This is an example of how to inspect sample images by using all the functions of det module. More details about this example.
int main()
{
std::wstring data_dir = L"../../../examples/data";
std::wstring trainedModelPath = data_dir + L"/models/lego_sample_model.srDet";
std::vector<std::wstring> imagePaths(5);
imagePaths[0] = data_dir + L"/images/Lego/sample1.png";
imagePaths[1] = data_dir + L"/images/Lego/sample2.png";
imagePaths[2] = data_dir + L"/images/Lego/sample3.png";
imagePaths[3] = data_dir + L"/images/Lego/sample4.png";
imagePaths[4] = data_dir + L"/images/Lego/sample5.png";
unsigned char* ucImageData = nullptr;
unsigned int* uiImageData = nullptr;
int verbosity = 3;
int displayTime = 100;
double resizeFactor = 1.0;
int gpuIndex = 0;
{
std::cout << "Saige Vision failed: Please check the file path of trained model." << std::endl;
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
int numberOfClassesOutput = 0;
int maxStrLenOfClassName = 0;
{
PrintErrorType(srStatus, verbosity);
return -1;
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
std::vector<std::string> classNames(numberOfClassesOutput);
std::vector<std::wstring> wClassNames(numberOfClassesOutput);
std::vector<int> classColorsOutput(numberOfClassesOutput);
std::vector<int> scoreThresholdOutput(numberOfClassesOutput);
std::vector<int> pixelSizeThresholdOutput(numberOfClassesOutput);
char* classNameOutput = new char[maxStrLenOfClassName];
wchar_t* wClassNameOutput = new wchar_t[maxStrLenOfClassName];
for (int i = 0; i < numberOfClassesOutput; i++)
{
{
PrintErrorType(srStatus, verbosity);
return -1;
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
classNames[i] = classNameOutput;
wClassNames[i] = wClassNameOutput;
}
delete[] classNameOutput;
delete[] wClassNameOutput;
{
PrintErrorType(srStatus, verbosity);
return -1;
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
#ifdef SR_USE_NEW_SCORE_THRESHOLD
std::vector<int> scoreThresholdInput(numberOfClassesOutput);
for (int i = 0; i < numberOfClassesOutput; i++)
{
scoreThresholdInput[i] = 50;
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
#endif
#ifdef SR_USE_NEW_PIXEL_SIZE_THRESHOLD
std::vector<int> pixelSizeThresholdInput(numberOfClassesOutput);
for (int i = 0; i < numberOfClassesOutput; i++)
{
pixelSizeThresholdInput[i] = 0;
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
#endif
#ifdef SR_FILTER_OBJECTS_BY_REGION
{
PrintErrorType(srStatus, verbosity);
return -1;
}
std::vector<float> filterObjSimpleParams(4);
filterObjSimpleParams[0] = 0.1f;
filterObjSimpleParams[1] = 0.1f;
filterObjSimpleParams[2] = 0.8f;
filterObjSimpleParams[3] = 0.8f;
{
PrintErrorType(srStatus, verbosity);
return -1;
}
#endif
#ifdef SR_USE_FORWARD_TIME_LIMIT
float timeLimit = 1100;
{
PrintErrorType(srStatus, verbosity);
return -1;
}
#endif
#ifndef SR_USE_FORWARD_TIME_LIMIT
{
PrintErrorType(srStatus, verbosity);
return -1;
}
#endif
#ifdef SR_FILTER_OUTPUT_BY_MAX_NUMBER
std::vector<int> maxNumberOfDetectionBoxes(numberOfClassesOutput);
for (size_t i = 0; i < numberOfClassesOutput; i++)
{
maxNumberOfDetectionBoxes[i] = 5;
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
#endif
#ifndef SR_FILTER_OUTPUT_BY_MAX_NUMBER
std::vector<int> maxNumberOfDetectionBoxes(numberOfClassesOutput);
for (size_t i = 0; i < numberOfClassesOutput; i++)
{
