From 86cad564e474d66bbdeb4537a5ac0f435763086d Mon Sep 17 00:00:00 2001 From: WenmuZhou <572459439@qq.com> Date: Mon, 7 Mar 2022 11:05:42 +0000 Subject: [PATCH] update db postprocess params --- deploy/cpp_infer/readme.md | 28 +- deploy/cpp_infer/readme_en.md | 2 +- deploy/cpp_infer/src/main.cpp | 468 +++++++++++++++++----------------- 3 files changed, 244 insertions(+), 254 deletions(-) diff --git a/deploy/cpp_infer/readme.md b/deploy/cpp_infer/readme.md index d9013662..725197ad 100644 --- a/deploy/cpp_infer/readme.md +++ b/deploy/cpp_infer/readme.md @@ -4,16 +4,20 @@ C++在性能计算上优于python，因此，在大多数CPU、GPU部署场景，多采用C++的部署方式，本节将介绍如何在Linux\Windows (CPU\GPU)环境下配置C++环境并完成 PaddleOCR模型部署。 -* [1. 准备环境](#1) - + [1.0 运行准备](#10) - + [1.1 编译opencv库](#11) - + [1.2 下载或者编译Paddle预测库](#12) - - [1.2.1 直接下载安装](#121) - - [1.2.2 预测库源码编译](#122) -* [2 开始运行](#2) - + [2.1 将模型导出为inference model](#21) - + [2.2 编译PaddleOCR C++预测demo](#22) - + [2.3运行demo](#23) +- [服务器端C++预测](#服务器端c预测) + - [1. 准备环境](#1-准备环境) + - [1.0 运行准备](#10-运行准备) + - [1.1 编译opencv库](#11-编译opencv库) + - [1.2 下载或者编译Paddle预测库](#12-下载或者编译paddle预测库) + - [1.2.1 直接下载安装](#121-直接下载安装) + - [1.2.2 预测库源码编译](#122-预测库源码编译) + - [2 开始运行](#2-开始运行) + - [2.1 将模型导出为inference model](#21-将模型导出为inference-model) + - [2.2 编译PaddleOCR C++预测demo](#22-编译paddleocr-c预测demo) + - [2.3 运行demo](#23-运行demo) + - [1. 只调用检测：](#1-只调用检测) + - [2. 只调用识别：](#2-只调用识别) + - [3. 调用串联：](#3-调用串联) @@ -103,7 +107,7 @@ opencv3/ #### 1.2.1 直接下载安装 -* [Paddle预测库官网](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。 +* [Paddle预测库官网](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#linux) 上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本（*建议选择paddle版本>=2.0.1版本的预测库* ）。 * 下载之后使用下面的方法解压。 @@ -249,7 +253,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir |gpu_id|int|0|GPU id，使用GPU时有效| |gpu_mem|int|4000|申请的GPU内存| |cpu_math_library_num_threads|int|10|CPU预测时的线程数，在机器核数充足的情况下，该值越大，预测速度越快| -|use_mkldnn|bool|true|是否使用mkldnn库| +|enable_mkldnn|bool|true|是否使用mkldnn库| - 检测模型相关 diff --git a/deploy/cpp_infer/readme_en.md b/deploy/cpp_infer/readme_en.md index 8c5a323a..6b1fa71c 100644 --- a/deploy/cpp_infer/readme_en.md +++ b/deploy/cpp_infer/readme_en.md @@ -231,7 +231,7 @@ More parameters are as follows, |gpu_id|int|0|GPU id when use_gpu is true| |gpu_mem|int|4000|GPU memory requested| |cpu_math_library_num_threads|int|10|Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed| -|use_mkldnn|bool|true|Whether to use mkdlnn library| +|enable_mkldnn|bool|true|Whether to use mkdlnn library| - Detection related parameters diff --git a/deploy/cpp_infer/src/main.cpp b/deploy/cpp_infer/src/main.cpp index b7a199b5..e1069a11 100644 --- a/deploy/cpp_infer/src/main.cpp +++ b/deploy/cpp_infer/src/main.cpp @@ -28,14 +28,14 @@ #include #include -#include #include +#include #include #include #include -#include #include "auto_log/autolog.h" +#include DEFINE_bool(use_gpu, false, "Infering with GPU or CPU."); DEFINE_int32(gpu_id, 0, "Device id of GPU to execute."); @@ -51,8 +51,8 @@ DEFINE_string(image_dir, "", "Dir of input image."); DEFINE_string(det_model_dir, "", "Path of det inference model."); DEFINE_int32(max_side_len, 960, "max_side_len of input image."); DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh."); -DEFINE_double(det_db_box_thresh, 0.5, "Threshold of det_db_box_thresh."); -DEFINE_double(det_db_unclip_ratio, 1.6, "Threshold of det_db_unclip_ratio."); +DEFINE_double(det_db_box_thresh, 0.6, "Threshold of det_db_box_thresh."); +DEFINE_double(det_db_unclip_ratio, 1.5, "Threshold of det_db_unclip_ratio."); DEFINE_bool(use_polygon_score, false, "Whether use polygon score."); DEFINE_bool(visualize, true, "Whether show the detection results."); // classification related @@ -62,281 +62,267 @@ DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh."); // recognition related