diff --git a/deploy/cpp_infer/include/args.h b/deploy/cpp_infer/include/args.h index fe5d45f6e0f2475089dfaaebc021a3e194a2cb22..473ff25d981f8409f60a43940aaaec376375adf5 100644 --- a/deploy/cpp_infer/include/args.h +++ b/deploy/cpp_infer/include/args.h @@ -46,6 +46,8 @@ DECLARE_int32(cls_batch_num); DECLARE_string(rec_model_dir); DECLARE_int32(rec_batch_num); DECLARE_string(rec_char_dict_path); +DECLARE_int32(rec_img_h); +DECLARE_int32(rec_img_w); // forward related DECLARE_bool(det); DECLARE_bool(rec); diff --git a/deploy/cpp_infer/include/ocr_rec.h b/deploy/cpp_infer/include/ocr_rec.h index f634b6dc8481ad062ced5ef9afef3fb0532d28a6..30f8efa9996a62adc74717dd46f2aef7fc96b091 100644 --- a/deploy/cpp_infer/include/ocr_rec.h +++ b/deploy/cpp_infer/include/ocr_rec.h @@ -45,7 +45,8 @@ public: const bool &use_mkldnn, const string &label_path, const bool &use_tensorrt, const std::string &precision, - const int &rec_batch_num) { + const int &rec_batch_num, const int &rec_img_h, + const int &rec_img_w) { this->use_gpu_ = use_gpu; this->gpu_id_ = gpu_id; this->gpu_mem_ = gpu_mem; @@ -54,6 +55,10 @@ public: this->use_tensorrt_ = use_tensorrt; this->precision_ = precision; this->rec_batch_num_ = rec_batch_num; + this->rec_img_h_ = rec_img_h; + this->rec_img_w_ = rec_img_w; + std::vector rec_image_shape = {3, rec_img_h, rec_img_w}; + this->rec_image_shape_ = rec_image_shape; this->label_list_ = Utility::ReadDict(label_path); this->label_list_.insert(this->label_list_.begin(), @@ -86,7 +91,9 @@ private: bool use_tensorrt_ = false; std::string precision_ = "fp32"; int rec_batch_num_ = 6; - + int rec_img_h_ = 32; + int rec_img_w_ = 320; + std::vector rec_image_shape_ = {3, rec_img_h_, rec_img_w_}; // pre-process CrnnResizeImg resize_op_; Normalize normalize_op_; diff --git a/deploy/cpp_infer/readme.md b/deploy/cpp_infer/readme.md index 4290fbb0fa03005970a0937d7d9daf5b6ba1995e..66c3a4c0719154152a2029572a8b88af3adcfcf4 100644 --- a/deploy/cpp_infer/readme.md +++ b/deploy/cpp_infer/readme.md @@ -323,6 +323,8 @@ More parameters are as follows, |rec_model_dir|string|-|Address of recognition inference model| |rec_char_dict_path|string|../../ppocr/utils/ppocr_keys_v1.txt|dictionary file| |rec_batch_num|int|6|batch size of recognition| +|rec_img_h|int|32|image height of recognition| +|rec_img_w|int|320|image width of recognition| * Multi-language inference is also supported in PaddleOCR, you can refer to [recognition tutorial](../../doc/doc_en/recognition_en.md) for more supported languages and models in PaddleOCR. Specifically, if you want to infer using multi-language models, you just need to modify values of `rec_char_dict_path` and `rec_model_dir`. diff --git a/deploy/cpp_infer/readme_ch.md b/deploy/cpp_infer/readme_ch.md index 95b28397d0fca72d28c4f981709754e13ca35e0d..47c7e032ebb350625adae8f500f91c0a7b96dbf4 100644 --- a/deploy/cpp_infer/readme_ch.md +++ b/deploy/cpp_infer/readme_ch.md @@ -336,6 +336,8 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir |rec_model_dir|string|-|识别模型inference model地址| |rec_char_dict_path|string|../../ppocr/utils/ppocr_keys_v1.txt|字典文件| |rec_batch_num|int|6|识别模型batchsize| +|rec_img_h|int|32|识别模型输入图像高度| +|rec_img_w|int|320|识别模型输入图像宽度| * PaddleOCR也支持多语言的预测，更多支持的语言和模型可以参考[识别文档](../../doc/doc_ch/recognition.md)中的多语言字典与模型部分，如果希望进行多语言预测，只需将修改`rec_char_dict_path`（字典文件路径）以及`rec_model_dir`（inference模型路径）字段即可。 diff --git a/deploy/cpp_infer/src/args.cpp b/deploy/cpp_infer/src/args.cpp index 82cfb54ae7f05e549d58d3824504cce9ba7c6329..fe58236734568035dfb26570df39f21154f4e9ef 100644 --- a/deploy/cpp_infer/src/args.cpp +++ b/deploy/cpp_infer/src/args.cpp @@ -47,6 +47,8 @@ DEFINE_string(rec_model_dir, "", "Path of rec inference model."); DEFINE_int32(rec_batch_num, 6, "rec_batch_num."); DEFINE_string(rec_char_dict_path, "../../ppocr/utils/ppocr_keys_v1.txt", "Path of dictionary."); +DEFINE_int32(rec_img_h, 32, "rec image height"); +DEFINE_int32(rec_img_w, 320, "rec image width"); // ocr forward related DEFINE_bool(det, true, "Whether use det in forward."); diff --git a/deploy/cpp_infer/src/ocr_rec.cpp b/deploy/cpp_infer/src/ocr_rec.cpp index 54ed3fefcb798033748ad6f9cde20375527b1774..f0adfcf0270b4a1b8b50025dc094eb8f05319819 100644 --- a/deploy/cpp_infer/src/ocr_rec.cpp +++ b/deploy/cpp_infer/src/ocr_rec.cpp @@ -39,7 +39,9 @@ void CRNNRecognizer::Run(std::vector img_list, auto preprocess_start = std::chrono::steady_clock::now(); int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_); int batch_num = end_img_no - beg_img_no; - float max_wh_ratio = 0; + int