openvino里的object detection demo 运行的时候有很多报错是怎么回事? 5C

代码如下:/*
// Copyright (c) 2018 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
*/
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include

#include
#include
#include

#include
#include
#include
#include "object_detection_demo.h"
#include "detectionoutput.h"

using namespace InferenceEngine;

bool ParseAndCheckCommandLine(int argc, char *argv[]) {
// ---------------------------Parsing and validation of input args--------------------------------------
slog::info << "Parsing input parameters" << slog::endl;

gflags::ParseCommandLineNonHelpFlags(&argc, &argv, true);
if (FLAGS_h) {
    showUsage();
    return false;
}

if (FLAGS_ni < 1) {
    throw std::logic_error("Parameter -ni should be greater than 0 (default: 1)");
}

if (FLAGS_i.empty()) {
    throw std::logic_error("Parameter -i is not set");
}

if (FLAGS_m.empty()) {
    throw std::logic_error("Parameter -m is not set");
}

return true;

}

/**

  • \brief The entry point for the Inference Engine object_detection demo application
  • \file object_detection_demo/main.cpp
  • \example object_detection_demo/main.cpp
    /
    int main(int argc, char *argv[]) {
    try {
    /
    * This demo covers certain topology and cannot be generalized for any object detection one **/
    slog::info << "InferenceEngine: " << GetInferenceEngineVersion() << "\n";

    // ------------------------------ Parsing and validation of input args ---------------------------------
    if (!ParseAndCheckCommandLine(argc, argv)) {
        return 0;
    }
    
    /** This vector stores paths to the processed images **/
    std::vector<std::string> images;
    parseImagesArguments(images);
    if (images.empty()) throw std::logic_error("No suitable images were found");
    // -----------------------------------------------------------------------------------------------------
    
    // --------------------------- 1. Load Plugin for inference engine -------------------------------------
    slog::info << "Loading plugin" << slog::endl;
    InferencePlugin plugin = PluginDispatcher({ FLAGS_pp, "../../../lib/intel64" , "" }).getPluginByDevice(FLAGS_d);
    
    /*If CPU device, load default library with extensions that comes with the product*/
    if (FLAGS_d.find("CPU") != std::string::npos) {
        /**
        * cpu_extensions library is compiled from "extension" folder containing
        * custom MKLDNNPlugin layer implementations. These layers are not supported
        * by mkldnn, but they can be useful for inferencing custom topologies.
        **/
        plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>());
    }
    
    if (!FLAGS_l.empty()) {
        // CPU(MKLDNN) extensions are loaded as a shared library and passed as a pointer to base extension
        IExtensionPtr extension_ptr = make_so_pointer<IExtension>(FLAGS_l);
        plugin.AddExtension(extension_ptr);
        slog::info << "CPU Extension loaded: " << FLAGS_l << slog::endl;
    }
    
    if (!FLAGS_c.empty()) {
        // clDNN Extensions are loaded from an .xml description and OpenCL kernel files
        plugin.SetConfig({ { PluginConfigParams::KEY_CONFIG_FILE, FLAGS_c } });
        slog::info << "GPU Extension loaded: " << FLAGS_c << slog::endl;
    }
    
    /** Setting plugin parameter for per layer metrics **/
    if (FLAGS_pc) {
        plugin.SetConfig({ { PluginConfigParams::KEY_PERF_COUNT, PluginConfigParams::YES } });
    }
    
    /** Printing plugin version **/
    printPluginVersion(plugin, std::cout);
    // -----------------------------------------------------------------------------------------------------
    
    // --------------------------- 2. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------
    std::string binFileName = fileNameNoExt(FLAGS_m) + ".bin";
    slog::info << "Loading network files:"
        "\n\t" << FLAGS_m <<
        "\n\t" << binFileName <<
        slog::endl;
    
    CNNNetReader networkReader;
    /** Read network model **/
    networkReader.ReadNetwork(FLAGS_m);
    
    /** Extract model name and load weigts **/
    networkReader.ReadWeights(binFileName);
    CNNNetwork network = networkReader.getNetwork();
    
    Precision p = network.getPrecision();
    // -----------------------------------------------------------------------------------------------------
    
    // --------------------------- 3. Configure input & output ---------------------------------------------
    
    // ------------------------------ Adding DetectionOutput -----------------------------------------------
    
    /**
    * The only meaningful difference between Faster-RCNN and SSD-like topologies is the interpretation
    * of the output data. Faster-RCNN has 2 output layers which (the same format) are presented inside SSD.
    *
    * But SSD has an additional post-processing DetectionOutput layer that simplifies output filtering.
    * So here we are adding 3 Reshapes and the DetectionOutput to the end of Faster-RCNN so it will return the
    * same result as SSD and we can easily parse it.
    */
    
    std::string firstLayerName = network.getInputsInfo().begin()->first;
    
    int inputWidth = network.getInputsInfo().begin()->second->getTensorDesc().getDims()[3];
    int inputHeight = network.getInputsInfo().begin()->second->getTensorDesc().getDims()[2];
    
    DataPtr bbox_pred_reshapeInPort = ((ICNNNetwork&)network).getData(FLAGS_bbox_name.c_str());
    if (bbox_pred_reshapeInPort == nullptr) {
        throw std::logic_error(std::string("Can't find output layer named ") + FLAGS_bbox_name);
    }
    
    SizeVector bbox_pred_reshapeOutDims = {
        bbox_pred_reshapeInPort->getTensorDesc().getDims()[0] *
        bbox_pred_reshapeInPort->getTensorDesc().getDims()[1], 1
    };
    DataPtr rois_reshapeInPort = ((ICNNNetwork&)network).getData(FLAGS_proposal_name.c_str());
    if (rois_reshapeInPort == nullptr) {
        throw std::logic_error(std::string("Can't find output layer named ") + FLAGS_proposal_name);
    }
    
    SizeVector rois_reshapeOutDims = { rois_reshapeInPort->getTensorDesc().getDims()[0] * rois_reshapeInPort->getTensorDesc().getDims()[1], 1 };
    
    DataPtr cls_prob_reshapeInPort = ((ICNNNetwork&)network).getData(FLAGS_prob_name.c_str());
    if (cls_prob_reshapeInPort == nullptr) {
        throw std::logic_error(std::string("Can't find output layer named ") + FLAGS_prob_name);
    }
    
    SizeVector cls_prob_reshapeOutDims = { cls_prob_reshapeInPort->getTensorDesc().getDims()[0] * cls_prob_reshapeInPort->getTensorDesc().getDims()[1], 1 };
    
    /*
    Detection output
    */
    
    int normalized = 0;
    int prior_size = normalized ? 4 : 5;
    int num_priors = rois_reshapeOutDims[0] / prior_size;
    
    // num_classes guessed from the output dims
    if (bbox_pred_reshapeOutDims[0] % (num_priors * 4) != 0) {
        throw std::logic_error("Can't guess number of classes. Something's wrong with output layers dims");
    }
    int num_classes = bbox_pred_reshapeOutDims[0] / (num_priors * 4);
    slog::info << "num_classes guessed: " << num_classes << slog::endl;
    
