mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-08-06 23:58:05 +00:00
142 lines
4.5 KiB
C++
142 lines
4.5 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.
|
||
|
||
#include "opencv2/core.hpp"
|
||
#include "opencv2/imgcodecs.hpp"
|
||
#include "opencv2/imgproc.hpp"
|
||
#include "paddle_api.h"
|
||
#include "paddle_inference_api.h"
|
||
#include <chrono>
|
||
#include <iomanip>
|
||
#include <iostream>
|
||
#include <ostream>
|
||
#include <vector>
|
||
|
||
#include <cstring>
|
||
#include <fstream>
|
||
#include <numeric>
|
||
|
||
#include <include/ocr_det.h>
|
||
|
||
namespace PaddleOCR {
|
||
|
||
void DBDetector::LoadModel(const std::string &model_dir, bool use_gpu,
|
||
const int gpu_id, const int min_subgraph_size,
|
||
const int batch_size) {
|
||
AnalysisConfig config;
|
||
config.SetModel(model_dir + "/model", model_dir + "/params");
|
||
|
||
// for cpu
|
||
config.DisableGpu();
|
||
config.EnableMKLDNN(); // 开启MKLDNN加速
|
||
config.SetCpuMathLibraryNumThreads(10);
|
||
|
||
// 使用ZeroCopyTensor，此处必须设置为false
|
||
config.SwitchUseFeedFetchOps(false);
|
||
// 若输入为多个，此处必须设置为true
|
||
config.SwitchSpecifyInputNames(true);
|
||
// config.SwitchIrDebug(true); //
|
||
// 可视化调试选项，若开启，则会在每个图优化过程后生成dot文件
|
||
// config.SwitchIrOptim(false);// 默认为true。如果设置为false，关闭所有优化
|
||
config.EnableMemoryOptim(); // 开启内存/显存复用
|
||
|
||
this->predictor_ = CreatePaddlePredictor(config);
|
||
// predictor_ = std::move(CreatePaddlePredictor(config)); // PaddleDetection
|
||
// usage
|
||
}
|
||
|
||
void DBDetector::Run(cv::Mat &img,
|
||
std::vector<std::vector<std::vector<int>>> &boxes) {
|
||
float ratio_h{};
|
||
float ratio_w{};
|
||
|
||
cv::Mat srcimg;
|
||
cv::Mat resize_img;
|
||
img.copyTo(srcimg);
|
||
this->resize_op_.Run(img, resize_img, this->max_side_len_, ratio_h, ratio_w);
|
||
|
||
this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
|
||
this->is_scale_);
|
||
|
||
float *input = new float[1 * 3 * resize_img.rows * resize_img.cols];
|
||
this->permute_op_.Run(&resize_img, input);
|
||
|
||
auto input_names = this->predictor_->GetInputNames();
|
||
auto input_t = this->predictor_->GetInputTensor(input_names[0]);
|
||
input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
|
||
input_t->copy_from_cpu(input);
|
||
|
||
this->predictor_->ZeroCopyRun();
|
||
|
||
std::vector<float> out_data;
|
||
auto output_names = this->predictor_->GetOutputNames();
|
||
auto output_t = this->predictor_->GetOutputTensor(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->copy_to_cpu(out_data.data());
|
||
|
||
int n2 = output_shape[2];
|
||
int n3 = output_shape[3];
|
||
int n = n2 * n3;
|
||
|
||
float *pred = new float[n];
|
||
unsigned char *cbuf = new unsigned char[n];
|
||
|
||
for (int i = 0; i < n; i++) {
|
||
pred[i] = float(out_data[i]);
|
||
cbuf[i] = (unsigned char)((out_data[i]) * 255);
|
||
}
|
||
|
||
cv::Mat cbuf_map(n2, n3, CV_8UC1, (unsigned char *)cbuf);
|
||
cv::Mat pred_map(n2, n3, CV_32F, (float *)pred);
|
||
|
||
const double threshold = 0.3 * 255;
|
||
const double maxvalue = 255;
|
||
cv::Mat bit_map;
|
||
cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
|
||
|
||
boxes = post_processor_.boxes_from_bitmap(pred_map, bit_map);
|
||
|
||
boxes = post_processor_.filter_tag_det_res(boxes, ratio_h, ratio_w, srcimg);
|
||
|
||
//// visualization
|
||
cv::Point rook_points[boxes.size()][4];
|
||
for (int n = 0; n < boxes.size(); n++) {
|
||
for (int m = 0; m < boxes[0].size(); m++) {
|
||
rook_points[n][m] = cv::Point(int(boxes[n][m][0]), int(boxes[n][m][1]));
|
||
}
|
||
}
|
||
|
||
cv::Mat img_vis;
|
||
srcimg.copyTo(img_vis);
|
||
for (int n = 0; n < boxes.size(); n++) {
|
||
const cv::Point *ppt[1] = {rook_points[n]};
|
||
int npt[] = {4};
|
||
cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
|
||
}
|
||
|
||
imwrite("./det_res.png", img_vis);
|
||
|
||
std::cout << "The detection visualized image saved in ./det_res.png"
|
||
<< std::endl;
|
||
|
||
delete[] input;
|
||
delete[] pred;
|
||
delete[] cbuf;
|
||
}
|
||
|
||
} // namespace PaddleOCR
|