Skip to content

P
PaddleOCR

s920243400 / PaddleOCR
与 Fork 源项目一致

Fork自 PaddlePaddle / PaddleOCR

通知 1
Star 1
Fork 0
P
PaddleOCR
提交 86cad564 编写于 作者: 文幕地方's avatar 文幕地方
浏览文件
操作

update db postprocess params

上级 5b333406
显示空白变更内容
内联 并排
Showing with 244 addition and 254 deletion
deploy/cpp_infer/readme.md
浏览文件 @ 86cad564
...@@ -4,16 +4,20 @@ ...@@ -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 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。 PaddleOCR模型部署。
* [1. 准备环境](#1) - [服务器端C++预测](#服务器端c预测)
+ [1.0 运行准备](#10) - [1. 准备环境](#1-准备环境)
+ [1.1 编译opencv库](#11) - [1.0 运行准备](#10-运行准备)
+ [1.2 下载或者编译Paddle预测库](#12) - [1.1 编译opencv库](#11-编译opencv库)
- [1.2.1 直接下载安装](#121) - [1.2 下载或者编译Paddle预测库](#12-下载或者编译paddle预测库)
- [1.2.2 预测库源码编译](#122) - [1.2.1 直接下载安装](#121-直接下载安装)
* [2 开始运行](#2) - [1.2.2 预测库源码编译](#122-预测库源码编译)
+ [2.1 将模型导出为inference model](#21) - [2 开始运行](#2-开始运行)
+ [2.2 编译PaddleOCR C++预测demo](#22) - [2.1 将模型导出为inference model](#21-将模型导出为inference-model)
+ [2.3运行demo](#23) - [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)
<a name="1"></a> <a name="1"></a>
...@@ -103,7 +107,7 @@ opencv3/ ...@@ -103,7 +107,7 @@ opencv3/
#### 1.2.1 直接下载安装 #### 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。 * 下载之后使用下面的方法解压。
...@@ -249,7 +253,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir ...@@ -249,7 +253,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
|gpu_id|int|0|GPU id，使用GPU时有效| |gpu_id|int|0|GPU id，使用GPU时有效|
|gpu_mem|int|4000|申请的GPU内存| |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| |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 - 检测模型相关
......
deploy/cpp_infer/readme_en.md
浏览文件 @ 86cad564
...@@ -231,7 +231,7 @@ More parameters are as follows, ...@@ -231,7 +231,7 @@ More parameters are as follows,
|gpu_id|int|0|GPU id when use_gpu is true| |gpu_id|int|0|GPU id when use_gpu is true|
|gpu_mem|int|4000|GPU memory requested| |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| |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 - Detection related parameters
......
deploy/cpp_infer/src/main.cpp
浏览文件 @ 86cad564
...@@ -28,14 +28,14 @@ ...@@ -28,14 +28,14 @@
#include <numeric> #include <numeric>
#include <glog/logging.h> #include <glog/logging.h>
#include <include/ocr_det.h>
#include <include/ocr_cls.h> #include <include/ocr_cls.h>
#include <include/ocr_det.h>
#include <include/ocr_rec.h> #include <include/ocr_rec.h>
#include <include/utility.h> #include <include/utility.h>
#include <sys/stat.h> #include <sys/stat.h>
#include <gflags/gflags.h>
#include "auto_log/autolog.h" #include "auto_log/autolog.h"
#include <gflags/gflags.h>
DEFINE_bool(use_gpu, false, "Infering with GPU or CPU."); DEFINE_bool(use_gpu, false, "Infering with GPU or CPU.");
DEFINE_int32(gpu_id, 0, "Device id of GPU to execute."); DEFINE_int32(gpu_id, 0, "Device id of GPU to execute.");
...@@ -51,8 +51,8 @@ DEFINE_string(image_dir, "", "Dir of input image."); ...@@ -51,8 +51,8 @@ DEFINE_string(image_dir, "", "Dir of input image.");
DEFINE_string(det_model_dir, "", "Path of det inference model."); DEFINE_string(det_model_dir, "", "Path of det inference model.");
DEFINE_int32(max_side_len, 960, "max_side_len of input image."); 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_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_box_thresh, 0.6, "Threshold of det_db_box_thresh.");
DEFINE_double(det_db_unclip_ratio, 1.6, "Threshold of det_db_unclip_ratio."); 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(use_polygon_score, false, "Whether use polygon score.");
DEFINE_bool(visualize, true, "Whether show the detection results."); DEFINE_bool(visualize, true, "Whether show the detection results.");
// classification related // classification related
...@@ -62,15 +62,14 @@ DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh."); ...@@ -62,15 +62,14 @@ DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
// recognition related // recognition related
