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Commit 0332ddcb authored by wangsen's avatar wangsen
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add yolov8

parent b10952cf
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CMakeLists.txt
View file @ 0332ddcb
...@@ -35,11 +35,13 @@ set(SOURCE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/src/main.cpp ...@@ -35,11 +35,13 @@ set(SOURCE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/src/main.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/YOLOV3.cpp ${CMAKE_CURRENT_SOURCE_DIR}/src/YOLOV3.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/YOLOV5.cpp ${CMAKE_CURRENT_SOURCE_DIR}/src/YOLOV5.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/YOLOV7.cpp ${CMAKE_CURRENT_SOURCE_DIR}/src/YOLOV7.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/YOLOV8.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/RetinaFace.cpp ${CMAKE_CURRENT_SOURCE_DIR}/src/RetinaFace.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Inference/DetectorSSD.cpp ${CMAKE_CURRENT_SOURCE_DIR}/src/Inference/DetectorSSD.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Inference/DetectorYOLOV3.cpp ${CMAKE_CURRENT_SOURCE_DIR}/src/Inference/DetectorYOLOV3.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Inference/DetectorYOLOV5.cpp ${CMAKE_CURRENT_SOURCE_DIR}/src/Inference/DetectorYOLOV5.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Inference/DetectorYOLOV7.cpp ${CMAKE_CURRENT_SOURCE_DIR}/src/Inference/DetectorYOLOV7.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Inference/DetectorYOLOV8.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Inference/DetectorRetinaFace.cpp ${CMAKE_CURRENT_SOURCE_DIR}/src/Inference/DetectorRetinaFace.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Utility/CommonUtility.cpp ${CMAKE_CURRENT_SOURCE_DIR}/src/Utility/CommonUtility.cpp
${CMAKE_CURRENT_SOURCE_DIR}/src/Utility/Filesystem.cpp) ${CMAKE_CURRENT_SOURCE_DIR}/src/Utility/Filesystem.cpp)
......
Resource/Configuration.xml
View file @ 0332ddcb
...@@ -201,4 +201,15 @@ ...@@ -201,4 +201,15 @@
<NMSThreshold>0.5</NMSThreshold> <NMSThreshold>0.5</NMSThreshold>
<ObjectThreshold>0.5</ObjectThreshold> <ObjectThreshold>0.5</ObjectThreshold>
</DetectorYOLOV7> </DetectorYOLOV7>
<!--YOLOV8检测器 -->
<DetectorYOLOV8>
<ModelPath>"../Resource/Models/Detector/YOLOV8/yolov8n_static.onnx"</ModelPath>
<ClassNameFile>"../Resource/Models/Detector/YOLOV8/coco.names"</ClassNameFile>
<UseFP16>0</UseFP16><!--是否使用FP16-->
<NumberOfClasses>80</NumberOfClasses><!--类别数(不包括背景类)，COCO:80,VOC:20-->
<ConfidenceThreshold>0.25</ConfidenceThreshold>
<NMSThreshold>0.5</NMSThreshold>
<!-- <ObjectThreshold>0.5</ObjectThreshold> -->
</DetectorYOLOV8>
</opencv_storage> </opencv_storage>
Resource/Models/Detector/YOLOV8/coco.names 0 → 100644
View file @ 0332ddcb
person
bicycle
car
motorbike
aeroplane
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
sofa
pottedplant
bed
diningtable
toilet
tvmonitor
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush
include/DetectorYOLOV8.h 0 → 100644
View file @ 0332ddcb
// YOLOV8检测器
#ifndef __DETECTOR_YOLOV8_H__
#define __DETECTOR_YOLOV8_H__
#include <string>
#include <migraphx/program.hpp>
#include <opencv2/opencv.hpp>
#include <CommonDefinition.h>
#include "Decoder.h"
using namespace std;
using namespace cv;
using namespace migraphx;
namespace migraphxSamples
{
// YOLOV8参数
typedef struct _YOLOV8Parameter
{
int numberOfClasses;// 类别数
float confidenceThreshold;// 置信度阈值
