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Commit a5e2341e authored by yaoht's avatar yaoht
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fix cpp inputdata channel range problem

parent e80d6c72
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Showing with 309 additions and 442 deletions
Src/YOLOX.cpp
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Src/YOLOX.h
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#ifndef __DETECTOR_YOLOX_H__ #ifndef __DETECTOR_YOLOX_H__
#define __DETECTOR_YOLOX_H__ #define __DETECTOR_YOLOX_H__
#include <migraphx/program.hpp>
#include <CommonDefinition.h> #include <CommonDefinition.h>
#include <migraphx/program.hpp>
namespace migraphxSamples namespace migraphxSamples {
{
typedef struct _YOLOXParameter typedef struct _YOLOXParameter {
{
int numberOfClasses; int numberOfClasses;
float confidenceThreshold; float confidenceThreshold;
float nmsThreshold; float nmsThreshold;
float objectThreshold; float objectThreshold;
}YOLOXParameter; } YOLOXParameter;
struct Object struct Object {
{
cv::Rect_<float> rect; cv::Rect_<float> rect;
int label; int label;
float prob; float prob;
}; };
struct GridAndStride struct GridAndStride {
{
int grid0; int grid0;
int grid1; int grid1;
int stride; int stride;
}; };
class DetectorYOLOX class DetectorYOLOX {
{ public:
public:
DetectorYOLOX(); DetectorYOLOX();
~DetectorYOLOX();
ErrorCode Initialize(InitializationParameterOfDetector initializationParameterOfDetector, bool dynamic); ~DetectorYOLOX();
void decode_outputs(float* prob, std::vector<Object>& objects, float scalew, float scaleh, const int img_w, const int img_h);
void generate_grids_and_stride(std::vector<int>& strides, std::vector<GridAndStride>& grid_strides);
void generate_yolox_proposals(std::vector<GridAndStride> grid_strides, float* feat_blob, float prob_threshold, std::vector<Object>& objects);
void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right);
void qsort_descent_inplace(std::vector<Object>& objects);
void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold);
inline float intersection_area(const Object& a, const Object& b);
ErrorCode Detect(const cv::Mat &srcImage, std::vector<std::size_t> &relInputShape, std::vector<ResultOfDetection> &resultsOfDetection, bool dynamic); ErrorCode Initialize(
InitializationParameterOfDetector initializationParameterOfDetector);
ErrorCode Detect(const cv::Mat &srcImage,
const std::vector<std::size_t> &relInputShape,
std::vector<ResultOfDetection> &resultsOfDetection);
private:
void decode_outputs(float *prob, std::vector<Object> &objects, float scalew,
float scaleh, const int img_w, const int img_h,
cv::Size inputSize);
void generate_grids_and_stride(std::vector<int> &strides,
std::vector<GridAndStride> &grid_strides,
cv::Size inputSize);
void generate_yolox_proposals(std::vector<GridAndStride> grid_strides,
float *feat_blob, float prob_threshold,
std::vector<Object> &objects);
void qsort_descent_inplace(std::vector<Object> &faceobjects, int left,
int right);
void qsort_descent_inplace(std::vector<Object> &objects);
void nms_sorted_bboxes(const std::vector<Object> &faceobjects,
std::vector<int> &picked, float nms_threshold);
inline float intersection_area(const Object &a, const Object &b);
private:
cv::FileStorage configurationFile; cv::FileStorage configurationFile;
migraphx::program net; migraphx::program net;
...@@ -57,15 +62,13 @@ private: ...@@ -57,15 +62,13 @@ private:
std::string inputName; std::string inputName;
std::string modelPath; std::string modelPath;
migraphx::shape inputShape; migraphx::shape inputShape;
bool useFP16; bool useFP16;
std::vector<std::string> classNames; std::vector<std::string> classNames;
YOLOXParameter yoloxParameter; YOLOXParameter yoloxParameter;
}; };
} } // namespace migraphxSamples
#endif #endif
Src/main.cpp
View file @ a5e2341e