maxNumberOfDetectionBoxes[i] = -1;
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
#endif
{
PrintErrorType(srStatus, verbosity);
return -1;
}
for (size_t i_img = 0; i_img < imagePaths.size(); i_img++)
{
int h = 0, w = 0;
if (inputImageType == eSRInputImageType::UIColorImage)
{
uiImageData = (unsigned int*)GetImageDataByInputImageType(imagePaths[i_img], inputImageType, h, w);
}
else
{
ucImageData = (unsigned char*)GetImageDataByInputImageType(imagePaths[i_img], inputImageType, h, w);
}
if (inputImageType == eSRInputImageType::ImageFilePathMultiByteChar)
{
size_t sizeOfChar = 0;
size_t sizeOfWChar = wcslen(imagePaths[i_img].c_str());
size_t bufferSize = sizeOfWChar * 2;
std::vector<char> imagePathMB(bufferSize);
sizeOfChar = wcstombs(
imagePathMB.data(),
imagePaths[i_img].c_str(),
bufferSize
);
if (sizeOfChar != sizeOfWChar)
{
std::wcout << "Saige Vision FAILED: Wrong Image path" << imagePaths[i_img] << std::endl;
continue;
}
}
else if (inputImageType == eSRInputImageType::ImageFilePathWideChar)
{
}
else if (inputImageType == eSRInputImageType::UCGrayImage)
{
}
else if (inputImageType == eSRInputImageType::UCColorImage)
{
}
else if (inputImageType == eSRInputImageType::UIColorImage)
{
}
{
PrintErrorType(srStatus, verbosity);
if (verbosity >= 3)
{
DisplayFailedImageOnCVWindow(imagePaths[i_img], "FAIL TO DETECT", "Detection Result", displayTime, resizeFactor);
}
continue;
}
int numberOfBoxesOutput = 0;
long inspectionTimeOutput = 0, loadImageTimeOutput = 0;
{
PrintErrorType(srStatus, verbosity);
continue;
}
{
PrintErrorType(srStatus, verbosity);
continue;
}
{
PrintErrorType(srStatus, verbosity);
continue;
}
std::vector<SR_DetectedBox> detectedBoxes(numberOfBoxesOutput);
{
PrintErrorType(srStatus, verbosity);
continue;
}
cv::Mat image = cv::Mat();
if (verbosity >= 3)
{
if (inputImageType == eSRInputImageType::UCGrayImage)
{
image = cv::Mat(h, w, CV_8UC1, ucImageData);
image = image.clone();
}
else if (inputImageType == eSRInputImageType::UCColorImage)
{
image = cv::Mat(h, w, CV_8UC3, ucImageData);
image = image.clone();
}
else if (inputImageType == eSRInputImageType::UIColorImage)
{
image = cv::Mat(h, w, CV_8UC4, uiImageData);
image = image.clone();
}
else
{
int isSuccess = OpenImageFile(imagePaths[i_img].c_str(), image);
if (isSuccess == 0)
{
std::cout << "Saige Vision FAILED: Fail to open image file to display" << std::endl;
continue;
}
}
if (image.channels() == 4)
{
cv::cvtColor(image, image, cv::COLOR_BGRA2BGR);
}
if (image.channels() == 1)
{
cv::cvtColor(image, image, cv::COLOR_GRAY2BGR);
}
}
if (verbosity >= 2)
{
std::cout << "====== Detection Test Result ======" << std::endl;
std::wcout << imagePaths[i_img] << std::endl;
std::cout << " - Number of final detected boxes: " << numberOfBoxesOutput << std::endl;
#ifdef SR_FILTER_OUTPUT_BY_MAX_NUMBER
int* numberOfBoxesBeforeFiltered = new int[numberOfClassesOutput];
std::cout << " - Number of raw detected boxes before filtering: " << std::endl;
for (size_t i_c = 0; i_c < numberOfClassesOutput; i_c++)
{
std::cout << " - " << classNames[i_c] << ": " << numberOfBoxesBeforeFiltered[i_c] << std::endl;
}
delete[] numberOfBoxesBeforeFiltered;
#endif
std::cout << " - Image Loading time: " << loadImageTimeOutput << std::endl << std::endl;
std::cout << " - Inspection time: " << inspectionTimeOutput << std::endl;
