使用Caffe基于cifar10进行物体识别

在http://blog.csdn.net/fengbingchun/article/details/72953284中对cifar10进行train，这里通过train得到的model，对图像进行识别。cifar10数据集共包括10类，按照0到9的顺序依次为airplane(飞机)、automobile(轿车)、bird(鸟)、cat(猫)、deer(鹿)、dog(狗)、frog(青蛙)、horse(房子)、ship(船)、truck(卡车)。

在识别前需要对原有的cifar10_quick_train_test.prototxt文件进行调整，调整后的内容如下：

name: "CIFAR10_quick"
layer {name: "data"type: "MemoryData"top: "data"top: "label"memory_data_param {batch_size: 1channels: 3height: 32width: 32}
}
layer {name: "conv1"type: "Convolution"bottom: "data"top: "conv1"param {lr_mult: 1}param {lr_mult: 2}convolution_param {num_output: 32pad: 2kernel_size: 5stride: 1weight_filler {type: "gaussian"std: 0.0001}bias_filler {type: "constant"}}
}
layer {name: "pool1"type: "Pooling"bottom: "conv1"top: "pool1"pooling_param {pool: MAXkernel_size: 3stride: 2}
}
layer {name: "relu1"type: "ReLU"bottom: "pool1"top: "pool1"
}
layer {name: "conv2"type: "Convolution"bottom: "pool1"top: "conv2"param {lr_mult: 1}param {lr_mult: 2}convolution_param {num_output: 32pad: 2kernel_size: 5stride: 1weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"}}
}
layer {name: "relu2"type: "ReLU"bottom: "conv2"top: "conv2"
}
layer {name: "pool2"type: "Pooling"bottom: "conv2"top: "pool2"pooling_param {pool: AVEkernel_size: 3stride: 2}
}
layer {name: "conv3"type: "Convolution"bottom: "pool2"top: "conv3"param {lr_mult: 1}param {lr_mult: 2}convolution_param {num_output: 64pad: 2kernel_size: 5stride: 1weight_filler {type: "gaussian"std: 0.01}bias_filler {type: "constant"}}
}
layer {name: "relu3"type: "ReLU"bottom: "conv3"top: "conv3"
}
layer {name: "pool3"type: "Pooling"bottom: "conv3"top: "pool3"pooling_param {pool: AVEkernel_size: 3stride: 2}
}
layer {name: "ip1"type: "InnerProduct"bottom: "pool3"top: "ip1"param {lr_mult: 1}param {lr_mult: 2}inner_product_param {num_output: 64weight_filler {type: "gaussian"std: 0.1}bias_filler {type: "constant"}}
}
layer {name: "ip2"type: "InnerProduct"bottom: "ip1"top: "ip2"param {lr_mult: 1}param {lr_mult: 2}inner_product_param {num_output: 10weight_filler {type: "gaussian"std: 0.1}bias_filler {type: "constant"}}
}
layer {name: "prob"type: "Softmax"bottom: "ip2"top: "prob"
}

