diff --git a/csrc/fastdeploy/vision/ocr/ppocr/classifier.cc b/csrc/fastdeploy/vision/ocr/ppocr/classifier.cc index 3cce19983..6654d71f7 100644 --- a/csrc/fastdeploy/vision/ocr/ppocr/classifier.cc +++ b/csrc/fastdeploy/vision/ocr/ppocr/classifier.cc @@ -46,7 +46,7 @@ bool Classifier::Initialize() { cls_thresh = 0.9; cls_image_shape = {3, 48, 192}; cls_batch_num = 1; - mean = {0.485f, 0.456f, 0.406f}; + mean = {0.5f, 0.5f, 0.5f}; scale = {0.5f, 0.5f, 0.5f}; is_scale = true; diff --git a/csrc/fastdeploy/vision/ocr/ppocr/dbdetector.cc b/csrc/fastdeploy/vision/ocr/ppocr/dbdetector.cc index 001401c6e..b79dfb0ed 100644 --- a/csrc/fastdeploy/vision/ocr/ppocr/dbdetector.cc +++ b/csrc/fastdeploy/vision/ocr/ppocr/dbdetector.cc @@ -144,10 +144,11 @@ bool DBDetector::Postprocess( std::vector>> boxes; - post_processor_.BoxesFromBitmap(pred_map, &boxes, bit_map, det_db_box_thresh, - det_db_unclip_ratio, det_db_score_mode); + boxes = + post_processor_.BoxesFromBitmap(pred_map, bit_map, det_db_box_thresh, + det_db_unclip_ratio, det_db_score_mode); - post_processor_.FilterTagDetRes(&boxes, ratio_h, ratio_w, im_info); + boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, im_info); // boxes to boxes_result for (int i = 0; i < boxes.size(); i++) { diff --git a/csrc/fastdeploy/vision/ocr/ppocr/ppocr_system_v2.cc b/csrc/fastdeploy/vision/ocr/ppocr/ppocr_system_v2.cc index a6907525d..62efefd83 100644 --- a/csrc/fastdeploy/vision/ocr/ppocr/ppocr_system_v2.cc +++ b/csrc/fastdeploy/vision/ocr/ppocr/ppocr_system_v2.cc @@ -36,7 +36,7 @@ void PPOCRSystemv2::Detect(cv::Mat* img, void PPOCRSystemv2::Recognize(cv::Mat* img, fastdeploy::vision::OCRResult* result) { std::tuple rec_result; - + this->recognizer->rec_image_shape[1] = 32; this->recognizer->Predict(img, &rec_result); result->text.push_back(std::get<0>(rec_result)); diff --git a/csrc/fastdeploy/vision/ocr/ppocr/ppocr_system_v3.cc b/csrc/fastdeploy/vision/ocr/ppocr/ppocr_system_v3.cc index ca07972ae..c5df69444 100644 --- a/csrc/fastdeploy/vision/ocr/ppocr/ppocr_system_v3.cc +++ b/csrc/fastdeploy/vision/ocr/ppocr/ppocr_system_v3.cc @@ -94,7 +94,6 @@ bool PPOCRSystemv3::Predict(cv::Mat* img, for (int i = 0; i < img_list.size(); i++) { if ((result->cls_labels)[i] % 2 == 1 && (result->cls_scores)[i] > this->classifier->cls_thresh) { - std::cout << "Rotate this image " << std::endl; cv::rotate(img_list[i], img_list[i], 1); } } diff --git a/csrc/fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.cc b/csrc/fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.cc index d235cef67..40869e063 100644 --- a/csrc/fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.cc +++ b/csrc/fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.cc @@ -250,9 +250,8 @@ float PostProcessor::BoxScoreFast(std::vector> box_array, //这个应该是DB（差分二值化）相关的内容，方法从 Bitmap 图中获取检测框 //涉及到box_thresh（低于这个阈值的boxs不予显示）和det_db_unclip_ratio（文本框扩张的系数，关系到文本框的大小） -void PostProcessor::BoxesFromBitmap( - const cv::Mat pred, std::vector>> *boxes, - const cv::Mat bitmap, const float &box_thresh, +std::vector>> PostProcessor::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) { const