float nmsThreshold;// NMS阈值
// float objectThreshold;//目标置信度值
}YOLOV8Parameter;
class DetectorYOLOV8
{
public:
DetectorYOLOV8();
~DetectorYOLOV8();
ErrorCode Initialize(InitializationParameterOfDetector initializationParameterOfDetector);
ErrorCode Detect(const cv::Mat &srcImage, std::vector<ResultOfDetection> &resultsOfDetection);
ErrorCode Detect(DCU_Frame &srcImage, std::vector<ResultOfDetection> &resultsOfDetection);
float* preprocess_Image = NULL;
private:
ErrorCode DoCommonInitialization(InitializationParameterOfDetector initializationParameterOfDetector);
private:
cv::FileStorage configurationFile;
InitializationParameterOfDetector initializationParameter;
FILE *logFile;
// net
migraphx::program net;
cv::Size inputSize;
string inputName;
migraphx::shape inputShape;
bool useFP16;
vector<string> classNames;
YOLOV8Parameter yolov8Parameter;
migraphx::parameter_map ParameterMap;
};
}
#endif
\ No newline at end of file
include/Sample.h
View file @ 0332ddcb
...@@ -15,7 +15,14 @@ void Sample_DetectorYOLOV5(int device); ...@@ -15,7 +15,14 @@ void Sample_DetectorYOLOV5(int device);
// YOLOV7 sample // YOLOV7 sample
void Sample_DetectorYOLOV7(int device); void Sample_DetectorYOLOV7(int device);
// YOLOV8 sample
void Sample_DetectorYOLOV8(int device);
// RetinaFace sample // RetinaFace sample
void Sample_DetectorRetinaFace(int device); void Sample_DetectorRetinaFace(int device);
#endif #endif
src/Inference/DetectorYOLOV8.cpp 0 → 100644
View file @ 0332ddcb
#include <DetectorYOLOV8.h>
#include <migraphx/onnx.hpp>
#include <migraphx/gpu/target.hpp>
#include <migraphx/gpu/hip.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/quantization.hpp>
#include <opencv2/dnn.hpp>
#include <CommonUtility.h>
#include <Filesystem.h>
#include <SimpleLog.h>
#include <fstream>
#include <hip/hip_runtime.h>
#include "hip/hip_runtime.h"
using namespace cv::dnn;
namespace migraphxSamples
{
DetectorYOLOV8::DetectorYOLOV8():logFile(NULL)
{
}
DetectorYOLOV8::~DetectorYOLOV8()
{
configurationFile.release();
}
ErrorCode DetectorYOLOV8::Initialize(InitializationParameterOfDetector initializationParameterOfDetector)
{
// 初始化(获取日志文件,加载配置文件等)
ErrorCode errorCode=DoCommonInitialization(initializationParameterOfDetector);
if(errorCode!=SUCCESS)
{
LOG_ERROR(logFile,"fail to DoCommonInitialization\n");
return errorCode;
}
LOG_INFO(logFile,"succeed to DoCommonInitialization\n");
// 获取配置文件参数
FileNode netNode = configurationFile["DetectorYOLOV8"];
string modelPath=initializationParameter.parentPath+(string)netNode["ModelPath"];
string pathOfClassNameFile=(string)netNode["ClassNameFile"];
yolov8Parameter.confidenceThreshold = (float)netNode["ConfidenceThreshold"];
yolov8Parameter.nmsThreshold = (float)netNode["NMSThreshold"];
// yolov8Parameter.objectThreshold = (float)netNode["ObjectThreshold"];
yolov8Parameter.numberOfClasses=(int)netNode["NumberOfClasses"];
useFP16=(bool)(int)netNode["UseFP16"];
// 加载模型
if(Exists(modelPath)==false)
{
LOG_ERROR(logFile,"%s not exist!\n",modelPath.c_str());
return MODEL_NOT_EXIST;
}
net = migraphx::parse_onnx(modelPath);
LOG_INFO(logFile,"succeed to load model: %s\n",GetFileName(modelPath).c_str());
// 获取模型输入属性
std::pair<std::string, migraphx::shape> inputAttribute=*(net.get_parameter_shapes().begin());
inputName=inputAttribute.first;
inputShape=inputAttribute.second;
inputSize=cv::Size(inputShape.lens()[3],inputShape.lens()[2]);// NCHW
// 设置模型为GPU模式
migraphx::target gpuTarget = migraphx::gpu::target{};
// 量化
if(useFP16)
{
