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cpp shitu code format

pull/1437/head
dongshuilong 2021-11-15 12:19:21 +08:00
parent a96305c225
commit 29f8fb830a
19 changed files with 1314 additions and 1291 deletions

129
deploy/cpp_shitu/include/feature_extracter.h
View File

@ -35,77 +35,76 @@ using namespace paddle_infer;
namespace Feature {
class FeatureExtracter {
public:
explicit FeatureExtracter(const YAML::Node &config_file) {
this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
if (config_file["Global"]["gpu_id"].IsDefined())
this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
else
this->gpu_id_ = 0;
this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
this->cpu_math_library_num_threads_ =
config_file["Global"]["cpu_num_threads"].as<int>();
this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
class FeatureExtracter {
public:
explicit FeatureExtracter(const YAML::Node &config_file) {
this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
if (config_file["Global"]["gpu_id"].IsDefined())
this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
else
this->gpu_id_ = 0;
this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
this->cpu_math_library_num_threads_ =
config_file["Global"]["cpu_num_threads"].as<int>();
this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
this->cls_model_path_ =
config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdmodel";
this->cls_params_path_ =
config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdiparams";
this->resize_size_ =
config_file["RecPreProcess"]["transform_ops"][0]["ResizeImage"]["size"]
.as<int>();
this->scale_ = config_file["RecPreProcess"]["transform_ops"][1]
["NormalizeImage"]["scale"]
.as<float>();
this->mean_ = config_file["RecPreProcess"]["transform_ops"][1]
["NormalizeImage"]["mean"]
.as<std::vector<float>>();
this->std_ = config_file["RecPreProcess"]["transform_ops"][1]
["NormalizeImage"]["std"]
.as<std::vector<float>>();
if (config_file["Global"]["rec_feature_normlize"].IsDefined())
this->feature_norm =
config_file["Global"]["rec_feature_normlize"].as<bool>();
this->cls_model_path_ =
config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdmodel";
this->cls_params_path_ =
config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
OS_PATH_SEP + "inference.pdiparams";
this->resize_size_ =
config_file["RecPreProcess"]["transform_ops"][0]["ResizeImage"]["size"]
.as<int>();
this->scale_ = config_file["RecPreProcess"]["transform_ops"][1]["NormalizeImage"]["scale"].as<float>();
this->mean_ = config_file["RecPreProcess"]["transform_ops"][1]
["NormalizeImage"]["mean"]
.as < std::vector < float >> ();
this->std_ = config_file["RecPreProcess"]["transform_ops"][1]
["NormalizeImage"]["std"]
.as < std::vector < float >> ();
if (config_file["Global"]["rec_feature_normlize"].IsDefined())
this->feature_norm =
config_file["Global"]["rec_feature_normlize"].as<bool>();
LoadModel(cls_model_path_, cls_params_path_);
}
LoadModel(cls_model_path_, cls_params_path_);
}
// Load Paddle inference model
void LoadModel(const std::string &model_path, const std::string &params_path);
// Load Paddle inference model
void LoadModel(const std::string &model_path, const std::string &params_path);
// Run predictor
void Run(cv::Mat &img, std::vector<float> &out_data,
std::vector<double> &times);
void FeatureNorm(std::vector<float> &feature);
// Run predictor
void Run(cv::Mat &img, std::vector<float> &out_data,
std::vector<double> &times);
std::shared_ptr<Predictor> predictor_;
void FeatureNorm(std::vector<float> &feature);
private:
bool use_gpu_ = false;
int gpu_id_ = 0;
int gpu_mem_ = 4000;
int cpu_math_library_num_threads_ = 4;
bool use_mkldnn_ = false;
bool use_tensorrt_ = false;
bool feature_norm = true;
bool use_fp16_ = false;
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> std_ = {0.229f, 0.224f, 0.225f};
float scale_ = 0.00392157;
int resize_size_ = 224;
int resize_short_ = 224;
std::string cls_model_path_;
std::string cls_params_path_;
std::shared_ptr <Predictor> predictor_;
// pre-process
ResizeImg resize_op_;
Normalize normalize_op_;
Permute permute_op_;
};
private:
bool use_gpu_ = false;
int gpu_id_ = 0;
int gpu_mem_ = 4000;
int cpu_math_library_num_threads_ = 4;
bool use_mkldnn_ = false;
bool use_tensorrt_ = false;
bool feature_norm = true;
bool use_fp16_ = false;
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> std_ = {0.229f, 0.224f, 0.225f};
float scale_ = 0.00392157;
int resize_size_ = 224;
int resize_short_ = 224;
std::string cls_model_path_;
std::string cls_params_path_;
// pre-process
ResizeImg resize_op_;
Normalize normalize_op_;
Permute permute_op_;
};
} // namespace Feature

80
deploy/cpp_shitu/include/nms.h
View File

@ -17,21 +17,21 @@
#include <algorithm>
#include <include/object_detector.h>
template <typename T>
template<typename T>
static inline bool SortScorePairDescend(const std::pair<float, T> &pair1,
const std::pair<float, T> &pair2) {
return pair1.first > pair2.first;
return pair1.first > pair2.first;
}
float RectOverlap(const Detection::ObjectResult &a,
const Detection::ObjectResult &b) {
float Aa = (a.rect[2] - a.rect[0] + 1) * (a.rect[3] - a.rect[1] + 1);
float Ab = (b.rect[2] - b.rect[0] + 1) * (b.rect[3] - b.rect[1] + 1);
float Aa = (a.rect[2] - a.rect[0] + 1) * (a.rect[3] - a.rect[1] + 1);
float Ab = (b.rect[2] - b.rect[0] + 1) * (b.rect[3] - b.rect[1] + 1);
int iou_w = max(min(a.rect[2], b.rect[2]) - max(a.rect[0], b.rect[0]) + 1, 0);
int iou_h = max(min(a.rect[3], b.rect[3]) - max(a.rect[1], b.rect[1]) + 1, 0);
float Aab = iou_w * iou_h;
return Aab / (Aa + Ab - Aab);
int iou_w = max(min(a.rect[2], b.rect[2]) - max(a.rect[0], b.rect[0]) + 1, 0);
int iou_h = max(min(a.rect[3], b.rect[3]) - max(a.rect[1], b.rect[1]) + 1, 0);
float Aab = iou_w * iou_h;
return Aab / (Aa + Ab - Aab);
}
// Get max scores with corresponding indices.
@ -40,46 +40,46 @@ float RectOverlap(const Detection::ObjectResult &a,
// top_k: if -1, keep all; otherwise, keep at most top_k.
// score_index_vec: store the sorted (score, index) pair.
inline void
GetMaxScoreIndex(const std::vector<Detection::ObjectResult> &det_result,
GetMaxScoreIndex(const std::vector <Detection::ObjectResult> &det_result,
const float threshold,
std::vector<std::pair<float, int>> &score_index_vec) {
// Generate index score pairs.
for (size_t i = 0; i < det_result.size(); ++i) {
if (det_result[i].confidence > threshold) {
score_index_vec.push_back(std::make_pair(det_result[i].confidence, i));
std::vector <std::pair<float, int>> &score_index_vec) {
// Generate index score pairs.
for (size_t i = 0; i < det_result.size(); ++i) {
if (det_result[i].confidence > threshold) {
score_index_vec.push_back(std::make_pair(det_result[i].confidence, i));
}
}
}
// Sort the score pair according to the scores in descending order
std::stable_sort(score_index_vec.begin(), score_index_vec.end(),
SortScorePairDescend<int>);
// Sort the score pair according to the scores in descending order
std::stable_sort(score_index_vec.begin(), score_index_vec.end(),
SortScorePairDescend<int>);
// // Keep top_k scores if needed.
// if (top_k > 0 && top_k < (int)score_index_vec.size())
// {
// score_index_vec.resize(top_k);
// }
// // Keep top_k scores if needed.
// if (top_k > 0 && top_k < (int)score_index_vec.size())
// {
// score_index_vec.resize(top_k);
// }
}
void NMSBoxes(const std::vector<Detection::ObjectResult> det_result,
void NMSBoxes(const std::vector <Detection::ObjectResult> det_result,
const float score_threshold, const float nms_threshold,
std::vector<int> &indices) {
int a = 1;
// Get top_k scores (with corresponding indices).
std::vector<std::pair<float, int>> score_index_vec;
GetMaxScoreIndex(det_result, score_threshold, score_index_vec);
int a = 1;
// Get top_k scores (with corresponding indices).
std::vector <std::pair<float, int>> score_index_vec;
GetMaxScoreIndex(det_result, score_threshold, score_index_vec);
// Do nms
indices.clear();
for (size_t i = 0; i < score_index_vec.size(); ++i) {
const int idx = score_index_vec[i].second;
bool keep = true;
for (int k = 0; k < (int)indices.size() && keep; ++k) {
const int kept_idx = indices[k];
float overlap = RectOverlap(det_result[idx], det_result[kept_idx]);
keep = overlap <= nms_threshold;
// Do nms
indices.clear();
for (size_t i = 0; i < score_index_vec.size(); ++i) {
const int idx = score_index_vec[i].second;
bool keep = true;
for (int k = 0; k < (int) indices.size() && keep; ++k) {
const int kept_idx = indices[k];
float overlap = RectOverlap(det_result[idx], det_result[kept_idx]);
keep = overlap <= nms_threshold;
}
if (keep)
indices.push_back(idx);
}
if (keep)
indices.push_back(idx);
}
}

