diff --git a/deploy/cpp_infer/include/utility.h b/deploy/cpp_infer/include/utility.h index 367e37e434b396ac1eae28961f366dc397ed446f..6e8173e007279319657250b376de022240bc6f62 100644 --- a/deploy/cpp_infer/include/utility.h +++ b/deploy/cpp_infer/include/utility.h @@ -44,6 +44,9 @@ public: inline static size_t argmax(ForwardIterator first, ForwardIterator last) { return std::distance(first, std::max_element(first, last)); } + + static void GetAllFiles(const char *dir_name, + std::vector &all_inputs); }; } // namespace PaddleOCR \ No newline at end of file diff --git a/deploy/cpp_infer/src/main.cpp b/deploy/cpp_infer/src/main.cpp index 588c8374ab341163835aea2ba6c7132640c74c64..f25e674b489ea92118fe45c63939fca203ce3823 100644 --- a/deploy/cpp_infer/src/main.cpp +++ b/deploy/cpp_infer/src/main.cpp @@ -27,9 +27,12 @@ #include #include +#include #include #include #include +#include +#include using namespace std; using namespace cv; @@ -47,13 +50,8 @@ int main(int argc, char **argv) { config.PrintConfigInfo(); std::string img_path(argv[2]); - - 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); - } + std::vector all_img_names; + Utility::GetAllFiles((char *)img_path.c_str(), all_img_names); DBDetector det(config.det_model_dir, config.use_gpu, config.gpu_id, config.gpu_mem, config.cpu_math_library_num_threads, @@ -76,18 +74,30 @@ int main(int argc, char **argv) { config.use_tensorrt, config.use_fp16); auto start = std::chrono::system_clock::now(); - std::vector>> boxes; - det.Run(srcimg, boxes); - - rec.Run(boxes, srcimg, cls); - auto end = std::chrono::system_clock::now(); - auto duration = - std::chrono::duration_cast(end - start); - std::cout << "Cost " - << double(duration.count()) * - std::chrono::microseconds::period::num / - std::chrono::microseconds::period::den - << "s" << std::endl; + + for (auto img_dir : all_img_names) { + LOG(INFO) << "The predict img: " << img_dir; + + cv::Mat srcimg = cv::imread(img_dir, cv::IMREAD_COLOR); + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " << img_path + << "\n"; + exit(1); + } + std::vector>> boxes; + + det.Run(srcimg, boxes); + + rec.Run(boxes, srcimg, cls); + auto end = std::chrono::system_clock::now(); + auto duration = + std::chrono::duration_cast(end - start); + std::cout << "Cost " + << double(duration.count()) * + std::chrono::microseconds::period::num / + std::chrono::microseconds::period::den + << "s" << std::endl; + } return 0; } diff --git a/deploy/cpp_infer/src/ocr_det.cpp b/deploy/cpp_infer/src/ocr_det.cpp index 9bfee6138577288156496d9b533b4da906ae7268..33ad468a33b42c3d9f25beb19452f2fa6a81db9e 100644 --- a/deploy/cpp_infer/src/ocr_det.cpp +++ b/deploy/cpp_infer/src/ocr_det.cpp @@ -30,6 +30,42 @@ void DBDetector::LoadModel(const std::string &model_dir) { this->use_fp16_ ? paddle_infer::Config::Precision::kHalf : paddle_infer::Config::Precision::kFloat32, false, false); + std::map> min_input_shape = { + {"x", {1, 3, 50, 50}}, + {"conv2d_92.tmp_0", {1, 96, 20, 20}}, + {"conv2d_91.tmp_0", {1, 96, 10, 10}}, + {"nearest_interp_v2_1.tmp_0", {1, 96, 10, 10}}, + {"nearest_interp_v2_2.tmp_0", {1, 96, 20, 20}}, + {"nearest_interp_v2_3.tmp_0", {1, 24, 20, 20}}, + {"nearest_interp_v2_4.tmp_0", {1, 24, 20, 20}}, + {"nearest_interp_v2_5.tmp_0", {1, 24, 20, 20}}, + {"elementwise_add_7", {1, 56, 2, 2}}, + {"nearest_interp_v2_0.tmp_0", {1, 96, 2, 2}}}; + std::map> max_input_shape = { + {"x", {1, 3, this->max_side_len_, this->max_side_len_}}, + {"conv2d_92.tmp_0", {1, 96, 400, 400}}, + {"conv2d_91.tmp_0", {1, 96, 200, 200}}, + {"nearest_interp_v2_1.tmp_0", {1, 96, 200, 200}}, + {"nearest_interp_v2_2.tmp_0", {1, 96, 400, 400}}, + {"nearest_interp_v2_3.tmp_0", {1, 24, 400, 