DEFINE_string(rec_model_dir, "", "Path of rec inference model."); DEFINE_int32(rec_batch_num, 6, "rec_batch_num."); -DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt", "Path of dictionary."); - +DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt", + "Path of dictionary."); using namespace std; using namespace cv; using namespace PaddleOCR; - -static bool PathExists(const std::string& path){ +static bool PathExists(const std::string &path) { #ifdef _WIN32 struct _stat buffer; return (_stat(path.c_str(), &buffer) == 0); #else struct stat buffer; return (stat(path.c_str(), &buffer) == 0); -#endif // !_WIN32 +#endif // !_WIN32 } - int main_det(std::vector cv_all_img_names) { - std::vector time_info = {0, 0, 0}; - DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, - FLAGS_gpu_mem, FLAGS_cpu_threads, - FLAGS_enable_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh, - FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio, - FLAGS_use_polygon_score, FLAGS_visualize, - FLAGS_use_tensorrt, FLAGS_precision); - - for (int i = 0; i < cv_all_img_names.size(); ++i) { -// LOG(INFO) << "The predict img: " << cv_all_img_names[i]; - - cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); - if (!srcimg.data) { - std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl; - exit(1); + std::vector time_info = {0, 0, 0}; + DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, + FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn, + FLAGS_max_side_len, FLAGS_det_db_thresh, + FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio, + FLAGS_use_polygon_score, FLAGS_visualize, FLAGS_use_tensorrt, + FLAGS_precision); + + for (int i = 0; i < cv_all_img_names.size(); ++i) { + // LOG(INFO) << "The predict img: " << cv_all_img_names[i]; + + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " + << cv_all_img_names[i] << endl; + exit(1); + } + std::vector>> boxes; + std::vector det_times; + + det.Run(srcimg, boxes, &det_times); + + time_info[0] += det_times[0]; + time_info[1] += det_times[1]; + time_info[2] += det_times[2]; + + cout << cv_all_img_names[i] << '\t'; + for (int n = 0; n < boxes.size(); n++) { + for (int m = 0; m < boxes[n].size(); m++) { + cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' '; } - std::vector>> boxes; - std::vector det_times; - - det.Run(srcimg, boxes, &det_times); - - time_info[0] += det_times[0]; - time_info[1] += det_times[1]; - time_info[2] += det_times[2]; - - if (FLAGS_benchmark) { - cout << cv_all_img_names[i] << '\t'; - for (int n = 0; n < boxes.size(); n++) { - for (int m = 0; m < boxes[n].size(); m++) { - cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' '; - } - } - cout << endl; - } } - + cout << endl; if (FLAGS_benchmark) { - AutoLogger autolog("ocr_det", - FLAGS_use_gpu, - FLAGS_use_tensorrt, - FLAGS_enable_mkldnn, - FLAGS_cpu_threads, - 1, - "dynamic", - FLAGS_precision, - time_info, - cv_all_img_names.size()); - autolog.report(); + cout << cv_all_img_names[i] << '\t'; + for (int n = 0; n < boxes.size(); n++) { + for (int m = 0; m < boxes[n].size(); m++) { + cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' '; + } + } + cout << endl; } - return 0; -} + } + if (FLAGS_benchmark) { + AutoLogger autolog("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt, + FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic", + FLAGS_precision, time_info, cv_all_img_names.size()); + autolog.report(); + } + return 0; +} int main_rec(std::vector cv_all_img_names) { - std::vector time_info = {0, 0, 0}; - - std::string char_list_file = FLAGS_char_list_file; - if (FLAGS_benchmark) - char_list_file = FLAGS_char_list_file.substr(6); - cout << "label file: " << char_list_file << endl; - - CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, - FLAGS_gpu_mem, FLAGS_cpu_threads, - FLAGS_enable_mkldnn, char_list_file, - FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num); + std::vector