DEFINE_string(rec_model_dir, "", "Path of rec inference model."); DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
DEFINE_int32(rec_batch_num, 6, "rec_batch_num."); 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 std;
using namespace cv; using namespace cv;
using namespace PaddleOCR; using namespace PaddleOCR;
static bool PathExists(const std::string &path) {
static bool PathExists(const std::string& path){
#ifdef _WIN32 #ifdef _WIN32
struct _stat buffer; struct _stat buffer;
return (_stat(path.c_str(), &buffer) == 0); return (_stat(path.c_str(), &buffer) == 0);
...@@ -80,22 +79,22 @@ static bool PathExists(const std::string& path){ ...@@ -80,22 +79,22 @@ static bool PathExists(const std::string& path){
#endif // !_WIN32 #endif // !_WIN32
} }
int main_det(std::vector<cv::String> cv_all_img_names) { int main_det(std::vector<cv::String> cv_all_img_names) {
std::vector<double> time_info = {0, 0, 0}; std::vector<double> time_info = {0, 0, 0};
DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
FLAGS_enable_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh, FLAGS_max_side_len, FLAGS_det_db_thresh,
FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio, FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
FLAGS_use_polygon_score, FLAGS_visualize, FLAGS_use_polygon_score, FLAGS_visualize, FLAGS_use_tensorrt,
FLAGS_use_tensorrt, FLAGS_precision); FLAGS_precision);
for (int i = 0; i < cv_all_img_names.size(); ++i) { for (int i = 0; i < cv_all_img_names.size(); ++i) {
// LOG(INFO) << "The predict img: " << cv_all_img_names[i]; // LOG(INFO) << "The predict img: " << cv_all_img_names[i];
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
if (!srcimg.data) { if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl; std::cerr << "[ERROR] image read failed! image path: "
<< cv_all_img_names[i] << endl;
exit(1); exit(1);
} }
std::vector<std::vector<std::vector<int>>> boxes; std::vector<std::vector<std::vector<int>>> boxes;
...@@ -107,6 +106,13 @@ int main_det(std::vector<cv::String> cv_all_img_names) { ...@@ -107,6 +106,13 @@ int main_det(std::vector<cv::String> cv_all_img_names) {
time_info[1] += det_times[1]; time_info[1] += det_times[1];
time_info[2] += det_times[2]; 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] << ' ';
}
}
cout << endl;
if (FLAGS_benchmark) { if (FLAGS_benchmark) {
cout << cv_all_img_names[i] << '\t'; cout << cv_all_img_names[i] << '\t';
for (int n = 0; n < boxes.size(); n++) { for (int n = 0; n < boxes.size(); n++) {
...@@ -119,22 +125,14 @@ int main_det(std::vector<cv::String> cv_all_img_names) { ...@@ -119,22 +125,14 @@ int main_det(std::vector<cv::String> cv_all_img_names) {
} }
if (FLAGS_benchmark) { if (FLAGS_benchmark) {
AutoLogger autolog("ocr_det", AutoLogger autolog("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
FLAGS_use_gpu, FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
FLAGS_use_tensorrt, FLAGS_precision, time_info, cv_all_img_names.size());
FLAGS_enable_mkldnn,
FLAGS_cpu_threads,
1,
"dynamic",
FLAGS_precision,
time_info,
cv_all_img_names.size());
autolog.report(); autolog.report();
} }
return 0; return 0;
} }
int main_rec(std::vector<cv::String> cv_all_img_names) { int main_rec(std::vector<cv::String> cv_all_img_names) {
std::vector<double> time_info = {0, 0, 0}; std::vector<double> time_info = {0, 0, 0};
...@@ -144,9 +142,9 @@ int main_rec(std::vector<cv::String> cv_all_img_names) { ...@@ -144,9 +142,9 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
cout << "label file: " << char_list_file << endl; cout << "label file: " << char_list_file << endl;
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
FLAGS_enable_mkldnn, char_list_file, char_list_file, FLAGS_use_tensorrt, FLAGS_precision,
FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num); FLAGS_rec_batch_num);
std::vector<cv::Mat> img_list; std::vector<cv::Mat> img_list;
for (int i = 0; i < cv_all_img_names.size(); ++i) { for (int i = 0; i < cv_all_img_names.size(); ++i) {