    LayerParams detectionOutParams;
    detectionOutParams.name = "detection_out";
    detectionOutParams.type = "DetectionOutput";
    detectionOutParams.precision = p;
    CNNLayerPtr detectionOutLayer = CNNLayerPtr(new CNNLayer(detectionOutParams));
    detectionOutLayer->params["background_label_id"] = "0";
    detectionOutLayer->params["code_type"] = "caffe.PriorBoxParameter.CENTER_SIZE";
    detectionOutLayer->params["eta"] = "1.0";
    detectionOutLayer->params["input_height"] = std::to_string(inputHeight);
    detectionOutLayer->params["input_width"] = std::to_string(inputWidth);
    detectionOutLayer->params["keep_top_k"] = "200";
    detectionOutLayer->params["nms_threshold"] = "0.3";
    detectionOutLayer->params["normalized"] = std::to_string(normalized);
    detectionOutLayer->params["num_classes"] = std::to_string(num_classes);
    detectionOutLayer->params["share_location"] = "0";
    detectionOutLayer->params["top_k"] = "400";
    detectionOutLayer->params["variance_encoded_in_target"] = "1";
    detectionOutLayer->params["visualize"] = "False";
    
    detectionOutLayer->insData.push_back(bbox_pred_reshapeInPort);
    detectionOutLayer->insData.push_back(cls_prob_reshapeInPort);
    detectionOutLayer->insData.push_back(rois_reshapeInPort);
    
    SizeVector detectionOutLayerOutDims = { 7, 200, 1, 1 };
    DataPtr detectionOutLayerOutPort = DataPtr(new Data("detection_out", detectionOutLayerOutDims, p,
        TensorDesc::getLayoutByDims(detectionOutLayerOutDims)));
    detectionOutLayerOutPort->creatorLayer = detectionOutLayer;
    detectionOutLayer->outData.push_back(detectionOutLayerOutPort);
    
    DetectionOutputPostProcessor detOutPostProcessor(detectionOutLayer.get());
    
    network.addOutput(FLAGS_bbox_name, 0);
    network.addOutput(FLAGS_prob_name, 0);
    network.addOutput(FLAGS_proposal_name, 0);
    
    // --------------------------- Prepare input blobs -----------------------------------------------------
    slog::info << "Preparing input blobs" << slog::endl;
    
    /** Taking information about all topology inputs **/
    InputsDataMap inputsInfo(network.getInputsInfo());
    
    /** SSD network has one input and one output **/
    if (inputsInfo.size() != 1 && inputsInfo.size() != 2) throw std::logic_error("Demo supports topologies only with 1 or 2 inputs");
    
    std::string imageInputName, imInfoInputName;
    
    InputInfo::Ptr inputInfo = inputsInfo.begin()->second;
    
    SizeVector inputImageDims;
    /** Stores input image **/
    
    /** Iterating over all input blobs **/
    for (auto & item : inputsInfo) {
        /** Working with first input tensor that stores image **/
        if (item.second->getInputData()->getTensorDesc().getDims().size() == 4) {
            imageInputName = item.first;
    
            slog::info << "Batch size is " << std::to_string(networkReader.getNetwork().getBatchSize()) << slog::endl;
    
            /** Creating first input blob **/
            Precision inputPrecision = Precision::U8;
            item.second->setPrecision(inputPrecision);
    
        }
        else if (item.second->getInputData()->getTensorDesc().getDims().size() == 2) {
            imInfoInputName = item.first;
    
            Precision inputPrecision = Precision::FP32;
            item.second->setPrecision(inputPrecision);
            if ((item.second->getTensorDesc().getDims()[1] != 3 && item.second->getTensorDesc().getDims()[1] != 6) ||
                item.second->getTensorDesc().getDims()[0] != 1) {
                throw std::logic_error("Invalid input info. Should be 3 or 6 values length");
            }
        }
    }
    
    // ------------------------------ Prepare output blobs -------------------------------------------------
    slog::info << "Preparing output blobs" << slog::endl;
    
    OutputsDataMap outputsInfo(network.getOutputsInfo());
    
    const int maxProposalCount = detectionOutLayerOutDims[1];
    const int objectSize = detectionOutLayerOutDims[0];
    
    /** Set the precision of output data provided by the user, should be called before load of the network to the plugin **/
    
    outputsInfo[FLAGS_bbox_name]->setPrecision(Precision::FP32);
    outputsInfo[FLAGS_prob_name]->setPrecision(Precision::FP32);
    outputsInfo[FLAGS_proposal_name]->setPrecision(Precision::FP32);
    // -----------------------------------------------------------------------------------------------------
    
    // --------------------------- 4. Loading model to the plugin ------------------------------------------
    slog::info << "Loading model to the plugin" << slog::endl;
    
    ExecutableNetwork executable_network = plugin.LoadNetwork(network, {});
    // -----------------------------------------------------------------------------------------------------
    
    // --------------------------- 5. Create infer request -------------------------------------------------
    InferRequest infer_request = executable_network.CreateInferRequest();
    // -----------------------------------------------------------------------------------------------------
    
    // --------------------------- 6. Prepare input --------------------------------------------------------
    /** Collect images data ptrs **/
    std::vector<std::shared_ptr<unsigned char>> imagesData, originalImagesData;
    std::vector<int> imageWidths, imageHeights;
    for (auto & i : images) {
        FormatReader::ReaderPtr reader(i.c_str());
        if (reader.get() == nullptr) {
            slog::warn << "Image " + i + " cannot be read!" << slog::endl;
            continue;
        }
        /** Store image data **/
        std::shared_ptr<unsigned char> originalData(reader->getData());
        std::shared_ptr<unsigned char> data(reader->getData(inputInfo->getTensorDesc().getDims()[3], inputInfo->getTensorDesc().getDims()[2]));
        if (data.get() != nullptr) {
            originalImagesData.push_back(originalData);
            imagesData.push_back(data);
            imageWidths.push_back(reader->width());
            imageHeights.push_back(reader->height());
        }
    }
    if (imagesData.empty()) throw std::logic_error("Valid input images were not found!");
    
    size_t batchSize = network.getBatchSize();
    slog::info << "Batch size is " << std::to_string(batchSize) << slog::endl;
    if (batchSize != imagesData.size()) {
        slog::warn << "Number of images " + std::to_string(imagesData.size()) + \
            " doesn't match batch size " + std::to_string(batchSize) << slog::endl;
        slog::warn << std::to_string(std::min(imagesData.size(), batchSize)) + \
            " images will be processed" << slog::endl;
        batchSize = std::min(batchSize, imagesData.size());
    }
    
    /** Creating input blob **/
    Blob::Ptr imageInput = infer_request.GetBlob(imageInputName);
    
    /** Filling input tensor with images. First b channel, then g and r channels **/
    size_t num_channels = imageInput->getTensorDesc().getDims()[1];
    size_t image_size = imageInput->getTensorDesc().getDims()[3] * imageInput->getTensorDesc().getDims()[2];
    
    unsigned char* data = static_cast<unsigned char*>(imageInput->buffer());
    
