#pragma once
#include "common_include.h"


// 接口导出
#ifndef BUILD_STATIC
# if defined(MI_ALGORITHM_LIB)
#  define MI_ALGORITHM_EXPORT __declspec(dllexport)
# else
#  define MI_ALGORITHM_EXPORT __declspec(dllimport)
# endif
#else
# define MI_ALGORITHM_EXPORT
#endif


namespace trtUtils
{
	namespace dataSets
	{
		const std::vector<std::string> coco80 = {
			"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
			"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
			"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
			"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
			"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
			"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
			"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
			"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
			"hair drier", "toothbrush"
		};
		const std::vector<std::string> coco91 = {
			"person", "bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light",
			"fire hydrant","street sign","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe",
			"hat","backpack","umbrella","shoe","eye glasses","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat",
			"baseball glove","skateboard","surfboard","tennis racket","bottle","plate","wine glass","cup","fork","knife","spoon","bowl","banana","apple",
			"sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","mirror","dining table","window",
			"desk","toilet","door","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","blender",
			"book","clock","vase","scissors","teddy bear","hair drier","toothbrush","hair brush"
		};
		const std::vector<std::string> voc20 = {
			"aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","diningtable",
			"dog","horse","motorbike","person","pottedplant","sheep","sofa","train","tvmonitor"
		};

		const std::vector<std::string> face2 = { "non-face", "face" };