...@@ -154,7 +152,8 @@ int main_rec(std::vector<cv::String> cv_all_img_names) { ...@@ -154,7 +152,8 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
if (!srcimg.data) { if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl; std::cerr << "[ERROR] image read failed! image path: "
<< cv_all_img_names[i] << endl;
exit(1); exit(1);
} }
img_list.push_back(srcimg); img_list.push_back(srcimg);
...@@ -166,39 +165,31 @@ int main_rec(std::vector<cv::String> cv_all_img_names) { ...@@ -166,39 +165,31 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
time_info[2] += rec_times[2]; time_info[2] += rec_times[2];
if (FLAGS_benchmark) { if (FLAGS_benchmark) {
AutoLogger autolog("ocr_rec", AutoLogger autolog("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
FLAGS_use_gpu, FLAGS_enable_mkldnn, FLAGS_cpu_threads,
FLAGS_use_tensorrt, FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
FLAGS_enable_mkldnn, time_info, cv_all_img_names.size());
FLAGS_cpu_threads,
FLAGS_rec_batch_num,
"dynamic",
FLAGS_precision,
time_info,
cv_all_img_names.size());
autolog.report(); autolog.report();
} }
return 0; return 0;
} }
int main_system(std::vector<cv::String> cv_all_img_names) { int main_system(std::vector<cv::String> cv_all_img_names) {
std::vector<double> time_info_det = {0, 0, 0}; std::vector<double> time_info_det = {0, 0, 0};
std::vector<double> time_info_rec = {0, 0, 0}; std::vector<double> time_info_rec = {0, 0, 0};
DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
FLAGS_enable_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh, FLAGS_max_side_len, FLAGS_det_db_thresh,
FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio, FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
FLAGS_use_polygon_score, FLAGS_visualize, FLAGS_use_polygon_score, FLAGS_visualize, FLAGS_use_tensorrt,
FLAGS_use_tensorrt, FLAGS_precision); FLAGS_precision);
Classifier *cls = nullptr; Classifier *cls = nullptr;
if (FLAGS_use_angle_cls) { if (FLAGS_use_angle_cls) {
cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
FLAGS_enable_mkldnn, FLAGS_cls_thresh, FLAGS_cls_thresh, FLAGS_use_tensorrt, FLAGS_precision);
FLAGS_use_tensorrt, FLAGS_precision);
} }
std::string char_list_file = FLAGS_char_list_file; std::string char_list_file = FLAGS_char_list_file;
...@@ -207,16 +198,17 @@ int main_system(std::vector<cv::String> cv_all_img_names) { ...@@ -207,16 +198,17 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
cout << "label file: " << char_list_file << endl; cout << "label file: " << char_list_file << endl;
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
FLAGS_enable_mkldnn, char_list_file, char_list_file, FLAGS_use_tensorrt, FLAGS_precision,
FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num); FLAGS_rec_batch_num);
for (int i = 0; i < cv_all_img_names.size(); ++i) { for (int i = 0; i < cv_all_img_names.size(); ++i) {
LOG(INFO) << "The predict img: " << cv_all_img_names[i]; LOG(INFO) << "The predict img: " << cv_all_img_names[i];
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
if (!srcimg.data) { if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl; std::cerr << "[ERROR] image read failed! image path: "
<< cv_all_img_names[i] << endl;
exit(1); exit(1);
} }
std::vector<std::vector<std::vector<int>>> boxes; std::vector<std::vector<std::vector<int>>> boxes;
...@@ -245,26 +237,14 @@ int main_system(std::vector<cv::String> cv_all_img_names) { ...@@ -245,26 +237,14 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
} }
if (FLAGS_benchmark) { if (FLAGS_benchmark) {
AutoLogger autolog_det("ocr_det", AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
FLAGS_use_gpu, FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
FLAGS_use_tensorrt, FLAGS_precision, time_info_det,
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()); 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(); autolog_det.report();
std::cout << endl; std::cout << endl;
autolog_rec.report(); autolog_rec.report();