177
deploy/cpp_shitu/include/object_detector.h
View File

@ -33,103 +33,106 @@ using namespace paddle_infer;
namespace Detection {
// Object Detection Result
struct ObjectResult {
// Rectangle coordinates of detected object: left, right, top, down
std::vector<int> rect;
// Class id of detected object
int class_id;
// Confidence of detected object
float confidence;
};
struct ObjectResult {
// Rectangle coordinates of detected object: left, right, top, down
std::vector<int> rect;
// Class id of detected object
int class_id;
// Confidence of detected object
float confidence;
};
// Generate visualization colormap for each class
std::vector<int> GenerateColorMap(int num_class);
std::vector<int> GenerateColorMap(int num_class);
// Visualiztion Detection Result
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector<ObjectResult> &results,
const std::vector<std::string> &lables,
const std::vector<int> &colormap, const bool is_rbox);
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector <ObjectResult> &results,
const std::vector <std::string> &lables,
const std::vector<int> &colormap, const bool is_rbox);
class ObjectDetector {
public:
explicit ObjectDetector(const YAML::Node &config_file) {
this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
if (config_file["Global"]["gpu_id"].IsDefined())
this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
this->cpu_math_library_num_threads_ =
config_file["Global"]["cpu_num_threads"].as<int>();
this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
this->model_dir_ =
config_file["Global"]["det_inference_model_dir"].as<std::string>();
this->threshold_ = config_file["Global"]["threshold"].as<float>();
this->max_det_results_ = config_file["Global"]["max_det_results"].as<int>();
this->image_shape_ =
config_file["Global"]["image_shape"].as<std::vector<int>>();
this->label_list_ =
config_file["Global"]["labe_list"].as<std::vector<std::string>>();
this->ir_optim_ = config_file["Global"]["ir_optim"].as<bool>();
this->batch_size_ = config_file["Global"]["batch_size"].as<int>();
class ObjectDetector {
public:
explicit ObjectDetector(const YAML::Node &config_file) {
this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
if (config_file["Global"]["gpu_id"].IsDefined())
this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
this->cpu_math_library_num_threads_ =
config_file["Global"]["cpu_num_threads"].as<int>();
this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
this->model_dir_ =
config_file["Global"]["det_inference_model_dir"].as<std::string>();
this->threshold_ = config_file["Global"]["threshold"].as<float>();
this->max_det_results_ = config_file["Global"]["max_det_results"].as<int>();
this->image_shape_ =
config_file["Global"]["image_shape"].as < std::vector < int >> ();
this->label_list_ =
config_file["Global"]["labe_list"].as < std::vector < std::string >> ();
this->ir_optim_ = config_file["Global"]["ir_optim"].as<bool>();
this->batch_size_ = config_file["Global"]["batch_size"].as<int>();
preprocessor_.Init(config_file["DetPreProcess"]["transform_ops"]);
LoadModel(model_dir_, batch_size_, run_mode);
}
preprocessor_.Init(config_file["DetPreProcess"]["transform_ops"]);
LoadModel(model_dir_, batch_size_, run_mode);
}
// Load Paddle inference model
void LoadModel(const std::string &model_dir, const int batch_size = 1,
const std::string &run_mode = "fluid");
// Load Paddle inference model
void LoadModel(const std::string &model_dir, const int batch_size = 1,
const std::string &run_mode = "fluid");
// Run predictor
void Predict(const std::vector<cv::Mat> imgs, const int warmup = 0,
const int repeats = 1,
std::vector<ObjectResult> *result = nullptr,
std::vector<int> *bbox_num = nullptr,
std::vector<double> *times = nullptr);
const std::vector<std::string> &GetLabelList() const {
return this->label_list_;
}
const float &GetThreshold() const { return this->threshold_; }
// Run predictor
void Predict(const std::vector <cv::Mat> imgs, const int warmup = 0,
const int repeats = 1,
std::vector <ObjectResult> *result = nullptr,
std::vector<int> *bbox_num = nullptr,
std::vector<double> *times = nullptr);
private:
bool use_gpu_ = true;
int gpu_id_ = 0;
int gpu_mem_ = 800;
int cpu_math_library_num_threads_ = 6;
std::string run_mode = "fluid";
bool use_mkldnn_ = false;
bool use_tensorrt_ = false;
bool batch_size_ = 1;
bool use_fp16_ = false;
std::string model_dir_;
float threshold_ = 0.5;
float max_det_results_ = 5;
std::vector<int> image_shape_ = {3, 640, 640};
std::vector<std::string> label_list_;
bool ir_optim_ = true;
bool det_permute_ = true;
bool det_postprocess_ = true;
int min_subgraph_size_ = 30;
bool use_dynamic_shape_ = false;
int trt_min_shape_ = 1;
int trt_max_shape_ = 1280;
int trt_opt_shape_ = 640;
bool trt_calib_mode_ = false;
const std::vector <std::string> &GetLabelList() const {
return this->label_list_;
}
// Preprocess image and copy data to input buffer
void Preprocess(const cv::Mat &image_mat);
// Postprocess result
void Postprocess(const std::vector<cv::Mat> mats,
std::vector<ObjectResult> *result, std::vector<int> bbox_num,
bool is_rbox);
const float &GetThreshold() const { return this->threshold_; }
std::shared_ptr<Predictor> predictor_;
Preprocessor preprocessor_;
ImageBlob inputs_;
std::vector<float> output_data_;
std::vector<int> out_bbox_num_data_;
};
private:
bool use_gpu_ = true;
int gpu_id_ = 0;
int gpu_mem_ = 800;
int cpu_math_library_num_threads_ = 6;
std::string run_mode = "fluid";
bool use_mkldnn_ = false;
bool use_tensorrt_ = false;
bool batch_size_ = 1;
bool use_fp16_ = false;
std::string model_dir_;
float threshold_ = 0.5;
float max_det_results_ = 5;
std::vector<int> image_shape_ = {3, 640, 640};
std::vector <std::string> label_list_;
bool ir_optim_ = true;
bool det_permute_ = true;
bool det_postprocess_ = true;
int min_subgraph_size_ = 30;
bool use_dynamic_shape_ = false;
int trt_min_shape_ = 1;
int trt_max_shape_ = 1280;
int trt_opt_shape_ = 640;
bool trt_calib_mode_ = false;
// Preprocess image and copy data to input buffer
void Preprocess(const cv::Mat &image_mat);
// Postprocess result
void Postprocess(const std::vector <cv::Mat> mats,
std::vector <ObjectResult> *result, std::vector<int> bbox_num,
bool is_rbox);
std::shared_ptr <Predictor> predictor_;
Preprocessor preprocessor_;
ImageBlob inputs_;
std::vector<float> output_data_;
std::vector<int> out_bbox_num_data_;
};
} // namespace Detection

36
deploy/cpp_shitu/include/preprocess_op.h
View File

@ -31,27 +31,27 @@ using namespace std;
namespace Feature {
class Normalize {
public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale);
};
class Normalize {
public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale);
};
// RGB -> CHW
class Permute {
public:
virtual void Run(const cv::Mat *im, float *data);
};
class Permute {
public:
virtual void Run(const cv::Mat *im, float *data);
};
class CenterCropImg {
public:
virtual void Run(cv::Mat &im, const int crop_size = 224);
};
class CenterCropImg {
public:
virtual void Run(cv::Mat &im, const int crop_size = 224);
};
class ResizeImg {
public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
int size = 0);
};
class ResizeImg {
public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
int size = 0);
};
} // namespace Feature

199
deploy/cpp_shitu/include/preprocess_op_det.h
View File

@ -31,125 +31,128 @@
namespace Detection {
// Object for storing all preprocessed data
class ImageBlob {
public:
// image width and height
std::vector<float> im_shape_;
// Buffer for image data after preprocessing
std::vector<float> im_data_;
// in net data shape(after pad)
std::vector<float> in_net_shape_;
// Evaluation image width and height
// std::vector<float> eval_im_size_f_;
// Scale factor for image size to origin image size
std::vector<float> scale_factor_;
};
class ImageBlob {
public:
// image width and height
std::vector<float> im_shape_;
// Buffer for image data after preprocessing
std::vector<float> im_data_;
// in net data shape(after pad)
std::vector<float> in_net_shape_;
// Evaluation image width and height
// std::vector<float> eval_im_size_f_;
// Scale factor for image size to origin image size
std::vector<float> scale_factor_;
};
// Abstraction of preprocessing opration class
class PreprocessOp {
public:
virtual void Init(const YAML::Node &item) = 0;
virtual void Run(cv::Mat *im, ImageBlob *data) = 0;
};
class PreprocessOp {
public:
virtual void Init(const YAML::Node &item) = 0;
class InitInfo : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {}
virtual void Run(cv::Mat *im, ImageBlob *data);
};
virtual void Run(cv::Mat *im, ImageBlob *data) = 0;
};
class NormalizeImage : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
mean_ = item["mean"].as<std::vector<float>>();
scale_ = item["std"].as<std::vector<float>>();
is_scale_ = item["is_scale"].as<bool>();
}
class InitInfo : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {}
virtual void Run(cv::Mat *im, ImageBlob *data);
virtual void Run(cv::Mat *im, ImageBlob *data);
};
private:
// CHW or HWC
std::vector<float> mean_;
std::vector<float> scale_;
bool is_scale_;
};
class NormalizeImage : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
mean_ = item["mean"].as < std::vector < float >> ();
scale_ = item["std"].as < std::vector < float >> ();
is_scale_ = item["is_scale"].as<bool>();
}
class Permute : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {}
virtual void Run(cv::Mat *im, ImageBlob *data);
};
virtual void Run(cv::Mat *im, ImageBlob *data);
class Resize : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
interp_ = item["interp"].as<int>();
// max_size_ = item["target_size"].as<int>();
keep_ratio_ = item["keep_ratio"].as<bool>();
target_size_ = item["target_size"].as<std::vector<int>>();
}
private:
// CHW or HWC
std::vector<float> mean_;
std::vector<float> scale_;
bool is_scale_;
};
// Compute best resize scale for x-dimension, y-dimension
std::pair<double, double> GenerateScale(const cv::Mat &im);
class Permute : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {}
virtual void Run(cv::Mat *im, ImageBlob *data);
virtual void Run(cv::Mat *im, ImageBlob *data);
};
private:
int interp_ = 2;
bool keep_ratio_;
std::vector<int> target_size_;
std::vector<int> in_net_shape_;
};
class Resize : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
interp_ = item["interp"].as<int>();
// max_size_ = item["target_size"].as<int>();
keep_ratio_ = item["keep_ratio"].as<bool>();
target_size_ = item["target_size"].as < std::vector < int >> ();
}
// Compute best resize scale for x-dimension, y-dimension
std::pair<double, double> GenerateScale(const cv::Mat &im);
virtual void Run(cv::Mat *im, ImageBlob *data);
private:
int interp_ = 2;
bool keep_ratio_;
std::vector<int> target_size_;
std::vector<int> in_net_shape_;
};
// Models with FPN need input shape % stride == 0
class PadStride : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
stride_ = item["stride"].as<int>();
}
class PadStride : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
stride_ = item["stride"].as<int>();
}
virtual void Run(cv::Mat *im, ImageBlob *data);
virtual void Run(cv::Mat *im, ImageBlob *data);
private:
int stride_;
};
private:
int stride_;
};
class Preprocessor {
public:
void Init(const YAML::Node &config_node) {
// initialize image info at first
ops_["InitInfo"] = std::make_shared<InitInfo>();
for (int i = 0; i < config_node.size(); ++i) {
if (config_node[i]["DetResize"].IsDefined()) {
ops_["Resize"] = std::make_shared<Resize>();
ops_["Resize"]->Init(config_node[i]["DetResize"]);
}
class Preprocessor {
public:
void Init(const YAML::Node &config_node) {
// initialize image info at first
ops_["InitInfo"] = std::make_shared<InitInfo>();
for (int i = 0; i < config_node.size(); ++i) {
if (config_node[i]["DetResize"].IsDefined()) {
ops_["Resize"] = std::make_shared<Resize>();
ops_["Resize"]->Init(config_node[i]["DetResize"]);
}
if (config_node[i]["DetNormalizeImage"].IsDefined()) {
ops_["NormalizeImage"] = std::make_shared<NormalizeImage>();
ops_["NormalizeImage"]->Init(config_node[i]["DetNormalizeImage"]);
}
if (config_node[i]["DetNormalizeImage"].IsDefined()) {
ops_["NormalizeImage"] = std::make_shared<NormalizeImage>();
ops_["NormalizeImage"]->Init(config_node[i]["DetNormalizeImage"]);
}
if (config_node[i]["DetPermute"].IsDefined()) {
ops_["Permute"] = std::make_shared<Permute>();
ops_["Permute"]->Init(config_node[i]["DetPermute"]);
}
if (config_node[i]["DetPermute"].IsDefined()) {
ops_["Permute"] = std::make_shared<Permute>();
ops_["Permute"]->Init(config_node[i]["DetPermute"]);
}
if (config_node[i]["DetPadStrid"].IsDefined()) {
ops_["PadStride"] = std::make_shared<PadStride>();
ops_["PadStride"]->Init(config_node[i]["DetPadStrid"]);
}
}
}
if (config_node[i]["DetPadStrid"].IsDefined()) {
ops_["PadStride"] = std::make_shared<PadStride>();
ops_["PadStride"]->Init(config_node[i]["DetPadStrid"]);
}
}
}
void Run(cv::Mat *im, ImageBlob *data);
void Run(cv::Mat *im, ImageBlob *data);
public:
static const std::vector<std::string> RUN_ORDER;
public:
static const std::vector <std::string> RUN_ORDER;
private:
std::unordered_map<std::string, std::shared_ptr<PreprocessOp>> ops_;
};
private:
std::unordered_map <std::string, std::shared_ptr<PreprocessOp>> ops_;
};
} // namespace Detection