std::cout << " - Detected boxes information" << std::endl;
}
for (int i = 0; i < numberOfBoxesOutput; i++)
{
int xmin = static_cast<int>(detectedBoxes[i].box.x_tl);
int ymin = static_cast<int>(detectedBoxes[i].box.y_tl);
int xmax = static_cast<int>(detectedBoxes[i].box.x_br);
int ymax = static_cast<int>(detectedBoxes[i].box.y_br);
if (verbosity >= 2)
{
printf(" - %d-th box info(label, score, x_topLeft, y_topLeft, x_bottomRight, y_bottomRight): %d, %0.2f, %d, %d, %d, %d\n",
i, detectedBoxes[i].box.classIdx, detectedBoxes[i].box.score, xmin, ymin, xmax, ymax);
}
if (verbosity >= 3)
{
int colorTmp = classColorsOutput[detectedBoxes[i].box.classIdx];
unsigned char r = (colorTmp >> 16) & 0xFF;
unsigned char g = (colorTmp >> 8) & 0xFF;
unsigned char b = (colorTmp) & 0xFF;
cv::Scalar classColor = cv::Scalar(b, g, r);
cv::Rect rect(xmin, ymin, xmax - xmin, ymax - ymin);
cv::rectangle(image, rect, classColor, 2, 8, 0);
cv::putText(image, classNames[detectedBoxes[i].box.classIdx], cv::Point(xmin, ymin), 2, 1.2, classColor, 3);
}
}
if (verbosity >= 3)
{
cv::namedWindow("Detection Result", cv::WINDOW_NORMAL);
cv::resizeWindow("Detection Result", image.size().width * resizeFactor, image.size().height * resizeFactor);
cv::imshow("Detection Result", image);
cv::waitKey(displayTime);
}
if (ucImageData != nullptr)
{
delete[] ucImageData;
}
if (uiImageData != nullptr)
{
delete[] uiImageData;
}
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
{
PrintErrorType(srStatus, verbosity);
return -1;
}
std::cout << "SaigeVision successfully completed, please press enter key to terminate" << std::endl;
std::cin.get();
return 0;
}
Header file for Detection Module.
eErrorType SR_PrintDetTestParameters(DetTestHandle handle)
Print a model parameters (Detection's class information, inspection options...)
Header file of util functions for example projects.
eSRInputImageType
Data Type of input Image for example projects.
Definition: SRUtils.h:23
eErrorType SR_GetDetTestPixelSizeThreshold(DetTestHandle handle, int *areaThreshold)
Get Detection's area threshold for each class.
eErrorType SR_SetDetTestPixelSizeThreshold(DetTestHandle handle, int *areaThreshold, bool writeOnModelFile=true)
Set Detection's area threshold for each class.
eErrorType SR_GetDetTestOneClassNameWithMultiByteChar(DetTestHandle handle, const int classIdx, char *className)
Get a multi byte char name of certain class by given index.
eErrorType SR_GetDetTestMaxStrLenOfClassName(DetTestHandle handle, int *len)
Get a maximum length of class name.
eErrorType SR_GetDetTestNumberOfClasses(DetTestHandle handle, int *numberOfClasses)
Get the number of classes.
eErrorType SR_GetDetTestOneClassColor(DetTestHandle handle, const int classIdx, int *classColor)
Get a color of certain class by given index.
eErrorType SR_GetDetTestOneClassNameWithWideChar(DetTestHandle handle, const int classIdx, wchar_t *className)
Get a wide char name of certain class by given index.
eErrorType SR_SetDetFilterObjSimpleParameters(DetTestHandle handle, float *simpleParams, bool writeOnModelFile=true)
Set the simple parameters of filtering object by Detection's filter object by region.
eErrorType SR_SetDetFilterObjByRegion(DetTestHandle handle, eFilterObjByRegionType filterObjType, bool writeOnModelFile=true)
Set the filtering object type by Detection's filter object by region.