#include <Filesystem.h>
#include <SimpleLog.h>
#include <YOLOX.h>
#include <stdio.h> #include <stdio.h>
#include <stdlib.h> #include <stdlib.h>
#include <string.h> #include <string.h>
#include <SimpleLog.h>
#include <Filesystem.h>
#include <YOLOX.h>
void MIGraphXSamplesUsage(char* programName) void MIGraphXSamplesUsage(char *programName) {
{
printf("Usage : %s <index> \n", programName); printf("Usage : %s <index> \n", programName);
printf("index:\n"); printf("index:\n");
printf("\t 0) YOLOX sample.\n"); printf("\t 0) YOLOX sample.\n");
// printf("\t 1) YOLOX Dynamic sample.\n"); 暂不支持
} }
void Sample_YOLOX(); void Sample_YOLOX();
void Sample_YOLOX_Dynamic();
int main(int argc, char *argv[]) int main(int argc, char *argv[]) {
{ if (argc < 2 || argc > 2) {
if (argc < 2 || argc > 2)
{
MIGraphXSamplesUsage(argv[0]); MIGraphXSamplesUsage(argv[0]);
return -1; return -1;
} }
if (!strncmp(argv[1], "-h", 2)) if (!strncmp(argv[1], "-h", 2)) {
{
MIGraphXSamplesUsage(argv[0]); MIGraphXSamplesUsage(argv[0]);
return 0; return 0;
} }
switch (*argv[1]) switch (*argv[1]) {
{ case '0': {
case '0': Sample_YOLOX();
{ break;
Sample_YOLOX(); }
break; case '1': {
} break;
case '1': }
{ default: {
// Sample_YOLOX_Dynamic(); 暂不支持 MIGraphXSamplesUsage(argv[0]);
break; break;
} }
default :
{
MIGraphXSamplesUsage(argv[0]);
break;
}
} }
return 0; return 0;
} }
void Sample_YOLOX() void Sample_YOLOX() {
{
// 创建YOLOX检测器 // 创建YOLOX检测器
migraphxSamples::DetectorYOLOX detector; migraphxSamples::DetectorYOLOX detector;
migraphxSamples::InitializationParameterOfDetector initParamOfDetectorYOLOX; migraphxSamples::InitializationParameterOfDetector initParamOfDetectorYOLOX;
initParamOfDetectorYOLOX.configFilePath = CONFIG_FILE; initParamOfDetectorYOLOX.configFilePath = CONFIG_FILE;
migraphxSamples::ErrorCode errorCode=detector.Initialize(initParamOfDetectorYOLOX, false); migraphxSamples::ErrorCode errorCode =
if(errorCode!=migraphxSamples::SUCCESS) detector.Initialize(initParamOfDetectorYOLOX, false);
{ if (errorCode != migraphxSamples::SUCCESS) {
LOG_ERROR(stdout, "fail to initialize detector!\n"); LOG_ERROR(stdout, "fail to initialize detector!\n");
exit(-1); exit(-1);
} }
LOG_INFO(stdout, "succeed to initialize detector\n"); LOG_INFO(stdout, "succeed to initialize detector\n");
// 读取测试图片 // 读取测试图片
cv::Mat srcImage = cv::imread("../Resource/Images/image_test.jpg",1); cv::Mat srcImage = cv::imread("../Resource/Images/image_test.jpg", 1);
// 静态推理固定尺寸 // 静态推理固定尺寸
std::vector<std::size_t> inputShape={1,3,640,640}; std::vector<std::size_t> inputShape = {1, 3, 640, 640};
// 推理 // 推理
std::vector<migraphxSamples::ResultOfDetection> predictions; std::vector<migraphxSamples::ResultOfDetection> predictions;
double time1 = cv::getTickCount(); double time1 = cv::getTickCount();
detector.Detect(srcImage,inputShape,predictions,false); detector.Detect(srcImage, inputShape, predictions, false);
double time2 = cv::getTickCount(); double time2 = cv::getTickCount();
double elapsedTime = (time2 - time1)*1000 / cv::getTickFrequency(); double elapsedTime = (time2 - time1) * 1000 / cv::getTickFrequency();
LOG_INFO(stdout, "inference time:%f ms\n", elapsedTime); LOG_INFO(stdout, "inference time:%f ms\n", elapsedTime);
// 获取推理结果 // 获取推理结果
LOG_INFO(stdout,"========== Detection Results ==========\n"); LOG_INFO(stdout, "========== Detection Results ==========\n");
for(int i=0;i<predictions.size();++i) for (int i = 0; i < predictions.size(); ++i) {
{ migraphxSamples::ResultOfDetection result = predictions[i];
migraphxSamples::ResultOfDetection result=predictions[i]; cv::rectangle(srcImage, result.boundingBox, cv::Scalar(0, 255, 255), 2);
cv::rectangle(srcImage,result.boundingBox,cv::Scalar(0,255,255),2);
std::string label = cv::format("%.2f", result.confidence); std::string label = cv::format("%.2f", result.confidence);