400}}, + {"nearest_interp_v2_4.tmp_0", {1, 24, 400, 400}}, + {"nearest_interp_v2_5.tmp_0", {1, 24, 400, 400}}, + {"elementwise_add_7", {1, 56, 400, 400}}, + {"nearest_interp_v2_0.tmp_0", {1, 96, 400, 400}}}; + std::map> opt_input_shape = { + {"x", {1, 3, 640, 640}}, + {"conv2d_92.tmp_0", {1, 96, 160, 160}}, + {"conv2d_91.tmp_0", {1, 96, 80, 80}}, + {"nearest_interp_v2_1.tmp_0", {1, 96, 80, 80}}, + {"nearest_interp_v2_2.tmp_0", {1, 96, 160, 160}}, + {"nearest_interp_v2_3.tmp_0", {1, 24, 160, 160}}, + {"nearest_interp_v2_4.tmp_0", {1, 24, 160, 160}}, + {"nearest_interp_v2_5.tmp_0", {1, 24, 160, 160}}, + {"elementwise_add_7", {1, 56, 40, 40}}, + {"nearest_interp_v2_0.tmp_0", {1, 96, 40, 40}}}; + + config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape, + opt_input_shape); } } else { config.DisableGpu(); @@ -48,7 +84,7 @@ void DBDetector::LoadModel(const std::string &model_dir) { config.SwitchIrOptim(true); config.EnableMemoryOptim(); - config.DisableGlogInfo(); + // config.DisableGlogInfo(); this->predictor_ = CreatePredictor(config); } diff --git a/deploy/cpp_infer/src/ocr_rec.cpp b/deploy/cpp_infer/src/ocr_rec.cpp index 76873dad3c871a027c7fccd88409227639edefdf..b09282b0283743b530cd5477dbe9c5ff751de93c 100644 --- a/deploy/cpp_infer/src/ocr_rec.cpp +++ b/deploy/cpp_infer/src/ocr_rec.cpp @@ -25,8 +25,9 @@ void CRNNRecognizer::Run(std::vector>> boxes, std::cout << "The predicted text is :" << std::endl; int index = 0; - for (int i = boxes.size() - 1; i >= 0; i--) { + for (int i = 0; i < boxes.size(); i++) { crop_img = GetRotateCropImage(srcimg, boxes[i]); + if (cls != nullptr) { crop_img = cls->Run(crop_img); } @@ -105,6 +106,15 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) { this->use_fp16_ ? paddle_infer::Config::Precision::kHalf : paddle_infer::Config::Precision::kFloat32, false, false); + std::map> min_input_shape = { + {"x", {1, 3, 32, 10}}}; + std::map> max_input_shape = { + {"x", {1, 3, 32, 2000}}}; + std::map> opt_input_shape = { + {"x", {1, 3, 32, 320}}}; + + config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape, + opt_input_shape); } } else { config.DisableGpu(); diff --git a/deploy/cpp_infer/src/preprocess_op.cpp b/deploy/cpp_infer/src/preprocess_op.cpp old mode 100755 new mode 100644 index fb7590e359da81e27c52c5a0037b93e19edb77df..28590e18520e6cc6f4e13260605366ec9bd93ed3 --- a/deploy/cpp_infer/src/preprocess_op.cpp +++ b/deploy/cpp_infer/src/preprocess_op.cpp @@ -77,19 +77,13 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img, int resize_h = int(float(h) * ratio); int resize_w = int(float(w) * ratio); - + resize_h = max(int(round(float(resize_h) / 32) * 32), 32); resize_w = max(int(round(float(resize_w) / 32) * 32), 32); - if (!use_tensorrt) { - cv::resize(img, resize_img, cv::Size(resize_w, resize_h)); - ratio_h = float(resize_h) / float(h); - ratio_w = float(resize_w) / float(w); - } else { - cv::resize(img, resize_img, cv::Size(640, 640)); - ratio_h = float(640) / float(h); - ratio_w = float(640) / float(w); - } + cv::resize(img, resize_img, cv::Size(resize_w, resize_h)); + ratio_h = float(resize_h) / float(h); + ratio_w = float(resize_w) / float(w); } void CrnnResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio, @@ -108,23 +102,12 @@ void CrnnResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio, resize_w = imgW; else resize_w = int(ceilf(imgH * ratio)); - if (!use_tensorrt) { - 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, - int(imgW - resize_img.cols), cv::BORDER_CONSTANT, - {127, 127, 127}); - } else { - int k = int(img.cols * 32 / img.rows); - if (k >= 100) { - cv::resize(img, resize_img, cv::Size(100, 32), 0.f, 0.f, - cv::INTER_LINEAR); - } else { - cv::resize(img, resize_img, cv::Size(k, 