eErrorType SR_SetDetTestForwardTimeLimitVariables(DetTestHandle handle, float forwardTimeLimit, bool bIsToUseForwardTimeLimit, bool writeOnModelFile=true)
Set the Forward time limit variables.
eErrorType SR_SetDetTestIsToUseForwardTimeLimit(DetTestHandle handle, bool bIsToUseForwardTimeLimit, bool writeOnModelFile=true)
Set the boolean whether the current model is using time limit or not.
eErrorType SR_MakeDetTestResult_ErrorType(DetTestResult *detTestResult)
Construct a DetTestResult.
eErrorType SR_CreateDetTestHandleByWideCharPath_ErrorType(DetTestHandle *detTestHandle, const wchar_t *filePath)
Create a DetTestHandle via wide char path.
void * DetTestResult
Detection inspection results handler.
Definition: DetectorTestDLL.h:67
void * DetTestHandle
Detection file handler.
Definition: DetectorTestDLL.h:52
eErrorType SR_DestroyDetTestHandle(DetTestHandle handle)
Destruct a DetTestHandle.
eErrorType SR_DeleteDetTestResult(DetTestResult detTestResult)
Destruct a DetTestResult.
eErrorType SR_ApplyDetTestFrom8bitCharGrayArray(DetTestHandle handle, const unsigned char *imageData, const int height, const int width, DetTestResult detTestResult)
Apply detection algorithm to 1-channel image by 8-bit data.
eErrorType SR_ApplyDetTestFrom8bitCharColorArray(DetTestHandle handle, const unsigned char *imageData, const int height, const int width, DetTestResult detTestResult)
Apply detection algorithm to 3-channels(BGR) image by 8-bit data.
eErrorType SR_ApplyDetTestFromImageFile(DetTestHandle handle, const char *imagePath, DetTestResult detTestResult)
Apply detection algorithm to image by file path.
eErrorType SR_ApplyDetTestFromImageFileByWideCharPath(DetTestHandle handle, const wchar_t *imagePath, DetTestResult detTestResult)
Apply detection algorithm to image by file path.
eErrorType SR_ApplyDetTestFromUnsignedIntColorArray(DetTestHandle handle, const unsigned int *imageData, const int height, const int width, DetTestResult detTestResult)
Apply detection algorithm to 4-channels(BGRA) image by 8-bit data.
eErrorType SR_GetDetTestResultNumberOfBoxesBeforeFiltered(DetTestResult detTestResult, int numClasses, int *numRawBoxes)
Get the number of raw detected boxes before filtered by Max number of boxes for each class.
eErrorType SR_GetDetTestLoadImageTime(DetTestResult detTestResult, long *imageLoadingTimeInLong)
Get the image loading time.
eErrorType SR_GetDetTestInspectionTime(DetTestResult detTestResult, long *inspectionTimeInLong)
Get the inspection time.
eErrorType SR_GetDetTestResultDetectedBoxes(DetTestResult detTestResult, const int numDetectedBoxes, SR_DetectedBox *detectedBoxes)
Get SR_DetectedBox.
eErrorType SR_GetDetTestResultNumberOfBoxes(DetTestResult detTestResult, int *numDetectedBoxes)
Get the number of detected boxes.
eErrorType SR_SetDetTestMaxNumberOfDetectionBoxesForEachClass(DetTestHandle handle, int numOfClasses, int *maxNumBoxes, bool writeOnModelFile=true)
Set Max number of boxes.
eErrorType SR_SetDetTestMinScoreThreshold(DetTestHandle handle, int numOfClasses, int *scoreThreshold, bool writeOnModelFile=true)
Set Detection's score threshold for each class.
eErrorType SR_GetDetTestMinScoreThreshold(DetTestHandle handle, int *scoreThreshold)
Get Detection's score threshold for each class.
eErrorType SR_LoadDetTestNetworkInGPU(DetTestHandle handle, int GPUNumber)
Allocate required cpu & gpu memory.