jasder
/
PaddleOCR
mirror of https://mirror.trustie.net/root/PaddleOCR.git
1
0
Fork 1
Code Issues Releases Wiki Activity

Merge pull request #1092 from prettyocean85/develop 

添加C++预测，批量识别代码
This commit is contained in:
Evezerest 2022-02-09 15:11:32 +08:00 committed by GitHub
parent 6b44a969d3 b7cc06aa1b
commit 69d78ddba3
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 115 additions and 72 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
deploy/cpp_infer/include/config.h
View File

@ -63,6 +63,8 @@ public:
this->cls_thresh = stod(config_map_["cls_thresh"]);
this->rec_batch_num = stoi(config_map_["rec_batch_num"]);
this->visualize = bool(stoi(config_map_["visualize"]));
}
@ -86,6 +88,8 @@ public:
double det_db_unclip_ratio = 2.0;
int rec_batch_num = 30;
std::string det_model_dir;
std::string rec_model_dir;

4
deploy/cpp_infer/include/ocr_rec.h
View File

@ -40,13 +40,14 @@ public:
const int &gpu_id, const int &gpu_mem,
const int &cpu_math_library_num_threads,
const bool &use_mkldnn, const bool &use_zero_copy_run,
const string &label_path) {
const string &label_path, const int& rec_batch_num) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
this->use_mkldnn_ = use_mkldnn;
this->use_zero_copy_run_ = use_zero_copy_run;
this->rec_batch_num_ = rec_batch_num;
this->label_list_ = Utility::ReadDict(label_path);
this->label_list_.push_back(" ");
@ -69,6 +70,7 @@ private:
int cpu_math_library_num_threads_ = 4;
bool use_mkldnn_ = false;
bool use_zero_copy_run_ = false;
int rec_batch_num_ = 30;
std::vector<std::string> label_list_;

6
deploy/cpp_infer/src/main.cpp
View File

@ -67,7 +67,7 @@ int main(int argc, char **argv) {
CRNNRecognizer rec(config.rec_model_dir, config.use_gpu, config.gpu_id,
config.gpu_mem, config.cpu_math_library_num_threads,
config.use_mkldnn, config.use_zero_copy_run,
config.char_list_file);
config.char_list_file, config.rec_batch_num);
#ifdef USE_MKL
#pragma omp parallel
@ -91,11 +91,11 @@ int main(int argc, char **argv) {
auto end = std::chrono::system_clock::now();
auto duration =
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
std::cout << "花费了"
std::cout << "cost"
<< double(duration.count()) *
std::chrono::microseconds::period::num /
std::chrono::microseconds::period::den
<< "秒" << std::endl;
<< "s" << std::endl;
return 0;
}