32), 0.f, 0.f, cv::INTER_LINEAR); - cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, int(100 - k), - cv::BORDER_CONSTANT, {127, 127, 127}); - } - } + + 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, + int(imgW - resize_img.cols), cv::BORDER_CONSTANT, + {127, 127, 127}); } void ClsResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, @@ -142,15 +125,11 @@ void ClsResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, else resize_w = int(ceilf(imgH * ratio)); - if (!use_tensorrt) { - cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f, - cv::INTER_LINEAR); - if (resize_w < imgW) { - cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, imgW - resize_w, - cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0)); - } - } else { - cv::resize(img, resize_img, cv::Size(100, 32), 0.f, 0.f, cv::INTER_LINEAR); + cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f, + cv::INTER_LINEAR); + if (resize_w < imgW) { + cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, imgW - resize_w, + cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0)); } } diff --git a/deploy/cpp_infer/src/utility.cpp b/deploy/cpp_infer/src/utility.cpp index c1c9d9382a06432daca71eb7b08acb8b19b8ee98..2cd84f7e8dbdd8144b5337f55b3f3a62ed43d5b3 100644 --- a/deploy/cpp_infer/src/utility.cpp +++ b/deploy/cpp_infer/src/utility.cpp @@ -12,12 +12,14 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include +#include #include #include +#include +#include #include -#include - namespace PaddleOCR { std::vector Utility::ReadDict(const std::string &path) { @@ -57,4 +59,37 @@ void Utility::VisualizeBboxes( << std::endl; } +// list all files under a directory +void Utility::GetAllFiles(const char *dir_name, + std::vector &all_inputs) { + if (NULL == dir_name) { + std::cout << " dir_name is null ! " << std::endl; + return; + } + struct stat s; + lstat(dir_name, &s); + if (!S_ISDIR(s.st_mode)) { + std::cout << "dir_name is not a valid directory !" << std::endl; + all_inputs.push_back(dir_name); + return; + } else { + struct dirent *filename; // return value for readdir() + DIR *dir; // return value for opendir() + dir = opendir(dir_name); + if (NULL == dir) { + std::cout << "Can not open dir " << dir_name << std::endl; + return; + } + std::cout << "Successfully opened the dir !" << std::endl; + while ((filename = readdir(dir)) != NULL) { + if (strcmp(filename->d_name, ".") == 0 || + strcmp(filename->d_name, "..") == 0) + continue; + // img_dir + std::string("/") + all_inputs[0]; + all_inputs.push_back(dir_name + std::string("/") + + std::string(filename->d_name)); + } + } +} + } // namespace PaddleOCR \ No newline at end of file diff --git a/deploy/cpp_infer/tools/build.sh b/deploy/cpp_infer/tools/build.sh index 606539487fce82adf817e7a3ee300e3bf890643b..79611300584755e531e6a2f645ab1a9420d3c5ad 100755 --- a/deploy/cpp_infer/tools/build.sh +++ b/deploy/cpp_infer/tools/build.sh @@ -12,9 +12,10 @@ cmake .. \ -DWITH_MKL=ON \ -DWITH_GPU=OFF \ -DWITH_STATIC_LIB=OFF \ - -DUSE_TENSORRT=OFF \ + -DWITH_TENSORRT=OFF \ -DOPENCV_DIR=${OPENCV_DIR} \ -DCUDNN_LIB=${CUDNN_LIB_DIR} \ -DCUDA_LIB=${CUDA_LIB_DIR} \ + -DTENSORRT_DIR=${TENSORRT_DIR} \ make -j diff --git a/deploy/cpp_infer/tools/config.txt b/deploy/cpp_infer/tools/config.txt index 0e5f8472ab62f5fc646738bc2974736a0564b343..d4d66d65225bc9d1d4d62f45550db71fb5d8414e 100644 --- a/deploy/cpp_infer/tools/config.txt +++ b/deploy/cpp_infer/tools/config.txt @@ -20,10 +20,10 @@ cls_thresh 0.9 # rec config rec_model_dir ./inference/ch_ppocr_mobile_v2.0_rec_infer/ -char_list_file ../../ppocr/utils/ppocr_keys_v1.txt +char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # show the detection results -visualize 