1.编译ssd-caffe,在.build/examples/ssd/下生成ssd_detect.bin
查看ssd_detect.cpp参数,输入测试网络,测试模型,图片/视频列表,文件类型(图像/视频),阈值。输出带有检测坐标信息的文本。
// This is a demo code for using a SSD model to do detection. // The code is modified from examples/cpp_classification/classification.cpp. // Usage: // ssd_detect [FLAGS] model_file weights_file list_file // // where model_file is the .prototxt file defining the network architecture, and // weights_file is the .caffemodel file containing the network parameters, and // list_file contains a list of image files with the format as follows: // folder/img1.JPEG // folder/img2.JPEG // list_file can also contain a list of video files with the format as follows: // folder/video1.mp4 // folder/video2.mp4 // #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_ONLY Caffe::set_mode(Caffe::CPU); #else Caffe::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_); } } /* Wrap the input layer of the network in separate cv::Mat objects * (one per channel). This way we save one memcpy operation and we * don't need to rely on cudaMemcpy2D. The last preprocessing * operation will write the separate channels directly to the input * layer. */ 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); else sample = img; cv::Mat sample_resized; if (sample.size() != input_geometry_) cv::resize(sample, sample_resized, input_geometry_); else sample_resized = sample; cv::Mat sample_float; if (num_channels_ == 3) sample_resized.convertTo(sample_float, CV_32FC3); else sample_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."); 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; #endif gflags::SetUsageMessage("Do detection using SSD mode.\n" "Usage:\n" " ssd_detect [FLAGS] model_file weights_file list_file\n"); gflags::ParseCommandLineFlags(&argc, &argv, true); if (argc < 4) { gflags::ShowUsageWithFlagsRestrict(argv[0], "examples/ssd/ssd_detect"); return 1; } const string& model_file = argv[1]; const string& weights_file = argv[2]; const string& mean_file = FLAGS_mean_file; const string& mean_value = FLAGS_mean_value; const string& file_type = FLAGS_file_type; const string& out_file = FLAGS_out_file; const float confidence_threshold = FLAGS_confidence_threshold; // Initialize the network. Detector detector(model_file, weights_file, mean_file, mean_value); // Set the output mode. std::streambuf* buf = std::cout.rdbuf(); std::ofstream outfile; if (!out_file.empty()) { outfile.open(out_file.c_str()); if (outfile.good()) { buf = outfile.rdbuf(); } } std::ostream out(buf); // Process image one by one. std::ifstream infile(argv[3]); std::string file; while (infile >> file) { if (file_type == "image") { cv::Mat img = cv::imread(file, -1); CHECK(!img.empty()) << "Unable to decode image " << file; 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: [image_id, label, score, xmin, ymin, xmax, ymax]. CHECK_EQ(d.size(), 7); const float score = d[2]; if (score >= confidence_threshold) { out << file << " "; out << static_cast<int>(d[1]) << " "; out << score << " "; out << static_cast<int>(d[3] * img.cols) << " "; out << static_cast<int>(d[4] * img.rows) << " "; out << static_cast<int>(d[5] * img.cols) << " "; out << static_cast<int>(d[6] * img.rows) << std::endl; } } } else if (file_type == "video") { cv::VideoCapture cap(file); if (!cap.isOpened()) { LOG(FATAL) << "Failed to open video: " << file; } cv::Mat img; int frame_count = 0; while (true) { bool success = cap.read(img); if (!success) { LOG(INFO) << "Process " << frame_count << " frames from " << file; 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: [image_id, label, score, xmin, ymin, xmax, ymax]. CHECK_EQ(d.size(), 7); const float score = d[2]; if (score >= confidence_threshold) { out << file << "_"; out << std::setfill('0') << std::setw(6) << frame_count << " "; out << static_cast<int>(d[1]) << " "; out << score << " "; out << static_cast<int>(d[3] * img.cols) << " "; out << static_cast<int>(d[4] * img.rows) << " "; out << static_cast<int>(d[5] * img.cols) << " "; out << static_cast<int>(d[6] * img.rows) << std::endl; } } ++frame_count; } if (cap.isOpened()) { cap.release(); } } else { LOG(FATAL) << "Unknown file_type: " << file_type; } } return 0; } #else int main(int argc, char** argv) { LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV."; } #endif // USE_OPENCV
2.批处理脚本
ssd_detect_pic.sh
#!/usr/bin/env sh ./build/examples/ssd/ssd_detect.bin \ models/VGGNet/SSD_300x300/deploy.prototxt \ models/VGGNet/SSD_300x300/SSD_300x300_iter_30000.caffemodel \ examples/images/name.txt \ --file_type image \ --out_file output.txt \ --confidence_threshold 0.45 echo "Done."
name.txt中的待处理图像列表为caffe根目录下的相对路径
ssd_detect_video.sh
#!/usr/bin/env sh ./build/examples/ssd/ssd_detect.bin \ models/VGGNet/SSD_300x300/deploy.prototxt \ models/VGGNet/SSD_300x300/SSD_300x300_iter_80000.caffemodel \ examples/videos/test.txt \ --file_type video \ --out_file output.txt \ --confidence_threshold 0.4 echo "Done."