migraphx::quantize_fp16(net);
}
// 编译模型
migraphx::compile_options options;
options.device_id=0; // 设置GPU设备，默认为0号设备(>=1.2版本中支持)
#ifdef DMA
options.offload_copy=false; // 设置offload_copy
#else
options.offload_copy=true; // 设置offload_copy
#endif
net.compile(gpuTarget,options);
LOG_INFO(logFile,"succeed to compile model: %s\n",GetFileName(modelPath).c_str());
// Run once by itself
#ifdef DMA
ParameterMap=CreateParameterMap(net);
net.eval(ParameterMap);
#else
migraphx::parameter_map inputData;
inputData[inputName]=migraphx::generate_argument(inputShape);
net.eval(inputData);
#endif
// 读取类别名
if(!pathOfClassNameFile.empty())
{
ifstream classNameFile(pathOfClassNameFile);
string line;
while (getline(classNameFile, line))
{
classNames.push_back(line);
}
}
else
{
classNames.resize(yolov8Parameter.numberOfClasses);
}
#ifdef DMA
hipMalloc((void**)&preprocess_Image, inputSize.height * inputSize.width * 3 * sizeof(float));
#endif
// log
LOG_INFO(logFile,"InputSize:%dx%d\n",inputSize.width,inputSize.height);
LOG_INFO(logFile,"InputName:%s\n",inputName.c_str());
LOG_INFO(logFile,"ConfidenceThreshold:%f\n",yolov8Parameter.confidenceThreshold);
// LOG_INFO(logFile,"objectThreshold:%f\n",yolov8Parameter.objectThreshold);
LOG_INFO(logFile,"NMSThreshold:%f\n",yolov8Parameter.nmsThreshold);
LOG_INFO(logFile,"NumberOfClasses:%d\n",yolov8Parameter.numberOfClasses);
return SUCCESS;
}
#ifdef DMA
__global__ void convert_bgrp_to_rgb_and_normalization_yolov8(unsigned char* srcImage, float* outImage, int width, int height)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
if (x < width * height)
{
unsigned char r = srcImage[x + width * height * 2];
unsigned char g = srcImage[x + width * height * 1];
unsigned char b = srcImage[x + width * height * 0];
float sum = 255.0;
outImage[x + width * height * 0] = r / sum;
outImage[x + width * height * 1] = g / sum;
outImage[x + width * height * 2] = b / sum;
}
}
__global__ void convert_yuv420p_to_rgb_and_normalization_yolov8(unsigned char* srcImage, float* outImage, int width, int height)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= width || y >= height)
return;
int index = y * width + x;
int yIndex = index;
int uIndex = (y / 2) * (width / 2) + (x / 2) + width * height;
int vIndex = (y / 2) * (width / 2) + (x / 2) + width * height * 5 / 4;
unsigned char yValue = srcImage[yIndex];
unsigned char uValue = srcImage[uIndex];
unsigned char vValue = srcImage[vIndex];
int r = yValue + 1.370705 * (vValue - 128);
int g = yValue - 0.698001 * (vValue - 128) - 0.337633 * (uValue - 128);
int b = yValue + 1.732446 * (uValue - 128);
outImage[x + width * height * 0] = (float)r;
outImage[x + width * height * 1] = (float)g;
outImage[x + width * height * 2] = (float)b;
}
__global__ void convert_rgba_to_rgb_and_normalization_yolov8(unsigned char* srcImage, float* outImage, int width, int height)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
if (x < width * height)
{
unsigned char r = srcImage[x * 4 + 0];
unsigned char g = srcImage[x * 4 + 1];
unsigned char b = srcImage[x * 4 + 2];
float sum = 255.0;
outImage[x + width * height * 0] = r / sum;
outImage[x + width * height * 1] = g / sum;
outImage[x + width * height * 2] = b / sum;
}
}
#endif
ErrorCode DetectorYOLOV8::Detect(const cv::Mat &srcImage,std::vector<ResultOfDetection> &resultsOfDetection)
{
if(srcImage.empty()||srcImage.type()!=CV_8UC3)
{
LOG_ERROR(logFile, "image error!\n");
return IMAGE_ERROR;
}
// 预处理并转换为NCHW
cv::Mat inputBlob;
blobFromImage(srcImage,
