ExampleClsRuntimeBatchInspection.cpp

This is an example of how to inspect sample images by using batch-inspection cls module. More details about this example.

 
#include "ClassifierTestDLL.h"
#include "SRUtils.h"
 
/* define this variable when user want to use new additional score other than preset values. */
//#define SR_USE_NEW_ADDITIONAL_SCORE
 
/* define this variable when user want to limit the forward time of classfication algorithm. */
//#define SR_USE_FORWARD_TIME_LIMIT
 
int main()
{
    /***********************************************************
    *    최대 이미지 수 설정 , Set max # of images to inspect at once
    *     - 한번에 검사할 최대 이미지 수를 설정합니다. (Set maximum number of images to inspect at once)
    *     - 기본적으로 2의 배수로 설정할 것을 추천드리며 크게 설정할 수록
    *       GPU 메모리 할당량이 커지니 메모리가 부족하다면 더 작은 수로 설정해주세요.
    *       (Recommend to set this value as multiple of 2, and if GPU memory is low, set the value as lower number.)
    *     - 최대 이미지 수 보다 많은 이미지가 들어올 시에 내부적으로 여러번의 검사를 수행하게 됩니다. 
    *       (If more images than nMaxImages are passed to the inspection function, multiple inspections are conducted internally.)
    ***********************************************************/
    int          nMaxImages = 8; /* 4, 8, 16, 32, 64 */
 
    std::wstring data_dir = L"../../../examples/data";
 
    // trained model path
    std::wstring trainedModelPath = data_dir + L"/models/textile_sample_model.srCls";
 
    // image paths to classify
    std::vector<std::wstring> imagePaths(6);
    imagePaths[0] = data_dir + L"/images/Textile/class1_ok/000001.png";
    imagePaths[1] = data_dir + L"/images/Textile/class1_ng/000002.png";
    imagePaths[2] = data_dir + L"/images/Textile/class2_ok/000001.png";
    imagePaths[3] = data_dir + L"/images/Textile/class2_ng/000002.png";
    imagePaths[4] = data_dir + L"/images/Textile/class3_ok/000001.png";
    imagePaths[5] = data_dir + L"/images/Textile/class3_ng/000002.png";
 
    eErrorType srStatus = eErrorType::NO_ERROR_OUT;
 
    /***********************************************************
    *    0. 이미지 인풋 타입 설정
    *    inputImageType -
    *             UCGrayImage:  from memory(8bit unsigned char gray image)
    *             UCColorImage: from memory(8bit unsigned char color image)
    *             UIColorImage: from memory(32bit unsigned int color image)
    ***********************************************************/
    //eSRInputImageType inputImageType = eSRInputImageType::UCGrayImage;
    eSRInputImageType inputImageType = eSRInputImageType::UCColorImage;
    //eSRInputImageType inputImageType = eSRInputImageType::UIColorImage;
 
    /***********************************************************
    *    0-1. 정보 띄우기 단계 설정 (Verbosity)
    *    verbosity -
    *             0:    only warning
    *             1:    warning + model info
    *             2:    warning + model info + result info
    *             3:    warning + model info + result info + image display
    *    displayTime - 결과 이미지 display 하는 시간 (milliseconds)
    *    resizeFactor - 결과 이미지 display 하는 스케일 (display 크기 = 원본 크기 * resizeFactor)
    ***********************************************************/
    int verbosity = 3;
    int displayTime = 100;
    double resizeFactor = 1.0;
 
    /***********************************************************
    *    0-2. GPU 번호 설정
    *    gpuIndex - 사용하고자 하는 gpu의 번호
    *       Multi GPU 가 가능한 License의 경우에 한하여 gpuIndex를
    *       0~3까지 설정해 줄 수 있다. 아닌 경우 gpuIndex는 0을 사용하여야 한다.
    ***********************************************************/
    int gpuIndex = 0;
 