1 +visualize 0 # use_tensorrt use_tensorrt 0 diff --git a/deploy/lite/readme_en.md b/deploy/lite/readme_en.md index ec36de35b8882d1b81d8e2e2f9aa5f68d510d28d..2c04c5673b1d7afec99d4e1219a20462c55be203 100644 --- a/deploy/lite/readme_en.md +++ b/deploy/lite/readme_en.md @@ -6,7 +6,7 @@ paddle-lite is a lightweight inference engine for PaddlePaddle. It provides effi ## 1. Preparation -### 运行准备 +### Preparation environment - Computer (for Compiling Paddle Lite) - Mobile phone (arm7 or arm8) @@ -87,8 +87,8 @@ The following table also provides a series of models that can be deployed on mob |Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch| |---|---|---|---|---|---|---| -|V2.0|extra-lightweight chinese OCR optimized model|7.8M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[download lin](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download lin](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9| -|V2.0(slim)|extra-lightweight chinese OCR optimized model|3.3M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9| +|V2.0|extra-lightweight chinese OCR optimized model|7.8M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9| +|V2.0(slim)|extra-lightweight chinese OCR optimized model|3.3M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9| If you directly use the model in the above table for deployment, you can skip the following steps and directly read [Section 2.2](#2.2 Run optimized model on Phone). diff --git a/doc/doc_en/models_list_en.md b/doc/doc_en/models_list_en.md index 5e5a6cfab0383c10414b7ae8cd77324f19848b97..9bee4aef5121b1964a9bdbdeeaad4e81dd9ff6d4 100644 --- a/doc/doc_en/models_list_en.md +++ b/doc/doc_en/models_list_en.md @@ -103,14 +103,14 @@ python3 generate_multi_language_configs.py -l it \ | german_mobile_v2.0_rec | ppocr/utils/dict/german_dict.txt | Lightweight model for German recognition|[rec_german_lite_train.yml](../../configs/rec/multi_language/rec_german_lite_train.yml)|2.65M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_train.tar) | | korean_mobile_v2.0_rec | ppocr/utils/dict/korean_dict.txt | Lightweight model for Korean recognition|[rec_korean_lite_train.yml](../../configs/rec/multi_language/rec_korean_lite_train.yml)|3.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_train.tar) | | japan_mobile_v2.0_rec | ppocr/utils/dict/japan_dict.txt | Lightweight model for Japanese recognition|[rec_japan_lite_train.yml](../../configs/rec/multi_language/rec_japan_lite_train.yml)|4.23M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_train.tar) | -| chinese_cht_mobile_v2.0_rec | ppocr/utils/dict/chinese_cht_dict.txt | Lightweight model for chinese cht recognition|rec_chinese_cht_lite_train.yml|5.63M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) | +| chinese_cht_mobile_v2.0_rec | ppocr/utils/dict/chinese_cht_dict.txt | Lightweight model for chinese cht recognition|rec_chinese_cht_lite_train.yml|5.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_train.tar) | | te_mobile_v2.0_rec | ppocr/utils/dict/te_dict.txt | Lightweight model for Telugu recognition|rec_te_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_train.tar) | | ka_mobile_v2.0_rec | ppocr/utils/dict/ka_dict.txt | Lightweight model for Kannada recognition|rec_ka_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_train.tar) | | ta_mobile_v2.0_rec | ppocr/utils/dict/ta_dict.txt | Lightweight model for Tamil recognition|rec_ta_lite_train.yml|2.63M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_train.tar) | -| latin_mobile_v2.0_rec | ppocr/utils/dict/latin_dict.txt | Lightweight model for latin recognition | [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) | -| arabic_mobile_v2.0_rec | ppocr/utils/dict/arabic_dict.txt | Lightweight model for arabic recognition | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) | -| cyrillic_mobile_v2.0_rec | ppocr/utils/dict/cyrillic_dict.txt | Lightweight model for cyrillic recognition | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) | -| devanagari_mobile_v2.0_rec | ppocr/utils/dict/devanagari_dict.txt | Lightweight model for devanagari recognition | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) | +| latin_mobile_v2.0_rec | ppocr/utils/dict/latin_dict.txt | Lightweight model for latin recognition | [rec_latin_lite_train.yml](../../configs/rec/multi_language/rec_latin_lite_train.yml) |2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_train.tar) | +| arabic_mobile_v2.0_rec | ppocr/utils/dict/arabic_dict.txt | Lightweight model for arabic recognition | [rec_arabic_lite_train.yml](../../configs/rec/multi_language/rec_arabic_lite_train.yml) |2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_train.tar) | +| cyrillic_mobile_v2.0_rec | ppocr/utils/dict/cyrillic_dict.txt | Lightweight model for cyrillic recognition | [rec_cyrillic_lite_train.yml](../../configs/rec/multi_language/rec_cyrillic_lite_train.yml) |2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_train.tar) | +| devanagari_mobile_v2.0_rec | ppocr/utils/dict/devanagari_dict.txt | Lightweight model for devanagari recognition | [rec_devanagari_lite_train.yml](../../configs/rec/multi_language/rec_devanagari_lite_train.yml) |2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_train.tar) | For more supported languages, please refer to : [Multi-language model](./multi_languages_en.md) diff --git a/doc/joinus.PNG b/doc/joinus.PNG index 571232b4734ca52d74dcd346b1c981470663e8f8..934655e9e97130021649fc9d2e23fa805b8c7ba6 100644 Binary files a/doc/joinus.PNG and b/doc/joinus.PNG differ diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 88bfc6dd80f7ec2c61b07163b0bb74ad3d64d5d5..7b543353d2f9accb94b0c855a1e4d199dec8a6aa 100755 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -21,6 +21,9 @@ import json from PIL import Image, ImageDraw, ImageFont import math from paddle import inference +import time +from ppocr.utils.logging import get_logger +logger = get_logger() def str2bool(v): @@ -40,6 +43,7 @@ inference_args_list = [ ['total_process_num', int, 1], ['process_id', int, 0], ['gpu_mem', int, 500], + ['cpu_threads', int, 10], # params for text detector ['image_dir', str, None], ['det_algorithm', str, 'DB'], @@ -128,19 +132,97 @@ def create_predictor(args, mode, logger): config.enable_use_gpu(args.gpu_mem, 0) if args.use_tensorrt: config.enable_tensorrt_engine( - precision_mode=inference.PrecisionType.Half - if args.use_fp16 else inference.PrecisionType.Float32, - max_batch_size=args.max_batch_size) + precision_mode=inference.PrecisionType.Float32, + max_batch_size=args.max_batch_size, + min_subgraph_size=3) # skip the minmum trt subgraph + if mode == "det" and "mobile" in model_file_path: + min_input_shape = { + "x": [1, 3, 50, 50], + "conv2d_92.tmp_0": [1, 96, 20, 20], + "conv2d_91.tmp_0": [1, 96, 10, 10], + "nearest_interp_v2_1.tmp_0": [1, 96, 10, 10], + "nearest_interp_v2_2.tmp_0": [1, 96, 20, 20], + "nearest_interp_v2_3.tmp_0": [1, 24, 20, 20], + "nearest_interp_v2_4.tmp_0": [1, 24, 20, 20], + "nearest_interp_v2_5.tmp_0": [1, 24, 20, 20], + "elementwise_add_7": [1, 56, 2, 2], + "nearest_interp_v2_0.tmp_0": [1, 96, 2, 2] + } + max_input_shape = { + "x": [1, 3, 2000, 2000], + "conv2d_92.tmp_0": [1, 96, 400, 400], + "conv2d_91.tmp_0": [1, 96, 200, 200], + "nearest_interp_v2_1.tmp_0": [1, 96, 200, 200], + "nearest_interp_v2_2.tmp_0": [1, 96, 400, 400], + "nearest_interp_v2_3.tmp_0": [1, 24, 400, 