<vp_objects::vp_sub_target>> sub_targets;
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// feature vector(for example, 128 or 256-dims array) created by infer nodes such as vp_feature_encoder_node.
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// each target has only one feature vector, the value will be override if multi vp_feature_encoder_node exist.
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// embeddings can be used for reid related works.
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std::vector<float> embeddings;
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// ba flags of the target, hold by this value (created/updated by ba nodes).
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// for example, 0001/0010/0100/1000 stands for 4 different flags, 1110 means 3 flags are on and another one is off, using ^|& operators to update and read.
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// if 0100 stands for 'Stop' flag of target, 'ba_flags|=0100' means set 'Stop' flag as On, '(ba_flags & 0100) == 0100' means 'Stop' flag is already On.
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int ba_flags;
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vp_frame_target(int x,
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int y,
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int width,
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int height,
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int primary_class_id,
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float primary_score,
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int frame_index,
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int channel_index,
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std::string primary_label = "");
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vp_frame_target(vp_rect rect,
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int primary_class_id,
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float primary_score,
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int frame_index,
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int channel_index,
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std::string primary_label = "");
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~vp_frame_target();
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// clone myself
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std::shared_ptr<vp_frame_target> clone();
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// rect area of target
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vp_rect get_rect() const;
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};
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}
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