65
deploy/cpp_shitu/include/vector_search.h
View File

@ -26,40 +26,45 @@
#include <map>
struct SearchResult {
std::vector<faiss::Index::idx_t> I;
std::vector<float> D;
int return_k;
std::vector <faiss::Index::idx_t> I;
std::vector<float> D;
int return_k;
};
class VectorSearch {
public:
explicit VectorSearch(const YAML::Node &config_file) {
// IndexProcess
this->index_dir =
config_file["IndexProcess"]["index_dir"].as<std::string>();
this->return_k = config_file["IndexProcess"]["return_k"].as<int>();
this->score_thres = config_file["IndexProcess"]["score_thres"].as<float>();
this->max_query_number =
config_file["Global"]["max_det_results"].as<int>() + 1;
LoadIdMap();
LoadIndexFile();
this->I.resize(this->return_k * this->max_query_number);
this->D.resize(this->return_k * this->max_query_number);
};
void LoadIdMap();
void LoadIndexFile();
const SearchResult &Search(float *feature, int query_number);
const std::string &GetLabel(faiss::Index::idx_t ind);
const float &GetThreshold() { return this->score_thres; }
explicit VectorSearch(const YAML::Node &config_file) {
// IndexProcess
this->index_dir =
config_file["IndexProcess"]["index_dir"].as<std::string>();
this->return_k = config_file["IndexProcess"]["return_k"].as<int>();
this->score_thres = config_file["IndexProcess"]["score_thres"].as<float>();
this->max_query_number =
config_file["Global"]["max_det_results"].as<int>() + 1;
LoadIdMap();
LoadIndexFile();
this->I.resize(this->return_k * this->max_query_number);
this->D.resize(this->return_k * this->max_query_number);
};
void LoadIdMap();
void LoadIndexFile();
const SearchResult &Search(float *feature, int query_number);
const std::string &GetLabel(faiss::Index::idx_t ind);
const float &GetThreshold() { return this->score_thres; }
private:
std::string index_dir;
int return_k = 5;
float score_thres = 0.5;
std::map<long int, std::string> id_map;
faiss::Index *index;
int max_query_number = 6;
std::vector<float> D;
std::vector<faiss::Index::idx_t> I;
SearchResult sr;
std::string index_dir;
int return_k = 5;
float score_thres = 0.5;
std::map<long int, std::string> id_map;
faiss::Index *index;
int max_query_number = 6;
std::vector<float> D;
std::vector <faiss::Index::idx_t> I;
SearchResult sr;
};

21
deploy/cpp_shitu/include/yaml_config.h
View File

@ -42,12 +42,17 @@
class YamlConfig {
public:
explicit YamlConfig(const std::string &path) {
config_file = ReadYamlConfig(path);
}
static std::vector<std::string> ReadDict(const std::string &path);
static std::map<int, std::string> ReadIndexId(const std::string &path);
static YAML::Node ReadYamlConfig(const std::string &path);
void PrintConfigInfo();
YAML::Node config_file;
explicit YamlConfig(const std::string &path) {
config_file = ReadYamlConfig(path);
}
static std::vector <std::string> ReadDict(const std::string &path);
static std::map<int, std::string> ReadIndexId(const std::string &path);
static YAML::Node ReadYamlConfig(const std::string &path);
void PrintConfigInfo();
YAML::Node config_file;
};