    /***********************************************************
    *    1.  모델 파일 handle 생성,
    *        Create handles for model files and inspection results
    ***********************************************************/
    ClsTestHandle handle = nullptr;
    srStatus = SR_CreateClsTestHandleByWideCharPath_ErrorType(&handle, trainedModelPath.c_str());
    if (srStatus != NO_ERROR_OUT || handle == nullptr)
    {
        std::wcout << "Saige Vision failed: Please check the file path of trained model." << std::endl;
    }
 
    int nTotalImages = imagePaths.size();
    std::vector<ClsTestResult> clsTestResults(nTotalImages);
    
    for (size_t i_img = 0; i_img < nTotalImages; i_img++)
    {
        srStatus = SR_MakeClsTestResult_ErrorType(&clsTestResults[i_img]);
    }
 
    /***********************************************************
    *    2. 클래스 정보 확인 (Optional)
    *       Check class information
    ***********************************************************/
    // Print classification Test Parameters() by calling one fuctions
    srStatus = SR_PrintClsTestParameters(handle);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that the handle is not created.
        SR_DestroyClsTestHandle(handle);
        return -1;
    }
 
    // Get classification test parameters one by one
    int numberOfClassesOutput = 0;
    int maxStrLenOfClassName = 0;
 
    // Get the number of classes
    srStatus = SR_GetClsTestNumberOfClasses(handle, &numberOfClassesOutput);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that input pointer is not allocated (nullptr).
        SR_DestroyClsTestHandle(handle);
        return -1;
    }
 
    // Get the maximum length of class names
    srStatus = SR_GetClsTestMaxStrLenOfClassName(handle, &maxStrLenOfClassName);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that input pointer is not allocated (nullptr).
        SR_DestroyClsTestHandle(handle);
        return -1;
    }
 
    std::vector<std::string> classNames(numberOfClassesOutput);
    std::vector<std::wstring> wClassNames(numberOfClassesOutput);
    std::vector<int> classColorsOutput(numberOfClassesOutput);
    std::vector<int> additionalScoreOutput(numberOfClassesOutput);
 
    char* classNameOutput = new char[maxStrLenOfClassName];
    wchar_t* wClassNameOutput = new wchar_t[maxStrLenOfClassName];
 
    for (int i = 0; i < numberOfClassesOutput; i++)
    {
        // Get a color of single class
        srStatus = SR_GetClsTestOneClassColor(handle, i, &(classColorsOutput[i]));
        if (srStatus != NO_ERROR_OUT)
        {
            PrintErrorType(srStatus, verbosity);
            // if you get this error, it's possible that the input index is out of bound(# of classes) or input pointer is not allocated (nullptr).
            SR_DestroyClsTestHandle(handle);
            return -1;
        }
 
        // Get a name of single class by multi byte char (char*)
        srStatus = SR_GetClsTestOneClassNameWithMultiByteChar(handle, i, classNameOutput);
        if (srStatus != NO_ERROR_OUT)
        {
            PrintErrorType(srStatus, verbosity);
            // if you get this error, it's possible that the input index is out of bound(# of classes) or input pointer is not allocated (nullptr).
            SR_DestroyClsTestHandle(handle);
            return -1;
        }
 
        // Get a name of single class by wide char (wchar_t*)
        srStatus = SR_GetClsTestOneClassNameWithWideChar(handle, i, wClassNameOutput);
        if (srStatus != NO_ERROR_OUT)
        {
            PrintErrorType(srStatus, verbosity);
            // if you get this error, it's possible that the input index is out of bound(# of classes) or input pointer is not allocated (nullptr).
            SR_DestroyClsTestHandle(handle);
            return -1;
        }
        classNames[i] = classNameOutput;
        wClassNames[i] = wClassNameOutput;
    }
 
    delete[] classNameOutput;
    delete[] wClassNameOutput;
 
    /***********************************************************
    *    3. 검사 옵션 확인 및 수정 (Optional)
    *       Check inspection options and set new values
    ***********************************************************/
    // Get the additional score for each class
    srStatus = SR_GetClsTestAdditionalScore(handle, additionalScoreOutput.data());
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that input pointer is not allocated (nullptr).
        SR_DestroyClsTestHandle(handle);
        return -1;
    }
 
