Github上找到了一个别人写的基于c_predict_api实现的ssd 但是这个版本的GetImageFile函数有问题，导致检测结果不正确，经过将近一周时间的的排除才发现了问题的所在(吐血)。下面是正确的代码(mxnet大法好!!!!!!!)
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#include <stdio.h> // Path for c_predict_api #include <mxnet/c_predict_api.h> #include <iostream> #include <fstream> #include <string> #include <vector> #include <opencv2/opencv.hpp> const mx_float DEFAULT_MEAN = 117.0; // Read file to buffer class BufferFile { public : std::string file_path_; int length_; char* buffer_; explicit BufferFile(std::string file_path) :file_path_(file_path) { std::ifstream ifs(file_path.c_str(), std::ios::in | std::ios::binary); if (!ifs) { std::cerr << "Can't open the file. Please check " << file_path << ". \n"; length_ = 0; buffer_ = NULL; return; } ifs.seekg(0, std::ios::end); length_ = ifs.tellg(); ifs.seekg(0, std::ios::beg); std::cout << file_path.c_str() << " ... "<< length_ << " bytes\n"; buffer_ = new char[sizeof(char) * length_]; ifs.read(buffer_, length_); ifs.close(); } int GetLength() { return length_; } char* GetBuffer() { return buffer_; } ~BufferFile() { if (buffer_) { delete[] buffer_; buffer_ = NULL; } } }; void GetImageFile(const std::string image_file, mx_float* image_data, const int channels, const cv::Size resize_size, const mx_float* mean_data = nullptr) { // Read all kinds of file into a BGR color 3 channels image cv::Mat im_ori = cv::imread(image_file, cv::IMREAD_COLOR); if (im_ori.empty()) { std::cerr << "Can't open the image. Please check " << image_file << ". \n"; assert(false); } cv::Mat im; cv::resize(im_ori, im, resize_size); float mean_b, mean_g, mean_r; mean_b = 104.0; mean_g = 117.0; mean_r = 123.0; for(int i=0; i < im.rows; ++i){ uchar* data = im.ptr<uchar>(i); for(int j=0; j< im.cols; ++j){ image_data[2*im.rows*im.cols+i*im.cols+j] = static_cast<mx_float>(*data++) - mean_b; image_data[im.rows*im.cols+i*im.cols+j] = static_cast<mx_float>(*data++) - mean_g; image_data[i*im.cols+j] = static_cast<mx_float>(*data++) - mean_r; } } } int main(int argc, char* argv[]) { if (argc < 4) { std::cout << "Usage: ./detect symbol_path params_path image_path" << std::endl; return 0; } std::string test_file; test_file = std::string(argv[3]); // Models path for your model, you have to modify it std::string json_file = std::string(argv[1]); std::string param_file = std::string(argv[2]); BufferFile json_data(json_file); BufferFile param_data(param_file); // Parameters int dev_type = 1; // 1: cpu, 2: gpu int dev_id = 0; // arbitrary. mx_uint num_input_nodes = 1; // 1 for feedforward const char* input_key[1] = {"data"}; const char** input_keys = input_key; // Image size and channels int width = 512; int height = 512; int channels = 3; const mx_uint input_shape_indptr[2] = { 0, 4 }; const mx_uint input_shape_data[4] = { 1, static_cast<mx_uint>(channels), static_cast<mx_uint>(height), static_cast<mx_uint>(width)}; PredictorHandle pred_hnd = 0; if (json_data.GetLength() == 0 || param_data.GetLength() == 0) { return -1; } // Create Predictor MXPredCreate((const char*)json_data.GetBuffer(), (const char*)param_data.GetBuffer(), static_cast<size_t>(param_data.GetLength()), dev_type, dev_id, num_input_nodes, input_keys, input_shape_indptr, input_shape_data, &pred_hnd); assert(pred_hnd); int image_size = width * height * channels; // // Read Image Data std::vector<mx_float> image_data = std::vector<mx_float>(image_size); GetImageFile(test_file, image_data.data(), channels, cv::Size(width, height)); // // Set Input Image int64 start = cv::getTickCount(); MXPredSetInput(pred_hnd, "data", image_data.data(), image_size); // // Do Predict Forward MXPredForward(pred_hnd); mx_uint output_index = 0; mx_uint *shape = 0; mx_uint shape_len; // Get Output Result MXPredGetOutputShape(pred_hnd, output_index, &shape, &shape_len); size_t size = 1; for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i]; std::vector<float> data(size); MXPredGetOutput(pred_hnd, 0, data.data(), size); int64 end = cv::getTickCount(); double secs = (end-start)/cv::getTickFrequency(); std::cout<<"time: " <<secs<<std::endl; assert(data.size() % 6 == 0); cv::Mat mat = cv::imread(test_file, 1); int orig_cols = mat.cols; int orig_rows = mat.rows; cv::resize(mat, mat, cv::Size(width, height)); for(int i=0; i<data.size(); i+=6){ if(data[i]<0) continue; int id = static_cast<int>(data[i]); float score = data[i+1]; if(score < 0.5) continue; std::cout<<score<<std::endl; int xmin = static_cast<int>((data[i+2])*width); int ymin = static_cast<int>((data[i+3])*height); int xmax = static_cast<int>((data[i+4])*width); int ymax = static_cast<int>((data[i+5])*height); cv::rectangle(mat, cv::Point(xmin, ymin), cv::Point(xmax, ymax), cv::Scalar(255, 0, 0), 2); } cv::resize(mat, mat, cv::Size(orig_cols, orig_rows)); cv::imshow(test_file, mat); cv::waitKey(0); return 0; } |