int min_size = 3; const int max_candidates = 1000; @@ -269,6 +268,8 @@ void PostProcessor::BoxesFromBitmap( int num_contours = contours.size() >= max_candidates ? max_candidates : contours.size(); + std::vector>> boxes; + for (int _i = 0; _i < num_contours; _i++) { if (contours[_i].size() <= 2) { continue; @@ -318,45 +319,42 @@ void PostProcessor::BoxesFromBitmap( 0, float(dest_height)))}; intcliparray.push_back(a); } - boxes->push_back(intcliparray); + boxes.push_back(intcliparray); } // end for - // return true; + return boxes; } -//方法根据识别结果获取目标框位置 -void PostProcessor::FilterTagDetRes( - std::vector>> *boxes, const float ratio_h, - const float ratio_w, - const std::map> &im_info) { +std::vector>> PostProcessor::FilterTagDetRes( + std::vector>> boxes, float ratio_h, + float ratio_w, const std::map> &im_info) { int oriimg_h = im_info.at("input_shape")[0]; int oriimg_w = im_info.at("input_shape")[1]; - for (int n = 0; n < boxes->size(); n++) { - (*boxes)[n] = OrderPointsClockwise((*boxes)[n]); - for (int m = 0; m < (*boxes)[0].size(); m++) { - (*boxes)[n][m][0] /= ratio_w; - (*boxes)[n][m][1] /= ratio_h; + std::vector>> root_points; + for (int n = 0; n < boxes.size(); n++) { + boxes[n] = OrderPointsClockwise(boxes[n]); + for (int m = 0; m < boxes[0].size(); m++) { + boxes[n][m][0] /= ratio_w; + boxes[n][m][1] /= ratio_h; - (*boxes)[n][m][0] = int(_min(_max((*boxes)[n][m][0], 0), oriimg_w - 1)); - (*boxes)[n][m][1] = int(_min(_max((*boxes)[n][m][1], 0), oriimg_h - 1)); + boxes[n][m][0] = int(_min(_max(boxes[n][m][0], 0), oriimg_w - 1)); + boxes[n][m][1] = int(_min(_max(boxes[n][m][1], 0), oriimg_h - 1)); } } - //此时已经拿到所有的点. 再进行下面的筛选 - for (int n = (*boxes).size() - 1; n >= 0; n--) { + for (int n = 0; n < boxes.size(); n++) { int rect_width, rect_height; - rect_width = int(sqrt(pow((*boxes)[n][0][0] - (*boxes)[n][1][0], 2) + - pow((*boxes)[n][0][1] - (*boxes)[n][1][1], 2))); - rect_height = int(sqrt(pow((*boxes)[n][0][0] - (*boxes)[n][3][0], 2) + - pow((*boxes)[n][0][1] - (*boxes)[n][3][1], 2))); - - //小于4的删除掉. erase配合逆序遍历. - if (rect_width <= 4 || rect_height <= 4) { - boxes->erase(boxes->begin() + n); - } + rect_width = int(sqrt(pow(boxes[n][0][0] - boxes[n][1][0], 2) + + pow(boxes[n][0][1] - boxes[n][1][1], 2))); + rect_height = int(sqrt(pow(boxes[n][0][0] - boxes[n][3][0], 2) + + pow(boxes[n][0][1] - boxes[n][3][1], 2))); + if (rect_width <= 4 || rect_height <= 4) continue; + root_points.push_back(boxes[n]); } + return root_points; } + } // namespace ocr } // namespace vision } // namespace fastdeploy \ No newline at end of file diff --git a/csrc/fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h b/csrc/fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h index 034ac3ec4..8422704fd 100644 --- a/csrc/fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h +++ b/csrc/fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h @@ -52,15 +52,13 @@ class PostProcessor { float BoxScoreFast(std::vector> box_array, cv::Mat pred); float PolygonScoreAcc(std::vector contour, cv::Mat pred); - void