3.自动标注
https://blog.csdn.net/sinat_30071459/article/details/50723212
该作者提供了一种手动画框标注图像的方法,得到对应图像的目标信息框并保存到txt,再用matlab将txt中的坐标对应生成到xml文件,其中框到的目标位置信息格式如下:
jpg 数字label x1 y1 x2 y2
而之前使用ssd_detect得到的输出文件中格式:
jpg 标签label confidence x1 y1 x2 y2
对比仅仅多了confidence列,因此很容易想到流程,将一批待标记的图,用现有的检测模型先获得txt位置坐标信息,去除confidence列,替换数字label为标签label,再用matlab生成xml文件。流程有点多,但是至少比直接在原图上标记来的方便。注:自动标注需要一个已经训练好的模型。
删除第三列
awk '{$3="";print $0}' output.txt > new_output.txt
待处理图像和转换后的txt文件放在img文件夹,执行VOC2007xml.m,Annotations生成xml文件,JPEGImages下对应jpg文件,最后JPEGImages文件数量<=img文件数量,原因是模型可能会比较差,部分图不一定检测到结果。
VOC2007xml.m
%% %自动标注 %用ssd_detect先处理待标记图像得到txt %转换txt %txt文件每行格式为:000002.jpg dog 44 28 132 121 %即每行由图片名、目标类型、包围框坐标组成,空格隔开 %如果一张图片有多个目标,则格式如下:(比如两个目标) %000002.jpg dog 44 28 132 121 %000002.jpg car 50 27 140 110 %包围框坐标为左上角和右下角 %原作者小咸鱼_CSDN:http://blog.csdn.net/sinat_30071459 %matlab程序增加溢出保护,有转换偏差可能出现坐标为-1,-2,或者图像宽高+1,+2的情况,这部分加入训练集会报错。 %详细见:https://blog.csdn.net/cgt19910923 %% clc; clear; %注意修改下面四个变量 imgpath='img\';%图像存放文件夹 txtpath='img\new_output.txt';%txt文件 xmlpath_new='Annotations/';%修改后的xml保存文件夹 foldername='VOC2007';%xml的folder字段名 fidin=fopen(txtpath,'r'); lastname='begin'; while ~feof(fidin) tline=fgetl(fidin); str = regexp(tline, ' ','split'); filepath=[imgpath,str{1}]; img=imread(filepath); [h,w,d]=size(img); imshow(img); rectangle('Position',[str2double(str{3}),str2double(str{4}),str2double(str{5})-str2double(str{3}),str2double(str{6})-str2double(str{4})],'LineWidth',4,'EdgeColor','r'); pause(0.1); if strcmp(str{1},lastname)%如果文件名相等,只需增加object object_node=Createnode.createElement('object'); Root.appendChild(object_node); node=Createnode.createElement('name'); node.appendChild(Createnode.createTextNode(sprintf('%s',str{2}))); object_node.appendChild(node); node=Createnode.createElement('pose'); node.appendChild(Createnode.createTextNode(sprintf('%s','Unspecified'))); object_node.appendChild(node); node=Createnode.createElement('truncated'); node.appendChild(Createnode.createTextNode(sprintf('%s','0'))); object_node.appendChild(node); node=Createnode.createElement('difficult'); node.appendChild(Createnode.createTextNode(sprintf('%s','0'))); object_node.appendChild(node); bndbox_node=Createnode.createElement('bndbox'); object_node.appendChild(bndbox_node); node=Createnode.createElement('xmin'); if str2double(str{3})<0 node.appendChild(Createnode.createTextNode(sprintf('%s','0'))); bndbox_node.appendChild(node); else node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(str{3})))); bndbox_node.appendChild(node); end node=Createnode.createElement('ymin'); if str2double(str{4})<0 node.appendChild(Createnode.createTextNode(sprintf('%s','0'))); bndbox_node.appendChild(node); else node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(str{4})))); bndbox_node.appendChild(node); end node=Createnode.createElement('xmax'); if str2double(str{5})>w node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(w)))); bndbox_node.appendChild(node); else node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(str{5})))); bndbox_node.appendChild(node); end node=Createnode.createElement('ymax'); if str2double(str{6})>h node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(h)))); bndbox_node.appendChild(node); else node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(str{6})))); bndbox_node.appendChild(node); end else %如果文件名不等,则需要新建xml copyfile(filepath, 'JPEGImages'); %先保存上一次的xml if exist('Createnode','var') tempname=lastname; tempname=strrep(tempname,'.jpg','.xml'); xmlwrite(tempname,Createnode); end Createnode=com.mathworks.xml.XMLUtils.createDocument('annotation'); Root=Createnode.getDocumentElement;%根节点 node=Createnode.createElement('folder'); node.appendChild(Createnode.createTextNode(sprintf('%s',foldername))); Root.appendChild(node); node=Createnode.createElement('filename'); node.appendChild(Createnode.createTextNode(sprintf('%s',str{1}))); Root.appendChild(node); source_node=Createnode.createElement('source'); Root.appendChild(source_node); node=Createnode.createElement('database'); node.appendChild(Createnode.createTextNode(sprintf('My