...@@ -272,29 +252,33 @@ int main_system(std::vector<cv::String> cv_all_img_names) { ...@@ -272,29 +252,33 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
return 0; return 0;
} }
void check_params(char *mode) {
void check_params(char* mode) { if (strcmp(mode, "det") == 0) {
if (strcmp(mode, "det")==0) {
if (FLAGS_det_model_dir.empty() || FLAGS_image_dir.empty()) { if (FLAGS_det_model_dir.empty() || FLAGS_image_dir.empty()) {
std::cout << "Usage[det]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ " std::cout << "Usage[det]: ./ppocr "
"--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
exit(1); exit(1);
} }
} }
if (strcmp(mode, "rec")==0) { if (strcmp(mode, "rec") == 0) {
if (FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) { if (FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) {
std::cout << "Usage[rec]: ./ppocr --rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " std::cout << "Usage[rec]: ./ppocr "
"--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
exit(1); exit(1);
} }
} }
if (strcmp(mode, "system")==0) { if (strcmp(mode, "system") == 0) {
if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) || if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() ||
FLAGS_image_dir.empty()) ||
(FLAGS_use_angle_cls && FLAGS_cls_model_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/ " std::cout << "Usage[system without angle cls]: ./ppocr "
"--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
std::cout << "Usage[system with angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ " std::cout << "Usage[system with angle cls]: ./ppocr "
"--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--use_angle_cls=true " << "--use_angle_cls=true "
<< "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ " << "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ "
<< "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
...@@ -302,15 +286,17 @@ void check_params(char* mode) { ...@@ -302,15 +286,17 @@ void check_params(char* mode) {
exit(1); exit(1);
} }
} }
if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" && FLAGS_precision != "int8") { if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" &&
FLAGS_precision != "int8") {
cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. " << endl; cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. " << endl;
exit(1); exit(1);
} }
} }
int main(int argc, char **argv) { int main(int argc, char **argv) {
if (argc<=1 || (strcmp(argv[1], "det")!=0 && strcmp(argv[1], "rec")!=0 && strcmp(argv[1], "system")!=0)) { 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; std::cout << "Please choose one mode of [det, rec, system] !" << std::endl;
return -1; return -1;
} }
...@@ -321,7 +307,8 @@ int main(int argc, char **argv) { ...@@ -321,7 +307,8 @@ int main(int argc, char **argv) {
check_params(argv[1]); check_params(argv[1]);
if (!PathExists(FLAGS_image_dir)) { if (!PathExists(FLAGS_image_dir)) {
std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir << endl; std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir
<< endl;
exit(1); exit(1);
} }
...@@ -329,14 +316,13 @@ int main(int argc, char **argv) { ...@@ -329,14 +316,13 @@ int main(int argc, char **argv) {
cv::glob(FLAGS_image_dir, cv_all_img_names); cv::glob(FLAGS_image_dir, cv_all_img_names);
std::cout << "total images num: " << cv_all_img_names.size() << endl; std::cout << "total images num: " << cv_all_img_names.size() << endl;
if (strcmp(argv[1], "det")==0) { if (strcmp(argv[1], "det") == 0) {
return main_det(cv_all_img_names); return main_det(cv_all_img_names);
} }
if (strcmp(argv[1], "rec")==0) { if (strcmp(argv[1], "rec") == 0) {
return main_rec(cv_all_img_names); return main_rec(cv_all_img_names);
} }
if (strcmp(argv[1], "system")==0) { if (strcmp(argv[1], "system") == 0) {
return main_system(cv_all_img_names); return main_system(cv_all_img_names);
} }
} }
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或