#ifdef SR_USE_NEW_ADDITIONAL_SCORE
    std::vector<int> additionalScore(numberOfClassesOutput);
 
    for (int i = 0; i < numberOfClassesOutput; i++)
    {
        additionalScore[i] = 10;
    }
    srStatus = SR_SetClsTestAdditionalScore(handle, numberOfClassesOutput, additionalScore.data(), true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that the input's [# of classes] does not match the model's or input pointer is not allocated (nullptr).
        SR_DestroyClsTestHandle(handle);
        return -1;
    }
 
#endif
#ifdef SR_USE_FORWARD_TIME_LIMIT
    float timeLimit = 1000; // milliseconds
    srStatus = SR_SetClsTestForwardTimeLimitVariables(handle, timeLimit, true, true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that handle is not created.
        SR_DestroyClsTestHandle(handle);
        return -1;
    }
#endif
#ifndef SR_USE_FORWARD_TIME_LIMIT
    srStatus = SR_SetClsTestIsToUseForwardTimeLimit(handle, false, true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that handle is not created.
        SR_DestroyClsTestHandle(handle);
        return -1;
    }
#endif
 
    /***********************************************************
    *    4. 메모리를 GPU 및 CPU에 할당합니다.
    *       Allocate GPU and CPU memory
    ***********************************************************/
    srStatus = SR_LoadClsTestNetworkInGPU(handle, gpuIndex, nMaxImages);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that the inserted index is not applicable.
        SR_DestroyClsTestHandle(handle);
        return -1;
    }
 
 
    std::vector<int>           heights(nTotalImages, 0);
    std::vector<int>           widths(nTotalImages, 0);
    unsigned char** ucImageData = new unsigned char*[nTotalImages]; // arrays of unsigned char image data
    unsigned int**  uiImageData = new unsigned int*[nTotalImages]; // arrays of unsigned int image data
    for (size_t i_img = 0; i_img < nTotalImages; i_img++)
    {
        uiImageData[i_img] = nullptr;
        ucImageData[i_img] = nullptr;
 
        if (inputImageType == eSRInputImageType::UIColorImage)
        {
            uiImageData[i_img] = (unsigned int*)GetImageDataByInputImageType(imagePaths[i_img], inputImageType, heights[i_img], widths[i_img]);
        }
        else
        {
            ucImageData[i_img] = (unsigned char*)GetImageDataByInputImageType(imagePaths[i_img], inputImageType, heights[i_img], widths[i_img]);
        }
    }
 
    /***********************************************************
    *    5. 이미지 검사하기, Inspect images
    ***********************************************************/
    eErrorType errorType = NO_ERROR_OUT;
 
    if (inputImageType == eSRInputImageType::UCGrayImage)
    {
        errorType = SR_ApplyClsTestFromMultiple8bitCharGrayArrays(handle, nTotalImages, ucImageData, heights.data(), widths.data(), clsTestResults.data());
    }
    else if (inputImageType == eSRInputImageType::UCColorImage)
    {
        errorType = SR_ApplyClsTestFromMultiple8bitCharColorArrays(handle, nTotalImages, ucImageData, heights.data(), widths.data(), clsTestResults.data());
    }
    else if (inputImageType == eSRInputImageType::UIColorImage)
    {
        errorType = SR_ApplyClsTestFromMultipleUnsignedIntColorArrays(handle, nTotalImages, uiImageData, heights.data(), widths.data(), clsTestResults.data());
    }
 
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        std::cout << "Fail to classify" << std::endl;
    }
    else
    {
        /***********************************************************
        *    6. 검사 결과 받아오기, Retrieve inspection results
        ***********************************************************/
        if (verbosity >= 2)
        {
            long inspectionTimeOutput = 0, loadImageTimeOutput = 0;
 
            // Get the inspection time
            srStatus = SR_GetClsTestInspectionTime(clsTestResults[0], &inspectionTimeOutput);
            if (srStatus != NO_ERROR_OUT)
            {
                // if you get this error, it's possible that the input pointer is not allocated (nullptr).
                PrintErrorType(srStatus, verbosity);
            }
 