		// flower_data
		const std::vector<std::string> flower_labels = { "dailsy", "dandelion", "rose", "sunflower", "tulip" };
	}
	namespace Colors
	{
		const std::vector<cv::Scalar> color80{
			cv::Scalar(128, 77, 207),cv::Scalar(65, 32, 208),cv::Scalar(0, 224, 45),cv::Scalar(3, 141, 219),cv::Scalar(80, 239, 253),cv::Scalar(239, 184, 12),
			cv::Scalar(7, 144, 145),cv::Scalar(161, 88, 57),cv::Scalar(0, 166, 46),cv::Scalar(218, 113, 53),cv::Scalar(193, 33, 128),cv::Scalar(190, 94, 113),
			cv::Scalar(113, 123, 232),cv::Scalar(69, 205, 80),cv::Scalar(18, 170, 49),cv::Scalar(89, 51, 241),cv::Scalar(153, 191, 154),cv::Scalar(27, 26, 69),
			cv::Scalar(20, 186, 194),cv::Scalar(210, 202, 167),cv::Scalar(196, 113, 204),cv::Scalar(9, 81, 88),cv::Scalar(191, 162, 67),cv::Scalar(227, 73, 120),
			cv::Scalar(177, 31, 19),cv::Scalar(133, 102, 137),cv::Scalar(146, 72, 97),cv::Scalar(145, 243, 208),cv::Scalar(2, 184, 176),cv::Scalar(219, 220, 93),
			cv::Scalar(238, 153, 134),cv::Scalar(197, 169, 160),cv::Scalar(204, 201, 106),cv::Scalar(13, 24, 129),cv::Scalar(40, 38, 4),cv::Scalar(5, 41, 34),
			cv::Scalar(46, 94, 129),cv::Scalar(102, 65, 107),cv::Scalar(27, 11, 208),cv::Scalar(191, 240, 183),cv::Scalar(225, 76, 38),cv::Scalar(193, 89, 124),
			cv::Scalar(30, 14, 175),cv::Scalar(144, 96, 90),cv::Scalar(181, 186, 86),cv::Scalar(102, 136, 34),cv::Scalar(158, 71, 15),cv::Scalar(183, 81, 247),
			cv::Scalar(73, 69, 89),cv::Scalar(123, 73, 232),cv::Scalar(4, 175, 57),cv::Scalar(87, 108, 23),cv::Scalar(105, 204, 142),cv::Scalar(63, 115, 53),
			cv::Scalar(105, 153, 126),cv::Scalar(247, 224, 137),cv::Scalar(136, 21, 188),cv::Scalar(122, 129, 78),cv::Scalar(145, 80, 81),cv::Scalar(51, 167, 149),
			cv::Scalar(162, 173, 20),cv::Scalar(252, 202, 17),cv::Scalar(10, 40, 3),cv::Scalar(150, 90, 254),cv::Scalar(169, 21, 68),cv::Scalar(157, 148, 180),
			cv::Scalar(131, 254, 90),cv::Scalar(7, 221, 102),cv::Scalar(19, 191, 184),cv::Scalar(98, 126, 199),cv::Scalar(210, 61, 56),cv::Scalar(252, 86, 59),
			cv::Scalar(102, 195, 55),cv::Scalar(160, 26, 91),cv::Scalar(60, 94, 66),cv::Scalar(204, 169, 193),cv::Scalar(126, 4, 181),cv::Scalar(229, 209, 196),
			cv::Scalar(195, 170, 186),cv::Scalar(155, 207, 148)
		};
		const std::vector<cv::Scalar> color91{
			cv::Scalar(148, 99, 164),cv::Scalar(65, 172, 90),cv::Scalar(18, 117, 190),cv::Scalar(173, 208, 229),cv::Scalar(37, 162, 147),cv::Scalar(121, 99, 42),
			cv::Scalar(218, 173, 104),cv::Scalar(193, 213, 138),cv::Scalar(142, 168, 45),cv::Scalar(107, 143, 94),cv::Scalar(242, 89, 7),cv::Scalar(87, 218, 248),
			cv::Scalar(126, 168, 9),cv::Scalar(86, 152, 105),cv::Scalar(155, 135, 251),cv::Scalar(73, 234, 44),cv::Scalar(177, 37, 42),cv::Scalar(219, 215, 54),
			cv::Scalar(124, 207, 143),cv::Scalar(7, 81, 209),cv::Scalar(254, 18, 130),cv::Scalar(71, 54, 73),cv::Scalar(172, 198, 63),cv::Scalar(64, 217, 224),
			cv::Scalar(105, 224, 25),cv::Scalar(41, 52, 130),cv::Scalar(220, 27, 193),cv::Scalar(65, 222, 86),cv::Scalar(250, 150, 201),cv::Scalar(201, 150, 105),
			cv::Scalar(104, 96, 142),cv::Scalar(111, 230, 54),cv::Scalar(105, 24, 22),cv::Scalar(42, 226, 101),cv::Scalar(67, 26, 144),cv::Scalar(155, 113, 106),
			cv::Scalar(152, 196, 216),cv::Scalar(58, 68, 152),cv::Scalar(68, 230, 213),cv::Scalar(169, 143, 129),cv::Scalar(191, 102, 41),cv::Scalar(5, 73, 170),
			cv::Scalar(15, 73, 233),cv::Scalar(95, 13, 71),cv::Scalar(25, 92, 218),cv::Scalar(85, 173, 16),cv::Scalar(247, 158, 17),cv::Scalar(36, 28, 8),
			cv::Scalar(31, 100, 134),cv::Scalar(131, 71, 45),cv::Scalar(158, 190, 91),cv::Scalar(90, 207, 220),cv::Scalar(125, 77, 228),cv::Scalar(40, 156, 67),
			cv::Scalar(35, 250, 69),cv::Scalar(229, 61, 245),cv::Scalar(210, 201, 106),cv::Scalar(184, 35, 131),cv::Scalar(47, 124, 120),cv::Scalar(1, 114, 23),
			cv::Scalar(99, 181, 17),cv::Scalar(77, 141, 151),cv::Scalar(79, 33, 95),cv::Scalar(194, 111, 146),cv::Scalar(187, 199, 138),cv::Scalar(129, 215, 40),
			cv::Scalar(160, 209, 144),cv::Scalar(139, 121, 58),cv::Scalar(97, 208, 197),cv::Scalar(185, 105, 171),cv::Scalar(160, 96, 136),cv::Scalar(232, 26, 26),
			cv::Scalar(34, 165, 109),cv::Scalar(19, 86, 215),cv::Scalar(205, 209, 199),cv::Scalar(131, 91, 25),cv::Scalar(51, 201, 16),cv::Scalar(64, 35, 128),
			cv::Scalar(120, 161, 247),cv::Scalar(123, 164, 190),cv::Scalar(15, 191, 40),cv::Scalar(11, 44, 117),cv::Scalar(198, 136, 70),cv::Scalar(14, 224, 240),
			cv::Scalar(60, 186, 193),cv::Scalar(253, 190, 129),cv::Scalar(134, 228, 173),cv::Scalar(219, 156, 214),cv::Scalar(137, 67, 254),cv::Scalar(178, 223, 250),
			cv::Scalar(219, 199, 139)
		};
		const std::vector<cv::Scalar> color20{
			cv::Scalar(128, 77, 207),cv::Scalar(65, 32, 208),cv::Scalar(0, 224, 45),cv::Scalar(3, 141, 219),cv::Scalar(80, 239, 253),cv::Scalar(239, 184, 12),
			cv::Scalar(7, 144, 145),cv::Scalar(161, 88, 57),cv::Scalar(0, 166, 46),cv::Scalar(218, 113, 53),cv::Scalar(193, 33, 128),cv::Scalar(190, 94, 113),
			cv::Scalar(113, 123, 232),cv::Scalar(69, 205, 80),cv::Scalar(18, 170, 49),cv::Scalar(89, 51, 241),cv::Scalar(153, 191, 154),cv::Scalar(27, 26, 69),
			cv::Scalar(20, 186, 194),cv::Scalar(210, 202, 167),cv::Scalar(196, 113, 204),cv::Scalar(9, 81, 88),cv::Scalar(191, 162, 67),cv::Scalar(227, 73, 120)
		};
	}

	// JC_Xiong-20240424 
	// 模型类型/功能，通过该枚举实例化模型对象
	enum class ME_ModelType
	{
		E_RESNET34 = 0,
		E_RESNET50,
		E_YOLOV8,
	};

	// 当前产品检测结果
	enum class ME_DetectRes
	{
		E_DETECT_OK = 0,
		E_DETECT_NG,
		E_DETECT_NONE,
	};