inputBlob,
1 / 255.0,
inputSize,
Scalar(0, 0, 0),
true,
false);
// 输入数据
migraphx::parameter_map inputData;
inputData[inputName]= migraphx::argument{inputShape, (float*)inputBlob.data};
// 推理
std::vector<migraphx::argument> inferenceResults = net.eval(inputData);
// 获取推理结果
std::vector<cv::Mat> outs;
migraphx::argument result = inferenceResults[0];
// 转换为cv::Mat
migraphx::shape outputShape = result.get_shape();
int shape[]={static_cast<int>(outputShape.lens()[0]),static_cast<int>(outputShape.lens()[1]),static_cast<int>(outputShape.lens()[2])};
cv::Mat out(3,shape,CV_32F);
memcpy(out.data,result.data(),sizeof(float)*outputShape.elements());
outs.push_back(out);
//获取先验框的个数
int numProposal = outs[0].size[2];
int numOut = outs[0].size[1];
//变换输出的维度
outs[0] = outs[0].reshape(1, numOut);
cv::transpose(outs[0], outs[0]);
float *data = (float *)outs[0].data;
//生成先验框
std::vector<float> confidences;
std::vector<cv::Rect> boxes;
std::vector<int> classIds;
float ratioh = (float)srcImage.rows / inputSize.height, ratiow = (float)srcImage.cols / inputSize.width;
// 计算x,y,w,h
for (int n = 0; n < numProposal; n++)
{
float *classes_scores = data + 4;
cv::Mat scores(1, classNames.size(), CV_32FC1, classes_scores);
cv::Point class_id;
double maxClassScore;
cv::minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);
if (maxClassScore > yolov8Parameter.confidenceThreshold)
{
confidences.push_back(maxClassScore);
classIds.push_back(class_id.x);
float x = data[0];
float y = data[1];
float w = data[2];
float h = data[3];
int left = int((x - 0.5 * w) * ratiow);
int top = int((y - 0.5 * h) * ratioh);
int width = int(w * ratiow);
int height = int(h * ratioh);
boxes.push_back(cv::Rect(left, top, width, height));
}
data += numOut;
}
//执行non maximum suppression消除冗余重叠boxes
std::vector<int> indices;
dnn::NMSBoxes(boxes, confidences, yolov8Parameter.confidenceThreshold, yolov8Parameter.nmsThreshold, indices);
for (size_t i = 0; i < indices.size(); ++i)
{
int idx = indices[i];
int classID=classIds[idx];
string className=classNames[classID];
float confidence=confidences[idx];
cv::Rect box = boxes[idx];
ResultOfDetection result;
result.boundingBox=box;
result.confidence=confidence;// confidence
result.classID=classID; // label
result.className=className;
resultsOfDetection.push_back(result);
}
return SUCCESS;
}
#ifdef DMA
ErrorCode DetectorYOLOV8::Detect(DCU_Frame &srcImage, std::vector<ResultOfDetection> &resultsOfDetection)
{
int block_size = 256;
int num_blocks = (srcImage.width * srcImage.height + block_size - 1) / block_size;
if(srcImage.format == AV_PIX_FMT_BGRP)
{
convert_bgrp_to_rgb_and_normalization_yolov8<<<num_blocks, block_size>>>(srcImage.dcu_data, preprocess_Image, srcImage.width, srcImage.height);
}
if(srcImage.format == AV_PIX_FMT_YUV420P)
{
convert_yuv420p_to_rgb_and_normalization_yolov8<<<num_blocks, block_size>>>(srcImage.dcu_data, preprocess_Image, srcImage.width, srcImage.height);
}
if(srcImage.format == AV_PIX_FMT_RGBA)
{
convert_rgba_to_rgb_and_normalization_yolov8<<<num_blocks, block_size>>>(srcImage.dcu_data, preprocess_Image, srcImage.width, srcImage.height);
}
// 输入数据
ParameterMap[inputName] = migraphx::argument{inputShape, preprocess_Image};
// 推理
std::vector<migraphx::argument> inferenceResults = net.eval(ParameterMap);
// 获取推理结果
std::vector<cv::Mat> outs;
migraphx::argument result = inferenceResults[0];
// 转换为cv::Mat
migraphx::shape outputShape = result.get_shape();