BoxesFromBitmap(const cv::Mat pred, - std::vector>> *boxes, - const cv::Mat bitmap, const float &box_thresh, - const float &det_db_unclip_ratio, - const std::string &det_db_score_mode); + std::vector>> 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>> *boxes, const float ratio_h, - const float ratio_w, + std::vector>> FilterTagDetRes( + std::vector>> boxes, float ratio_h, + float ratio_w, const std::map> &im_info); private: diff --git a/examples/vision/ocr/PPOCRSystemv2/cpp/infer.cc b/examples/vision/ocr/PPOCRSystemv2/cpp/infer.cc index 8e5061f67..e6dd96504 100644 --- a/examples/vision/ocr/PPOCRSystemv2/cpp/infer.cc +++ b/examples/vision/ocr/PPOCRSystemv2/cpp/infer.cc @@ -65,8 +65,6 @@ void CpuInfer(const std::string& det_model_dir, if (!rec_model_dir.empty()) { auto rec_option = fastdeploy::RuntimeOption(); rec_option.UseCpu(); - rec_option.UsePaddleBackend(); // OCRv2的rec模型暂不支持ORT后端 - rec_model = fastdeploy::vision::ocr::Recognizer( rec_model_file, rec_params_file, rec_label, rec_option); diff --git a/examples/vision/ocr/PPOCRSystemv3/python/infer.py b/examples/vision/ocr/PPOCRSystemv3/python/infer.py index 3703afc9b..1b22c95e9 100644 --- a/examples/vision/ocr/PPOCRSystemv3/python/infer.py +++ b/examples/vision/ocr/PPOCRSystemv3/python/infer.py @@ -65,7 +65,6 @@ def build_det_option(args): def build_cls_option(args): option = fd.RuntimeOption() - option.use_paddle_backend() if args.device.lower() == "gpu": option.use_gpu() diff --git a/examples/vision/ocr/README.md b/examples/vision/ocr/README.md index 893989d10..76d52693e 100644 --- a/examples/vision/ocr/README.md +++ b/examples/vision/ocr/README.md @@ -1,15 +1,15 @@ # PaddleOCR 模型部署 -## 模型版本说明 +## PaddleOCR系列预测模型下载 -- [PaddleOCR Release/2.5](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.5) +| 模型 | 参数文件大小 | 说明 | 模型格式 | 版本 | +|:---------------------------------------------------------------- |:----- |:----- | :------- | :--- | +| [ch_PP-OCRv3_det](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) | 3.8MB | OCRv3的检测模型 | PADDLE | [Release/2.6](https://github.com/PaddlePaddle/PaddleOCR) | +| [ch_PP-OCRv3_rec](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) | 12.4MB | OCRv3的识别模型 | PADDLE | [Release/2.6](https://github.com/PaddlePaddle/PaddleOCR) | +| [ch_ppocr_mobile_v2.0_cls](https://bj.bcebos.com/paddlehub/fastdeploy/ch_ppocr_mobile_v2.0_cls_infer.tar.gz) | 1.38MB | OCRv2和v3通用的分类模型 | PADDLE | [Release/2.6](https://github.com/PaddlePaddle/PaddleOCR) | +| [ch_PP-OCRv2_det](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar)| 3MB | OCRv2的检测模型 | PADDLE | [Release/2.6](https://github.com/PaddlePaddle/PaddleOCR) | +| [ch_PP-OCRv2_rec](https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_rec_infer) | 8.5MB | OCRv2的识别模型 | PADDLE | [Release/2.6](https://github.com/PaddlePaddle/PaddleOCR) | -目前FastDeploy支持如下模型的部署 - -- [PaddleOCRv3系列模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/doc/doc_ch/models_list.md) - -## 准备PaddleOCRv3部署模型 -用户在[PP-OCR系列模型列表](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/doc/doc_ch/models_list.md)下载相应的的OCRv3系列推理模型即可. ## 详细部署文档