Database'))); source_node.appendChild(node); node=Createnode.createElement('annotation'); node.appendChild(Createnode.createTextNode(sprintf('VOC2007'))); source_node.appendChild(node); node=Createnode.createElement('image'); node.appendChild(Createnode.createTextNode(sprintf('flickr'))); source_node.appendChild(node); node=Createnode.createElement('flickrid'); node.appendChild(Createnode.createTextNode(sprintf('NULL'))); source_node.appendChild(node); owner_node=Createnode.createElement('owner'); Root.appendChild(owner_node); node=Createnode.createElement('flickrid'); node.appendChild(Createnode.createTextNode(sprintf('NULL'))); owner_node.appendChild(node); node=Createnode.createElement('name'); node.appendChild(Createnode.createTextNode(sprintf('xiaoxianyu'))); owner_node.appendChild(node); size_node=Createnode.createElement('size'); Root.appendChild(size_node); node=Createnode.createElement('width'); node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(w)))); size_node.appendChild(node); node=Createnode.createElement('height'); node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(h)))); size_node.appendChild(node); node=Createnode.createElement('depth'); node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(d)))); size_node.appendChild(node); node=Createnode.createElement('segmented'); node.appendChild(Createnode.createTextNode(sprintf('%s','0'))); Root.appendChild(node); object_node=Createnode.createElement('object'); Root.appendChild(object_node); node=Createnode.createElement('name'); node.appendChild(Createnode.createTextNode(sprintf('%s',str{2}))); object_node.appendChild(node); node=Createnode.createElement('pose'); node.appendChild(Createnode.createTextNode(sprintf('%s','Unspecified'))); object_node.appendChild(node); node=Createnode.createElement('truncated'); node.appendChild(Createnode.createTextNode(sprintf('%s','0'))); object_node.appendChild(node); node=Createnode.createElement('difficult'); node.appendChild(Createnode.createTextNode(sprintf('%s','0'))); object_node.appendChild(node); bndbox_node=Createnode.createElement('bndbox'); object_node.appendChild(bndbox_node); node=Createnode.createElement('xmin'); if str2double(str{3})<0 node.appendChild(Createnode.createTextNode(sprintf('%s','0'))); bndbox_node.appendChild(node); else node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(str{3})))); bndbox_node.appendChild(node); end node=Createnode.createElement('ymin'); if str2double(str{4})<0 node.appendChild(Createnode.createTextNode(sprintf('%s','0'))); bndbox_node.appendChild(node); else node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(str{4})))); bndbox_node.appendChild(node); end node=Createnode.createElement('xmax'); if str2double(str{5})>w node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(w)))); bndbox_node.appendChild(node); else node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(str{5})))); bndbox_node.appendChild(node); end node=Createnode.createElement('ymax'); if str2double(str{6})>h node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(h)))); bndbox_node.appendChild(node); else node.appendChild(Createnode.createTextNode(sprintf('%s',num2str(str{6})))); bndbox_node.appendChild(node); end lastname=str{1}; end %处理最后一行 if feof(fidin) tempname=lastname; tempname=strrep(tempname,'.jpg','.xml'); xmlwrite(tempname,Createnode); end end fclose(fidin); file=dir(pwd); for i=1:length(file) if length(file(i).name)>=4 && strcmp(file(i).name(end-3:end),'.xml') fold=fopen(file(i).name,'r'); fnew=fopen([xmlpath_new file(i).name],'w'); line=1; while ~feof(fold) tline=fgetl(fold); if line==1 line=2; continue; end expression = ' '; replace=char(9); newStr=regexprep(tline,expression,replace); fprintf(fnew,'%s\n',newStr); end fprintf('已处理%s\n',file(i).name); fclose(fold); fclose(fnew); delete(file(i).name); end end
标注程序下载:https://pan.baidu.com/s/1dWTjAPKi4HcVs-RW7JEATQ