136
deploy/cpp_shitu/readme.md
View File

@ -6,10 +6,7 @@
## 1. 准备环境
### 运行准备
- Linux环境，推荐使用docker。
- Windows环境，目前支持基于`Visual Studio 2019 Community`进行编译；此外，如果您希望通过生成`sln解决方案`的方式进行编译，可以参考该文档：[https://zhuanlan.zhihu.com/p/145446681](https://zhuanlan.zhihu.com/p/145446681)
* 该文档主要介绍基于Linux环境下的PaddleClas C++预测流程，如果需要在Windows环境下使用预测库进行C++预测，具体编译方法请参考[Windows下编译教程](./docs/windows_vs2019_build.md)。
- Linux环境，推荐使用ubuntu docker。
### 1.1 编译opencv库
@ -103,7 +100,7 @@ make -j
make inference_lib_dist
```
更多编译参数选项可以参考Paddle C++预测库官网：[https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)。
更多编译参数选项可以参考[Paddle C++预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)。
* 编译完成之后，可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。
@ -137,29 +134,27 @@ tar -xvf paddle_inference.tgz
### 1.3 安装faiss库
```shell
# 下载 faiss
git clone https://github.com/facebookresearch/faiss.git
cd faiss
cmake -B build . -DFAISS_ENABLE_PYTHON=OFF -DCMAKE_INSTALL_PREFIX=${faiss_install_path}
cmake -B build . -DFAISS_ENABLE_PYTHON=OFF -DCMAKE_INSTALL_PREFIX=${faiss_install_path}
make -C build -j faiss
make -C build install
```
## 2 开始运行
### 2.1 将模型导出为inference model
* 可以参考[模型导出](../../tools/export_model.py)，导出`inference model`，用于模型预测。得到预测模型后，假设模型文件放在`inference`目录下，则目录结构如下。
在安装`faiss`前，请安装`openblas`，`ubuntu`系统中安装命令如下：
```shell
apt-get install libopenblas-dev
```
inference/
|--cls_infer.pdmodel
|--cls_infer.pdiparams
```
**注意**：上述文件中，`cls_infer.pdmodel`文件存储了模型结构信息，`cls_infer.pdiparams`文件存储了模型参数信息。注意两个文件的路径需要与配置文件`tools/config.txt`中的`cls_model_path`和`cls_params_path`参数对应一致。
注意本教程以安装faiss cpu版本为例，安装时请参考[faiss](https://github.com/facebookresearch/faiss)官网文档，根据需求自行安装。
## 2 代码编译
### 2.2 编译PaddleClas C++预测demo
* 编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。
编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。同时，编译过程中需要下载编译`yaml-cpp`等C++库，请保持联网环境。
```shell
@ -169,11 +164,12 @@ sh tools/build.sh
具体地，`tools/build.sh`中内容如下。
```shell
OPENCV_DIR=your_opencv_dir
LIB_DIR=your_paddle_inference_dir
CUDA_LIB_DIR=your_cuda_lib_dir
CUDNN_LIB_DIR=your_cudnn_lib_dir
TENSORRT_DIR=your_tensorrt_lib_dir
OPENCV_DIR=${opencv_install_dir}
LIB_DIR=${paddle_inference_dir}
CUDA_LIB_DIR=/usr/local/cuda/lib64
CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
FAISS_DIR=${faiss_install_dir}
FAISS_WITH_MKL=OFF
BUILD_DIR=build
rm -rf ${BUILD_DIR}
@ -182,14 +178,14 @@ cd ${BUILD_DIR}
cmake .. \
-DPADDLE_LIB=${LIB_DIR} \
-DWITH_MKL=ON \
-DDEMO_NAME=clas_system \
-DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \
-DWITH_TENSORRT=OFF \
-DTENSORRT_DIR=${TENSORRT_DIR} \
-DUSE_TENSORRT=OFF \
-DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \
-DFAISS_DIR=${FAISS_DIR} \
-DFAISS_WITH_MKL=${FAISS_WITH_MKL}
make -j
```
@ -197,47 +193,75 @@ make -j
上述命令中，
* `OPENCV_DIR`为opencv编译安装的地址（本例中为`opencv-3.4.7/opencv3`文件夹的路径）；
* `LIB_DIR`为下载的Paddle预测库（`paddle_inference`文件夹），或编译生成的Paddle预测库（`build/paddle_inference_install_dir`文件夹）的路径；
* `CUDA_LIB_DIR`为cuda库文件地址，在docker中为`/usr/local/cuda/lib64`；
* `CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
* `TENSORRT_DIR`是tensorrt库文件地址，在dokcer中为`/usr/local/TensorRT6-cuda10.0-cudnn7/`，TensorRT需要结合GPU使用。
在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`clas_system`的可执行文件。
* `FAISS_DIR`是faiss的安装地址
* `FAISS_WITH_MKL`是指在编译faiss的过程中，是否使用了mkldnn，本文档中编译faiss，没有使用，而使用了openblas，故设置为`OFF`，若使用了mkldnn，则为`ON`.
### 运行demo
* 首先修改`tools/config.txt`中对应字段：
* use_gpu：是否使用GPU；
* gpu_id：使用的GPU卡号；
* gpu_mem：显存；
* cpu_math_library_num_threads：底层科学计算库所用线程的数量；
* use_mkldnn：是否使用MKLDNN加速；
* use_tensorrt: 是否使用tensorRT进行加速；
* use_fp16：是否使用半精度浮点数进行计算，该选项仅在use_tensorrt为true时有效；
* cls_model_path：预测模型结构文件路径；
* cls_params_path：预测模型参数文件路径；
* resize_short_size：预处理时图像缩放大小；
* crop_size：预处理时图像裁剪后的大小。
在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`pp_shitu`的可执行文件。
* 然后修改`tools/run.sh`：
* `./build/clas_system ./tools/config.txt ./docs/imgs/ILSVRC2012_val_00000666.JPEG`
* 上述命令中分别为：编译得到的可执行文件`clas_system`；运行时的配置文件`config.txt`；待预测的图像。
## 3 运行demo
* 最后执行以下命令，完成对一幅图像的分类。
- 请参考[识别快速开始文档](../../docs/zh_CN/quick_start/quick_start_recognition.md)，下载好相应的 轻量级通用主体检测模型、轻量级通用识别模型及瓶装饮料测试数据并解压。
```shell
sh tools/run.sh
```
```shell
mkdir models
cd models
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar
cd ..
mkdir data
cd data
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
tar -xf drink_dataset_v1.0.tar
cd ..
```
* 最终屏幕上会输出结果，如下图所示。
- 将相应的yaml文件拷到`test`文件夹下
<div align="center">
<img src="./docs/imgs/cpp_infer_result.png" width="600">
</div>
```shell
cp ../configs/inference_drink.yaml .
```
- 将`inference_drink.yaml`中的相对路径，改成基于本目录的路径或者绝对路径。涉及到的参数有
其中`class id`表示置信度最高的类别对应的id，score表示图片属于该类别的概率。
- Global.infer_imgs ：此参数可以是具体的图像地址，也可以是图像集所在的目录
- Global.det_inference_model_dir ： 检测模型存储目录
- Global.rec_inference_model_dir ： 识别模型存储目录
- IndexProcess.index_dir ： 检索库的存储目录，在示例中，检索库在下载的demo数据中。
- 字典转换
由于python的检索库的字典，使用`pickle`进行的序列化存储，导致C++不方便读取，因此进行转换
```shell
python tools/transform_id_map.py -c inference_drink.yaml
```
转换成功后，在`IndexProcess.index_dir`目录下生成`id_map.txt`，方便c++ 读取。
- 执行程序
```shell
./build/pp_shitu -c inference_drink.yaml
# or
./build/pp_shitu -config inference_drink.yaml
```
若对图像集进行检索，则可能得到，如下结果。注意，此结果只做展示，具体以实际运行结果为准。
同时，需注意的是，由于opencv 版本问题，会导致图像在预处理的过程中，resize产生细微差别，导致python 和c++结果，轻微不同，如bbox相差几个像素，检索结果小数点后3位diff等。但不会改变最终检索label。
![](../../docs/images/quick_start/shitu_c++_result.png)
## 4 使用自己模型
使用自己训练的模型，可以参考[模型导出](../../docs/zh_CN/inference_deployment/export_model.md)，导出`inference model`，用于模型预测。
同时注意修改`yaml`文件中具体参数。

188
deploy/cpp_shitu/src/feature_extracter.cpp
View File

@ -18,102 +18,102 @@
namespace Feature {
void FeatureExtracter::LoadModel(const std::string &model_path,
const std::string &params_path) {
paddle_infer::Config config;
config.SetModel(model_path, params_path);
void FeatureExtracter::LoadModel(const std::string &model_path,
const std::string &params_path) {
paddle_infer::Config config;
config.SetModel(model_path, params_path);
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
config.EnableTensorRtEngine(
1 << 20, 1, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
false, false);
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
config.EnableTensorRtEngine(
1 << 20, 1, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
false, false);
}
} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10);
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
// true for multiple input
config.SwitchSpecifyInputNames(true);
config.SwitchIrOptim(true);
config.EnableMemoryOptim();
config.DisableGlogInfo();
this->predictor_ = CreatePredictor(config);
}
} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10);
void FeatureExtracter::Run(cv::Mat &img, std::vector<float> &out_data,
std::vector<double> &times) {
cv::Mat resize_img;
std::vector<double> time;
auto preprocess_start = std::chrono::system_clock::now();
this->resize_op_.Run(img, resize_img, this->resize_short_,
this->resize_size_);
this->normalize_op_.Run(&resize_img, this->mean_, this->std_, this->scale_);
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
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 preprocess_end = std::chrono::system_clock::now();
auto infer_start = std::chrono::system_clock::now();
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = output_t->shape();
int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
std::multiplies<int>());
out_data.resize(out_num);
output_t->CopyToCpu(out_data.data());
auto infer_end = std::chrono::system_clock::now();
auto postprocess_start = std::chrono::system_clock::now();
if (this->feature_norm)
FeatureNorm(out_data);
auto postprocess_end = std::chrono::system_clock::now();
std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start;
time.push_back(double(preprocess_diff.count()));
std::chrono::duration<float> inference_diff = infer_end - infer_start;
double inference_cost_time = double(inference_diff.count());
time.push_back(inference_cost_time);
// std::chrono::duration<float> postprocess_diff =
// postprocess_end - postprocess_start;
time.push_back(0);
// std::cout << "result: " << std::endl;
// std::cout << "\tclass id: " << maxPosition << std::endl;
// std::cout << std::fixed << std::setprecision(10)
// << "\tscore: " << double(out_data[maxPosition]) << std::endl;
times[0] += time[0];
times[1] += time[1];
times[2] += time[2];
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
// true for multiple input
config.SwitchSpecifyInputNames(true);
config.SwitchIrOptim(true);
config.EnableMemoryOptim();
config.DisableGlogInfo();
this->predictor_ = CreatePredictor(config);
}
void FeatureExtracter::Run(cv::Mat &img, std::vector<float> &out_data,
std::vector<double> &times) {
cv::Mat resize_img;
std::vector<double> time;
auto preprocess_start = std::chrono::system_clock::now();
this->resize_op_.Run(img, resize_img, this->resize_short_,
this->resize_size_);
this->normalize_op_.Run(&resize_img, this->mean_, this->std_, this->scale_);
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
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 preprocess_end = std::chrono::system_clock::now();
auto infer_start = std::chrono::system_clock::now();
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = output_t->shape();
int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
std::multiplies<int>());
out_data.resize(out_num);
output_t->CopyToCpu(out_data.data());
auto infer_end = std::chrono::system_clock::now();
auto postprocess_start = std::chrono::system_clock::now();
if (this->feature_norm)
FeatureNorm(out_data);
auto postprocess_end = std::chrono::system_clock::now();
std::chrono::duration<float> preprocess_diff =
preprocess_end - preprocess_start;
time.push_back(double(preprocess_diff.count()));
std::chrono::duration<float> inference_diff = infer_end - infer_start;
double inference_cost_time = double(inference_diff.count());
time.push_back(inference_cost_time);
// std::chrono::duration<float> postprocess_diff =
// postprocess_end - postprocess_start;
time.push_back(0);
// std::cout << "result: " << std::endl;
// std::cout << "\tclass id: " << maxPosition << std::endl;
// std::cout << std::fixed << std::setprecision(10)
// << "\tscore: " << double(out_data[maxPosition]) << std::endl;
times[0] += time[0];
times[1] += time[1];
times[2] += time[2];
}
void FeatureExtracter::FeatureNorm(std::vector<float> &featuer) {
float featuer_sqrt = std::sqrt(std::inner_product(
featuer.begin(), featuer.end(), featuer.begin(), 0.0f));
for (int i = 0; i < featuer.size(); ++i)
featuer[i] /= featuer_sqrt;
}
void FeatureExtracter::FeatureNorm(std::vector<float> &featuer) {
float featuer_sqrt = std::sqrt(std::inner_product(
featuer.begin(), featuer.end(), featuer.begin(), 0.0f));
for (int i = 0; i < featuer.size(); ++i)
featuer[i] /= featuer_sqrt;
}
} // namespace Feature

458
deploy/cpp_shitu/src/main.cpp
View File

@ -37,260 +37,262 @@
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 <Detection::ObjectResult> &im_result,
std::vector<int> &im_bbox_num, 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);
// printf("total images = %d, batch_size = %d, total steps = %d\n",
// all_img_paths.size(), batch_size, steps);
for (int idx = 0; idx < steps; idx++) {
int left_image_cnt = all_img_paths.size() - idx * batch_size;
if (left_image_cnt > batch_size) {
left_image_cnt = batch_size;
}
// for (int bs = 0; bs < left_image_cnt; bs++) {
// std::string image_file_path = all_img_paths.at(idx * batch_size+bs);
// cv::Mat im = cv::imread(image_file_path, 1);
// batch_imgs.insert(batch_imgs.end(), im);
// }
int steps = ceil(float(all_img_paths.size()) / batch_size);
// printf("total images = %d, batch_size = %d, total steps = %d\n",
// all_img_paths.size(), batch_size, steps);
for (int idx = 0; idx < steps; idx++) {
int left_image_cnt = all_img_paths.size() - idx * batch_size;
if (left_image_cnt > batch_size) {
left_image_cnt = batch_size;
}
// for (int bs = 0; bs < left_image_cnt; bs++) {
// std::string image_file_path = all_img_paths.at(idx * batch_size+bs);
// cv::Mat im = cv::imread(image_file_path, 1);
// batch_imgs.insert(batch_imgs.end(), im);
// }
