PaddleOCR/deploy/cpp_infer/include/postprocess_op.h

128 lines
4.4 KiB
C++

// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "include/clipper.h"
#include "include/utility.h"
namespace PaddleOCR {
class DBPostProcessor {
public:
void GetContourArea(const std::vector<std::vector<float>> &box,
float unclip_ratio, float &distance);
cv::RotatedRect UnClip(const std::vector<std::vector<float>> &box,
const float &unclip_ratio);
float **Mat2Vec(const cv::Mat &mat);
std::vector<std::vector<int>>
OrderPointsClockwise(const std::vector<std::vector<int>> &pts);
std::vector<std::vector<float>> GetMiniBoxes(const cv::RotatedRect &box,
float &ssid);
float BoxScoreFast(const std::vector<std::vector<float>> &box_array,
const cv::Mat &pred);
float PolygonScoreAcc(const std::vector<cv::Point> &contour,
const cv::Mat &pred);
std::vector<std::vector<std::vector<int>>>
BoxesFromBitmap(const cv::Mat &pred, const cv::Mat &bitmap,
const float &box_thresh, const float &det_db_unclip_ratio,
const std::string &det_db_score_mode);
void FilterTagDetRes(std::vector<std::vector<std::vector<int>>> &boxes,
float ratio_h, float ratio_w, const cv::Mat &srcimg);
private:
static bool XsortInt(const std::vector<int> &a, const std::vector<int> &b);
static bool XsortFp32(const std::vector<float> &a,
const std::vector<float> &b);
std::vector<std::vector<float>> Mat2Vector(const cv::Mat &mat);
inline int _max(int a, int b) const noexcept { return a >= b ? a : b; }
inline int _min(int a, int b) const noexcept { return a >= b ? b : a; }
template <class T> inline T clamp(T x, T min, T max) const noexcept {
if (x > max)
return max;
if (x < min)
return min;
return x;
}
inline float clampf(float x, float min, float max) const noexcept {
if (x > max)
return max;
if (x < min)
return min;
return x;
}
};
class TablePostProcessor {
public:
void init(const std::string &label_path, bool merge_no_span_structure = true);
void Run(const std::vector<float> &loc_preds,
const std::vector<float> &structure_probs,
std::vector<float> &rec_scores,
const std::vector<int> &loc_preds_shape,
const std::vector<int> &structure_probs_shape,
std::vector<std::vector<std::string>> &rec_html_tag_batch,
std::vector<std::vector<std::vector<int>>> &rec_boxes_batch,
const std::vector<int> &width_list,
const std::vector<int> &height_list);
private:
std::vector<std::string> label_list_;
const std::string end = "eos";
const std::string beg = "sos";
};
class PicodetPostProcessor {
public:
void init(const std::string &label_path, const double score_threshold = 0.4,
const double nms_threshold = 0.5,
const std::vector<int> &fpn_stride = {8, 16, 32, 64});
void Run(std::vector<StructurePredictResult> &results,
const std::vector<std::vector<float>> &outs,
const std::vector<int> &ori_shape,
const std::vector<int> &resize_shape, int eg_max);
inline size_t fpn_stride_size() const { return fpn_stride_.size(); }
private:
StructurePredictResult disPred2Bbox(const std::vector<float> &bbox_pred,
int label, float score, int x, int y,
int stride,
const std::vector<int> &im_shape,
int reg_max);
void nms(std::vector<StructurePredictResult> &input_boxes,
float nms_threshold);
std::vector<int> fpn_stride_ = {8, 16, 32, 64};
std::vector<std::string> label_list_;
double score_threshold_ = 0.4;
double nms_threshold_ = 0.5;
int num_class_ = 5;
};
} // namespace PaddleOCR