int shape[]={static_cast<int>(outputShape.lens()[0]), static_cast<int>(outputShape.lens()[1]), static_cast<int>(outputShape.lens()[2])};
cv::Mat out(3, shape, CV_32F, (unsigned char*)result.data());
outs.push_back(out);
//获取先验框的个数
int numProposal = outs[0].size[1];
int numOut = outs[0].size[2];
//变换输出的维度
outs[0] = outs[0].reshape(0, numProposal);
// 生成先验框
std::vector<float> confidences;
std::vector<cv::Rect> boxes;
std::vector<int> classIds;
float ratioh = (float)srcImage.rows / inputSize.height, ratiow = (float)srcImage.cols / inputSize.width;
// 计算x,y,w,h
for (int n = 0; n < numProposal; n++)
{
float *classes_scores = data + 4;
cv::Mat scores(1, classNames.size(), CV_32FC1, classes_scores);
cv::Point class_id;
double maxClassScore;
cv::minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);
if (maxClassScore > yolov8Parameter.confidenceThreshold)
{
confidences.push_back(maxClassScore);
classIds.push_back(class_id.x);
float x = data[0];
float y = data[1];
float w = data[2];
float h = data[3];
int left = int((x - 0.5 * w) * ratiow);
int top = int((y - 0.5 * h) * ratioh);
int width = int(w * ratiow);
int height = int(h * ratioh);
boxes.push_back(cv::Rect(left, top, width, height));
}
data += numOut;
}
std::vector<int> indices;
dnn::NMSBoxes(boxes, confidences, yolov8Parameter.confidenceThreshold, yolov8Parameter.nmsThreshold, indices);
for (size_t i = 0; i < indices.size(); ++i)
{
int idx = indices[i];
int classID=classIds[idx];
string className=classNames[classID];
float confidence=confidences[idx];
cv::Rect box = boxes[idx];
ResultOfDetection result;
result.boundingBox=box;
result.confidence=confidence;
result.classID=classID;
result.className=className;
resultsOfDetection.push_back(result);
}
return SUCCESS;
}
#endif
ErrorCode DetectorYOLOV8::DoCommonInitialization(InitializationParameterOfDetector initializationParameterOfDetector)
{
initializationParameter=initializationParameterOfDetector;
// 获取日志文件
logFile=LogManager::GetInstance()->GetLogFile(initializationParameter.logName);
// 加载配置文件
std::string configFilePath=initializationParameter.configFilePath;
if(!Exists(configFilePath))
{
LOG_ERROR(logFile, "no configuration file!\n");
return CONFIG_FILE_NOT_EXIST;
}
if(!configurationFile.open(configFilePath, FileStorage::READ))
{
LOG_ERROR(logFile, "fail to open configuration file\n");
return FAIL_TO_OPEN_CONFIG_FILE;
}
LOG_INFO(logFile, "succeed to open configuration file\n");
// 修改父路径
std::string &parentPath = initializationParameter.parentPath;
if (!parentPath.empty())
{
if(!IsPathSeparator(parentPath[parentPath.size() - 1]))
{
parentPath+=PATH_SEPARATOR;
}
}
return SUCCESS;
}
}
src/YOLOV8.cpp 0 → 100644
View file @ 0332ddcb
#include <Sample.h>
#include <SimpleLog.h>
#include <Filesystem.h>
#include <DetectorYOLOV8.h>
#include <sys/time.h>
#include <Decoder.h>
#include <Queuethread.h>
using namespace cv;
using namespace std;
using namespace cv::dnn;
using namespace migraphx;
using namespace migraphxSamples;
static void DecoderThreadFunc(Queue* queue)
{
int ret, end = 0;
int frame_cnt = 0;
Queue* que = queue;
Decoder decoder(que->device);
InitializationParameterOfDecoder initParamOfDecoderYOLOV8;
#ifndef DMA
initParamOfDecoderYOLOV8.src_filename = "../Resource/Images/Mean.mp4";
if( que->device == _HW)
{
initParamOfDecoderYOLOV8.str_devid[0] = {0};
initParamOfDecoderYOLOV8.xcoder_params = "out=hw";
initParamOfDecoderYOLOV8.dec_name = "h264_ni_quadra_dec";
initParamOfDecoderYOLOV8.filters_descr = "ni_quadra_scale=640:640:format=bgrp,hwdownload,format=bgrp";
} else if (que->device == _HW_DMA) {