// Store all detected result
std::vector<Detection::ObjectResult> result;
std::vector<int> bbox_num;
std::vector<double> det_times;
bool is_rbox = false;
if (run_benchmark) {
det->Predict(batch_imgs, 10, 10, &result, &bbox_num, &det_times);
} else {
det->Predict(batch_imgs, 0, 1, &result, &bbox_num, &det_times);
// get labels and colormap
auto labels = det->GetLabelList();
auto colormap = Detection::GenerateColorMap(labels.size());
// Store all detected result
std::vector <Detection::ObjectResult> result;
std::vector<int> bbox_num;
std::vector<double> det_times;
bool is_rbox = false;
if (run_benchmark) {
det->Predict(batch_imgs, 10, 10, &result, &bbox_num, &det_times);
} else {
det->Predict(batch_imgs, 0, 1, &result, &bbox_num, &det_times);
// get labels and colormap
auto labels = det->GetLabelList();
auto colormap = Detection::GenerateColorMap(labels.size());
int item_start_idx = 0;
for (int i = 0; i < left_image_cnt; i++) {
cv::Mat im = batch_imgs[i];
int detect_num = 0;
int item_start_idx = 0;
for (int i = 0; i < left_image_cnt; i++) {
cv::Mat im = batch_imgs[i];
int detect_num = 0;
for (int j = 0; j < bbox_num[i]; j++) {
Detection::ObjectResult item = result[item_start_idx + j];
if (item.confidence < det->GetThreshold() || item.class_id == -1) {
continue;
}
detect_num += 1;
im_result.push_back(item);
if (visual_det) {
if (item.rect.size() > 6) {
is_rbox = true;
printf(
"class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
item.class_id, item.confidence, item.rect[0], item.rect[1],
item.rect[2], item.rect[3], item.rect[4], item.rect[5],
item.rect[6], item.rect[7]);
} else {
printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
item.class_id, item.confidence, item.rect[0], item.rect[1],
item.rect[2], item.rect[3]);
for (int j = 0; j < bbox_num[i]; j++) {
Detection::ObjectResult item = result[item_start_idx + j];
if (item.confidence < det->GetThreshold() || item.class_id == -1) {
continue;
}
detect_num += 1;
im_result.push_back(item);
if (visual_det) {
if (item.rect.size() > 6) {
is_rbox = true;
printf(
"class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
item.class_id, item.confidence, item.rect[0], item.rect[1],
item.rect[2], item.rect[3], item.rect[4], item.rect[5],
item.rect[6], item.rect[7]);
} else {
printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
item.class_id, item.confidence, item.rect[0], item.rect[1],
item.rect[2], item.rect[3]);
}
}
}
im_bbox_num.push_back(detect_num);
item_start_idx = item_start_idx + bbox_num[i];
// Visualization result
if (visual_det) {
std::cout << all_img_paths.at(idx * batch_size + i)
<< " The number of detected box: " << detect_num
<< std::endl;
cv::Mat vis_img = Detection::VisualizeResult(im, im_result, labels,
colormap, is_rbox);
std::vector<int> compression_params;
compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
compression_params.push_back(95);
std::string output_path(output_dir);
if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
output_path += OS_PATH_SEP;
}
std::string image_file_path = all_img_paths.at(idx * batch_size + i);
output_path +=
image_file_path.substr(image_file_path.find_last_of('/') + 1);
cv::imwrite(output_path, vis_img, compression_params);
printf("Visualized output saved as %s\n", output_path.c_str());
}
}
}
}
im_bbox_num.push_back(detect_num);
item_start_idx = item_start_idx + bbox_num[i];
// Visualization result
if (visual_det) {
std::cout << all_img_paths.at(idx * batch_size + i)
<< " The number of detected box: " << detect_num
<< std::endl;
cv::Mat vis_img = Detection::VisualizeResult(im, im_result, labels,
colormap, is_rbox);
std::vector<int> compression_params;
compression_params.push_back(CV_IMWRITE_JPEG_QUALITY);
compression_params.push_back(95);
std::string output_path(output_dir);
if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
output_path += OS_PATH_SEP;
}
std::string image_file_path = all_img_paths.at(idx * batch_size + i);
output_path +=
image_file_path.substr(image_file_path.find_last_of('/') + 1);
cv::imwrite(output_path, vis_img, compression_params);
printf("Visualized output saved as %s\n", output_path.c_str());
}
}
det_t[0] += det_times[0];
det_t[1] += det_times[1];
det_t[2] += det_times[2];
}
det_t[0] += det_times[0];
det_t[1] += det_times[1];
det_t[2] += det_times[2];
}
}
void PrintResult(std::string &img_path,
std::vector<Detection::ObjectResult> &det_result,
std::vector <Detection::ObjectResult> &det_result,
std::vector<int> &indeices, VectorSearch &vector_search,
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,
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])
.c_str());
}
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,
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])
.c_str());
}
}
int main(int argc, char **argv) {
google::ParseCommandLineFlags(&argc, &argv, true);
std::string yaml_path = "";
if (FLAGS_config == "" && FLAGS_c == "") {
std::cerr << "[ERROR] usage: " << std::endl
<< argv[0] << " -c $yaml_path" << std::endl
<< "or:" << std::endl
<< argv[0] << " -config $yaml_path" << std::endl;
exit(1);
} else if (FLAGS_config != "") {
yaml_path = FLAGS_config;
} else {
yaml_path = FLAGS_c;
}
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);
// config
const int batch_size = config.config_file["Global"]["batch_size"].as<int>();
bool visual_det = false;
if (config.config_file["Global"]["visual_det"].IsDefined()) {
visual_det = config.config_file["Global"]["visual_det"].as<bool>();
}
bool run_benchmark = false;
if (config.config_file["Global"]["benchmark"].IsDefined()) {
run_benchmark = config.config_file["Global"]["benchmark"].as<bool>();
}
int max_det_results = 5;
if (config.config_file["Global"]["max_det_results"].IsDefined()) {
max_det_results = config.config_file["Global"]["max_det_results"].as<int>();
}
float rec_nms_thresold = 0.05;
if (config.config_file["Global"]["rec_nms_thresold"].IsDefined()) {
rec_nms_thresold =
config.config_file["Global"]["rec_nms_thresold"].as<float>();
}
// load image_file_path
std::string path =
config.config_file["Global"]["infer_imgs"].as<std::string>();
std::vector<std::string> img_files_list;
if (cv::utils::fs::isDirectory(path)) {
std::vector<cv::String> filenames;
cv::glob(path, filenames);
for (auto f : filenames) {
img_files_list.push_back(f);
}
} else {
img_files_list.push_back(path);
}
std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
// for time log
std::vector<double> cls_times = {0, 0, 0};
std::vector<double> det_times = {0, 0, 0};
// for read images
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;
// for vector search
std::vector<float> features;
std::vector<float> feature;
// for nms
std::vector<int> indeices;
int warmup_iter = img_files_list.size() > 5 ? 5 : 0;
for (int idx = 0; idx < img_files_list.size(); ++idx) {
std::string img_path = img_files_list[idx];
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << img_path
<< "\n";
exit(-1);
}
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
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, run_benchmark);
// select max_det_results bbox
if (det_result.size() > max_det_results) {
det_result.resize(max_det_results);
}
// step2: add the whole image for recognition to improve recall
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;
for (int j = 0; j < det_result.size(); ++j) {
int w = det_result[j].rect[2] - det_result[j].rect[0];
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);
features.insert(features.end(), feature.begin(), feature.end());
google::ParseCommandLineFlags(&argc, &argv, true);
std::string yaml_path = "";
if (FLAGS_config == "" && FLAGS_c == "") {
std::cerr << "[ERROR] usage: " << std::endl
<< argv[0] << " -c $yaml_path" << std::endl
<< "or:" << std::endl
<< argv[0] << " -config $yaml_path" << std::endl;
exit(1);
} else if (FLAGS_config != "") {
yaml_path = FLAGS_config;
} else {
yaml_path = FLAGS_c;
}
// step4: get search result
search_result = searcher.Search(features.data(), det_result.size());
YamlConfig config(yaml_path);
config.PrintConfigInfo();
// 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];
// initialize detector, rec_Model, vector_search
Feature::FeatureExtracter feature_extracter(config.config_file);
Detection::ObjectDetector detector(config.config_file);
VectorSearch searcher(config.config_file);
// config
const int batch_size = config.config_file["Global"]["batch_size"].as<int>();
bool visual_det = false;
if (config.config_file["Global"]["visual_det"].IsDefined()) {
visual_det = config.config_file["Global"]["visual_det"].as<bool>();
}
bool run_benchmark = false;
if (config.config_file["Global"]["benchmark"].IsDefined()) {
run_benchmark = config.config_file["Global"]["benchmark"].as<bool>();
}
int max_det_results = 5;
if (config.config_file["Global"]["max_det_results"].IsDefined()) {
max_det_results = config.config_file["Global"]["max_det_results"].as<int>();
}
float rec_nms_thresold = 0.05;
if (config.config_file["Global"]["rec_nms_thresold"].IsDefined()) {
rec_nms_thresold =
config.config_file["Global"]["rec_nms_thresold"].as<float>();
}
NMSBoxes(det_result, searcher.GetThreshold(), rec_nms_thresold, indeices);
// print result
PrintResult(img_path, det_result, indeices, searcher, search_result);
// load image_file_path
std::string path =
config.config_file["Global"]["infer_imgs"].as<std::string>();
std::vector <std::string> img_files_list;
if (cv::utils::fs::isDirectory(path)) {
std::vector <cv::String> filenames;
cv::glob(path, filenames);
for (auto f : filenames) {
img_files_list.push_back(f);
}
} else {
img_files_list.push_back(path);
}
std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
// for time log
std::vector<double> cls_times = {0, 0, 0};
std::vector<double> det_times = {0, 0, 0};
// for read images
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;
// for vector search
std::vector<float> features;
std::vector<float> feature;
// for nms
std::vector<int> indeices;
// for postprocess
batch_imgs.clear();
img_paths.clear();
det_bbox_num.clear();
det_result.clear();
feature.clear();
features.clear();
indeices.clear();
}
int warmup_iter = img_files_list.size() > 5 ? 5 : 0;
for (int idx = 0; idx < img_files_list.size(); ++idx) {
std::string img_path = img_files_list[idx];
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << img_path
<< "\n";
exit(-1);
}
cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
std::string presion = "fp32";
batch_imgs.push_back(srcimg);
img_paths.push_back(img_path);
// if (config.use_fp16)
// presion = "fp16";
// if (config.benchmark) {
// AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt,
// config.use_mkldnn, config.cpu_threads, 1,
// "1, 3, 224, 224", presion, cls_times,
// img_files_list.size());
// autolog.report();
// }
return 0;
// step1: get all detection results
DetPredictImage(batch_imgs, img_paths, batch_size, &detector, det_result,
det_bbox_num, det_times, visual_det, run_benchmark);
// select max_det_results bbox
if (det_result.size() > max_det_results) {
det_result.resize(max_det_results);
}
// step2: add the whole image for recognition to improve recall
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;
for (int j = 0; j < det_result.size(); ++j) {
int w = det_result[j].rect[2] - det_result[j].rect[0];
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);
features.insert(features.end(), feature.begin(), feature.end());
}
// step4: get search result
search_result = searcher.Search(features.data(), det_result.size());
// 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);
// print result
PrintResult(img_path, det_result, indeices, searcher, search_result);
// for postprocess
batch_imgs.clear();
img_paths.clear();
det_bbox_num.clear();
det_result.clear();
feature.clear();
features.clear();
indeices.clear();
}
std::string presion = "fp32";
// if (config.use_fp16)
// presion = "fp16";
// if (config.benchmark) {
// AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt,
// config.use_mkldnn, config.cpu_threads, 1,
// "1, 3, 224, 224", presion, cls_times,
// img_files_list.size());
// autolog.report();
// }
return 0;
}

644
deploy/cpp_shitu/src/object_detector.cpp
