mirrors
/
PaddleClas
mirror of https://github.com/PaddlePaddle/PaddleClas.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

add flexible configuration for disable det model(C++)

pull/2337/head
HydrogenSulfate 2022-09-22 02:42:48 +00:00
parent 6cf954e1c7
commit b31d67ea32
1 changed files with 295 additions and 281 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

86
deploy/cpp_shitu/src/main.cpp
View File

@ -37,17 +37,14 @@
using namespace std;
using namespace cv;
DEFINE_string(config,
"", "Path of yaml file");
DEFINE_string(c,
"", "Path of yaml file");
DEFINE_string(config, "", "Path of yaml file");
DEFINE_string(c, "", "Path of yaml file");
void DetPredictImage(const std::vector <cv::Mat> &batch_imgs,
const std::vector <std::string> &all_img_paths,
void DetPredictImage(const std::vector<cv::Mat> &batch_imgs,
const std::vector<std::string> &all_img_paths,
const int batch_size, Detection::ObjectDetector *det,
std::vector <Detection::ObjectResult> &im_result,
std::vector<int> &im_bbox_num, std::vector<double> &det_t,
const bool visual_det = false,
std::vector<Detection::ObjectResult> &im_result,
std::vector<double> &det_t, const bool visual_det = false,
const bool run_benchmark = false,
const std::string &output_dir = "output") {
int steps = ceil(float(all_img_paths.size()) / batch_size);
@ -65,7 +62,7 @@ void DetPredictImage(const std::vector <cv::Mat> &batch_imgs,
// }
// Store all detected result
std::vector <Detection::ObjectResult> result;
std::vector<Detection::ObjectResult> result;
std::vector<int> bbox_num;
std::vector<double> det_times;
bool is_rbox = false;
@ -104,7 +101,7 @@ void DetPredictImage(const std::vector <cv::Mat> &batch_imgs,
}
}
}
im_bbox_num.push_back(detect_num);
// im_bbox_num.push_back(detect_num);
item_start_idx = item_start_idx + bbox_num[i];
// Visualization result
@ -136,16 +133,17 @@ void DetPredictImage(const std::vector <cv::Mat> &batch_imgs,
}
void PrintResult(std::string &img_path,
std::vector <Detection::ObjectResult> &det_result,
std::vector<int> &indeices, VectorSearch &vector_search,
std::vector<Detection::ObjectResult> &det_result,
std::vector<int> &indeices, VectorSearch *vector_search_ptr,
SearchResult &search_result) {
printf("%s:\n", img_path.c_str());
for (int i = 0; i < indeices.size(); ++i) {
int t = indeices[i];
printf("\tresult%d: bbox[%d, %d, %d, %d], score: %f, label: %s\n", i,
printf(
"\tresult%d: bbox[%d, %d, %d, %d], score: %f, label: %s\n", i,
det_result[t].rect[0], det_result[t].rect[1], det_result[t].rect[2],
det_result[t].rect[3], det_result[t].confidence,
vector_search.GetLabel(search_result.I[search_result.return_k * t])
vector_search_ptr->GetLabel(search_result.I[search_result.return_k * t])
.c_str());
}
}
@ -168,10 +166,21 @@ int main(int argc, char **argv) {
YamlConfig config(yaml_path);
config.PrintConfigInfo();
// initialize detector, rec_Model, vector_search
Feature::FeatureExtracter feature_extracter(config.config_file);
Detection::ObjectDetector detector(config.config_file);
VectorSearch searcher(config.config_file);
// initialize detector
Detection::ObjectDetector *detector_ptr = nullptr;
if (config.config_file["Global"]["det_inference_model_dir"].Type() !=
YAML::NodeType::Null &&
!config.config_file["Global"]["det_inference_model_dir"]
.as<std::string>()
.empty()) {
detector_ptr = new Detection::ObjectDetector(config.config_file);
}
// initialize feature_extractor
Feature::FeatureExtracter *feature_extracter_ptr =
new Feature::FeatureExtracter(config.config_file);
// initialize vector_searcher
VectorSearch *vector_searcher_ptr = new VectorSearch(config.config_file);
// config
const int batch_size = config.config_file["Global"]["batch_size"].as<int>();
@ -196,9 +205,9 @@ int main(int argc, char **argv) {
// load image_file_path
std::string path =
config.config_file["Global"]["infer_imgs"].as<std::string>();