LOG_ERROR(stdout, "Error program param or cmake param, not USE_P2P can`t set '--dma'!\n");
que->finish();
return;
}
#else
if( que->device == _HW_DMA) {
initParamOfDecoderYOLOV8.str_devid[0] = {0};
initParamOfDecoderYOLOV8.xcoder_params = "out=hw";
initParamOfDecoderYOLOV8.dec_name = "h264_ni_quadra_dec";
initParamOfDecoderYOLOV8.filters_descr = "ni_quadra_scale=640:640:format=rgba:is_p2p=1";
initParamOfDecoderYOLOV8.src_filename = "../Resource/Images/cr_1920x1080.h264";
} else {
LOG_ERROR(stdout, "Error program param or cmake param, USE_P2P need set '--dma'!\n");
que->finish();
return;
}
#endif
ret = decoder.DecoderInit(initParamOfDecoderYOLOV8);
if (ret == -1)
{
que->finish();
return;
}
while(true)
{
if (av_read_frame(decoder.fmt_ctx, decoder.pkt) < 0)
{
if(end == 2)
{
que->DecodeEnd = true;
break;
}
end = 1;
}
if (decoder.pkt->stream_index == decoder.video_stream_idx) {
if(!end) {
ret = avcodec_send_packet(decoder.video_dec_ctx, decoder.pkt);
} else {
ret = avcodec_send_packet(decoder.video_dec_ctx, NULL);
}
if (ret < 0 && ret != AVERROR_EOF) {
fprintf(stderr, "Error submitting a packet for decoding\n");
que->DecodeEnd = true;
break;
}
while (ret >= 0 || end == 1)
{
ret = avcodec_receive_frame(decoder.video_dec_ctx, decoder.frame);
if (ret == AVERROR(EAGAIN)) {
break;
} else if (ret == AVERROR_EOF ) {
end = 2;
break;
} else if (ret < 0) {
av_log(NULL, AV_LOG_ERROR, "Error while receiving a frame from the decoder\n");
que->finish();
return;
}
decoder.frame->pts = decoder.frame->best_effort_timestamp;
frame_cnt++;
if (que->device == CPU)
{
cv::Mat srcImage = cv::Mat::zeros(decoder.frame->height*3/2, decoder.frame->width, CV_8UC1);
memcpy(srcImage.data, (unsigned char*)decoder.frame->data[0], decoder.frame->width * decoder.frame->height);
memcpy(srcImage.data + decoder.frame->width * decoder.frame->height, (unsigned char*)decoder.frame->data[1], decoder.frame->width * decoder.frame->height/4);
memcpy(srcImage.data + decoder.frame->width * decoder.frame->height*5/4, (unsigned char*)decoder.frame->data[2], decoder.frame->width * decoder.frame->height/4);
cvtColor(srcImage, srcImage, COLOR_YUV420p2RGB);
que->enQueue(srcImage);
}
if (que->device == _HW || que->device == _HW_DMA)
{
if (av_buffersrc_add_frame_flags(decoder.buffersrc_ctx, decoder.frame, AV_BUFFERSRC_FLAG_KEEP_REF) < 0) {
av_log(NULL, AV_LOG_ERROR, "Error while feeding the filtergraph\n");
break;
}
while (1)
{
ret = av_buffersink_get_frame(decoder.buffersink_ctx, decoder.filt_frame);
if (ret == AVERROR(EAGAIN))
{
break;
}
else if(ret == AVERROR_EOF)
{
end = 2;
break;
}
if (ret < 0)
{
que->finish();
return;
}
#ifndef DMA
if (que->device == _HW)
{
cv::Mat srcImage;
switch (decoder.filt_frame->format)
{
case AV_PIX_FMT_BGRP:
{
srcImage = cv::Mat::zeros(decoder.filt_frame->height, decoder.filt_frame->width, CV_8UC3);
cv::Mat mat_r = cv::Mat(decoder.filt_frame->height, decoder.filt_frame->width, CV_8UC1, (unsigned char*)decoder.filt_frame->data[0]);
cv::Mat mat_g = cv::Mat(decoder.filt_frame->height, decoder.filt_frame->width, CV_8UC1, (unsigned char*)decoder.filt_frame->data[1]);
cv::Mat mat_b = cv::Mat(decoder.filt_frame->height, decoder.filt_frame->width, CV_8UC1, (unsigned char*)decoder.filt_frame->data[2]);
cv::Mat Channels[3]{mat_r, mat_g, mat_b};
cv::merge(Channels, 3, srcImage);
break;
}
case AV_PIX_FMT_YUV420P:
{
srcImage = cv::Mat::zeros(decoder.filt_frame->height*3/2, decoder.filt_frame->width, CV_8UC1);