	// 图像分类返回结果
	typedef struct MS_Classification
	{
		MS_Classification() :mDetectRes(ME_DetectRes::E_DETECT_OK), mConfidence(0.0), mLabel("")
		{}

		ME_DetectRes mDetectRes;
		double mConfidence;
		std::string mLabel;
	}MS_ClassificationParam;

	struct Box
	{
		float left, top, right, bottom, confidence;
		int label;
		std::vector<cv::Point2i> land_marks;

		Box() = default;
		Box(float left, float top, float right, float bottom, float confidence, int label) :
			left(left), top(top), right(right), bottom(bottom), confidence(confidence), label(label) {}

		Box(float left, float top, float right, float bottom, float confidence, int label, int numLandMarks) :
			left(left), top(top), right(right), bottom(bottom), confidence(confidence), label(label)
		{
			land_marks.reserve(numLandMarks);
		}
	};

	// 目标检测返回结果
	typedef struct MS_ObjectDetect
	{
		MS_ObjectDetect() :mDetectRes(ME_DetectRes::E_DETECT_OK), mBoxVec(std::vector<Box>())
		{}

		ME_DetectRes mDetectRes;
		std::vector<Box> mBoxVec;
	}MS_ObjectDetectParam;


	struct MR_Result
	{
		MR_Result() : mObjectDecRes(), mClassifyDecRes()
		{}

		MS_ClassificationParam mClassifyDecRes;
		std::vector<std::vector<Box>> mObjectDecRes;
	};

	struct InitParameter
	{
		InitParameter() :num_class(5), dynamic_batch(false), batch_size(1), dst_h(0), dst_w(0), scale(255.0f),
			meanVec{ 0.0f,0.0f,0.0f }, stdVec{ 1.0f,1.0f,1.0f }, iou_thresh(0.5), conf_thresh(0.5), topK(1000),
			save_path(""), char_width(11), det_info_render_width(15), font_scale(0.6), is_show(false), is_save(false)
		{}


		ME_ModelType m_modelType;								// 模型类型
		int num_class;											// flower_data
		std::vector<std::string> class_names;
		std::vector<std::string> input_output_names;

		bool dynamic_batch;
		int batch_size;			// 推理的批量数据
		MN_VisionImage::MS_ImageParam mImage;
		int dst_h, dst_w;		// 输入到模型的图像宽高

		float scale;
		std::vector<float> stdVec;
		std::vector<float> meanVec;

		float iou_thresh;
		float conf_thresh;

		int topK;
		std::string save_path;

		std::string winname = "TensorRT-Infer";
		int char_width;
		int det_info_render_width;
		double font_scale;
		bool is_show;
		bool is_save;
	};

	// legacy
	struct CandidateObject
	{
		float mBboxAndkeyPoints[14]; // bbox:[x y w h] +  5 facial key points:[x1 y1 x2 y2 ...x5 y5]
		float mScore;
		bool  mIsGood;
		CandidateObject()
		{
			std::fill_n(mBboxAndkeyPoints, 14, FLT_MAX);
			mScore = FLT_MAX;
			mIsGood = true;
		}
		CandidateObject(float* bboxAndkeyPoints, float score, bool isGood) :
			mScore(score),
			mIsGood(isGood)
		{
			memcpy(mBboxAndkeyPoints, bboxAndkeyPoints, 14 * sizeof(float));
		}
	};



	enum class InputStream { IMAGE, VIDEO, CAMERA };

	enum class ColorMode { RGB, GRAY };

	struct AffineMat
	{
		float v0, v1, v2;
		float v3, v4, v5;
	};


	void saveBinaryFile(float* vec, size_t len, const std::string& file);

	std::vector<uint8_t> readBinaryFile(const std::string& file);

	std::vector<unsigned char> loadModel(const std::string& file);

	std::string getSystemTimeStr();

	bool setInputStream(const InputStream& source, const std::string& imagePath, const std::string& videoPath, const int& cameraID,
		cv::VideoCapture& capture, int& totalBatches, int& delayTime, InitParameter& param);

	void setRenderWindow(InitParameter& param);

	std::string getTimeStamp();

	void show(const std::vector<std::vector<Box>>& objectss,
		const std::vector<std::string>& classNames,
		const int& cvDelayTime, std::vector<cv::Mat>& imgsBatch);

	void save(const std::vector<std::vector<Box>>& objectss,
		const std::vector<std::string>& classNames,
		const std::string& savePath, std::vector<cv::Mat>& imgsBatch,
		const int& batchSize, const int& batchi);

	class HostTimer
	{
	public:
		HostTimer();
		float getUsedTime(); // while timing for cuda code, add "cudaDeviceSynchronize();" before this
		~HostTimer();

	private:
		std::chrono::steady_clock::time_point t1;
		std::chrono::steady_clock::time_point t2;
	};


	class DeviceTimer
	{
	public:
		DeviceTimer();
		float getUsedTime();
		// overload
		DeviceTimer(cudaStream_t ctream);
		float getUsedTime(cudaStream_t ctream);

		~DeviceTimer();

	private:
		cudaEvent_t start, end;
	};
}