From 1210dbf55f9fbd96dddfc21d40af2d7cd3f65ffa Mon Sep 17 00:00:00 2001 From: tink2123 Date: Fri, 8 Apr 2022 07:34:53 +0000 Subject: [PATCH] add err no --- deploy/pdserving/general_detection_op.cpp | 367 ++++++++++++++++++++++ deploy/pdserving/pipeline_http_client.py | 19 +- deploy/pdserving/web_service.py | 4 +- 3 files changed, 382 insertions(+), 8 deletions(-) create mode 100644 deploy/pdserving/general_detection_op.cpp diff --git a/deploy/pdserving/general_detection_op.cpp b/deploy/pdserving/general_detection_op.cpp new file mode 100644 index 00000000..7d918295 --- /dev/null +++ b/deploy/pdserving/general_detection_op.cpp @@ -0,0 +1,367 @@ +// 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 "core/general-server/op/general_detection_op.h" +#include "core/predictor/framework/infer.h" +#include "core/predictor/framework/memory.h" +#include "core/predictor/framework/resource.h" +#include "core/util/include/timer.h" +#include +#include +#include +#include + +/* +#include "opencv2/imgcodecs/legacy/constants_c.h" +#include "opencv2/imgproc/types_c.h" +*/ + +namespace baidu { +namespace paddle_serving { +namespace serving { + +using baidu::paddle_serving::Timer; +using baidu::paddle_serving::predictor::MempoolWrapper; +using baidu::paddle_serving::predictor::general_model::Tensor; +using baidu::paddle_serving::predictor::general_model::Response; +using baidu::paddle_serving::predictor::general_model::Request; +using baidu::paddle_serving::predictor::InferManager; +using baidu::paddle_serving::predictor::PaddleGeneralModelConfig; + +int GeneralDetectionOp::inference() { + VLOG(2) << "Going to run inference"; + const std::vector pre_node_names = pre_names(); + if (pre_node_names.size() != 1) { + LOG(ERROR) << "This op(" << op_name() + << ") can only have one predecessor op, but received " + << pre_node_names.size(); + return -1; + } + const std::string pre_name = pre_node_names[0]; + + const GeneralBlob *input_blob = get_depend_argument(pre_name); + if (!input_blob) { + LOG(ERROR) << "input_blob is nullptr,error"; + return -1; + } + uint64_t log_id = input_blob->GetLogId(); + VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name; + + GeneralBlob *output_blob = mutable_data(); + if (!output_blob) { + LOG(ERROR) << "output_blob is nullptr,error"; + return -1; + } + output_blob->SetLogId(log_id); + + if (!input_blob) { + LOG(ERROR) << "(logid=" << log_id + << ") Failed mutable depended argument, op:" << pre_name; + return -1; + } + + const TensorVector *in = &input_blob->tensor_vector; + TensorVector *out = &output_blob->tensor_vector; + + int batch_size = input_blob->_batch_size; + VLOG(2) << "(logid=" << log_id << ") input batch size: " << batch_size; + + output_blob->_batch_size = batch_size; + + std::vector input_shape; + int in_num = 0; + void *databuf_data = NULL; + char *databuf_char = NULL; + size_t databuf_size = 0; + // now only support single string + char *total_input_ptr = static_cast(in->at(0).data.data()); + std::string base64str = total_input_ptr; + + float ratio_h{}; + float ratio_w{}; + + cv::Mat img = Base2Mat(base64str); + cv::Mat srcimg; + cv::Mat resize_img; + + cv::Mat resize_img_rec; + cv::Mat crop_img; + img.copyTo(srcimg); + + this->resize_op_.Run(img, resize_img, this->max_side_len_, ratio_h, ratio_w, + this->use_tensorrt_); + + this->normalize_op_.Run(&resize_img, this->mean_det, this->scale_det, + this->is_scale_); + + std::vector input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f); + this->permute_op_.Run(&resize_img, input.data()); + + TensorVector *real_in = new TensorVector(); + if (!real_in) { + LOG(ERROR) << "real_in is nullptr,error"; + return -1; + } + + for (int i = 0; i < in->size(); ++i) { + input_shape = {1, 3, resize_img.rows, resize_img.cols}; + in_num = std::accumulate(input_shape.begin(), input_shape.end(), 1, + std::multiplies()); + databuf_size = in_num * sizeof(float); + databuf_data = MempoolWrapper::instance().malloc(databuf_size); + if (!databuf_data) { + LOG(ERROR) << "Malloc failed, size: " << databuf_size; + return -1; + } + memcpy(databuf_data, input.data(), databuf_size); + databuf_char = reinterpret_cast(databuf_data); + paddle::PaddleBuf paddleBuf(databuf_char, databuf_size); + paddle::PaddleTensor tensor_in; + tensor_in.name = in->at(i).name; + tensor_in.dtype = paddle::PaddleDType::FLOAT32; + tensor_in.shape = {1, 3, resize_img.rows, resize_img.cols}; + tensor_in.lod = in->at(i).lod; + tensor_in.data = paddleBuf; + real_in->push_back(tensor_in); + } + + Timer timeline; + int64_t start = timeline.TimeStampUS(); + timeline.Start(); + + if (InferManager::instance().infer(engine_name().c_str(), real_in, out, + batch_size)) { + LOG(ERROR) << "(logid=" << log_id + << ") Failed do infer in fluid model: " << engine_name().c_str(); + return -1; + } + delete real_in; + + std::vector output_shape; + int out_num = 0; + void *databuf_data_out = NULL; + char *databuf_char_out = NULL; + size_t databuf_size_out = 0; + // this is special add for PaddleOCR postprecess + int infer_outnum = out->size(); + for (int k = 0; k < infer_outnum; ++k) { + int n2 = out->at(k).shape[2]; + int n3 = out->at(k).shape[3]; + int n = n2 * n3; + + float *out_data = static_cast(out->at(k).data.data()); + std::vector pred(n, 0.0); + std::vector cbuf(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.data()); + cv::Mat pred_map(n2, n3, CV_32F, (float *)pred.data()); + + const double threshold = this->det_db_thresh_ * 255; + const double maxvalue = 255; + cv::Mat bit_map; + cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY); + cv::Mat dilation_map; + cv::Mat dila_ele = + cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2)); + cv::dilate(bit_map, dilation_map, dila_ele); + boxes = post_processor_.BoxesFromBitmap(pred_map, dilation_map, + this->det_db_box_thresh_, + this->det_db_unclip_ratio_); + + boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg); + + float max_wh_ratio = 0.0f; + std::vector crop_imgs; + std::vector resize_imgs; + int max_resize_w = 0; + int max_resize_h = 0; + int box_num = boxes.size(); + std::vector> output_rec; + for (int i = 0; i < box_num; ++i) { + cv::Mat line_img = GetRotateCropImage(img, boxes[i]); + float wh_ratio = float(line_img.cols) / float(line_img.rows); + max_wh_ratio = max_wh_ratio > wh_ratio ? max_wh_ratio : wh_ratio; + crop_imgs.push_back(line_img); + } + + for (int i = 0; i < box_num; ++i) { + cv::Mat resize_img; + crop_img = crop_imgs[i]; + this->resize_op_rec.Run(crop_img, resize_img, max_wh_ratio, + this->use_tensorrt_); + + this->normalize_op_.Run(&resize_img, this->mean_rec, this->scale_rec, + this->is_scale_); + + max_resize_w = std::max(max_resize_w, resize_img.cols); + max_resize_h = std::max(max_resize_h, resize_img.rows); + resize_imgs.push_back(resize_img); + } + int buf_size = 3 * max_resize_h * max_resize_w; + output_rec = std::vector>( + box_num, std::vector(buf_size, 0.0f)); + for (int i = 0; i < box_num; ++i) { + resize_img_rec = resize_imgs[i]; + + this->permute_op_.Run(&resize_img_rec, output_rec[i].data()); + } + + // Inference. + output_shape = {box_num, 3, max_resize_h, max_resize_w}; + out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1, + std::multiplies()); + databuf_size_out = out_num * sizeof(float); + databuf_data_out = MempoolWrapper::instance().malloc(databuf_size_out); + if (!databuf_data_out) { + LOG(ERROR) << "Malloc failed, size: " << databuf_size_out; + return -1; + } + int offset = buf_size * sizeof(float); + for (int i = 0; i < box_num; ++i) { + memcpy(databuf_data_out + i * offset, output_rec[i].data(), offset); + } + databuf_char_out = reinterpret_cast(databuf_data_out); + paddle::PaddleBuf paddleBuf(databuf_char_out, databuf_size_out); + paddle::PaddleTensor tensor_out; + tensor_out.name = "x"; + tensor_out.dtype = paddle::PaddleDType::FLOAT32; + tensor_out.shape = output_shape; + tensor_out.data = paddleBuf; + out->push_back(tensor_out); + } + out->erase(out->begin(), out->begin() + infer_outnum); + + int64_t end = timeline.TimeStampUS(); + CopyBlobInfo(input_blob, output_blob); + AddBlobInfo(output_blob, start); + AddBlobInfo(output_blob, end); + return 0; +} + +cv::Mat GeneralDetectionOp::Base2Mat(std::string &base64_data) { + cv::Mat img; + std::string s_mat; + s_mat = base64Decode(base64_data.data(), base64_data.size()); + std::vector base64_img(s_mat.begin(), s_mat.end()); + img = cv::imdecode(base64_img, cv::IMREAD_COLOR); // CV_LOAD_IMAGE_COLOR + return img; +} + +std::string GeneralDetectionOp::base64Decode(const char *Data, int DataByte) { + const char DecodeTable[] = { + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, + 62, // '+' + 0, 0, 0, + 63, // '/' + 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, // '0'-'9' + 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, // 'A'-'Z' + 0, 0, 0, 0, 0, 0, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, + 