time_info = {0, 0, 0}; - std::vector img_list; - for (int i = 0; i < cv_all_img_names.size(); ++i) { - LOG(INFO) << "The predict img: " << cv_all_img_names[i]; + std::string char_list_file = FLAGS_char_list_file; + if (FLAGS_benchmark) + char_list_file = FLAGS_char_list_file.substr(6); + cout << "label file: " << char_list_file << endl; - cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); - if (!srcimg.data) { - std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl; - exit(1); - } - img_list.push_back(srcimg); - } - std::vector rec_times; - rec.Run(img_list, &rec_times); - time_info[0] += rec_times[0]; - time_info[1] += rec_times[1]; - time_info[2] += rec_times[2]; - - if (FLAGS_benchmark) { - AutoLogger autolog("ocr_rec", - FLAGS_use_gpu, - FLAGS_use_tensorrt, - FLAGS_enable_mkldnn, - FLAGS_cpu_threads, - FLAGS_rec_batch_num, - "dynamic", - FLAGS_precision, - time_info, - cv_all_img_names.size()); - autolog.report(); + CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, + FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn, + char_list_file, FLAGS_use_tensorrt, FLAGS_precision, + FLAGS_rec_batch_num); + + std::vector img_list; + for (int i = 0; i < cv_all_img_names.size(); ++i) { + LOG(INFO) << "The predict img: " << cv_all_img_names[i]; + + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " + << cv_all_img_names[i] << endl; + exit(1); } - return 0; -} + img_list.push_back(srcimg); + } + std::vector rec_times; + rec.Run(img_list, &rec_times); + time_info[0] += rec_times[0]; + time_info[1] += rec_times[1]; + time_info[2] += rec_times[2]; + if (FLAGS_benchmark) { + AutoLogger autolog("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt, + FLAGS_enable_mkldnn, FLAGS_cpu_threads, + FLAGS_rec_batch_num, "dynamic", FLAGS_precision, + time_info, cv_all_img_names.size()); + autolog.report(); + } + return 0; +} int main_system(std::vector cv_all_img_names) { - std::vector time_info_det = {0, 0, 0}; - std::vector time_info_rec = {0, 0, 0}; - - DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, - FLAGS_gpu_mem, FLAGS_cpu_threads, - FLAGS_enable_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh, - FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio, - FLAGS_use_polygon_score, FLAGS_visualize, - FLAGS_use_tensorrt, FLAGS_precision); - - Classifier *cls = nullptr; - if (FLAGS_use_angle_cls) { - cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, - FLAGS_gpu_mem, FLAGS_cpu_threads, - FLAGS_enable_mkldnn, FLAGS_cls_thresh, - FLAGS_use_tensorrt, FLAGS_precision); - } + std::vector time_info_det = {0, 0, 0}; + std::vector time_info_rec = {0, 0, 0}; - std::string char_list_file = FLAGS_char_list_file; - if (FLAGS_benchmark) - char_list_file = FLAGS_char_list_file.substr(6); - cout << "label file: " << char_list_file << endl; - - CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, - FLAGS_gpu_mem, FLAGS_cpu_threads, - FLAGS_enable_mkldnn, char_list_file, - FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num); - - for (int i = 0; i < cv_all_img_names.size(); ++i) { - LOG(INFO) << "The predict img: " << cv_all_img_names[i]; - - cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); - if (!srcimg.data) { - std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl; - exit(1); - } - std::vector>> boxes; - std::vector det_times; - std::vector rec_times; - - det.Run(srcimg, boxes, &det_times); - time_info_det[0] += det_times[0]; - time_info_det[1] += det_times[1]; - time_info_det[2] += det_times[2]; - - std::vector img_list; - for (int j = 0; j < boxes.size(); j++) { - cv::Mat crop_img; - crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]); - if (cls != nullptr) { - crop_img = cls->Run(crop_img); - } - img_list.push_back(crop_img); - } + DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, + FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn, + FLAGS_max_side_len, FLAGS_det_db_thresh, + FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio, + FLAGS_use_polygon_score, FLAGS_visualize, FLAGS_use_tensorrt, + FLAGS_precision); + + Classifier *cls = nullptr; + if (FLAGS_use_angle_cls) { + cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, + FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn, + FLAGS_cls_thresh, FLAGS_use_tensorrt, FLAGS_precision); + } + + std::string char_list_file = FLAGS_char_list_file; + if (FLAGS_benchmark) + char_list_file = FLAGS_char_list_file.substr(6); + cout << "label file: " << char_list_file << endl; + + CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, + FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn, + char_list_file, FLAGS_use_tensorrt, FLAGS_precision, + FLAGS_rec_batch_num); + + for (int i = 0; i < cv_all_img_names.size(); ++i) { + LOG(INFO) << "The predict img: " << cv_all_img_names[i]; - rec.Run(img_list, &rec_times); - time_info_rec[0] += rec_times[0]; - time_info_rec[1] += rec_times[1]; - time_info_rec[2] += rec_times[2]; + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " + << cv_all_img_names[i] << endl; + exit(1); } - - if (FLAGS_benchmark) { - AutoLogger autolog_det("ocr_det", - FLAGS_use_gpu, - FLAGS_use_tensorrt, - FLAGS_enable_mkldnn, - FLAGS_cpu_threads, - 1, - "dynamic", - FLAGS_precision, - time_info_det, - cv_all_img_names.size()); - AutoLogger autolog_rec("ocr_rec", - FLAGS_use_gpu, - FLAGS_use_tensorrt, - FLAGS_enable_mkldnn, - FLAGS_cpu_threads, - FLAGS_rec_batch_num, - "dynamic", - FLAGS_precision, - time_info_rec, - cv_all_img_names.size()); - autolog_det.report(); - std::cout << endl; - autolog_rec.report(); - } - return 0; -} + std::vector>> boxes; + std::vector det_times; + std::vector rec_times; + det.Run(srcimg, boxes, &det_times); + time_info_det[0] += det_times[0]; + time_info_det[1] += det_times[1]; + time_info_det[2] += det_times[2]; -void check_params(char* mode) { - if (strcmp(mode, "det")==0) { - if (FLAGS_det_model_dir.empty() || FLAGS_image_dir.empty()) { - std::cout << "Usage[det]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ " - << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; - exit(1); - } + std::vector img_list; + for (int j = 0; j < boxes.size(); j++) { + cv::Mat crop_img; + crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]); + if (cls != nullptr) { + crop_img = cls->Run(crop_img); + } + img_list.push_back(crop_img); } - if (strcmp(mode, "rec")==0) { - if (FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) { - std::cout << "Usage[rec]: ./ppocr --rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " - << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; - exit(1); - } + + rec.Run(img_list, &rec_times); + time_info_rec[0] += rec_times[0]; + time_info_rec[1] += rec_times[1]; + time_info_rec[2] += rec_times[2]; + } + + if (FLAGS_benchmark) { + AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt, + FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic", + FLAGS_precision, time_info_det, + cv_all_img_names.size()); + AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt, + FLAGS_enable_mkldnn, FLAGS_cpu_threads, + FLAGS_rec_batch_num, "dynamic", FLAGS_precision, + time_info_rec, cv_all_img_names.size()); + autolog_det.report(); + std::cout << endl; + autolog_rec.report(); + } + return 0; +} + +void check_params(char *mode) { + if (strcmp(mode, "det") == 0) { + if (FLAGS_det_model_dir.empty() || FLAGS_image_dir.empty()) { + std::cout << "Usage[det]: ./ppocr " + "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ " + << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; + exit(1); } - if (strcmp(mode, "system")==0) { - if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) || - (FLAGS_use_angle_cls && FLAGS_cls_model_dir.empty())) { - std::cout << "Usage[system without angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ " - << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " - << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; - std::cout << "Usage[system with angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ " - << "--use_angle_cls=true " - << "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ " - << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " - << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; - exit(1); - } + } + if (strcmp(mode, "rec") == 0) { + if (FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) { + std::cout << "Usage[rec]: ./ppocr " + "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " + << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; + exit(1); } - if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" && FLAGS_precision != "int8") { - cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. " << endl; - exit(1); + } + if (strcmp(mode, "system") == 0) { + if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() || + FLAGS_image_dir.empty()) || + (FLAGS_use_angle_cls && FLAGS_cls_model_dir.empty())) { + std::cout << "Usage[system without angle cls]: ./ppocr " + "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ " + << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " + << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; + std::cout << "Usage[system with angle cls]: ./ppocr " + "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ " + << "--use_angle_cls=true " + << "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ " + << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " + << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; + exit(1); } + } + if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" && + FLAGS_precision != "int8") { + cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. " << endl; + exit(1); + } } - int main(int argc, char **argv) { - if (argc<=1 || (strcmp(argv[1], "det")!=0 && strcmp(argv[1], "rec")!=0 && strcmp(argv[1], "system")!=0)) { - std::cout << "Please choose one mode of [det, rec, system] !" << std::endl; - return -1; - } - std::cout << "mode: " << argv[1] << endl; - - // Parsing command-line - google::ParseCommandLineFlags(&argc, &argv, true); - check_params(argv[1]); - - if (!PathExists(FLAGS_image_dir)) { - std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir << endl; - exit(1); - } - - std::vector cv_all_img_names; - cv::glob(FLAGS_image_dir, cv_all_img_names); - std::cout << "total images num: " << cv_all_img_names.size() << endl; - - if (strcmp(argv[1], "det")==0) { - return main_det(cv_all_img_names); - } - if (strcmp(argv[1], "rec")==0) { - return main_rec(cv_all_img_names); - } - if (strcmp(argv[1], "system")==0) { - return main_system(cv_all_img_names); - } + if (argc <= 1 || + (strcmp(argv[1], "det") != 0 && strcmp(argv[1], "rec") != 0 && + strcmp(argv[1], "system") != 0)) { + std::cout << "Please choose one mode of [det, rec, system] !" << std::endl; + return -1; + } + std::cout << "mode: " << argv[1] << endl; + + // Parsing command-line + google::ParseCommandLineFlags(&argc, &argv, true); + check_params(argv[1]); + + if (!PathExists(FLAGS_image_dir)) { + std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir + << endl; + exit(1); + } + + std::vector cv_all_img_names; + cv::glob(FLAGS_image_dir, cv_all_img_names); + std::cout << "total images num: " << cv_all_img_names.size() << endl; + if (strcmp(argv[1], "det") == 0) { + return main_det(cv_all_img_names); + } + if (strcmp(argv[1], "rec") == 0) { + return main_rec(cv_all_img_names); + } + if (strcmp(argv[1], "system") == 0) { + return main_system(cv_all_img_names); + } } -- GitLab