View File

@ -22,344 +22,344 @@ using namespace paddle_infer;
namespace Detection {
// Load Model and create model predictor
void ObjectDetector::LoadModel(const std::string &model_dir,
const int batch_size,
const std::string &run_mode) {
paddle_infer::Config config;
std::string prog_file = model_dir + OS_PATH_SEP + "inference.pdmodel";
std::string params_file = model_dir + OS_PATH_SEP + "inference.pdiparams";
config.SetModel(prog_file, params_file);
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
config.SwitchIrOptim(this->ir_optim_);
// // use tensorrt
// if (run_mode != "fluid") {
// auto precision = paddle_infer::Config::Precision::kFloat32;
// if (run_mode == "trt_fp32") {
// precision = paddle_infer::Config::Precision::kFloat32;
// }
// else if (run_mode == "trt_fp16") {
// precision = paddle_infer::Config::Precision::kHalf;
// }
// else if (run_mode == "trt_int8") {
// precision = paddle_infer::Config::Precision::kInt8;
// } else {
// printf("run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or
// 'trt_int8'");
// }
// set tensorrt
void ObjectDetector::LoadModel(const std::string &model_dir,
const int batch_size,
const std::string &run_mode) {
paddle_infer::Config config;
std::string prog_file = model_dir + OS_PATH_SEP + "inference.pdmodel";
std::string params_file = model_dir + OS_PATH_SEP + "inference.pdiparams";
config.SetModel(prog_file, params_file);
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
config.SwitchIrOptim(this->ir_optim_);
// // use tensorrt
// if (run_mode != "fluid") {
// auto precision = paddle_infer::Config::Precision::kFloat32;
// if (run_mode == "trt_fp32") {
// precision = paddle_infer::Config::Precision::kFloat32;
// }
// else if (run_mode == "trt_fp16") {
// precision = paddle_infer::Config::Precision::kHalf;
// }
// else if (run_mode == "trt_int8") {
// precision = paddle_infer::Config::Precision::kInt8;
// } else {
// printf("run_mode should be 'fluid', 'trt_fp32', 'trt_fp16' or
// 'trt_int8'");
// }
// set tensorrt
if (this->use_tensorrt_) {
config.EnableTensorRtEngine(
1 << 30, batch_size, this->min_subgraph_size_,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
false, this->trt_calib_mode_);
// set use dynamic shape
if (this->use_dynamic_shape_) {
// set DynamicShsape for image tensor
const std::vector<int> min_input_shape = {1, 3, this->trt_min_shape_,
this->trt_min_shape_};
const std::vector<int> max_input_shape = {1, 3, this->trt_max_shape_,
this->trt_max_shape_};
const std::vector<int> opt_input_shape = {1, 3, this->trt_opt_shape_,
this->trt_opt_shape_};
const std::map<std::string, std::vector<int>> map_min_input_shape = {
{"image", min_input_shape}};
const std::map<std::string, std::vector<int>> map_max_input_shape = {
{"image", max_input_shape}};
const std::map<std::string, std::vector<int>> map_opt_input_shape = {
{"image", opt_input_shape}};
if (this->use_tensorrt_) {
config.EnableTensorRtEngine(
1 << 30, batch_size, this->min_subgraph_size_,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
false, this->trt_calib_mode_);
// set use dynamic shape
if (this->use_dynamic_shape_) {
// set DynamicShsape for image tensor
const std::vector<int> min_input_shape = {1, 3, this->trt_min_shape_,
this->trt_min_shape_};
const std::vector<int> max_input_shape = {1, 3, this->trt_max_shape_,
this->trt_max_shape_};
const std::vector<int> opt_input_shape = {1, 3, this->trt_opt_shape_,
this->trt_opt_shape_};
const std::map <std::string, std::vector<int>> map_min_input_shape = {
{"image", min_input_shape}};
const std::map <std::string, std::vector<int>> map_max_input_shape = {
{"image", max_input_shape}};
const std::map <std::string, std::vector<int>> map_opt_input_shape = {
{"image", opt_input_shape}};
config.SetTRTDynamicShapeInfo(map_min_input_shape, map_max_input_shape,
map_opt_input_shape);
std::cout << "TensorRT dynamic shape enabled" << std::endl;
}
config.SetTRTDynamicShapeInfo(map_min_input_shape, map_max_input_shape,
map_opt_input_shape);
std::cout << "TensorRT dynamic shape enabled" << std::endl;
}
}
// } else if (this->device_ == "XPU"){
// config.EnableXpu(10*1024*1024);
} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10);
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
config.SwitchIrOptim(this->ir_optim_);
config.DisableGlogInfo();
// Memory optimization
config.EnableMemoryOptim();
predictor_ = std::move(CreatePredictor(config));
}
// } else if (this->device_ == "XPU"){
// config.EnableXpu(10*1024*1024);
} else {
config.DisableGpu();
if (this->use_mkldnn_) {
config.EnableMKLDNN();
// cache 10 different shapes for mkldnn to avoid memory leak
config.SetMkldnnCacheCapacity(10);
}
config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
}
config.SwitchUseFeedFetchOps(false);
config.SwitchIrOptim(this->ir_optim_);
config.DisableGlogInfo();
// Memory optimization
config.EnableMemoryOptim();
predictor_ = std::move(CreatePredictor(config));
}
// Visualiztion MaskDetector results
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector<ObjectResult> &results,
const std::vector<std::string> &lables,
const std::vector<int> &colormap,
const bool is_rbox = false) {
cv::Mat vis_img = img.clone();
for (int i = 0; i < results.size(); ++i) {
// Configure color and text size
std::ostringstream oss;
oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
oss << lables[results[i].class_id] << " ";
oss << results[i].confidence;
std::string text = oss.str();
int c1 = colormap[3 * results[i].class_id + 0];
int c2 = colormap[3 * results[i].class_id + 1];
int c3 = colormap[3 * results[i].class_id + 2];
cv::Scalar roi_color = cv::Scalar(c1, c2, c3);
int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL;
double font_scale = 0.5f;
float thickness = 0.5;
cv::Size text_size =
cv::getTextSize(text, font_face, font_scale, thickness, nullptr);
cv::Point origin;
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector <ObjectResult> &results,
const std::vector <std::string> &lables,
const std::vector<int> &colormap,
const bool is_rbox = false) {
cv::Mat vis_img = img.clone();
for (int i = 0; i < results.size(); ++i) {
// Configure color and text size
std::ostringstream oss;
oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
oss << lables[results[i].class_id] << " ";
oss << results[i].confidence;
std::string text = oss.str();
int c1 = colormap[3 * results[i].class_id + 0];
int c2 = colormap[3 * results[i].class_id + 1];
int c3 = colormap[3 * results[i].class_id + 2];
cv::Scalar roi_color = cv::Scalar(c1, c2, c3);
int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL;
double font_scale = 0.5f;
float thickness = 0.5;
cv::Size text_size =
cv::getTextSize(text, font_face, font_scale, thickness, nullptr);
cv::Point origin;
if (is_rbox) {
// Draw object, text, and background
for (int k = 0; k < 4; k++) {
cv::Point pt1 = cv::Point(results[i].rect[(k * 2) % 8],
results[i].rect[(k * 2 + 1) % 8]);
cv::Point pt2 = cv::Point(results[i].rect[(k * 2 + 2) % 8],
results[i].rect[(k * 2 + 3) % 8]);
cv::line(vis_img, pt1, pt2, roi_color, 2);
}
} else {
int w = results[i].rect[2] - results[i].rect[0];
int h = results[i].rect[3] - results[i].rect[1];
cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[1], w, h);
// Draw roi object, text, and background
cv::rectangle(vis_img, roi, roi_color, 2);
if (is_rbox) {
// Draw object, text, and background
for (int k = 0; k < 4; k++) {
cv::Point pt1 = cv::Point(results[i].rect[(k * 2) % 8],
results[i].rect[(k * 2 + 1) % 8]);
cv::Point pt2 = cv::Point(results[i].rect[(k * 2 + 2) % 8],
results[i].rect[(k * 2 + 3) % 8]);
cv::line(vis_img, pt1, pt2, roi_color, 2);
}
} else {
int w = results[i].rect[2] - results[i].rect[0];
int h = results[i].rect[3] - results[i].rect[1];
cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[1], w, h);
// Draw roi object, text, and background
cv::rectangle(vis_img, roi, roi_color, 2);
}
origin.x = results[i].rect[0];
origin.y = results[i].rect[1];
// Configure text background
cv::Rect text_back =
cv::Rect(results[i].rect[0], results[i].rect[1] - text_size.height,
text_size.width, text_size.height);
// Draw text, and background
cv::rectangle(vis_img, text_back, roi_color, -1);
cv::putText(vis_img, text, origin, font_face, font_scale,
cv::Scalar(255, 255, 255), thickness);
}
return vis_img;
}
origin.x = results[i].rect[0];
origin.y = results[i].rect[1];
// Configure text background
cv::Rect text_back =
cv::Rect(results[i].rect[0], results[i].rect[1] - text_size.height,
text_size.width, text_size.height);
// Draw text, and background
cv::rectangle(vis_img, text_back, roi_color, -1);
cv::putText(vis_img, text, origin, font_face, font_scale,
cv::Scalar(255, 255, 255), thickness);
}
return vis_img;
}
void ObjectDetector::Preprocess(const cv::Mat &ori_im) {
// Clone the image : keep the original mat for postprocess
cv::Mat im = ori_im.clone();
cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
preprocessor_.Run(&im, &inputs_);
}
void ObjectDetector::Postprocess(const std::vector<cv::Mat> mats,
std::vector<ObjectResult> *result,
std::vector<int> bbox_num,
bool is_rbox = false) {
result->clear();
int start_idx = 0;
for (int im_id = 0; im_id < mats.size(); im_id++) {
cv::Mat raw_mat = mats[im_id];
int rh = 1;
int rw = 1;
// if (config_.arch_ == "Face") {
// rh = raw_mat.rows;
// rw = raw_mat.cols;
// }
for (int j = start_idx; j < start_idx + bbox_num[im_id]; j++) {
if (is_rbox) {
// Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 10]));
// Confidence score
float score = output_data_[1 + j * 10];
int x1 = (output_data_[2 + j * 10] * rw);
int y1 = (output_data_[3 + j * 10] * rh);
int x2 = (output_data_[4 + j * 10] * rw);
int y2 = (output_data_[5 + j * 10] * rh);
int x3 = (output_data_[6 + j * 10] * rw);
int y3 = (output_data_[7 + j * 10] * rh);
int x4 = (output_data_[8 + j * 10] * rw);
int y4 = (output_data_[9 + j * 10] * rh);
ObjectResult result_item;
result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4};
result_item.class_id = class_id;
result_item.confidence = score;
result->push_back(result_item);
} else {
// Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 6]));
// Confidence score
float score = output_data_[1 + j * 6];
int xmin = (output_data_[2 + j * 6] * rw);
int ymin = (output_data_[3 + j * 6] * rh);
int xmax = (output_data_[4 + j * 6] * rw);
int ymax = (output_data_[5 + j * 6] * rh);
int wd = xmax - xmin;
int hd = ymax - ymin;
ObjectResult result_item;
result_item.rect = {xmin, ymin, xmax, ymax};
result_item.class_id = class_id;
result_item.confidence = score;
result->push_back(result_item);
}
}
start_idx += bbox_num[im_id];
}
}
void ObjectDetector::Predict(const std::vector<cv::Mat> imgs, const int warmup,
const int repeats,
std::vector<ObjectResult> *result,
std::vector<int> *bbox_num,
std::vector<double> *times) {
auto preprocess_start = std::chrono::steady_clock::now();
int batch_size = imgs.size();
// in_data_batch
std::vector<float> in_data_all;
std::vector<float> im_shape_all(batch_size * 2);
std::vector<float> scale_factor_all(batch_size * 2);
// Preprocess image
for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
cv::Mat im = imgs.at(bs_idx);
Preprocess(im);
im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];
scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
// TODO: reduce cost time
in_data_all.insert(in_data_all.end(), inputs_.im_data_.begin(),
inputs_.im_data_.end());
}
// Prepare input tensor
auto input_names = predictor_->GetInputNames();
for (const auto &tensor_name : input_names) {
auto in_tensor = predictor_->GetInputHandle(tensor_name);
if (tensor_name == "image") {
int rh = inputs_.in_net_shape_[0];
int rw = inputs_.in_net_shape_[1];
in_tensor->Reshape({batch_size, 3, rh, rw});
in_tensor->CopyFromCpu(in_data_all.data());
} else if (tensor_name == "im_shape") {
in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(im_shape_all.data());
} else if (tensor_name == "scale_factor") {
in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(scale_factor_all.data());
}
}
auto preprocess_end = std::chrono::steady_clock::now();
// Run predictor
// warmup
