ssd_detection.cpp代碼

#include "head.h" #include <caffe/caffe.hpp> #ifdef USE_OPENCV #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #endif // USE_OPENCV #include <algorithm> #include <iomanip> #include <iosfwd> #include <memory> #include <string> #include <utility> #include <vector>#ifdef USE_OPENCV using namespace caffe; // NOLINT(build/namespaces)class Detector { public:Detector(const string& model_file,const string& weights_file,const string& mean_file,const string& mean_value);std::vector<vector<float> > Detect(const cv::Mat& img);private:void SetMean(const string& mean_file, const string& mean_value);void WrapInputLayer(std::vector<cv::Mat>* input_channels);void Preprocess(const cv::Mat& img,std::vector<cv::Mat>* input_channels);private:shared_ptr<Net<float> > net_;cv::Size input_geometry_;int num_channels_;cv::Mat mean_; };Detector::Detector(const string& model_file,const string& weights_file,const string& mean_file,const string& mean_value) { #ifdef CPU_ONLYCaffe::set_mode(Caffe::CPU); #elseCaffe::set_mode(Caffe::GPU); #endif/* Load the network. */net_.reset(new Net<float>(model_file, TEST));net_->CopyTrainedLayersFrom(weights_file);CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";Blob<float>* input_layer = net_->input_blobs()[0];num_channels_ = input_layer->channels();CHECK(num_channels_ == 3 || num_channels_ == 1)<< "Input layer should have 1 or 3 channels.";input_geometry_ = cv::Size(input_layer->width(), input_layer->height());/* Load the binaryproto mean file. */SetMean(mean_file, mean_value); }std::vector<vector<float> > Detector::Detect(const cv::Mat& img) {Blob<float>* input_layer = net_->input_blobs()[0];input_layer->Reshape(1, num_channels_,input_geometry_.height, input_geometry_.width);/* Forward dimension change to all layers. */net_->Reshape();std::vector<cv::Mat> input_channels;WrapInputLayer(&input_channels);Preprocess(img, &input_channels);net_->Forward();/* Copy the output layer to a std::vector */Blob<float>* result_blob = net_->output_blobs()[0];const float* result = result_blob->cpu_data();const int num_det = result_blob->height();vector<vector<float> > detections;for (int k = 0; k < num_det; ++k) {if (result[0] == -1) {// Skip invalid detection.result += 7;continue;}vector<float> detection(result, result + 7);detections.push_back(detection);result += 7;}return detections; }/* Load the mean file in binaryproto format. */ void Detector::SetMean(const string& mean_file, const string& mean_value) {cv::Scalar channel_mean;if (!mean_file.empty()) {CHECK(mean_value.empty()) <<"Cannot specify mean_file and mean_value at the same time";BlobProto blob_proto;ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);/* Convert from BlobProto to Blob<float> */Blob<float> mean_blob;mean_blob.FromProto(blob_proto);CHECK_EQ(mean_blob.channels(), num_channels_)<< "Number of channels of mean file doesn't match input layer.";/* The format of the mean file is planar 32-bit float BGR or grayscale. */std::vector<cv::Mat> channels;float* data = mean_blob.mutable_cpu_data();for (int i = 0; i < num_channels_; ++i) {/* Extract an individual channel. */cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);channels.push_back(channel);data += mean_blob.height() * mean_blob.width();}/* Merge the separate channels into a single image. */cv::Mat mean;cv::merge(channels, mean);/* Compute the global mean pixel value and create a mean image* filled with this value. */channel_mean = cv::mean(mean);mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);}if (!mean_value.empty()) {CHECK(mean_file.empty()) <<"Cannot specify mean_file and mean_value at the same time";stringstream ss(mean_value);vector<float> values;string item;while (getline(ss, item, ',')) {float value = std::atof(item.c_str());values.push_back(value);}CHECK(values.size() == 1 || values.size() == num_channels_) <<"Specify either 1 mean_value or as many as channels: " << num_channels_;std::vector<cv::Mat> channels;for (int i = 0; i < num_channels_; ++i) {/* Extract an individual channel. */cv::Mat channel(input_geometry_.height, input_geometry_.width, CV_32FC1,cv::Scalar(values[i]));channels.push_back(channel);}cv::merge(channels, mean_);} }void Detector::WrapInputLayer(std::vector<cv::Mat>* input_channels) {Blob<float>* input_layer = net_->input_blobs()[0];int width = input_layer->width();int height = input_layer->height();float* input_data = input_layer->mutable_cpu_data();for (int i = 0; i < input_layer->channels(); ++i){cv::Mat channel(height, width, CV_32FC1, input_data);input_channels->push_back(channel);input_data += width * height;} }void Detector::Preprocess(const cv::Mat& img,std::vector<cv::Mat>* input_channels) {/* Convert the input image to the input image format of the network. */cv::Mat sample;if (img.channels() == 3 && num_channels_ == 1)cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);else if (img.channels() == 4 && num_channels_ == 1)cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);else if (img.channels() == 4 && num_channels_ == 3)cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);else if (img.channels() == 1 && num_channels_ == 3)cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);elsesample = img;cv::Mat sample_resized;if (sample.size() != input_geometry_)cv::resize(sample, sample_resized, input_geometry_);elsesample_resized = sample;cv::Mat sample_float;if (num_channels_ == 3)sample_resized.convertTo(sample_float, CV_32FC3);elsesample_resized.convertTo(sample_float, CV_32FC1);cv::Mat sample_normalized;cv::subtract(sample_float, mean_, sample_normalized);/* This operation will write the separate BGR planes directly to the* input layer of the network because it is wrapped by the cv::Mat* objects in input_channels. */cv::split(sample_normalized, *input_channels);CHECK(reinterpret_cast<float*>(input_channels->at(0).data)== net_->input_blobs()[0]->cpu_data())<< "Input channels are not wrapping the input layer of the network."; }DEFINE_string(mean_file, "","The mean file used to subtract from the input image."); DEFINE_string(mean_value, "104,117,123","If specified, can be one value or can be same as image channels"" - would subtract from the corresponding channel). Separated by ','.""Either mean_file or mean_value should be provided, not both."); DEFINE_string(file_type, "image","The file type in the list_file. Currently support image and video."); DEFINE_string(out_file, "","If provided, store the detection results in the out_file."); DEFINE_double(confidence_threshold, 0.01,"Only store detections with score higher than the threshold.");//string num2label(int num) //{ // string labels[25]{"", "airplane", "bicycle", "bird", "boat", "bottle", "bus", "car", // "cat", "chair", "cow", "table", "dog", "horse", "motobike", // "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; // return labels[num]; //}string num2label(int num) {string labels[2]{"", "car"};return labels[num]; } cv::Point pt1; cv::Point pt2;/*********************************************************//*********************************************************/ /*********************************************************//*********************************************************/int main(int argc, char** argv) {::google::InitGoogleLogging(argv[0]);// Print output to stderr (while still logging)FLAGS_alsologtostderr = 1;#ifndef GFLAGS_GFLAGS_H_namespace gflags = google; #endifconst