std::vector <std::string> img_files_list;
std::vector<std::string> img_files_list;
if (cv::utils::fs::isDirectory(path)) {
std::vector <cv::String> filenames;
std::vector<cv::String> filenames;
cv::glob(path, filenames);
for (auto f : filenames) {
img_files_list.push_back(f);
@ -213,11 +222,11 @@ int main(int argc, char **argv) {
std::vector<double> search_times = {0, 0, 0};
int instance_num = 0;
// for read images
std::vector <cv::Mat> batch_imgs;
std::vector <std::string> img_paths;
std::vector<cv::Mat> batch_imgs;
std::vector<std::string> img_paths;
// for detection
std::vector <Detection::ObjectResult> det_result;
std::vector<int> det_bbox_num;
std::vector<Detection::ObjectResult> det_result;
// for vector search
std::vector<float> features;
std::vector<float> feature;
@ -243,9 +252,11 @@ int main(int argc, char **argv) {
batch_imgs.push_back(srcimg);
img_paths.push_back(img_path);
// step1: get all detection results
DetPredictImage(batch_imgs, img_paths, batch_size, &detector, det_result,
det_bbox_num, det_times, visual_det, false);
// step1: get all detection results if enable detector
if (detector_ptr != nullptr) {
DetPredictImage(batch_imgs, img_paths, batch_size, detector_ptr,
det_result, det_times, visual_det, false);
}
// select max_det_results bbox
if (det_result.size() > max_det_results) {
@ -257,7 +268,6 @@ int main(int argc, char **argv) {
Detection::ObjectResult result_whole_img = {
{0, 0, srcimg.cols - 1, srcimg.rows - 1}, 0, 1.0};
det_result.push_back(result_whole_img);
det_bbox_num[0] = det_result.size() + 1;
// step3: extract feature for all boxes in an inmage
SearchResult search_result;
@ -266,20 +276,22 @@ int main(int argc, char **argv) {
int h = det_result[j].rect[3] - det_result[j].rect[1];
cv::Rect rect(det_result[j].rect[0], det_result[j].rect[1], w, h);
cv::Mat crop_img = srcimg(rect);
feature_extracter.Run(crop_img, feature, cls_times);
feature_extracter_ptr->Run(crop_img, feature, cls_times);
features.insert(features.end(), feature.begin(), feature.end());
}
// step4: get search result
auto search_start = std::chrono::steady_clock::now();
search_result = searcher.Search(features.data(), det_result.size());
search_result =
vector_searcher_ptr->Search(features.data(), det_result.size());
auto search_end = std::chrono::steady_clock::now();
// nms for search result
for (int i = 0; i < det_result.size(); ++i) {
det_result[i].confidence = search_result.D[search_result.return_k * i];
}
NMSBoxes(det_result, searcher.GetThreshold(), rec_nms_thresold, indeices);
NMSBoxes(det_result, vector_searcher_ptr->GetThreshold(), rec_nms_thresold,
indeices);
auto nms_end = std::chrono::steady_clock::now();
std::chrono::duration<float> search_diff = search_end - search_start;
search_times[1] += double(search_diff.count() * 1000);
@ -289,12 +301,12 @@ int main(int argc, char **argv) {
// print result
if (not benchmark or (benchmark and idx >= warmup_iter))
PrintResult(img_path, det_result, indeices, searcher, search_result);
PrintResult(img_path, det_result, indeices, vector_searcher_ptr,
search_result);
// for postprocess
batch_imgs.clear();
img_paths.clear();
det_bbox_num.clear();
det_result.clear();
feature.clear();
features.clear();
@ -320,10 +332,12 @@ int main(int argc, char **argv) {
config.config_file["Global"]["cpu_num_threads"].as<int>();
int batch_size = config.config_file["Global"]["batch_size"].as<int>();
std::vector<int> shape =
config.config_file["Global"]["image_shape"].as < std::vector < int >> ();
config.config_file["Global"]["image_shape"].as<std::vector<int>>();
std::string det_shape = std::to_string(shape[0]);
for (int i = 1; i < shape.size(); ++i)
for (int i = 1; i < shape.size(); ++i) {
det_shape = det_shape + ", " + std::to_string(shape[i]);
}
AutoLogger autolog_det("Det", use_gpu, use_tensorrt, enable_mkldnn,
cpu_num_threads, batch_size, det_shape, presion,