400], + "nearest_interp_v2_4.tmp_0": [1, 24, 400, 400], + "nearest_interp_v2_5.tmp_0": [1, 24, 400, 400], + "elementwise_add_7": [1, 56, 400, 400], + "nearest_interp_v2_0.tmp_0": [1, 96, 400, 400] + } + opt_input_shape = { + "x": [1, 3, 640, 640], + "conv2d_92.tmp_0": [1, 96, 160, 160], + "conv2d_91.tmp_0": [1, 96, 80, 80], + "nearest_interp_v2_1.tmp_0": [1, 96, 80, 80], + "nearest_interp_v2_2.tmp_0": [1, 96, 160, 160], + "nearest_interp_v2_3.tmp_0": [1, 24, 160, 160], + "nearest_interp_v2_4.tmp_0": [1, 24, 160, 160], + "nearest_interp_v2_5.tmp_0": [1, 24, 160, 160], + "elementwise_add_7": [1, 56, 40, 40], + "nearest_interp_v2_0.tmp_0": [1, 96, 40, 40] + } + if mode == "det" and "server" in model_file_path: + min_input_shape = { + "x": [1, 3, 50, 50], + "conv2d_59.tmp_0": [1, 96, 20, 20], + "nearest_interp_v2_2.tmp_0": [1, 96, 20, 20], + "nearest_interp_v2_3.tmp_0": [1, 24, 20, 20], + "nearest_interp_v2_4.tmp_0": [1, 24, 20, 20], + "nearest_interp_v2_5.tmp_0": [1, 24, 20, 20] + } + max_input_shape = { + "x": [1, 3, 2000, 2000], + "conv2d_59.tmp_0": [1, 96, 400, 400], + "nearest_interp_v2_2.tmp_0": [1, 96, 400, 400], + "nearest_interp_v2_3.tmp_0": [1, 24, 400, 400], + "nearest_interp_v2_4.tmp_0": [1, 24, 400, 400], + "nearest_interp_v2_5.tmp_0": [1, 24, 400, 400] + } + opt_input_shape = { + "x": [1, 3, 640, 640], + "conv2d_59.tmp_0": [1, 96, 160, 160], + "nearest_interp_v2_2.tmp_0": [1, 96, 160, 160], + "nearest_interp_v2_3.tmp_0": [1, 24, 160, 160], + "nearest_interp_v2_4.tmp_0": [1, 24, 160, 160], + "nearest_interp_v2_5.tmp_0": [1, 24, 160, 160] + } + elif mode == "rec": + min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]} + max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]} + opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]} + elif mode == "cls": + min_input_shape = {"x": [args.rec_batch_num, 3, 48, 10]} + max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]} + opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]} + else: + min_input_shape = {"x": [1, 3, 10, 10]} + max_input_shape = {"x": [1, 3, 1000, 1000]} + opt_input_shape = {"x": [1, 3, 500, 500]} + config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape, + opt_input_shape) + else: config.disable_gpu() - config.set_cpu_math_library_num_threads(6) + if hasattr(args, "cpu_threads"): + config.set_cpu_math_library_num_threads(args.cpu_threads) + else: + config.set_cpu_math_library_num_threads( + 10) # default cpu threads as 10 if args.enable_mkldnn: # cache 10 different shapes for mkldnn to avoid memory leak config.set_mkldnn_cache_capacity(10) config.enable_mkldnn() - # TODO LDOUBLEV: fix mkldnn bug when bach_size > 1 - # config.set_mkldnn_op({'conv2d', 'depthwise_conv2d', 'pool2d', 'batch_norm'}) - args.rec_batch_num = 1 # enable memory optim config.enable_memory_optim() @@ -203,7 +285,7 @@ def draw_ocr(image, txts=None, scores=None, drop_score=0.5, - font_path="./doc/simfang.ttf"): + font_path="./doc/fonts/simfang.ttf"): """ Visualize the results of OCR detection and recognition args: @@ -411,22 +493,4 @@ def draw_boxes(image, boxes, scores=None, drop_score=0.5): if __name__ == '__main__': - test_img = "./doc/test_v2" - predict_txt = "./doc/predict.txt" - f = open(predict_txt, 'r') - data = f.readlines() - img_path, anno = data[0].strip().split('\t') - img_name = os.path.basename(img_path) - img_path = os.path.join(test_img, img_name) - image = Image.open(img_path) - - data = json.loads(anno) - boxes, txts, scores = [], [], [] - for dic in data: - boxes.append(dic['points']) - txts.append(dic['transcription']) - scores.append(round(dic['scores'], 3)) - - new_img = draw_ocr(image, boxes, txts, scores) - - cv2.imwrite(img_name, new_img) + pass