imgH = this->rec_image_shape_[1]; + int imgW = this->rec_image_shape_[2]; + float max_wh_ratio = imgW * 1.0 / imgH; for (int ino = beg_img_no; ino < end_img_no; ino++) { int h = img_list[indices[ino]].rows; int w = img_list[indices[ino]].cols; @@ -47,28 +49,28 @@ void CRNNRecognizer::Run(std::vector img_list, max_wh_ratio = max(max_wh_ratio, wh_ratio); } - int batch_width = 0; + int batch_width = imgW; std::vector norm_img_batch; for (int ino = beg_img_no; ino < end_img_no; ino++) { cv::Mat srcimg; img_list[indices[ino]].copyTo(srcimg); cv::Mat resize_img; this->resize_op_.Run(srcimg, resize_img, max_wh_ratio, - this->use_tensorrt_); + this->use_tensorrt_, this->rec_image_shape_); this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, this->is_scale_); norm_img_batch.push_back(resize_img); batch_width = max(resize_img.cols, batch_width); } - std::vector input(batch_num * 3 * 32 * batch_width, 0.0f); + std::vector input(batch_num * 3 * imgH * batch_width, 0.0f); this->permute_op_.Run(norm_img_batch, input.data()); auto preprocess_end = std::chrono::steady_clock::now(); preprocess_diff += preprocess_end - preprocess_start; // Inference. auto input_names = this->predictor_->GetInputNames(); auto input_t = this->predictor_->GetInputHandle(input_names[0]); - input_t->Reshape({batch_num, 3, 32, batch_width}); + input_t->Reshape({batch_num, 3, imgH, batch_width}); auto inference_start = std::chrono::steady_clock::now(); input_t->CopyFromCpu(input.data()); this->predictor_->Run(); @@ -142,13 +144,14 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) { precision = paddle_infer::Config::Precision::kInt8; } config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false); - + int imgH = this->rec_image_shape_[1]; + int imgW = this->rec_image_shape_[2]; std::map> min_input_shape = { - {"x", {1, 3, 32, 10}}, {"lstm_0.tmp_0", {10, 1, 96}}}; + {"x", {1, 3, imgH, 10}}, {"lstm_0.tmp_0", {10, 1, 96}}}; std::map> max_input_shape = { - {"x", {1, 3, 32, 2000}}, {"lstm_0.tmp_0", {1000, 1, 96}}}; + {"x", {1, 3, imgH, 2000}}, {"lstm_0.tmp_0", {1000, 1, 96}}}; std::map> opt_input_shape = { - {"x", {1, 3, 32, 320}}, {"lstm_0.tmp_0", {25, 1, 96}}}; + {"x", {1, 3, imgH, imgW}}, {"lstm_0.tmp_0", {25, 1, 96}}}; config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape, opt_input_shape); diff --git a/deploy/cpp_infer/src/paddleocr.cpp b/deploy/cpp_infer/src/paddleocr.cpp index e7b3777e78efc5e5dbf35180b2794968aea5706f..cd620a9206cad8ec2b1cd5924c714a8a1fa989b6 100644 --- a/deploy/cpp_infer/src/paddleocr.cpp +++ b/deploy/cpp_infer/src/paddleocr.cpp @@ -39,7 +39,8 @@ PPOCR::PPOCR() { this->recognizer_ = new CRNNRecognizer( FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_rec_char_dict_path, - FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num); + FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num, + FLAGS_rec_img_h, FLAGS_rec_img_w); } }; diff --git a/deploy/cpp_infer/src/preprocess_op.cpp b/deploy/cpp_infer/src/preprocess_op.cpp index 14e8bd1d8425fa6c539c4f3673ea861e24b3b3c8..fff49ba2c2cd0e68f0c1d93e5877ab6276bdc337 100644 --- a/deploy/cpp_infer/src/preprocess_op.cpp +++ b/deploy/cpp_infer/src/preprocess_op.cpp @@ -41,16 +41,17 @@ void Permute::Run(const cv::Mat *im, float *data) { } void PermuteBatch::Run(const std::vector imgs, float *data) { - for (int j = 0; j < imgs.size(); j ++){ - int rh = imgs[j].rows; - int rw = imgs[j].cols; - int rc = imgs[j].channels(); - for (int i = 0; i < rc; ++i) { - cv::extractChannel(imgs[j], cv::Mat(rh, rw, CV_32FC1, data + (j * rc + i) * rh * rw), i); - } + for (int j = 0; j < imgs.size(); j++) { + int rh = imgs[j].rows; + int rw = imgs[j].cols; + int rc = imgs[j].channels(); + for (int i = 0; i < rc; ++i) { + cv::extractChannel( + imgs[j], cv::Mat(rh, rw, CV_32FC1, data + (j * rc + i) * rh * rw), i); } + } } - + void Normalize::Run(cv::Mat *im, const std::vector &mean, const std::vector &scale, const bool is_scale) { double e = 1.0; @@ -101,8 +102,8 @@ void CrnnResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio, imgC = rec_image_shape[0]; imgH = rec_image_shape[1]; imgW = rec_image_shape[2]; - - imgW = int(32 * wh_ratio); + + imgW = int(imgH * wh_ratio); float ratio = float(img.cols) / float(img.rows); int resize_w, resize_h; @@ -111,7 +112,7 @@ void CrnnResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio, resize_w = imgW; else resize_w = int(ceilf(imgH * ratio)); - + cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f, cv::INTER_LINEAR); cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0,