yolo_world/easydeploy/deepstream/custom_mmyolo_bbox_parser/nvdsparsebbox_mmyolo.cpp

#include "nvdsinfer_custom_impl.h"
#include <cassert>
#include <iostream>

/**
 * Function expected by DeepStream for decoding the MMYOLO output.
 *
 * C-linkage [extern "C"] was written to prevent name-mangling. This function must return true after
 * adding all bounding boxes to the objectList vector.
 *
 * @param [outputLayersInfo] std::vector of NvDsInferLayerInfo objects with information about the output layer.
 * @param [networkInfo] NvDsInferNetworkInfo object with information about the MMYOLO network.
 * @param [detectionParams] NvDsInferParseDetectionParams with information about some config params.
 * @param [objectList] std::vector of NvDsInferParseObjectInfo objects to which bounding box information must
 * be stored.
 *
 * @return true
 */

// This is just the function prototype. The definition is written at the end of the file.
extern "C" bool NvDsInferParseCustomMMYOLO(
	std::vector<NvDsInferLayerInfo> const& outputLayersInfo,
	NvDsInferNetworkInfo const& networkInfo,
	NvDsInferParseDetectionParams const& detectionParams,
	std::vector<NvDsInferParseObjectInfo>& objectList);

static __inline__ float clamp(float& val, float min, float max)
{
	return val > min ? (val < max ? val : max) : min;
}

static std::vector<NvDsInferParseObjectInfo> decodeMMYoloTensor(
	const int* num_dets,
	const float* bboxes,
	const float* scores,
	const int* labels,
	const float& conf_thres,
	const unsigned int& img_w,
	const unsigned int& img_h
)
{
	std::vector<NvDsInferParseObjectInfo> bboxInfo;
	size_t nums = num_dets[0];
	for (size_t i = 0; i < nums; i++)
	{
		float score = scores[i];
		if (score < conf_thres)continue;
		float x0 = (bboxes[i * 4]);
		float y0 = (bboxes[i * 4 + 1]);
		float x1 = (bboxes[i * 4 + 2]);
		float y1 = (bboxes[i * 4 + 3]);
		x0 = clamp(x0, 0.f, img_w);
		y0 = clamp(y0, 0.f, img_h);
		x1 = clamp(x1, 0.f, img_w);
		y1 = clamp(y1, 0.f, img_h);
		NvDsInferParseObjectInfo obj;
		obj.left = x0;
		obj.top = y0;
		obj.width = x1 - x0;
		obj.height = y1 - y0;
		obj.detectionConfidence = score;
		obj.classId = labels[i];
		bboxInfo.push_back(obj);
	}

	return bboxInfo;
}

/* C-linkage to prevent name-mangling */
extern "C" bool NvDsInferParseCustomMMYOLO(
	std::vector<NvDsInferLayerInfo> const& outputLayersInfo,
	NvDsInferNetworkInfo const& networkInfo,
	NvDsInferParseDetectionParams const& detectionParams,
	std::vector<NvDsInferParseObjectInfo>& objectList)
{

// Some assertions and error checking.
	if (outputLayersInfo.empty() || outputLayersInfo.size() != 4)
	{
		std::cerr << "Could not find output layer in bbox parsing" << std::endl;
		return false;
	}

//	Score threshold of bboxes.
	const float conf_thres = detectionParams.perClassThreshold[0];

// Obtaining the output layer.
	const NvDsInferLayerInfo& num_dets = outputLayersInfo[0];
	const NvDsInferLayerInfo& bboxes = outputLayersInfo[1];
	const NvDsInferLayerInfo& scores = outputLayersInfo[2];
	const NvDsInferLayerInfo& labels = outputLayersInfo[3];

// num_dets(int) bboxes(float) scores(float) labels(int)
	assert (num_dets.dims.numDims == 2);
	assert (bboxes.dims.numDims == 3);
	assert (scores.dims.numDims == 2);
	assert (labels.dims.numDims == 2);


// Decoding the output tensor of MMYOLO to the NvDsInferParseObjectInfo format.
	std::vector<NvDsInferParseObjectInfo> objects =
		decodeMMYoloTensor(
			(const int*)(num_dets.buffer),
			(const float*)(bboxes.buffer),
			(const float*)(scores.buffer),
			(const int*)(labels.buffer),
			conf_thres,
			networkInfo.width,
			networkInfo.height
		);

	objectList.clear();
	objectList = objects;
	return true;
}

/* Check that the custom function has been defined correctly */
CHECK_CUSTOM_PARSE_FUNC_PROTOTYPE(NvDsInferParseCustomMMYOLO);