    /** Iterate over all input images **/
    for (size_t image_id = 0; image_id < std::min(imagesData.size(), batchSize); ++image_id) {
        /** Iterate over all pixel in image (b,g,r) **/
        for (size_t pid = 0; pid < image_size; pid++) {
            /** Iterate over all channels **/
            for (size_t ch = 0; ch < num_channels; ++ch) {
                /**          [images stride + channels stride + pixel id ] all in bytes            **/
                data[image_id * image_size * num_channels + ch * image_size + pid] = imagesData.at(image_id).get()[pid*num_channels + ch];
            }
        }
    }
    
    if (imInfoInputName != "") {
        Blob::Ptr input2 = infer_request.GetBlob(imInfoInputName);
        auto imInfoDim = inputsInfo.find(imInfoInputName)->second->getTensorDesc().getDims()[1];
    
        /** Fill input tensor with values **/
        float *p = input2->buffer().as<PrecisionTrait<Precision::FP32>::value_type*>();
    
        for (size_t image_id = 0; image_id < std::min(imagesData.size(), batchSize); ++image_id) {
            p[image_id * imInfoDim + 0] = static_cast<float>(inputsInfo[imageInputName]->getTensorDesc().getDims()[2]);
            p[image_id * imInfoDim + 1] = static_cast<float>(inputsInfo[imageInputName]->getTensorDesc().getDims()[3]);
            for (int k = 2; k < imInfoDim; k++) {
                p[image_id * imInfoDim + k] = 1.0f;  // all scale factors are set to 1.0
            }
        }
    }
    // -----------------------------------------------------------------------------------------------------
    
    // ---------------------------- 7. Do inference --------------------------------------------------------
    slog::info << "Start inference (" << FLAGS_ni << " iterations)" << slog::endl;
    
    typedef std::chrono::high_resolution_clock Time;
    typedef std::chrono::duration<double, std::ratio<1, 1000>> ms;
    typedef std::chrono::duration<float> fsec;
    
    double total = 0.0;
    /** Start inference & calc performance **/
    for (int iter = 0; iter < FLAGS_ni; ++iter) {
        auto t0 = Time::now();
        infer_request.Infer();
        auto t1 = Time::now();
        fsec fs = t1 - t0;
        ms d = std::chrono::duration_cast<ms>(fs);
        total += d.count();
    }
    // -----------------------------------------------------------------------------------------------------
    
    // ---------------------------- 8. Process output ------------------------------------------------------
    slog::info << "Processing output blobs" << slog::endl;
    
    Blob::Ptr bbox_output_blob = infer_request.GetBlob(FLAGS_bbox_name);
    Blob::Ptr prob_output_blob = infer_request.GetBlob(FLAGS_prob_name);
    Blob::Ptr rois_output_blob = infer_request.GetBlob(FLAGS_proposal_name);
    
    std::vector<Blob::Ptr> detOutInBlobs = { bbox_output_blob, prob_output_blob, rois_output_blob };
    
    Blob::Ptr output_blob = std::make_shared<TBlob<float>>(Precision::FP32, Layout::NCHW, detectionOutLayerOutDims);
    output_blob->allocate();
    std::vector<Blob::Ptr> detOutOutBlobs = { output_blob };
    
    detOutPostProcessor.execute(detOutInBlobs, detOutOutBlobs, nullptr);
    
    const float* detection = static_cast<PrecisionTrait<Precision::FP32>::value_type*>(output_blob->buffer());
    
    std::vector<std::vector<int> > boxes(batchSize);
    std::vector<std::vector<int> > classes(batchSize);
    
    /* Each detection has image_id that denotes processed image */
    for (int curProposal = 0; curProposal < maxProposalCount; curProposal++) {
        float image_id = detection[curProposal * objectSize + 0];
        float label = detection[curProposal * objectSize + 1];
        float confidence = detection[curProposal * objectSize + 2];
        float xmin = detection[curProposal * objectSize + 3] * imageWidths[image_id];
        float ymin = detection[curProposal * objectSize + 4] * imageHeights[image_id];
        float xmax = detection[curProposal * objectSize + 5] * imageWidths[image_id];
        float ymax = detection[curProposal * objectSize + 6] * imageHeights[image_id];
    
        /* MKLDnn and clDNN have little differente in DetectionOutput layer, so we need this check */
        if (image_id < 0 || confidence == 0) {
            continue;
        }
    
        std::cout << "[" << curProposal << "," << label << "] element, prob = " << confidence <<
            "    (" << xmin << "," << ymin << ")-(" << xmax << "," << ymax << ")" << " batch id : " << image_id;
    
        if (confidence > 0.5) {
            /** Drawing only objects with >50% probability **/
            classes[image_id].push_back(static_cast<int>(label));
            boxes[image_id].push_back(static_cast<int>(xmin));
            boxes[image_id].push_back(static_cast<int>(ymin));
            boxes[image_id].push_back(static_cast<int>(xmax - xmin));
            boxes[image_id].push_back(static_cast<int>(ymax - ymin));
            std::cout << " WILL BE PRINTED!";
        }
        std::cout << std::endl;
    }
    
    for (size_t batch_id = 0; batch_id < batchSize; ++batch_id) {
        addRectangles(originalImagesData[batch_id].get(), imageHeights[batch_id], imageWidths[batch_id], boxes[batch_id], classes[batch_id]);
        const std::string image_path = "out_" + std::to_string(batch_id) + ".bmp";
        if (writeOutputBmp(image_path, originalImagesData[batch_id].get(), imageHeights[batch_id], imageWidths[batch_id])) {
            slog::info << "Image " + image_path + " created!" << slog::endl;
        }
        else {
            throw std::logic_error(std::string("Can't create a file: ") + image_path);
        }
    }
    // -----------------------------------------------------------------------------------------------------
    std::cout << std::endl << "total inference time: " << total << std::endl;
    std::cout << "Average running time of one iteration: " << total / static_cast<double>(FLAGS_ni) << " ms" << std::endl;
    std::cout << std::endl << "Throughput: " << 1000 * static_cast<double>(FLAGS_ni) * batchSize / total << " FPS" << std::endl;
    std::cout << std::endl;
    
    /** Show performace results **/
    if (FLAGS_pc) {
        printPerformanceCounts(infer_request, std::cout);
    }
    