可视化结果如下图(https://ethereon.github.io/netscope/quickstart.html )：

从网上找了10幅较标准的图像，如下：

测试代码如下：

#include "funset.hpp"
#include "common.hpp"int cifar10_predict()
{
#ifdef CPU_ONLYcaffe::Caffe::set_mode(caffe::Caffe::CPU);
#elsecaffe::Caffe::set_mode(caffe::Caffe::GPU);
#endifconst std::string param_file{ "E:/GitCode/Caffe_Test/test_data/model/cifar10/cifar10_quick_train_test_.prototxt" };const std::string trained_filename{ "E:/GitCode/Caffe_Test/test_data/model/cifar10/cifar10_quick_iter_4000.caffemodel.h5" };const std::string image_path{ "E:/GitCode/Caffe_Test/test_data/images/object_recognition/" };const std::string mean_file{"E:/GitCode/Caffe_Test/test_data/model/cifar10/mean.binaryproto"};caffe::Net<float> caffe_net(param_file, caffe::TEST);caffe_net.CopyTrainedLayersFromHDF5(trained_filename);const boost::shared_ptr<caffe::Blob<float> > blob_by_name = caffe_net.blob_by_name("data");int image_channel = blob_by_name->channels();int image_height = blob_by_name->height();int image_width = blob_by_name->width();int num_outputs = caffe_net.num_outputs();const std::vector<caffe::Blob<float>*>& output_blobs = caffe_net.output_blobs();int require_blob_index{ -1 };const int digit_category_num{ 10 };for (int i = 0; i < output_blobs.size(); ++i) {if (output_blobs[i]->count() == digit_category_num)require_blob_index = i;}if (require_blob_index == -1) {fprintf(stderr, "ouput blob don't match\n");return -1;}std::vector<int> target{ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };std::vector<int> result;// read mean datacaffe::BlobProto image_mean; // storage order: rr..rrgg..ggbb..bbif (!caffe::ReadProtoFromBinaryFile(mean_file, &image_mean)) {fprintf(stderr, "parse mean file fail\n");return -1;}if (image_channel != image_mean.channels() || image_height != image_mean.height() || image_width != image_mean.width() ||image_channel != 3) {fprintf(stderr, "their dimension dismatch\n");return -1;}cv::Mat mat_mean(image_height, image_width, CV_32FC3, const_cast<float*>(image_mean.data().data()));for (auto num : target) {std::string str = std::to_string(num);str += ".jpg";str = image_path + str;cv::Mat mat = cv::imread(str.c_str(), 1);if (!mat.data) {fprintf(stderr, "load image error: %s\n", str.c_str());return -1;}if (image_channel == 1)cv::cvtColor(mat, mat, CV_BGR2GRAY);else if (image_channel == 4)cv::cvtColor(mat, mat, CV_BGR2BGRA);cv::resize(mat, mat, cv::Size(image_width, image_height));mat.convertTo(mat, CV_32FC3);// Note: need to subtract meanstd::vector<cv::Mat> mat_tmp2; //b,g,rcv::split(mat, mat_tmp2);cv::Mat mat_tmp3(image_height, image_width, CV_32FC3);float* p = (float*)mat_tmp3.data;memcpy(p, mat_tmp2[2].data, image_height * image_width * sizeof(float));memcpy(p + image_height * image_width, mat_tmp2[1].data, image_height * image_width * sizeof(float));memcpy(p + image_height * image_width * 2, mat_tmp2[0].data, image_height * image_width * sizeof(float));cv::subtract(mat_tmp3, mat_mean, mat_tmp3);boost::shared_ptr<caffe::MemoryDataLayer<float> > memory_data_layer =boost::static_pointer_cast<caffe::MemoryDataLayer<float>>(caffe_net.layer_by_name("data"));float dummy_label[1] {0};memory_data_layer->Reset((float*)(mat_tmp3.data), dummy_label, 1); // rr..rrgg..ggbb..bbfloat loss{ 0.0 };const std::vector<caffe::Blob<float>*>& results = caffe_net.ForwardPrefilled(&loss); // Net forwardconst float* output = results[require_blob_index]->cpu_data();float tmp{ -1 };int pos{ -1 };fprintf(stderr, "actual digit is: %d\n", target[num]);for (int j = 0; j < 10; j++) {printf("Probability to be Number %d is: %.3f\n", j, output[j]);if (tmp < output[j]) {pos = j;tmp = output[j];}}result.push_back(pos);}for (auto i = 0; i < 10; i++)fprintf(stderr, "actual digit is: %d, result digit is: %d\n", target[i], result[i]);fprintf(stderr, "predict finish\n");return 0;
}

测试结果如下：

其中鹿和青蛙识别错误。

GitHub：https://github.com/fengbingchun/Caffe_Test

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