172
deploy/cpp_infer/src/ocr_rec.cpp
View File

@ -14,6 +14,20 @@
#include <include/ocr_rec.h>
template <typename T>
vector<int> argsort(const std::vector<T>& array)
{
const int array_len(array.size());
std::vector<int> array_index(array_len, 0);
for (int i = 0; i < array_len; ++i)
array_index[i] = i;
std::sort(array_index.begin(), array_index.end(),
[&array](int pos1, int pos2) {return (array[pos1] < array[pos2]); });
return array_index;
}
namespace PaddleOCR {
void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
@ -22,100 +36,122 @@ void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
img.copyTo(srcimg);
cv::Mat crop_img;
cv::Mat resize_img;
std::vector<float> width_list;
std::vector<cv::Mat> img_list;
for (int i = boxes.size() - 1; i >= 0; i--) {
crop_img = GetRotateCropImage(srcimg, boxes[i]);
if (cls != nullptr) {
crop_img = cls->Run(crop_img);
}
img_list.push_back(crop_img);
float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
width_list.push_back(wh_ratio);
}
//sort box
vector<int> sort_index = argsort(width_list);
int batch_num1 = this->rec_batch_num_;//batchsize
std::cout << "The predicted text is :" << std::endl;
int index = 0;
for (int i = boxes.size() - 1; i >= 0; i--) {
crop_img = GetRotateCropImage(srcimg, boxes[i]);
if (cls != nullptr) {
crop_img = cls->Run(crop_img);
int beg_img_no = 0;
int end_img_no = 0;
for (int beg_img_no = 0; beg_img_no < img_list.size(); beg_img_no += batch_num1)
{
float max_wh_ratio = 0;
end_img_no = min((int)boxes.size(), beg_img_no + batch_num1);
int batch_num = min(end_img_no - beg_img_no, batch_num1);
max_wh_ratio = width_list[sort_index[end_img_no - 1]];
int imgW1 = int(32 * max_wh_ratio);
int nqu, nra;
nqu = imgW1 / 4;
nra = imgW1 % 4;
int imgW = imgW1;
if (nra > 0)
{
imgW = int(4 * (nqu + 1));
}
std::vector<float> input(batch_num * 3 * 32 * imgW, 0.0f);//batchsize input
for (int i = beg_img_no; i < end_img_no; i++)
{
crop_img = img_list[sort_index[i]];
this->resize_op_.Run(crop_img, resize_img, max_wh_ratio);//resize
this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
this->is_scale_);
float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
cv::Mat padding_im;
cv::copyMakeBorder(resize_img, padding_im, 0, 0, 0, int(imgW - resize_img.cols), cv::BORDER_CONSTANT, { 0, 0, 0 });//padding image
this->resize_op_.Run(crop_img, resize_img, wh_ratio);
this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
this->is_scale_);
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
// Inference.
if (this->use_zero_copy_run_) {
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.data());
this->predictor_->ZeroCopyRun();
} else {
paddle::PaddleTensor input_t;
input_t.shape = {1, 3, resize_img.rows, resize_img.cols};
input_t.data =
paddle::PaddleBuf(input.data(), input.size() * sizeof(float));
input_t.dtype = PaddleDType::FLOAT32;
std::vector<paddle::PaddleTensor> outputs;
this->predictor_->Run({input_t}, &outputs, 1);
this->permute_op_.Run(&padding_im, input.data() + (i - beg_img_no) * 3 * padding_im.rows * padding_im.cols);
}
auto input_names = this->predictor_->GetInputNames();
auto input_t = this->predictor_->GetInputTensor(input_names[0]);
input_t->Reshape({ batch_num, 3, 32, imgW });
input_t->copy_from_cpu(input.data());
this->predictor_->ZeroCopyRun();
std::vector<int64_t> rec_idx;
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputTensor(output_names[0]);
auto rec_idx_lod = output_t->lod();
auto shape_out = output_t->shape();
int out_num = std::accumulate(shape_out.begin(), shape_out.end(), 1,
std::multiplies<int>());
auto rec_idx_lod = output_t->lod()[0];
std::vector<int> output_shape = output_t->shape();
int out_num = 1;
for (int i = 0; i < output_shape.size(); ++i) {
out_num *= output_shape[i];
}
rec_idx.resize(out_num);
output_t->copy_to_cpu(rec_idx.data());
std::vector<int> pred_idx;
for (int n = int(rec_idx_lod[0][0]); n < int(rec_idx_lod[0][1]); n++) {
pred_idx.push_back(int(rec_idx[n]));
}
if (pred_idx.size() < 1e-3)
continue;
index += 1;
std::cout << index << "\t";
for (int n = 0; n < pred_idx.size(); n++) {
std::cout << label_list_[pred_idx[n]];
}
output_t->copy_to_cpu(rec_idx.data());//output data
std::vector<float> predict_batch;
auto output_t_1 = this->predictor_->GetOutputTensor(output_names[1]);
auto predict_lod = output_t_1->lod();
auto predict_lod = output_t_1->lod()[0];
auto predict_shape = output_t_1->shape();
int out_num_1 = std::accumulate(predict_shape.begin(), predict_shape.end(),
1, std::multiplies<int>());
int out_num_1 = 1;
for (int i = 0; i < predict_shape.size(); ++i) {
out_num_1 *= predict_shape[i];
}
predict_batch.resize(out_num_1);
output_t_1->copy_to_cpu(predict_batch.data());
int argmax_idx;
int blank = predict_shape[1];
float score = 0.f;
int count = 0;
float max_value = 0.0f;
for (int n = predict_lod[0][0]; n < predict_lod[0][1] - 1; n++) {
argmax_idx =
int(Utility::argmax(&predict_batch[n * predict_shape[1]],
&predict_batch[(n + 1) * predict_shape[1]]));
max_value =
float(*std::max_element(&predict_batch[n * predict_shape[1]],
&predict_batch[(n + 1) * predict_shape[1]]));
if (blank - 1 - argmax_idx > 1e-5) {
score += max_value;
count += 1;
}
for (int j = 0; j < rec_idx_lod.size() - 1; j++)
{
std::vector<int> pred_idx;
float score = 0.f;
int count = 0;
float max_value = 0.0f;
for (int n = int(rec_idx_lod[j]); n < int(rec_idx_lod[j + 1]); n++) {
pred_idx.push_back(int(rec_idx[n]));
}
if (pred_idx.size() < 1e-3)
continue;
index += 1;
std::cout << index << "\t";
for (int n = 0; n < pred_idx.size(); n++) {
std::cout << label_list_[pred_idx[n]];
}
for (int n = predict_lod[j]; n < predict_lod[j + 1] - 1; n++) {
argmax_idx =
int(Utility::argmax(&predict_batch[n * predict_shape[1]],
&predict_batch[(n + 1) * predict_shape[1]]));
max_value = predict_batch[n * predict_shape[1] + argmax_idx];
if (blank - 1 - argmax_idx > 1e-5) {
score += max_value;
count += 1;
}
}
score /= count;
std::cout << "\tscore: " << score << std::endl;
}
score /= count;
std::cout << "\tscore: " << score << std::endl;
}
}

1
deploy/cpp_infer/tools/config.txt
View File

@ -21,6 +21,7 @@ cls_thresh 0.9
# rec config
rec_model_dir ./inference/rec_crnn
char_list_file ../../ppocr/utils/ppocr_keys_v1.txt
rec_batch_num 30
# show the detection results
visualize 1