    }
    catch (const std::exception& error) {
    slog::err << error.what() << slog::endl;
    return 1;
    }
    catch (...) {
    slog::err << "Unknown/internal exception happened." << slog::endl;
    return 1;
    }

    slog::info << "Execution successful" << slog::endl;
    return 0;
    }

有如下报错:严重性 代码 说明 项目 文件 行 禁止显示状态
错误 LNK2019 无法解析的外部符号 CreateFormatReader,该符号在函数 "public: cdecl FormatReader::ReaderPtr::ReaderPtr(char const *)" (??0ReaderPtr@FormatReader@@QEAA@PEBD@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误(活动) 无法引用 函数 "InferenceEngine::make_so_pointer(const std::string &name) [其中 T=InferenceEngine::IExtension]" (已声明 所在行数:164,所属文件:"c:\Users\颜俊毅\Desktop\dldt-2018\inference-engine\include\details\ie_so_pointer.hpp") -- 它是已删除的函数 88999 c:\Users\颜俊毅\Documents\Visual Studio 2015\Projects\88999\88999\7521.cpp 102
错误 LNK2019 无法解析的外部符号 __imp_CreateDefaultAllocator,该符号在函数 "protected: virtual class std::shared_ptr const & __cdecl InferenceEngine::TBlob >::getAllocator(void)const " (?getAllocator@?$TBlob@HU?$enable_if@$00X@std@@@InferenceEngine@@MEBAAEBV?$shared_ptr@VIAllocator@InferenceEngine@@@std@@XZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "
declspec(dllimport) public: cdecl InferenceEngine::BlockingDesc::BlockingDesc(class std::vector > const &,class std::vector > const &)" (imp_??0BlockingDesc@InferenceEngine@@QEAA@AEBV?$vector@_KV?$allocator@_K@std@@@std@@0@Z),该符号在函数 "public: cdecl DetectionOutputPostProcessor::DetectionOutputPostProcessor(class InferenceEngine::CNNLayer const *)" (??0DetectionOutputPostProcessor@@QEAA@PEBVCNNLayer@InferenceEngine@@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "
declspec(dllimport) public: virtual cdecl InferenceEngine::BlockingDesc::~BlockingDesc(void)" (imp_??1BlockingDesc@InferenceEngine@@UEAA@XZ),该符号在函数 "public: cdecl DetectionOutputPostProcessor::DetectionOutputPostProcessor(class InferenceEngine::CNNLayer const *)" (??0DetectionOutputPostProcessor@@QEAA@PEBVCNNLayer@InferenceEngine@@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "
declspec(dllimport) public: cdecl InferenceEngine::TensorDesc::TensorDesc(class InferenceEngine::Precision const &,class std::vector >,class InferenceEngine::BlockingDesc const &)" (imp_??0TensorDesc@InferenceEngine@@QEAA@AEBVPrecision@1@V?$vector@_KV?$allocator@_K@std@@@std@@AEBVBlockingDesc@1@@Z),该符号在函数 "public: cdecl DetectionOutputPostProcessor::DetectionOutputPostProcessor(class InferenceEngine::CNNLayer const *)" (??0DetectionOutputPostProcessor@@QEAA@PEBVCNNLayer@InferenceEngine@@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "
declspec(dllimport) public: cdecl InferenceEngine::TensorDesc::TensorDesc(class InferenceEngine::Precision const &,class std::vector >,enum InferenceEngine::Layout)" (imp_??0TensorDesc@InferenceEngine@@QEAA@AEBVPrecision@1@V?$vector@_KV?$allocator@_K@std@@@std@@W4Layout@1@@Z),该符号在函数 "public: cdecl InferenceEngine::Blob::Blob(class InferenceEngine::Precision,enum InferenceEngine::Layout,class std::vector > const &)" (??0Blob@InferenceEngine@@QEAA@VPrecision@1@W4Layout@1@AEBV?$vector@_KV?$allocator@_K@std@@@std@@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "
declspec(dllimport) public: virtual cdecl InferenceEngine::TensorDesc::~TensorDesc(void)" (imp_??1TensorDesc@InferenceEngine@@UEAA@XZ),该符号在函数 "public: cdecl InferenceEngine::Blob::Blob(class InferenceEngine::TensorDesc)" (??0Blob@InferenceEngine@@QEAA@VTensorDesc@1@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "
declspec(dllimport) public: class std::vector > & cdecl InferenceEngine::TensorDesc::getDims(void)" (imp_?getDims@TensorDesc@InferenceEngine@@QEAAAEAV?$vector@_KV?$allocator@_K@std@@@std@@XZ),该符号在函数 "public: virtual void cdecl InferenceEngine::TBlob >::allocate(void)" (?allocate@?$TBlob@HU?$enable_if@$00X@std@@@InferenceEngine@@UEAAXXZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "
declspec(dllimport) public: class std::vector > const & cdecl InferenceEngine::TensorDesc::getDims(void)const " (imp_?getDims@TensorDesc@InferenceEngine@@QEBAAEBV?$vector@_KV?$allocator@_K@std@@@std@@XZ),该符号在函数 "public: unsigned int64 __cdecl InferenceEngine::Blob::byteSize(void)const " (?byteSize@Blob@InferenceEngine@@QEBA_KXZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "
declspec(dllimport) public: static enum InferenceEngine::Layout cdecl InferenceEngine::TensorDesc::getLayoutByDims(class std::vector >)" (imp_?getLayoutByDims@TensorDesc@InferenceEngine@@SA?AW4Layout@2@V?$vector@_KV?$allocator@_K@std@@@std@@@Z),该符号在函数 main 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "__declspec(dllimport) public: cdecl InferenceEngine::TensorDesc::TensorDesc(class InferenceEngine::TensorDesc const &)" (imp_??0TensorDesc@InferenceEngine@@QEAA@AEBV01@@Z),该符号在函数 "public: cdecl InferenceEngine::TBlob >::TBlob >(class InferenceEngine::TensorDesc const &)" (??0?$TBlob@HU?$enable_if@$00X@std@@@InferenceEngine@@QEAA@AEBVTensorDesc@1@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "
declspec(dllimport) public: cdecl InferenceEngine::Data::Data(class std::basic_string,class std::allocator > const &,class std::vector > const &,class InferenceEngine::Precision,enum InferenceEngine::Layout)" (imp_??0Data@InferenceEngine@@QEAA@AEBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@AEBV?$vector@_KV?$allocator@_K@std@@@3@VPrecision@1@W4Layout@1@@Z),该符号在函数 main 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "__declspec(dllimport) public: class InferenceEngine::TensorDesc const & cdecl InferenceEngine::Data::getTensorDesc(void)const " (imp_?getTensorDesc@Data@InferenceEngine@@QEBAAEBVTensorDesc@2@XZ),该符号在函数 "public: virtual class std::map,class std::allocator >,class std::vector >,struct std::less,class std::allocator > >,class std::allocator,class std::allocator > const ,class std::vector > > > > cdecl InferenceEngine::CNNNetwork::getInputShapes(void)" (?getInputShapes@CNNNetwork@InferenceEngine@@UEAA?AV?$map@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V?$vector@_KV?$allocator@_K@std@@@2@U?$less@V?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@@2@V?$allocator@U?$pair@$$CBV?$basic_string@DU?$char_traits@D@std@@V?$allocator@D@2@@std@@V?$vector@_KV?$allocator@_K@std@@@2@@std@@@2@@std@@XZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "
declspec(dllimport) public: void cdecl InferenceEngine::Data::setPrecision(class InferenceEngine::Precision const &)" (imp_?setPrecision@Data@InferenceEngine@@QEAAXAEBVPrecision@2@@Z),该符号在函数 "public: void cdecl InferenceEngine::InputInfo::setPrecision(class InferenceEngine::Precision)" (?setPrecision@InputInfo@InferenceEngine@@QEAAXVPrecision@2@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2019 无法解析的外部符号 "
declspec(dllimport) public: cdecl InferenceEngine::Data::~Data(void)" (imp_??1Data@InferenceEngine@@QEAA@XZ),该符号在函数 "public: void * __cdecl InferenceEngine::Data::scalar deleting destructor'(unsigned int)" (??_GData@InferenceEngine@@QEAAPEAXI@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 __imp_findPlugin,该符号在函数 "public: class InferenceEngine::details::SOPointer<class InferenceEngine::IInferencePlugin,class InferenceEngine::details::SharedObjectLoader> __cdecl InferenceEngine::PluginDispatcher::getSuitablePlugin(enum InferenceEngine::TargetDevice)const " (?getSuitablePlugin@PluginDispatcher@InferenceEngine@@QEBA?AV?$SOPointer@VIInferencePlugin@InferenceEngine@@VSharedObjectLoader@details@2@@details@2@W4TargetDevice@2@@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 __imp_GetInferenceEngineVersion,该符号在函数 main 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 __imp_CreateCNNNetReader,该符号在函数 "public: __cdecl InferenceEngine::CNNNetReader::CNNNetReader(void)" (??0CNNNetReader@InferenceEngine@@QEAA@XZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "__declspec(dllimport) public: __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::CpuExtensions(void)" (__imp_??0CpuExtensions@Cpu@Extensions@InferenceEngine@@QEAA@XZ),该符号在函数 "public: __cdecl std::_Ref_count_obj<class InferenceEngine::Extensions::Cpu::CpuExtensions>::_Ref_count_obj<class InferenceEngine::Extensions::Cpu::CpuExtensions><>(void)" (??$?0$$V@?$_Ref_count_obj@VCpuExtensions@Cpu@Extensions@InferenceEngine@@@std@@QEAA@XZ) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1
错误 LNK2019 无法解析的外部符号 "__declspec(dllimport) public: virtual __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::~CpuExtensions(void)" (__imp_??1CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAA@XZ),该符号在函数 "public: virtual void * __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::
scalar deleting destructor'(unsigned int)" (??_GCpuExtensions@Cpu@Extensions@InferenceEngine@@UEAAPEAXI@Z) 中被引用 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2001 无法解析的外部符号 "public: virtual void __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::GetVersion(struct InferenceEngine::Version const * &)const " (?GetVersion@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEBAXAEAPEBUVersion@4@@Z) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2001 无法解析的外部符号 "public: virtual void __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::Release(void)" (?Release@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAAXXZ) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2001 无法解析的外部符号 "public: virtual void __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::SetLogCallback(class InferenceEngine::IErrorListener &)" (?SetLogCallback@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAAXAEAVIErrorListener@4@@Z) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2001 无法解析的外部符号 "public: virtual void __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::Unload(void)" (?Unload@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAAXXZ) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2001 无法解析的外部符号 "public: virtual enum InferenceEngine::StatusCode __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::getFactoryFor(class InferenceEngine::ILayerImplFactory * &,class InferenceEngine::CNNLayer const *,struct InferenceEngine::ResponseDesc *)" (?getFactoryFor@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAA?AW4StatusCode@4@AEAPEAVILayerImplFactory@4@PEBVCNNLayer@4@PEAUResponseDesc@4@@Z) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2001 无法解析的外部符号 "public: virtual enum InferenceEngine::StatusCode __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::getPrimitiveTypes(char * * &,unsigned int &,struct InferenceEngine::ResponseDesc *)" (?getPrimitiveTypes@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAA?AW4StatusCode@4@AEAPEAPEADAEAIPEAUResponseDesc@4@@Z) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK2001 无法解析的外部符号 "public: virtual enum InferenceEngine::StatusCode __cdecl InferenceEngine::Extensions::Cpu::CpuExtensions::getShapeInferImpl(class std::shared_ptr &,char const *,struct InferenceEngine::ResponseDesc *)" (?getShapeInferImpl@CpuExtensions@Cpu@Extensions@InferenceEngine@@UEAA?AW4StatusCode@4@AEAV?$shared_ptr@VIShapeInferImpl@InferenceEngine@@@std@@PEBDPEAUResponseDesc@4@@Z) 88999 c:\Users\颜俊毅\documents\visual studio 2015\Projects\88999\88999\7521.obj 1