label = result.className + " " + label; label = result.className + " " + label;
int left = predictions[i].boundingBox.x; int left = predictions[i].boundingBox.x;
int top = predictions[i].boundingBox.y; int top = predictions[i].boundingBox.y;
int baseLine; int baseLine;
cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); cv::Size labelSize =
cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
top = max(top, labelSize.height); top = max(top, labelSize.height);
cv::putText(srcImage, label, cv::Point(left, top-10), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 255), 2); cv::putText(srcImage, label, cv::Point(left, top - 10),
cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 255), 2);
LOG_INFO(stdout,"box:%d %d %d %d,label:%d,confidence:%f\n",predictions[i].boundingBox.x,
predictions[i].boundingBox.y,predictions[i].boundingBox.width,predictions[i].boundingBox.height,predictions[i].classID,predictions[i].confidence); LOG_INFO(stdout, "box:%d %d %d %d,label:%d,confidence:%f\n",
} predictions[i].boundingBox.x, predictions[i].boundingBox.y,
cv::imwrite("Result.jpg",srcImage); predictions[i].boundingBox.width,
LOG_INFO(stdout,"Detection results have been saved to ./Result.jpg\n"); predictions[i].boundingBox.height, predictions[i].classID,
predictions[i].confidence);
}
void Sample_YOLOX_Dynamic()
{
// 创建YOLOX检测器
migraphxSamples::DetectorYOLOX detector;
migraphxSamples::InitializationParameterOfDetector initParamOfDetectorYOLOX;
initParamOfDetectorYOLOX.configFilePath = CONFIG_FILE;
migraphxSamples::ErrorCode errorCode=detector.Initialize(initParamOfDetectorYOLOX, true);
if(errorCode!=migraphxSamples::SUCCESS)
{
LOG_ERROR(stdout, "fail to initialize detector!\n");
exit(-1);
}
LOG_INFO(stdout, "succeed to initialize detector\n");
// 读取测试图像
std::vector<cv::Mat> srcImages;
cv::String folder = "../Resource/Images/DynamicPics";
std::vector<cv::String> imagePathList;
cv::glob(folder,imagePathList);
for (int i = 0; i < imagePathList.size(); ++i)
{
cv:: Mat srcImage=cv::imread(imagePathList[i], 1);
srcImages.push_back(srcImage);
}
// 设置动态推理shape
std::vector<std::vector<std::size_t>> inputShapes;
inputShapes.push_back({1,3,416,416});
inputShapes.push_back({1,3,608,608});
for (int i = 0; i < srcImages.size(); ++i)
{
// 推理
std::vector<migraphxSamples::ResultOfDetection> predictions;
double time1 = cv::getTickCount();
detector.Detect(srcImages[i], inputShapes[i], predictions, true);
double time2 = cv::getTickCount();
double elapsedTime = (time2 - time1)*1000 / cv::getTickFrequency();
LOG_INFO(stdout, "inference image%d time:%f ms\n", i, elapsedTime);
// 获取推理结果
LOG_INFO(stdout,"========== Detection Image%d Results ==========\n", i);
for(int j=0;j<predictions.size();++j)
{
migraphxSamples::ResultOfDetection result=predictions[j];
cv::rectangle(srcImages[i],result.boundingBox,cv::Scalar(0,255,255),2);
std::string label = cv::format("%.2f", result.confidence);
label = result.className + " " + label;
int left = predictions[j].boundingBox.x;
int top = predictions[j].boundingBox.y;
int baseLine;
cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
top = max(top, labelSize.height);
cv::putText(srcImages[i], label, cv::Point(left, top-10), cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 255), 2);
LOG_INFO(stdout,"box:%d %d %d %d,label:%d,confidence:%f\n",predictions[j].boundingBox.x,
predictions[j].boundingBox.y,predictions[j].boundingBox.width,predictions[j].boundingBox.height,predictions[j].classID,predictions[j].confidence);
}
std::string imgName = cv::format("Result%d.jpg", i);
cv::imwrite(imgName, srcImages[i]);
LOG_INFO(stdout,"Detection results have been saved to ./Result%d.jpg\n", i);
} }
cv::imwrite("Result.jpg", srcImage);
LOG_INFO(stdout, "Detection results have been saved to ./Result.jpg\n");
} }
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