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add use_dilation to cpp

pull/5648/head
WenmuZhou 2022-03-07 14:22:28 +00:00
parent 05ed75457d
commit 2e984a5e19
3 changed files with 33 additions and 27 deletions
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10
deploy/cpp_infer/include/ocr_det.h
View File

@ -45,8 +45,9 @@ public:
const double &det_db_thresh,
const double &det_db_box_thresh,
const double &det_db_unclip_ratio,
const bool &use_polygon_score, const bool &visualize,
const bool &use_tensorrt, const std::string &precision) {
const bool &use_polygon_score, const bool &use_dilation,
const bool &visualize, const bool &use_tensorrt,
const std::string &precision) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
@ -59,6 +60,7 @@ public:
this->det_db_box_thresh_ = det_db_box_thresh;
this->det_db_unclip_ratio_ = det_db_unclip_ratio;
this->use_polygon_score_ = use_polygon_score;
this->use_dilation_ = use_dilation;
this->visualize_ = visualize;
this->use_tensorrt_ = use_tensorrt;
@ -71,7 +73,8 @@ public:
void LoadModel(const std::string &model_dir);
// Run predictor
void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes, std::vector<double> *times);
void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes,
std::vector<double> *times);
private:
std::shared_ptr<Predictor> predictor_;
@ -88,6 +91,7 @@ private:
double det_db_box_thresh_ = 0.5;
double det_db_unclip_ratio_ = 2.0;
bool use_polygon_score_ = false;
bool use_dilation_ = false;
bool visualize_ = true;
bool use_tensorrt_ = false;

9
deploy/cpp_infer/src/main.cpp
View File

@ -54,6 +54,7 @@ DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh.");
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(use_dilation, false, "Whether use the dilation on output map.");
DEFINE_bool(visualize, true, "Whether show the detection results.");
// classification related
DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls.");
@ -85,8 +86,8 @@ int main_det(std::vector<cv::String> cv_all_img_names) {
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);
FLAGS_use_polygon_score, FLAGS_use_dilation, 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];
@ -175,8 +176,8 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
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);
FLAGS_use_polygon_score, FLAGS_use_dilation, FLAGS_visualize,
FLAGS_use_tensorrt, FLAGS_precision);
Classifier *cls = nullptr;
if (FLAGS_use_angle_cls) {

41
deploy/cpp_infer/src/ocr_det.cpp
View File

@ -14,7 +14,6 @@
#include <include/ocr_det.h>
namespace PaddleOCR {
void DBDetector::LoadModel(const std::string &model_dir) {
@ -30,13 +29,10 @@ void DBDetector::LoadModel(const std::string &model_dir) {
if (this->precision_ == "fp16") {
precision = paddle_infer::Config::Precision::kHalf;
}
if (this->precision_ == "int8") {
if (this->precision_ == "int8") {
precision = paddle_infer::Config::Precision::kInt8;
}
config.EnableTensorRtEngine(
1 << 20, 10, 3,
precision,
false, false);
}
config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
std::map<std::string, std::vector<int>> min_input_shape = {
{"x", {1, 3, 50, 50}},
{"conv2d_92.tmp_0", {1, 96, 20, 20}},
@ -105,7 +101,7 @@ void DBDetector::Run(cv::Mat &img,
cv::Mat srcimg;
cv::Mat resize_img;
img.copyTo(srcimg);
auto preprocess_start = std::chrono::steady_clock::now();
this->resize_op_.Run(img, resize_img, this->max_side_len_, ratio_h, ratio_w,
this->use_tensorrt_);
@ -116,16 +112,16 @@ void DBDetector::Run(cv::Mat &img,
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
auto preprocess_end = std::chrono::steady_clock::now();
// Inference.
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputHandle(input_names[0]);
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
auto inference_start = std::chrono::steady_clock::now();
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
std::vector<float> out_data;
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
@ -136,7 +132,7 @@ void DBDetector::Run(cv::Mat &img,
out_data.resize(out_num);
output_t->CopyToCpu(out_data.data());
auto inference_end = std::chrono::steady_clock::now();
auto postprocess_start = std::chrono::steady_clock::now();
int n2 = output_shape[2];
int n3 = output_shape[3];
@ -157,24 +153,29 @@ void DBDetector::Run(cv::Mat &img,
const double maxvalue = 255;
cv::Mat bit_map;
cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
cv::Mat dilation_map;
cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
cv::dilate(bit_map, dilation_map, dila_ele);
if (this->use_dilation_) {
cv::Mat dila_ele =
cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
cv::dilate(bit_map, bit_map, dila_ele);
}
boxes = post_processor_.BoxesFromBitmap(
pred_map, dilation_map, this->det_db_box_thresh_,
this->det_db_unclip_ratio_, this->use_polygon_score_);
pred_map, bit_map, this->det_db_box_thresh_, this->det_db_unclip_ratio_,
this->use_polygon_score_);
boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
auto postprocess_end = std::chrono::steady_clock::now();
std::cout << "Detected boxes num: " << boxes.size() << endl;
std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start;
times->push_back(double(preprocess_diff.count() * 1000));
std::chrono::duration<float> inference_diff = inference_end - inference_start;
times->push_back(double(inference_diff.count() * 1000));
std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
std::chrono::duration<float> postprocess_diff =
postprocess_end - postprocess_start;
times->push_back(double(postprocess_diff.count() * 1000));
//// visualization
if (this->visualize_) {
Utility::VisualizeBboxes(srcimg, boxes);