错误 LNK1120 27 个无法解析的外部命令 88999 c:\users\颜俊毅\documents\visual studio 2015\Projects\88999\x64\Debug\88999.exe 1

yuzying
yuzying 我也遇到这个问题,你解决了吗?
19 天之前 回复

1个回答

Csdn user default icon
上传中...
上传图片
插入图片
抄袭、复制答案,以达到刷声望分或其他目的的行为,在CSDN问答是严格禁止的,一经发现立刻封号。是时候展现真正的技术了!
其他相关推荐
这个报错是怎么回事,运行时候出错
10-19 10:16:05.169 3781-3781/com.example.yizhangbiao1995.myapplication E/AndroidRuntime: FATAL EXCEPTION: mainrn Process: com.example.yizhangbiao1995.myapplication, PID: 3781rn java.lang.IllegalArgumentException: Illegal character in scheme at index 0: 118.228.175.123:9001/loginrn at java.net.URI.create(URI.java:730)rn at org.apache.http.client.methods.HttpPost.(HttpPost.java:79)rn at com.example.yizhangbiao1995.myapplication.MainActivity$1.onClick(MainActivity.java:85)rn at android.view.View.performClick(View.java:4756)rn at android.view.View$PerformClick.run(View.java:19749)rn at android.os.Handler.handleCallback(Handler.java:739)rn at android.os.Handler.dispatchMessage(Handler.java:95)rn at android.os.Looper.loop(Looper.java:135)rn at android.app.ActivityThread.main(ActivityThread.java:5221)rn at java.lang.reflect.Method.invoke(Native Method)rn at java.lang.reflect.Method.invoke(Method.java:372)rn at com.android.internal.os.ZygoteInit$MethodAndArgsCaller.run(ZygoteInit.java:899)rn at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:694)rn10-19 10:16:07.155 3781-3781/? I/Process: Sending signal. PID: 3781 SIG: 9rnrnrnrnrnrnrn[code=java]package com.example.yizhangbiao1995.myapplication;rnrnimport android.content.BroadcastReceiver;rnimport android.content.Context;rnimport android.content.Intent;rnimport android.content.IntentFilter;rnimport android.os.Bundle;rnimport android.support.annotation.NonNull;rnimport android.support.design.widget.BottomNavigationView;rnimport android.support.v7.app.AppCompatActivity;rnimport android.util.Log;rnimport android.view.MenuItem;rnimport android.view.View;rnimport android.widget.Button;rnimport android.widget.EditText;rnimport android.widget.FrameLayout;rnimport android.widget.ImageView;rnimport android.widget.TextView;rnimport android.widget.Toast;rnrnimport org.apache.http.HttpEntity;rnimport org.apache.http.HttpResponse;rnimport org.apache.http.NameValuePair;rnimport org.apache.http.client.entity.UrlEncodedFormEntity;rnimport org.apache.http.client.methods.HttpPost;rnimport org.apache.http.entity.StringEntity;rnimport org.apache.http.impl.client.DefaultHttpClient;rnimport org.apache.http.message.BasicNameValuePair;rnimport org.apache.http.protocol.HTTP;rnimport org.apache.http.util.EntityUtils;rnimport org.json.*;rnimport java.io.IOException;rnimport java.io.UnsupportedEncodingException;rnrnimport org.apache.http.HttpEntity;rnimport org.apache.http.ParseException;rnimport org.apache.http.entity.StringEntity;rnimport org.apache.http.util.EntityUtils;rnimport java.io.OutputStream;rnimport java.net.HttpURLConnection;rnimport java.net.URL;rnimport java.util.ArrayList;rnimport java.util.HashMap;rnimport java.util.List;rnimport java.util.Map;rnimport java.util.jar.Attributes;rnrnimport static android.R.attr.path;rnrnpublic class MainActivity extends AppCompatActivity rnrn EditText ed1,ed2;rn Button bt,bt2;rn protected void onCreate(Bundle savedInstanceState) rn super.onCreate(savedInstanceState);rn setContentView(R.layout.login);rn ed1=(EditText)findViewById(R.id.editText3);rn ed2=(EditText)findViewById(R.id.editText4);rn bt=(Button)findViewById(R.id.button);rn bt2=(Button)findViewById(R.id.button2);rn bt.setOnClickListener(new View.OnClickListener() rn @Overridern public void onClick(View v) rn final String username=ed1.getText().toString();rn final String password=ed2.getText().toString();rn String url = "118.228.175.123:9001/login";rn /*List list = new ArrayList();rn list.add(username);rn list.add(password);rn JSONArray array = new JSONArray();rn array.put(list);rn String content = String.valueOf(array);rn Toast.makeText(MainActivity.this, content, Toast.LENGTH_SHORT).show();*/rn try rn JSONObject jsonObject = new JSONObject();rn jsonObject.put("username",username);rn jsonObject.put("password",password);rn String jsonString = jsonObject.toString();rn //指定Post参数rn Log.i("MyActivity","MyClass.getView() - get item number");rn DefaultHttpClient httpClient = new DefaultHttpClient();rnrnrnrn HttpPost post = new HttpPost(url);rn StringEntity entity = new StringEntity(jsonObject.toString(), HTTP.UTF_8);rn post.setEntity(entity);rn rn catch (JSONException e) rn // TODO Auto-generated catch blockrn e.printStackTrace();rn rn catch (UnsupportedEncodingException e)rn rn rn if(ed1.getText().toString().equals("yzb")&&ed2.getText().toString().equals("123"))rn rn Toast.makeText(MainActivity.this, "登录成功", Toast.LENGTH_SHORT).show();rn Intent intent=new Intent( MainActivity.this,denglu.class);rn startActivity(intent);rn finish();rn rn elsern rn Toast.makeText(MainActivity.this, "登录失败", Toast.LENGTH_SHORT).show();rn rn rn );rn bt2.setOnClickListener(new View.OnClickListener() rn @Overridern public void onClick(View v) rn Intent intent1=new Intent( MainActivity.this,zhucce.class);rn startActivity(intent1);rn finish();rn rn );rn rnrn @Overridern protected void onDestroy() rn super.onDestroy();rn rnrnrn[/code]
openvino Demo
openvino demo资源。
object detection
multiple ovject detection
运行OpenVINO的demo示例时遇到的小问题
安装好OpenVINO后,运行demo程序时遇到如下问题: 运行demo_squeezenet_download_convert_run.bat *###############|| Run Inference Engine classification sample||############### 等待 2 秒,按一个键继续 ... 已复制 1 个文件。 系统找不到指...
在delphi里使用microsoft SQLDMO object library报错是怎么回事?
我定义了一个SQL SERVER 对象后,它报要建一个HOST APPLICATION
运行时候报错···??怎么回事呢,请进~~~!!
rn分析器错误信息: 未能加载文件或程序集“Infragistics2.WebUI.WebCombo.v7.1, Version=7.1.20071.40, Culture=neutral, PublicKeyToken=7dd5c3163f2cd0cb”或它的某一个依赖项。系统找不到指定的文件。rnrn源错误: rnrnrn行 51: rn行 52: rn[color=#FF0000]行 53: [/color]rn行 54: rn行 55: rn rn
运行时候 web.config 报错···怎么回事呢··??