memcpy(srcImage.data, (unsigned char*)decoder.filt_frame->data[0], decoder.filt_frame->width * decoder.filt_frame->height);
memcpy(srcImage.data + decoder.filt_frame->width * decoder.filt_frame->height, (unsigned char*)decoder.filt_frame->data[1], decoder.filt_frame->width * decoder.filt_frame->height/4);
memcpy(srcImage.data + decoder.filt_frame->width * decoder.filt_frame->height*5/4, (unsigned char*)decoder.filt_frame->data[2], decoder.filt_frame->width * decoder.filt_frame->height/4);
cvtColor(srcImage, srcImage, COLOR_YUV420p2RGB);
break;
}
case AV_PIX_FMT_RGBA:
{
srcImage = cv::Mat::zeros(decoder.filt_frame->height, decoder.filt_frame->width, CV_8UC4);
memcpy(srcImage.data, (unsigned char*)decoder.filt_frame->data[0], decoder.filt_frame->width * decoder.filt_frame->height * 4);
cvtColor(srcImage, srcImage, COLOR_BGRA2RGB);
break;
}
default:
break;
}
que->enQueue(srcImage);
av_frame_unref(decoder.filt_frame);
}
#else
if (que->device == _HW_DMA)
{
DCU_Frame dcu_frame;
AVHWFramesContext *hwfc = (AVHWFramesContext *)decoder.filt_frame->hw_frames_ctx->data;
switch (hwfc->sw_format)
{
case AV_PIX_FMT_BGRP:
{
dcu_frame.format = AV_PIX_FMT_BGRP;
dcu_frame.data_len = decoder.filt_frame->width * decoder.filt_frame->height * 3;
dcu_frame.srcImage = cv::Mat::zeros(decoder.filt_frame->height, decoder.filt_frame->width, CV_8UC3);
break;
}
case AV_PIX_FMT_YUV420P:
{
dcu_frame.format = AV_PIX_FMT_YUV420P;
dcu_frame.data_len = decoder.filt_frame->width * decoder.filt_frame->height * 3 / 2;
dcu_frame.srcImage = cv::Mat::zeros(decoder.filt_frame->height*3/2, decoder.filt_frame->width, CV_8UC1);
break;
}
case AV_PIX_FMT_RGBA:
{
dcu_frame.format = AV_PIX_FMT_RGBA;
dcu_frame.data_len = decoder.filt_frame->width * decoder.filt_frame->height * 4;
dcu_frame.srcImage = cv::Mat::zeros(decoder.filt_frame->height, decoder.filt_frame->width, CV_8UC4);
break;
}
default:
break;
}
dcu_frame.width = decoder.filt_frame->width;
dcu_frame.height = decoder.filt_frame->height;
hipMalloc((void**)&(dcu_frame.dcu_data), dcu_frame.data_len * sizeof(unsigned char));
ret = decoder.retrieve_filter_frame(dcu_frame, decoder.filt_frame);
if (ret)
av_log(NULL, AV_LOG_ERROR, "Error while retrieve_filter_frame with p2p.\n");
if(dcu_frame.format == AV_PIX_FMT_BGRP)
{
cv::Mat mat_b = cv::Mat(decoder.filt_frame->height, decoder.filt_frame->width, CV_8UC1, (unsigned char*)dcu_frame.srcImage.data);
cv::Mat mat_g = cv::Mat(decoder.filt_frame->height, decoder.filt_frame->width, CV_8UC1, (unsigned char*)(dcu_frame.srcImage.data + decoder.filt_frame->height * decoder.filt_frame->width));
cv::Mat mat_r = cv::Mat(decoder.filt_frame->height, decoder.filt_frame->width, CV_8UC1, (unsigned char*)(dcu_frame.srcImage.data + decoder.filt_frame->height * decoder.filt_frame->width * 2));
cv::Mat Channels[3]{mat_r, mat_g, mat_b};
cv::merge(Channels, 3, dcu_frame.srcImage);
}
if(dcu_frame.format == AV_PIX_FMT_YUV420P)
cvtColor(dcu_frame.srcImage, dcu_frame.srcImage, COLOR_YUV420p2RGB);
if(dcu_frame.format == AV_PIX_FMT_RGBA)
cvtColor(dcu_frame.srcImage, dcu_frame.srcImage, COLOR_BGRA2RGB);
queue->enQueue(dcu_frame);
av_frame_unref(decoder.filt_frame);
}
#endif
}
}
av_frame_unref(decoder.frame);
}
}
av_packet_unref(decoder.pkt);
}
LOG_INFO(stdout, "Decoder: ####### frame count: %d\n", frame_cnt);
que->finish();
}
static void DetectorThreadFunc(Queue* que)
{
Queue* queue = que;
// DetectorYOLOV8 Init
DetectorYOLOV8 detector;
InitializationParameterOfDetector initParamOfDetectorYOLOV8;
initParamOfDetectorYOLOV8.parentPath = "";