string& model_file("deploy.prototxt"); //prototxt文件const string& weights_file("VGG_VOC0712_SSD_300x300_iter_15000.caffemodel");; //caffemodel文件const string& mean_file = FLAGS_mean_file;const string& mean_value = FLAGS_mean_value;const float confidence_threshold = FLAGS_confidence_threshold;// 加載網絡Detector detector(model_file, weights_file, mean_file, mean_value);//cvNamedWindow("SSD", 0);cv::VideoCapture cap("2.mp4");if (!cap.isOpened()){LOG(FATAL) << "Failed to open video: ";}cv::Mat img;int frame_count = 0;while (true){bool success = cap.read(img);if (!success){LOG(INFO) << "Process " << frame_count << " frames from ";break;}CHECK(!img.empty()) << "Error when read frame";std::vector<vector<float> > detections = detector.Detect(img);/* Print the detection results. */for (int i = 0; i < detections.size(); ++i){const vector<float>& d = detections[i];// Detection format: d[image_id, label, score, xmin, ymin, xmax, ymax].CHECK_EQ(d.size(), 7);const float score = d[2];if (score >= 0.6) //【置信度】{//pt1.x = d[3] * img.cols;//pt1.y = d[4] * img.rows;//pt2.x = d[5] * img.cols;//pt2.y = d[6] * img.rows;//rectangle(img, pt1, pt2, cv::Scalar(0, 0, 255), 3, 8, 0);int posx = static_cast<int>(d[3] * img.cols);int posy = static_cast<int>(d[4] * img.rows);int posw = static_cast<int>(d[5] * img.cols) - posx;int posh = static_cast<int>(d[6] * img.rows) - posy;cv::Rect pos(posx, posy, posw, posh);cv::rectangle(img, pos, cv::Scalar(0, static_cast<int>(d[1]) / 21.0 * 255, 255)); //細、橙色框char buffer[50];_gcvt(score, 2, buffer);std::string words = std::string(buffer);words = num2label(static_cast<int>(d[1])) + " : " + words;cv::putText(img, words, cv::Point(posx, posy), CV_FONT_HERSHEY_COMPLEX, 0.4, cv::Scalar(0, static_cast<int>(d[1]) / 21.0 * 255, 255));}}cv::imshow("SSD", img);cv::waitKey(24);++frame_count;}//if (cap.isOpened()) //釋放相機cap{cap.release();}return 0; } #else int main(int argc, char** argv) {LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV."; } #endif // USE_OPENCV

head.h代碼

#ifndef CAFFE_REG_H #define CAFFE_REG_H #include "caffe/common.hpp" #include "caffe/layers/input_layer.hpp" #include "caffe/layers/inner_product_layer.hpp" #include "caffe/layers/dropout_layer.hpp" #include "caffe/layers/conv_layer.hpp" #include "caffe/layers/relu_layer.hpp" #include "caffe/layers/pooling_layer.hpp" #include "caffe/layers/lrn_layer.hpp" #include "caffe/layers/softmax_layer.hpp" #include "caffe/layers/normalize_layer.hpp" #include "caffe/layers/permute_layer.hpp" #include "caffe/layers/flatten_layer.hpp" #include "caffe/layers/prior_box_layer.hpp" #include "caffe/layers/concat_layer.hpp" #include "caffe/layers/reshape_layer.hpp" #include "caffe/layers/softmax_layer.hpp" #include "caffe/layers/detection_output_layer.hpp" namespace caffe {extern INSTANTIATE_CLASS(InputLayer);extern INSTANTIATE_CLASS(ConvolutionLayer);REGISTER_LAYER_CLASS(Convolution);extern INSTANTIATE_CLASS(InnerProductLayer);extern INSTANTIATE_CLASS(DropoutLayer);extern INSTANTIATE_CLASS(ReLULayer);REGISTER_LAYER_CLASS(ReLU);extern INSTANTIATE_CLASS(PoolingLayer);REGISTER_LAYER_CLASS(Pooling);extern INSTANTIATE_CLASS(LRNLayer);//REGISTER_LAYER_CLASS(LRN); extern INSTANTIATE_CLASS(SoftmaxLayer);//REGISTER_LAYER_CLASS(Softmax); extern INSTANTIATE_CLASS(NormalizeLayer);//REGISTER_LAYER_CLASS(Normalize); extern INSTANTIATE_CLASS(PermuteLayer);//REGISTER_LAYER_CLASS(Permute); extern INSTANTIATE_CLASS(FlattenLayer);extern INSTANTIATE_CLASS(PriorBoxLayer);extern INSTANTIATE_CLASS(ConcatLayer);extern INSTANTIATE_CLASS(ReshapeLayer);extern INSTANTIATE_CLASS(SoftmaxLayer);REGISTER_LAYER_CLASS(Softmax);extern INSTANTIATE_CLASS(DetectionOutputLayer); } #endif

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