rn行 51: rn行 52: rn[color=#FF0000]行 53: [/color]rn行 54: rn行 55: rn[color=#008000]rn就是上面红的报错,是不是我应该在我的电脑上安装些什么东西啊???(这是我刚刚接手的程序,前一个程序员没有交接就走了,所以我晕,请大家帮忙看看··)[/color]rn rn
OBJECT classid里classid是怎么回事
我需要客户端自动检测是否安装了JVM,如果没有就从指定的URL下载JVM并进行安装。然后浏览器执行插件,并下载和显示Appletrnrn我在网上找到了这样一段代码:rn<OBJECT classid="clsid:8AD9C840-044E-11D1-B3E9-00805F499D93".............rn说是可以实现,但是没说清楚这个classid怎么设置,哪为高手能指点一下?
连接SQLSERVER2005的时候报错,请问是怎么回事.
string strConnection,strSQL;rnDataSet objDataSet = new DataSet();rnOleDbConnection objConnection = null;rnOleDbDataAdapter objAdapter = null;rnstrConnection ="Server=LCEC2000;Database=term;User ID=sa;Password=12345678;Trusted_Connection=False"; rnstrSQL = "SELECT TermID,TermName FROM term;";rnobjConnection = new OleDbConnection(strConnection);rnobjAdapter = new OleDbDataAdapter(strSQL,objConnection);rnobjAdapter.Fill(objDataSet,"term");rn数据库的名字是lgtest,用户名是sa,密码12345678rnobjConnection = new OleDbConnection(strConnection);这句话报错.rn错误内容是System.ArgumentException: OLE DB ConnectionString rn 'Provider=SQLOLEDB;'
时候出现运行超时是怎么回事
win 2000 高级服务器版rnsql 2000rnrn做一个查询程序rn当输入10 或者以下的数字时候查询得以进行rn当大于100 肯定就不行了 好长时间没有响应 最后提示超时已过时!rnrn查询的字段是int型rn
运行的时候,提示如下信息是怎么回事?
提示信息先是“There were deployment errors.Continue?”rnrn然后点击“yes”之后,显示“The configuration data for this product is corrupt.Contact your support personnel.”rnrn这方面的错误有可能是什么原因导致的?
2D Object Detection and Recognition
2D Object Detection and Recognition-Models, Algorithms and Networks\2D Object Detection and Recognition-Models, Algorithms and Networks.
Robust Real-time Object Detection
这是Paul Viola和 Michael J. Jones两位大牛于2001发表的有关快速人脸检测的论文,堪称是人脸检测的历史转折点,绝对的经典!
Salient Object Detection
regard saliency map computation as a regression problem
Object Detection and Pose Tracking
2011 Object Detection and Pose Tracking
目标检测(Object Detection)
1、南京大学吴建鑫教授的主页:http://cs.nju.edu.cn/wujx/        基于轮廓线索的实时人体检测:Real-Time Human Detection Using Contour Cues,其中可下载C4的源码
Scale-Transferrable Object Detection
CVPR2018,上海交大的一篇论文,欢迎各位下载!!!!!!!!!!!!!!!!!!!!!!
image object detection
计算机视觉、数字图像处理方面 目标检测 超像素分割方法
object detection and tracking
目标检测与跟踪
CNN object detection review
从RCNN到fasterRCNN 再到SSD
object detection 物体识别
这是一个通过opencv读取图片,并进行物体识别的demo,能够正常识别图像,且注释很清楚
目标检测(Object Detection)算法合集
本文总结了近几年来的目标检测算法paper的pdf文档和在github上的代码地址
Multiple Kernels for Object Detection
Multiple Kernels for Object Detection
YOLO_V3 object detection
Implement a YOLO (v3) object detector from scratch in PyTorch
Regionlets for Generic Object Detection
Regionlets for Generic Object Detection
YOLO object detection——视频
对象检测算法是目前最先进的技术,它跑赢了R-CNN以及R—CNN的变种。我将会回顾一下近些年来一些不同的物体检测算法的进步,然后,逐步潜入到YOLO理论的讲解以及使用tensorflow来编程实现!
Smarty运行Demo报错.
版本:3.08rn错误如下:rnWarning: rename(/Applications/XAMPP/xamppfiles/temp/wrtIgaeRC,./templates_c/c0360d049dff10f364dfc53ba2cc3958abf6ee6d.file.index.tpl.cache.php) [function.rename]: Permission denied in /Applications/XAMPP/xamppfiles/htdocs/Smarty/libs/sysplugins/smarty_internal_write_file.php on line 48rnrnWarning: chmod() [function.chmod]: No such file or directory in /Applications/XAMPP/xamppfiles/htdocs/Smarty/libs/sysplugins/smarty_internal_write_file.php on line 50rnrnWarning: rename(/Applications/XAMPP/xamppfiles/temp/wrtWBA9ZM,./cache/c0360d049dff10f364dfc53ba2cc3958abf6ee6d.index.tpl.php) [function.rename]: Permission denied in /Applications/XAMPP/xamppfiles/htdocs/Smarty/libs/sysplugins/smarty_internal_write_file.php on line 48rnrnWarning: chmod() [function.chmod]: No such file or directory in /Applications/XAMPP/xamppfiles/htdocs/Smarty/libs/sysplugins/smarty_internal_write_file.php on line 50rnrnWarning: filemtime() [function.filemtime]: stat failed for ./cache/c0360d049dff10f364dfc53ba2cc3958abf6ee6d.index.tpl.php in /Applications/XAMPP/xamppfiles/htdocs/Smarty/libs/sysplugins/smarty_internal_cacheresource_file.php on line 101rnrnWarning: include(./templates_c/c0360d049dff10f364dfc53ba2cc3958abf6ee6d.file.index.tpl.cache.php) [function.include]: failed to open stream: No such file or directory in /Applications/XAMPP/xamppfiles/htdocs/Smarty/libs/sysplugins/smarty_internal_template.php on line 434rnrnWarning: include() [function.include]: Failed opening './templates_c/c0360d049dff10f364dfc53ba2cc3958abf6ee6d.file.index.tpl.cache.php' for inclusion (include_path='.:/Applications/XAMPP/xamppfiles/lib/php:/Applications/XAMPP/xamppfiles/lib/php/pear:/Applications/XAMPP/xamppfiles//htdocs/Smarty/libs/Smarty.class.php') in /Applications/XAMPP/xamppfiles/htdocs/Smarty/libs/sysplugins/smarty_internal_template.php on line 434rnrnWarning: rename(/Applications/XAMPP/xamppfiles/temp/wrtV7bIg0,./templates_c/be98117032cd14e85d5c695d0f4535a870319c8a.file.debug.tpl.php) [function.rename]: Permission denied in /Applications/XAMPP/xamppfiles/htdocs/Smarty/libs/sysplugins/smarty_internal_write_file.php on line 48rnrnWarning: chmod() [function.chmod]: No such file or directory in /Applications/XAMPP/xamppfiles/htdocs/Smarty/libs/sysplugins/smarty_internal_write_file.php on line 50rnrnWarning: include(./templates_c/be98117032cd14e85d5c695d0f4535a870319c8a.file.debug.tpl.php) [function.include]: failed to open stream: No such file or directory in /Applications/XAMPP/xamppfiles/htdocs/Smarty/libs/sysplugins/smarty_internal_template.php on line 434rnrnWarning: include() [function.include]: Failed opening './templates_c/be98117032cd14e85d5c695d0f4535a870319c8a.file.debug.tpl.php' for inclusion (include_path='.:/Applications/XAMPP/xamppfiles/lib/php:/Applications/XAMPP/xamppfiles/lib/php/pear:/Applications/XAMPP/xamppfiles//htdocs/Smarty/libs/Smarty.class.php') in /Applications/XAMPP/xamppfiles/htdocs/Smarty/libs/sysplugins/smarty_internal_template.php on line 434rnrnrn
fckeditor报错是怎么回事
我用的vb.net看到网上说这个编辑器不错,按照说明配置web.config和dll安装工具箱也装了,结果能显示出工具栏,但是编辑器没法写入任何字符,页面一开始就报错,查看详细后显示:rnURL request: "myweb1/fckeditor/fckstyles.xml"rnServer response:rnstatus:404rnResponse text:rnrn rn.rn.rn.rn.rn.rn "/myweb1" 应用程序中的服务器错误。 rn.rn.rn.rnrn然后有个确定按钮,点击后编辑器没有办法写入字符,点任何按钮都会出现一层白膜一样的东东(应该是层吧,偶是小白),盖住我的整个网页。请问各位大侠,怎么解决这个问题?