initParamOfDetectorYOLOV8.configFilePath = CONFIG_FILE;
initParamOfDetectorYOLOV8.logName = "";
ErrorCode errorCode=detector.Initialize(initParamOfDetectorYOLOV8);
if(errorCode!=SUCCESS)
{
LOG_ERROR(stdout, "fail to initialize detector!\n");
exit(-1);
}
LOG_INFO(stdout, "succeed to initialize detector\n");
int frame_cnt = 0;
double start_time = getTickCount();
while (!queue->DecodeEnd) {
#ifdef DMA
DCU_Frame dcu_frame;
queue->deQueue(&dcu_frame);
if(dcu_frame.srcImage.empty()) {
continue;
}
#else
cv::Mat InferImage;
queue->deQueue(&InferImage);
if (InferImage.empty()) {
continue;
}
#endif
// detect
std::vector<ResultOfDetection> predictions;
double time1 = getTickCount();
#ifdef DMA
detector.Detect(dcu_frame, predictions);
#else
detector.Detect(InferImage, predictions);
#endif
double time2 = getTickCount();
double elapsedTime = (time2 - time1)*1000 / getTickFrequency();
LOG_INFO(stdout, "inference time:%f ms\n", elapsedTime);
frame_cnt++;
#ifdef DMA
hipFree(dcu_frame.dcu_data);
#endif
// process result
LOG_INFO(stdout,"////////////////Detection Results////////////////\n");
for( int i = 0; i < predictions.size(); ++i)
{
ResultOfDetection result = predictions[i];
#ifdef DMA
cv::rectangle(dcu_frame.srcImage, result.boundingBox, Scalar(0,255,255),2);
cv::putText(dcu_frame.srcImage, result.className, cv::Point(result.boundingBox.x, result.boundingBox.y-20), cv::FONT_HERSHEY_PLAIN, 2.0, Scalar(0, 0, 255), 2);
#else
cv::rectangle(InferImage, result.boundingBox, Scalar(0,255,255),2);
cv::putText(InferImage, result.className, cv::Point(result.boundingBox.x, result.boundingBox.y-20), cv::FONT_HERSHEY_PLAIN, 2.0, Scalar(0, 0, 255), 2);
#endif
LOG_INFO(stdout,"box:%d %d %d %d,label:%d,confidence:%f\n",result.boundingBox.x,
result.boundingBox.y,result.boundingBox.width,result.boundingBox.height,result.classID,result.confidence);
}
// X11 display can`t support in docker.
/*namedWindow("video", WINDOW_NORMAL | WINDOW_KEEPRATIO);
imshow("video", InferImage);
if (waitKey(10) == 'q') {
break;
}*/
}
#ifdef DMA
hipFree(detector.preprocess_Image);
#endif
double end_time = getTickCount();
fprintf(stdout, "Finish ####### frame_cnt: %d, Inference fps: %.2f, all time: %.2f ms\n", frame_cnt, float(frame_cnt/((end_time - start_time)/getTickFrequency())), (end_time - start_time)/getTickFrequency()*1000);
queue->finish();
}
void Sample_DetectorYOLOV8(int device)
{
Queue* queue = new Queue(1);
queue->device = device;
std::thread ThreadDecoder(DecoderThreadFunc, queue);
std::thread ThreadDetector(DetectorThreadFunc, queue);
ThreadDecoder.join();
ThreadDetector.join();
delete queue;
queue = NULL;
return;
}
src/main.cpp
View file @ 0332ddcb
...@@ -36,7 +36,7 @@ int main(int argc, char *argv[]) ...@@ -36,7 +36,7 @@ int main(int argc, char *argv[])
} }
int opt, index; int opt, index;
int device = -1; int device = -1;
const char* Nets[] = {"YOLOV3", "YOLOV5", "YOLOV7", "SSD", "RetinaFace"}; const char* Nets[] = {"YOLOV3", "YOLOV5", "YOLOV7", "SSD", "RetinaFace", "YOLOV8"};
while ((opt = getopt_long(argc, argv, "chdn:", long_options, NULL)) != -1) while ((opt = getopt_long(argc, argv, "chdn:", long_options, NULL)) != -1)
{ {
switch (opt) switch (opt)
...@@ -91,6 +91,10 @@ int main(int argc, char *argv[]) ...@@ -91,6 +91,10 @@ int main(int argc, char *argv[])
{ {
Sample_DetectorRetinaFace(device); Sample_DetectorRetinaFace(device);
} }
case 5:
{
Sample_DetectorYOLOV8(device);
}
break; break;
} }
......
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