for (int i = 0; i < warmup; i++) {
predictor_->Run();
// Get output tensor
auto output_names = predictor_->GetOutputNames();
auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = out_tensor->shape();
auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
// Calculate output length
int output_size = 1;
for (int j = 0; j < output_shape.size(); ++j) {
output_size *= output_shape[j];
void ObjectDetector::Preprocess(const cv::Mat &ori_im) {
// Clone the image : keep the original mat for postprocess
cv::Mat im = ori_im.clone();
cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
preprocessor_.Run(&im, &inputs_);
}
if (output_size < 6) {
std::cerr << "[WARNING] No object detected." << std::endl;
}
output_data_.resize(output_size);
out_tensor->CopyToCpu(output_data_.data());
void ObjectDetector::Postprocess(const std::vector <cv::Mat> mats,
std::vector <ObjectResult> *result,
std::vector<int> bbox_num,
bool is_rbox = false) {
result->clear();
int start_idx = 0;
for (int im_id = 0; im_id < mats.size(); im_id++) {
cv::Mat raw_mat = mats[im_id];
int rh = 1;
int rw = 1;
// if (config_.arch_ == "Face") {
// rh = raw_mat.rows;
// rw = raw_mat.cols;
// }
for (int j = start_idx; j < start_idx + bbox_num[im_id]; j++) {
if (is_rbox) {
// Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 10]));
// Confidence score
float score = output_data_[1 + j * 10];
int x1 = (output_data_[2 + j * 10] * rw);
int y1 = (output_data_[3 + j * 10] * rh);
int x2 = (output_data_[4 + j * 10] * rw);
int y2 = (output_data_[5 + j * 10] * rh);
int x3 = (output_data_[6 + j * 10] * rw);
int y3 = (output_data_[7 + j * 10] * rh);
int x4 = (output_data_[8 + j * 10] * rw);
int y4 = (output_data_[9 + j * 10] * rh);
int out_bbox_num_size = 1;
for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
out_bbox_num_size *= out_bbox_num_shape[j];
}
out_bbox_num_data_.resize(out_bbox_num_size);
out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
}
ObjectResult result_item;
result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4};
result_item.class_id = class_id;
result_item.confidence = score;
result->push_back(result_item);
} else {
// Class id
int class_id = static_cast<int>(round(output_data_[0 + j * 6]));
// Confidence score
float score = output_data_[1 + j * 6];
int xmin = (output_data_[2 + j * 6] * rw);
int ymin = (output_data_[3 + j * 6] * rh);
int xmax = (output_data_[4 + j * 6] * rw);
int ymax = (output_data_[5 + j * 6] * rh);
int wd = xmax - xmin;
int hd = ymax - ymin;
bool is_rbox = false;
auto inference_start = std::chrono::steady_clock::now();
for (int i = 0; i < repeats; i++) {
predictor_->Run();
// Get output tensor
auto output_names = predictor_->GetOutputNames();
auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = out_tensor->shape();
auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
// Calculate output length
int output_size = 1;
for (int j = 0; j < output_shape.size(); ++j) {
output_size *= output_shape[j];
ObjectResult result_item;
result_item.rect = {xmin, ymin, xmax, ymax};
result_item.class_id = class_id;
result_item.confidence = score;
result->push_back(result_item);
}
}
start_idx += bbox_num[im_id];
}
}
is_rbox = output_shape[output_shape.size() - 1] % 10 == 0;
if (output_size < 6) {
std::cerr << "[WARNING] No object detected." << std::endl;
void ObjectDetector::Predict(const std::vector <cv::Mat> imgs, const int warmup,
const int repeats,
std::vector <ObjectResult> *result,
std::vector<int> *bbox_num,
std::vector<double> *times) {
auto preprocess_start = std::chrono::steady_clock::now();
int batch_size = imgs.size();
// in_data_batch
std::vector<float> in_data_all;
std::vector<float> im_shape_all(batch_size * 2);
std::vector<float> scale_factor_all(batch_size * 2);
// Preprocess image
for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
cv::Mat im = imgs.at(bs_idx);
Preprocess(im);
im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];
scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
// TODO: reduce cost time
in_data_all.insert(in_data_all.end(), inputs_.im_data_.begin(),
inputs_.im_data_.end());
}
// Prepare input tensor
auto input_names = predictor_->GetInputNames();
for (const auto &tensor_name : input_names) {
auto in_tensor = predictor_->GetInputHandle(tensor_name);
if (tensor_name == "image") {
int rh = inputs_.in_net_shape_[0];
int rw = inputs_.in_net_shape_[1];
in_tensor->Reshape({batch_size, 3, rh, rw});
in_tensor->CopyFromCpu(in_data_all.data());
} else if (tensor_name == "im_shape") {
in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(im_shape_all.data());
} else if (tensor_name == "scale_factor") {
in_tensor->Reshape({batch_size, 2});
in_tensor->CopyFromCpu(scale_factor_all.data());
}
}
auto preprocess_end = std::chrono::steady_clock::now();
// Run predictor
// warmup
for (int i = 0; i < warmup; i++) {
predictor_->Run();
// Get output tensor
auto output_names = predictor_->GetOutputNames();
auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = out_tensor->shape();
auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
// Calculate output length
int output_size = 1;
for (int j = 0; j < output_shape.size(); ++j) {
output_size *= output_shape[j];
}
if (output_size < 6) {
std::cerr << "[WARNING] No object detected." << std::endl;
}
output_data_.resize(output_size);
out_tensor->CopyToCpu(output_data_.data());
int out_bbox_num_size = 1;
for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
out_bbox_num_size *= out_bbox_num_shape[j];
}
out_bbox_num_data_.resize(out_bbox_num_size);
out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
}
bool is_rbox = false;
auto inference_start = std::chrono::steady_clock::now();
for (int i = 0; i < repeats; i++) {
predictor_->Run();
// Get output tensor
auto output_names = predictor_->GetOutputNames();
auto out_tensor = predictor_->GetOutputHandle(output_names[0]);
std::vector<int> output_shape = out_tensor->shape();
auto out_bbox_num = predictor_->GetOutputHandle(output_names[1]);
std::vector<int> out_bbox_num_shape = out_bbox_num->shape();
// Calculate output length
int output_size = 1;
for (int j = 0; j < output_shape.size(); ++j) {
output_size *= output_shape[j];
}
is_rbox = output_shape[output_shape.size() - 1] % 10 == 0;
if (output_size < 6) {
std::cerr << "[WARNING] No object detected." << std::endl;
}
output_data_.resize(output_size);
out_tensor->CopyToCpu(output_data_.data());
int out_bbox_num_size = 1;
for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
out_bbox_num_size *= out_bbox_num_shape[j];
}
out_bbox_num_data_.resize(out_bbox_num_size);
out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
}
auto inference_end = std::chrono::steady_clock::now();
auto postprocess_start = std::chrono::steady_clock::now();
// Postprocessing result
result->clear();
Postprocess(imgs, result, out_bbox_num_data_, is_rbox);
bbox_num->clear();
for (int k = 0; k < out_bbox_num_data_.size(); k++) {
int tmp = out_bbox_num_data_[k];
bbox_num->push_back(tmp);
}
auto postprocess_end = std::chrono::steady_clock::now();
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() / repeats * 1000));
std::chrono::duration<float> postprocess_diff =
postprocess_end - postprocess_start;
times->push_back(double(postprocess_diff.count() * 1000));
}
output_data_.resize(output_size);
out_tensor->CopyToCpu(output_data_.data());
int out_bbox_num_size = 1;
for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
out_bbox_num_size *= out_bbox_num_shape[j];
std::vector<int> GenerateColorMap(int num_class) {
auto colormap = std::vector<int>(3 * num_class, 0);
for (int i = 0; i < num_class; ++i) {
int j = 0;
int lab = i;
while (lab) {
colormap[i * 3] |= (((lab >> 0) & 1) << (7 - j));
colormap[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j));
colormap[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j));
++j;
lab >>= 3;
}
}
return colormap;
}
out_bbox_num_data_.resize(out_bbox_num_size);
out_bbox_num->CopyToCpu(out_bbox_num_data_.data());
}
auto inference_end = std::chrono::steady_clock::now();
auto postprocess_start = std::chrono::steady_clock::now();
// Postprocessing result
result->clear();
Postprocess(imgs, result, out_bbox_num_data_, is_rbox);
bbox_num->clear();
for (int k = 0; k < out_bbox_num_data_.size(); k++) {
int tmp = out_bbox_num_data_[k];
bbox_num->push_back(tmp);
}
auto postprocess_end = std::chrono::steady_clock::now();
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() / repeats * 1000));
std::chrono::duration<float> postprocess_diff =
postprocess_end - postprocess_start;
times->push_back(double(postprocess_diff.count() * 1000));
}
std::vector<int> GenerateColorMap(int num_class) {
auto colormap = std::vector<int>(3 * num_class, 0);
for (int i = 0; i < num_class; ++i) {
int j = 0;
int lab = i;
while (lab) {
colormap[i * 3] |= (((lab >> 0) & 1) << (7 - j));
colormap[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j));
colormap[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j));
++j;
lab >>= 3;
}
}
return colormap;
}
} // namespace Detection

104
deploy/cpp_shitu/src/preprocess_op.cpp
View File

@ -32,60 +32,60 @@
namespace Feature {
void Permute::Run(const cv::Mat *im, float *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
}
}
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale) {
(*im).convertTo(*im, CV_32FC3, scale);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / std[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / std[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
void Permute::Run(const cv::Mat *im, float *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
}
}
}
}
void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
int resize_w = img.cols;
int resize_h = img.rows;
int w_start = int((resize_w - crop_size) / 2);
int h_start = int((resize_h - crop_size) / 2);
cv::Rect rect(w_start, h_start, crop_size, crop_size);
img = img(rect);
}
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size, int size) {
int resize_h = 0;
int resize_w = 0;
if (size > 0) {
resize_h = size;
resize_w = size;
} else {
int w = img.cols;
int h = img.rows;
float ratio = 1.f;
if (h < w) {
ratio = float(resize_short_size) / float(h);
} else {
ratio = float(resize_short_size) / float(w);
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale) {
(*im).convertTo(*im, CV_32FC3, scale);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / std[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / std[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
}
}
}
void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
int resize_w = img.cols;
int resize_h = img.rows;
int w_start = int((resize_w - crop_size) / 2);
int h_start = int((resize_h - crop_size) / 2);
cv::Rect rect(w_start, h_start, crop_size, crop_size);
img = img(rect);
}
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size, int size) {
int resize_h = 0;
int resize_w = 0;
if (size > 0) {
resize_h = size;
resize_w = size;
} else {
int w = img.cols;
int h = img.rows;
float ratio = 1.f;
if (h < w) {
ratio = float(resize_short_size) / float(h);
} else {
ratio = float(resize_short_size) / float(w);
}
resize_h = round(float(h) * ratio);
resize_w = round(float(w) * ratio);
}
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
resize_h = round(float(h) * ratio);
resize_w = round(float(w) * ratio);
}
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
} // namespace Feature

194
deploy/cpp_shitu/src/preprocess_op_det.cpp
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@ -19,112 +19,112 @@
namespace Detection {
void InitInfo::Run(cv::Mat *im, ImageBlob *data) {
data->im_shape_ = {static_cast<float>(im->rows),
static_cast<float>(im->cols)};
data->scale_factor_ = {1., 1.};
data->in_net_shape_ = {static_cast<float>(im->rows),
static_cast<float>(im->cols)};
}
void NormalizeImage::Run(cv::Mat *im, ImageBlob *data) {
double e = 1.0;
if (is_scale_) {
e /= 255.0;
}
(*im).convertTo(*im, CV_32FC3, e);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean_[0]) / scale_[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean_[1]) / scale_[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean_[2]) / scale_[2];