报错是怎么回事
<%rnstrconn="DRIVER=Microsoft Access Driver (*.mdb);DBQ=" & Server.MapPath("2651.mdb") rn rnset conn = server.createobject("adodb.connection") rn rnconn.open strconn rn%>报错信息rnMicrosoft OLE DB Provider for ODBC Drivers (0x80004005)rn/sum.asp, 第 6 行rnrnrn浏览器类型:rnMozilla/4.0 (compatible; MSIE 6.0; Windows NT
这样报错是怎么回事??
Delphi 6 BDE调用Mysql5.0 ODBC 报如下错误rnrnGeneral sql error rn[MySQL][myodbc 5.00.10] option type out of rangernrn高手请帮助我看看是什么意思啊?如何解决?
json报错是怎么回事?
[color=#FF0000] 环境:jdk1.6 MyEclipse 8.6 for spring[/color]rn今年寻思着研究下json但是不想居然出现这个。。请大神指点。。rnrn[color=#FF0000]servlet代码:[/color]rn[code=java]rnresponse.setContentType("text/html");rnresponse.setCharacterEncoding("utf-8");rnrequest.setCharacterEncoding("utf-8");rnPrintWriter out = response.getWriter();rnList users=new ArrayList();rnUser u1=new User("张三", 12);rnUser u2=new User("小美", 15);rnusers.add(u1);rnusers.add(u2);rnJSONArray json=JSONArray.fromObject(users);rnout.write(json.toString());rnout.close();rn[/code]rnrn[color=#FF0000]jar :json-lib-2.2.2-jdk15.jar[/color]rnrn错误:rn[code=text]rnnet.sf.json.JSONException: java.lang.SecurityException: class "org.apache.commons.collections.FastHashMap"'s signer information does not match signer information of other classes in the same packagernat net.sf.json.JSONObject._fromBean(JSONObject.java:953)rnat net.sf.json.JSONObject.fromObject(JSONObject.java:192)rnat net.sf.json.JSONArray._processValue(JSONArray.java:2557)rnat net.sf.json.JSONArray.processValue(JSONArray.java:2588)rnat net.sf.json.JSONArray.addValue(JSONArray.java:2575)rnat net.sf.json.JSONArray._fromCollection(JSONArray.java:1082)rnat net.sf.json.JSONArray.fromObject(JSONArray.java:145)rnat net.sf.json.JSONArray.fromObject(JSONArray.java:127)rnat servlet.JsonAjax.doGet(JsonAjax.java:38)rnat servlet.JsonAjax.doPost(JsonAjax.java:55)rnat javax.servlet.http.HttpServlet.service(HttpServlet.java:637)rnat javax.servlet.http.HttpServlet.service(HttpServlet.java:717)rnat org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:290)rnat org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:206)rnat org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:233)rnat org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:191)rnat org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:127)rnat org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:102)rnat org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:109)rnat org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:293)rnat org.apache.coyote.http11.Http11Processor.process(Http11Processor.java:859)rnat org.apache.coyote.http11.Http11Protocol$Http11ConnectionHandler.process(Http11Protocol.java:602)rnat org.apache.tomcat.util.net.JIoEndpoint$Worker.run(JIoEndpoint.java:489)rnat java.lang.Thread.run(Thread.java:619)rnCaused by: java.lang.SecurityException: class "org.apache.commons.collections.FastHashMap"'s signer information does not match signer information of other classes in the same packagernat java.lang.ClassLoader.checkCerts(ClassLoader.java:776)rnat java.lang.ClassLoader.preDefineClass(ClassLoader.java:488)rnat java.lang.ClassLoader.defineClass(ClassLoader.java:615)rnat java.security.SecureClassLoader.defineClass(SecureClassLoader.java:124)rnat org.apache.catalina.loader.WebappClassLoader.findClassInternal(WebappClassLoader.java:2818)rnat org.apache.catalina.loader.WebappClassLoader.findClass(WebappClassLoader.java:1159)rnat org.apache.catalina.loader.WebappClassLoader.loadClass(WebappClassLoader.java:1647)rnat org.apache.catalina.loader.WebappClassLoader.loadClass(WebappClassLoader.java:1526)rnat java.lang.ClassLoader.loadClassInternal(ClassLoader.java:320)rnat org.apache.commons.beanutils.ConvertUtilsBean.(ConvertUtilsBean.java:125)rnat org.apache.commons.beanutils.BeanUtilsBean.(BeanUtilsBean.java:110)rnat org.apache.commons.beanutils.BeanUtilsBean$1.initialValue(BeanUtilsBean.java:68)rnat org.apache.commons.beanutils.ContextClassLoaderLocal.get(ContextClassLoaderLocal.java:91)rnat org.apache.commons.beanutils.BeanUtilsBean.getInstance(BeanUtilsBean.java:78)rnat org.apache.commons.beanutils.PropertyUtilsBean.getInstance(PropertyUtilsBean.java:101)rnat org.apache.commons.beanutils.PropertyUtils.getPropertyDescriptors(PropertyUtils.java:342)rnat net.sf.json.JSONObject._fromBean(JSONObject.java:901)rn[/code]
phpMyAdmin里的运行信息有一项select非常高是怎么回事?
我的网站phpMyAdmin里的运行信息有一项select非常高是怎么回事?rnrn它代表是查询量非常大吗?是查询次数多还是查询io大?
运行程序的时候报错:(
配置错误 rn说明: 在处理向该请求提供服务所需的配置文件时出错。请检查下面的特定错误详细信息并适当地修改配置文件。 rnrn分析器错误信息: 在应用程序级别以外使用注册为 allowDefinition='MachineToApplication' 的节是错误的。导致该错误的原因可能是在 IIS 中没有将虚拟目录作为应用程序进行配置。rnrn源错误: rnrnrn行 36: “Passport”和“None”rn行 37: -->rn行 38: rn行 39: rn行 40:
运行的时候报错
在运行的时候出现这种错误:找不到类型或面名空间名称"StringBuilder"这是为什么
运行的时候布局文件报错
它说我布局文件73行错误,我看了一下,原来真的view写成小写开头了 但是布局文件也没报红,真奇怪。
jsp访问的时候有乱码是怎么回事?
源文件是正常的。但是服务开启后,访问却是乱码。这是什么情况导致的》?
用myeclipse9开发android,在很多代码提示的时候很卡是怎么回事
用myeclipse9开发android,在很多代码提示的时候很卡是怎么回事rn比如控件的imageview1.打完点后就卡在那很长时间,我机子是四核的不是太差啊。
编译是报错和运行是报错有和区别?
1.ArrayListlist=new ArrayList();rn2.list.Add(new ObjectOne());rn3.list.Add(new ObjectOne());rn4.list.Add(new ObjectOne());rn5.Collections.sort(list);rn6.class ObjectOnern7. private int x=0;rn8. private int y=0;rnrn9.rnrn第5行有错,我认为是因为Collections是个接口,不能直接使用,但题目说是第五行编译是报错,但编译是报错和运行是报错有和区别?如何判断代码段中的编译是报错和运行是报错?谢谢
在spark环境中运行demo的时候报错
在spark环境中运行demo的时候报错,怎么解决? /usr/spark-1.6/bin/spark-submit --class org.apache.spark.examples.SparkPi --master yarn  --deploy-mode client --executor-cores 1 --queue thequeue /usr/spark-1.6/lib
在JSP页面里,能点出来,运行的时候报错,在.java文件里能运行
An error occurred at line: 43 in the jsp file: /userInfo.jsprnThe method getUserPageBySel(String, String, int, int) is undefined for the type CustomerDaoImplrn40: List list = null;rn41: rn42: if(whe == null)rn43: list = cusImpl.getUserPageBySel("allUsers", "", 3, 1);rnrn怎么解决啊?
相关热词 c++和c#哪个就业率高 c# 批量动态创建控件 c# 模块和程序集的区别 c# gmap 截图 c# 验证码图片生成类 c# 再次尝试 连接失败 c#开发编写规范 c# 压缩图片好麻烦 c#计算数组中的平均值 c#获取路由参数