            // Get the image-loading time
            srStatus = SR_GetClsTestLoadImageTime(clsTestResults[0], &loadImageTimeOutput);
            if (srStatus != NO_ERROR_OUT)
            {
                // if you get this error, it's possible that the input pointer is not allocated (nullptr).
                PrintErrorType(srStatus, verbosity);
            }
            std::cout << "====== classification Test Result (Total) ======" << std::endl;
            std::cout << "  - Total Image Loading time: " << loadImageTimeOutput << std::endl;
            std::cout << "  - Total Inspection time: " << inspectionTimeOutput << std::endl;
        }
 
        for (size_t i_img = 0; i_img < nTotalImages; i_img++)
        {
            SR_ClassifiedResult clsResult;
            srStatus = SR_GetClsTestClassifiedResult(clsTestResults[i_img], &clsResult);
            if (srStatus != NO_ERROR_OUT)
            {
                PrintErrorType(srStatus, verbosity);
                // if you get this error, it's possible that input pointer is not allocated (nullptr).
                continue; // jump to next image
            }
 
            // Display and draw
            if (verbosity >= 2)
            {
                std::cout << "====== classification Test Result (Single) ======" << std::endl;
                std::wcout << imagePaths[i_img] << std::endl;
                for (int j = 0; j < numberOfClassesOutput; j++)
                {
                    std::cout << "  - " << j << "-th class score (original, with additional): " << clsResult.scoresOriginal[j] << '\t' << clsResult.scoresIncludingAdd[j] << std::endl;
                }
                std::cout << "  - Result Class: " << clsResult.classIdx << std::endl;
            }
 
            if (verbosity >= 3)
            {
                cv::Mat image = cv::Mat();
 
                if (inputImageType == eSRInputImageType::UCGrayImage)
                {
                    image = cv::Mat(heights[i_img], widths[i_img], CV_8UC1, ucImageData[i_img]);
                    image = image.clone(); /* to prevent modifying raw data */
                }
                else if (inputImageType == eSRInputImageType::UCColorImage)
                {
                    image = cv::Mat(heights[i_img], widths[i_img], CV_8UC3, ucImageData[i_img]);
                    image = image.clone(); /* to prevent modifying raw data */
                }
                else if (inputImageType == eSRInputImageType::UIColorImage)
                {
                    image = cv::Mat(heights[i_img], widths[i_img], CV_8UC4, uiImageData[i_img]);
                    image = image.clone(); /* to prevent modifying raw data */
                }
                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);
                }
 
                // get class color
                int colorTmp = classColorsOutput[clsResult.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::putText(image, classNames[clsResult.classIdx],
                    cv::Point(int(image.size().width * 0.1), int(image.size().height * 0.9)), // Coordinates
                    cv::FONT_HERSHEY_COMPLEX_SMALL, // Font
                    2.0, // Scale. 2.0 = 2x bigger
                    classColor, // BGR Color
                    4 // Line Thickness (Optional)
                );
 
                cv::namedWindow("classification Result", cv::WINDOW_NORMAL);
                cv::resizeWindow("classification Result", image.size().width * resizeFactor, image.size().height * resizeFactor);
                cv::imshow("classification Result", image);
                cv::waitKey(displayTime);
            }
        }
    }
 
    /***********************************************************
    *    7. Handle 파괴, Destruct handles
    ***********************************************************/
    srStatus = SR_DestroyClsTestHandle(handle);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that the input pointer is not allocated (nullptr).
        return -1;
    }
 
    for (size_t i_img = 0; i_img < nTotalImages; i_img++)
    {
        srStatus = SR_DeleteClsTestResult(clsTestResults[i_img]);
        if (srStatus != NO_ERROR_OUT)
        {
            PrintErrorType(srStatus, verbosity);
            // if you get this error, it's possible that the input pointer is not allocated (nullptr).
            return -1;
        }
        if (ucImageData[i_img] != nullptr)
        {
            delete[] ucImageData[i_img];
            ucImageData[i_img] = nullptr;
        }
        if (uiImageData[i_img] != nullptr)
        {
            delete[] uiImageData[i_img];
            uiImageData[i_img] = nullptr;
        }
    }
    delete[] uiImageData;
    delete[] ucImageData;
 
    std::cout << "SaigeVision successfully completed, please press enter key to terminate" << std::endl;
    std::cin.get();
 
    return 0;
}