void InitInfo::Run(cv::Mat *im, ImageBlob *data) {
data->im_shape_ = {static_cast<float>(im->rows),
static_cast<float>(im->cols)};
data->scale_factor_ = {1., 1.};
data->in_net_shape_ = {static_cast<float>(im->rows),
static_cast<float>(im->cols)};
}
}
}
void Permute::Run(cv::Mat *im, ImageBlob *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
(data->im_data_).resize(rc * rh * rw);
float *base = (data->im_data_).data();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i);
}
}
void NormalizeImage::Run(cv::Mat *im, ImageBlob *data) {
double e = 1.0;
if (is_scale_) {
e /= 255.0;
}
(*im).convertTo(*im, CV_32FC3, e);
for (int h = 0; h < im->rows; h++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean_[0]) / scale_[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean_[1]) / scale_[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean_[2]) / scale_[2];
}
}
}
void Resize::Run(cv::Mat *im, ImageBlob *data) {
auto resize_scale = GenerateScale(*im);
data->im_shape_ = {static_cast<float>(im->cols * resize_scale.first),
static_cast<float>(im->rows * resize_scale.second)};
data->in_net_shape_ = {static_cast<float>(im->cols * resize_scale.first),
static_cast<float>(im->rows * resize_scale.second)};
cv::resize(*im, *im, cv::Size(), resize_scale.first, resize_scale.second,
interp_);
data->im_shape_ = {
static_cast<float>(im->rows), static_cast<float>(im->cols),
};
data->scale_factor_ = {
resize_scale.second, resize_scale.first,
};
}
void Permute::Run(cv::Mat *im, ImageBlob *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
(data->im_data_).resize(rc * rh * rw);
float *base = (data->im_data_).data();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i);
}
}
std::pair<double, double> Resize::GenerateScale(const cv::Mat &im) {
std::pair<double, double> resize_scale;
int origin_w = im.cols;
int origin_h = im.rows;
void Resize::Run(cv::Mat *im, ImageBlob *data) {
auto resize_scale = GenerateScale(*im);
data->im_shape_ = {static_cast<float>(im->cols * resize_scale.first),
static_cast<float>(im->rows * resize_scale.second)};
data->in_net_shape_ = {static_cast<float>(im->cols * resize_scale.first),
static_cast<float>(im->rows * resize_scale.second)};
cv::resize(*im, *im, cv::Size(), resize_scale.first, resize_scale.second,
interp_);
data->im_shape_ = {
static_cast<float>(im->rows), static_cast<float>(im->cols),
};
data->scale_factor_ = {
resize_scale.second, resize_scale.first,
};
}
if (keep_ratio_) {
int im_size_max = std::max(origin_w, origin_h);
int im_size_min = std::min(origin_w, origin_h);
int target_size_max =
*std::max_element(target_size_.begin(), target_size_.end());
int target_size_min =
*std::min_element(target_size_.begin(), target_size_.end());
double scale_min =
static_cast<double>(target_size_min) / static_cast<double>(im_size_min);
double scale_max =
static_cast<double>(target_size_max) / static_cast<double>(im_size_max);
double scale_ratio = std::min(scale_min, scale_max);
resize_scale = {scale_ratio, scale_ratio};
} else {
resize_scale.first =
static_cast<double>(target_size_[1]) / static_cast<double>(origin_w);
resize_scale.second =
static_cast<double>(target_size_[0]) / static_cast<double>(origin_h);
}
return resize_scale;
}
std::pair<double, double> Resize::GenerateScale(const cv::Mat &im) {
std::pair<double, double> resize_scale;
int origin_w = im.cols;
int origin_h = im.rows;
void PadStride::Run(cv::Mat *im, ImageBlob *data) {
if (stride_ <= 0) {
return;
}
int rc = im->channels();
int rh = im->rows;
int rw = im->cols;
int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_;
int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_;
cv::copyMakeBorder(*im, *im, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT,
cv::Scalar(0));
data->in_net_shape_ = {
static_cast<float>(im->rows), static_cast<float>(im->cols),
};
}
if (keep_ratio_) {
int im_size_max = std::max(origin_w, origin_h);
int im_size_min = std::min(origin_w, origin_h);
int target_size_max =
*std::max_element(target_size_.begin(), target_size_.end());
int target_size_min =
*std::min_element(target_size_.begin(), target_size_.end());
double scale_min =
static_cast<double>(target_size_min) / static_cast<double>(im_size_min);
double scale_max =
static_cast<double>(target_size_max) / static_cast<double>(im_size_max);
double scale_ratio = std::min(scale_min, scale_max);
resize_scale = {scale_ratio, scale_ratio};
} else {
resize_scale.first =
static_cast<double>(target_size_[1]) / static_cast<double>(origin_w);
resize_scale.second =
static_cast<double>(target_size_[0]) / static_cast<double>(origin_h);
}
return resize_scale;
}
void PadStride::Run(cv::Mat *im, ImageBlob *data) {
if (stride_ <= 0) {
return;
}
int rc = im->channels();
int rh = im->rows;
int rw = im->cols;
int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_;
int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_;
cv::copyMakeBorder(*im, *im, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT,
cv::Scalar(0));
data->in_net_shape_ = {
static_cast<float>(im->rows), static_cast<float>(im->cols),
};
}
// Preprocessor op running order
const std::vector<std::string> Preprocessor::RUN_ORDER = {
"InitInfo", "Resize", "NormalizeImage", "PadStride", "Permute"};
const std::vector <std::string> Preprocessor::RUN_ORDER = {
"InitInfo", "Resize", "NormalizeImage", "PadStride", "Permute"};
void Preprocessor::Run(cv::Mat *im, ImageBlob *data) {
for (const auto &name : RUN_ORDER) {
if (ops_.find(name) != ops_.end()) {
ops_[name]->Run(im, data);
void Preprocessor::Run(cv::Mat *im, ImageBlob *data) {
for (const auto &name : RUN_ORDER) {
if (ops_.find(name) != ops_.end()) {
ops_[name]->Run(im, data);
}
}
}
}
}
} // namespace Detection

58
deploy/cpp_shitu/src/vector_search.cpp
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@ -20,43 +20,43 @@
#include <regex>
void VectorSearch::LoadIndexFile() {
std::string file_path = this->index_dir + OS_PATH_SEP + "vector.index";
const char *fname = file_path.c_str();
this->index = faiss::read_index(fname, 0);
std::string file_path = this->index_dir + OS_PATH_SEP + "vector.index";
const char *fname = file_path.c_str();
this->index = faiss::read_index(fname, 0);
}
void VectorSearch::LoadIdMap() {
std::string file_path = this->index_dir + OS_PATH_SEP + "id_map.txt";
std::ifstream in(file_path);
std::string line;
std::vector<std::string> m_vec;
if (in) {
while (getline(in, line)) {
std::regex ws_re("\\s+");
std::vector<std::string> v(
std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1),
std::sregex_token_iterator());
if (v.size() != 2) {
std::cout << "The number of element for each line in : " << file_path
<< "must be 2, exit the program..." << std::endl;
exit(1);
} else
this->id_map.insert(std::pair<long int, std::string>(
std::stol(v[0], nullptr, 10), v[1]));
std::string file_path = this->index_dir + OS_PATH_SEP + "id_map.txt";
std::ifstream in(file_path);
std::string line;
std::vector <std::string> m_vec;
if (in) {
while (getline(in, line)) {
std::regex ws_re("\\s+");
std::vector <std::string> v(
std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1),
std::sregex_token_iterator());
if (v.size() != 2) {
std::cout << "The number of element for each line in : " << file_path
<< "must be 2, exit the program..." << std::endl;
exit(1);
} else
this->id_map.insert(std::pair<long int, std::string>(
std::stol(v[0], nullptr, 10), v[1]));
}
}
}
}
const SearchResult &VectorSearch::Search(float *feature, int query_number) {
this->D.resize(this->return_k * query_number);
this->I.resize(this->return_k * query_number);
this->index->search(query_number, feature, return_k, D.data(), I.data());
this->sr.return_k = this->return_k;
this->sr.D = this->D;
this->sr.I = this->I;
return this->sr;
this->D.resize(this->return_k * query_number);
this->I.resize(this->return_k * query_number);
this->index->search(query_number, feature, return_k, D.data(), I.data());
this->sr.return_k = this->return_k;
this->sr.D = this->D;
this->sr.I = this->I;
return this->sr;
}
const std::string &VectorSearch::GetLabel(faiss::Index::idx_t ind) {
return this->id_map.at(ind);
return this->id_map.at(ind);
}

90
deploy/cpp_shitu/src/yaml_config.cpp
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@ -19,60 +19,60 @@
#include <include/yaml_config.h>
std::vector<std::string> YamlConfig::ReadDict(const std::string &path) {
std::ifstream in(path);
std::string line;
std::vector<std::string> m_vec;
if (in) {
while (getline(in, line)) {
m_vec.push_back(line);
std::vector <std::string> YamlConfig::ReadDict(const std::string &path) {
std::ifstream in(path);
std::string line;
std::vector <std::string> m_vec;
if (in) {
while (getline(in, line)) {
m_vec.push_back(line);
}
} else {
std::cout << "no such label file: " << path << ", exit the program..."
<< std::endl;
exit(1);
}
} else {
std::cout << "no such label file: " << path << ", exit the program..."
<< std::endl;
exit(1);
}
return m_vec;
return m_vec;
}
std::map<int, std::string> YamlConfig::ReadIndexId(const std::string &path) {
std::ifstream in(path);
std::string line;
std::map<int, std::string> m_vec;
if (in) {
while (getline(in, line)) {
std::regex ws_re("\\s+");
std::vector<std::string> v(
std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1),
std::sregex_token_iterator());
if (v.size() != 3) {
std::cout << "The number of element for each line in : " << path
<< "must be 3, exit the program..." << std::endl;
exit(1);
} else
m_vec.insert(std::pair<int, std::string>(stoi(v[0]), v[2]));
std::ifstream in(path);
std::string line;
std::map<int, std::string> m_vec;
if (in) {
while (getline(in, line)) {
std::regex ws_re("\\s+");
std::vector <std::string> v(
std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1),
std::sregex_token_iterator());
if (v.size() != 3) {
std::cout << "The number of element for each line in : " << path
<< "must be 3, exit the program..." << std::endl;
exit(1);
} else
m_vec.insert(std::pair<int, std::string>(stoi(v[0]), v[2]));
}
}
}
return m_vec;
return m_vec;
}
YAML::Node YamlConfig::ReadYamlConfig(const std::string &path) {
YAML::Node config;
try {
config = YAML::LoadFile(path);
} catch (YAML::BadFile &e) {
std::cout << "Something wrong in yaml file, please check yaml file"
<< std::endl;
exit(1);
}
return config;
YAML::Node config;
try {
config = YAML::LoadFile(path);
} catch (YAML::BadFile &e) {
std::cout << "Something wrong in yaml file, please check yaml file"
<< std::endl;
exit(1);
}
return config;
}
void YamlConfig::PrintConfigInfo() {
std::cout << this->config_file << std::endl;
// for (YAML::const_iterator
// it=config_file.begin();it!=config_file.end();++it)
// {
// std::cout << it->as<std::string>() << "\n";
// }
std::cout << this->config_file << std::endl;
// for (YAML::const_iterator
// it=config_file.begin();it!=config_file.end();++it)
// {
// std::cout << it->as<std::string>() << "\n";
// }
}

8
deploy/cpp_shitu/tools/build.sh
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@ -1,8 +1,8 @@
OPENCV_DIR=/work/project/project/cpp_infer/opencv-3.4.7/opencv3
LIB_DIR=/work/project/project/cpp_infer/paddle_inference/
OPENCV_DIR=${opencv_install_dir}
LIB_DIR=${paddle_inference_dir}
CUDA_LIB_DIR=/usr/local/cuda/lib64
CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
FAISS_DIR=/work/project/project/cpp_infer/faiss/faiss_install
FAISS_DIR=${faiss_install_dir}
FAISS_WITH_MKL=OFF
BUILD_DIR=build
@ -21,4 +21,4 @@ cmake .. \
-DFAISS_DIR=${FAISS_DIR} \
-DFAISS_WITH_MKL=${FAISS_WITH_MKL}
make -j
make -j

17
deploy/cpp_shitu/tools/config.txt
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@ -1,17 +0,0 @@
# model load config
use_gpu 0
gpu_id 0
gpu_mem 4000
cpu_threads 10
use_mkldnn 1
use_tensorrt 0
use_fp16 0
# cls config
cls_model_path /PaddleClas/inference/cls_infer.pdmodel
cls_params_path /PaddleClas/inference/cls_infer.pdiparams
resize_short_size 256
crop_size 224
# for log env info
benchmark 0

1
deploy/cpp_shitu/tools/run.sh
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@ -1 +0,0 @@
./build/clas_system ../configs/inference_rec.yaml

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