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, // 'a'-'z' + }; + + std::string strDecode; + int nValue; + int i = 0; + while (i < DataByte) { + if (*Data != '\r' && *Data != '\n') { + nValue = DecodeTable[*Data++] << 18; + nValue += DecodeTable[*Data++] << 12; + strDecode += (nValue & 0x00FF0000) >> 16; + if (*Data != '=') { + nValue += DecodeTable[*Data++] << 6; + strDecode += (nValue & 0x0000FF00) >> 8; + if (*Data != '=') { + nValue += DecodeTable[*Data++]; + strDecode += nValue & 0x000000FF; + } + } + i += 4; + } else // 回车换行,跳过 + { + Data++; + i++; + } + } + return strDecode; +} + +cv::Mat +GeneralDetectionOp::GetRotateCropImage(const cv::Mat &srcimage, + std::vector> box) { + cv::Mat image; + srcimage.copyTo(image); + std::vector> points = box; + + int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]}; + int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]}; + int left = int(*std::min_element(x_collect, x_collect + 4)); + int right = int(*std::max_element(x_collect, x_collect + 4)); + int top = int(*std::min_element(y_collect, y_collect + 4)); + int bottom = int(*std::max_element(y_collect, y_collect + 4)); + + cv::Mat img_crop; + image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop); + + for (int i = 0; i < points.size(); i++) { + points[i][0] -= left; + points[i][1] -= top; + } + + int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) + + pow(points[0][1] - points[1][1], 2))); + int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) + + pow(points[0][1] - points[3][1], 2))); + + cv::Point2f pts_std[4]; + pts_std[0] = cv::Point2f(0., 0.); + pts_std[1] = cv::Point2f(img_crop_width, 0.); + pts_std[2] = cv::Point2f(img_crop_width, img_crop_height); + pts_std[3] = cv::Point2f(0.f, img_crop_height); + + cv::Point2f pointsf[4]; + pointsf[0] = cv::Point2f(points[0][0], points[0][1]); + pointsf[1] = cv::Point2f(points[1][0], points[1][1]); + pointsf[2] = cv::Point2f(points[2][0], points[2][1]); + pointsf[3] = cv::Point2f(points[3][0], points[3][1]); + + cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std); + + cv::Mat dst_img; + cv::warpPerspective(img_crop, dst_img, M, + cv::Size(img_crop_width, img_crop_height), + cv::BORDER_REPLICATE); + + if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) { + cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth()); + cv::transpose(dst_img, srcCopy); + cv::flip(srcCopy, srcCopy, 0); + return srcCopy; + } else { + return dst_img; + } +} + +DEFINE_OP(GeneralDetectionOp); + +} // namespace serving +} // namespace paddle_serving +} // namespace baidu diff --git a/deploy/pdserving/pipeline_http_client.py b/deploy/pdserving/pipeline_http_client.py index a0ade26c..e49174f1 100644 --- a/deploy/pdserving/pipeline_http_client.py +++ b/deploy/pdserving/pipeline_http_client.py @@ -34,16 +34,23 @@ test_img_dir = args.image_dir for idx, img_file in enumerate(os.listdir(test_img_dir)): with open(os.path.join(test_img_dir, img_file), 'rb') as file: image_data1 = file.read() + # print file name print('{}{}{}'.format('*' * 10, img_file, '*' * 10)) image = cv2_to_base64(image_data1) data = {"key": ["image"], "value": [image]} r = requests.post(url=url, data=json.dumps(data)) - all_result = r.json()["value"][0] - for item in eval(all_result): - print(item) - #print("len result:", len(result)) - #print(eval(result[0])) - + result = r.json() + print("erro_no:{}, err_msg:{}".format(result["err_no"], result["err_msg"])) + # check success + if result["err_no"] == 0: + ocr_result = result["value"][0] + for item in eval(ocr_result): + # return transcription and points + print("{}, {}".format(item[0], item[1])) + else: + print( + "For details about error message, see PipelineServingLogs/pipeline.log" + ) print("==> total number of test imgs: ", len(os.listdir(test_img_dir))) diff --git a/deploy/pdserving/web_service.py b/deploy/pdserving/web_service.py index e6b13fcc..07fd6102 100644 --- a/deploy/pdserving/web_service.py +++ b/deploy/pdserving/web_service.py @@ -139,13 +139,13 @@ class RecOp(Op): rec_batch_res = self.ocr_reader.postprocess( fetch_data, with_score=True) for res in rec_batch_res: - rec_list.append(res[0]) + rec_list.append(res) elif isinstance(fetch_data, list): for one_batch in fetch_data: one_batch_res = self.ocr_reader.postprocess( one_batch, with_score=True) for res in one_batch_res: - rec_list.append(res[0]) + rec_list.append(res) result_list = [] for i in range(dt_num): text = rec_list[i] -- GitLab