mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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204 lines
7.1 KiB
C++
204 lines
7.1 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/vision/facedet/contrib/ultraface.h"
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#include "fastdeploy/utils/perf.h"
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#include "fastdeploy/vision/utils/utils.h"
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namespace fastdeploy {
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namespace vision {
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namespace facedet {
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UltraFace::UltraFace(const std::string& model_file,
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const std::string& params_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format) {
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if (model_format == ModelFormat::ONNX) {
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valid_cpu_backends = {Backend::ORT};
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valid_gpu_backends = {Backend::ORT, Backend::TRT};
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} else {
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valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
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valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
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}
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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runtime_option.params_file = params_file;
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initialized = Initialize();
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}
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bool UltraFace::Initialize() {
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// parameters for preprocess
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size = {320, 240};
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if (!InitRuntime()) {
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FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
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return false;
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}
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// Check if the input shape is dynamic after Runtime already initialized,
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is_dynamic_input_ = false;
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auto shape = InputInfoOfRuntime(0).shape;
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for (int i = 0; i < shape.size(); ++i) {
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// if height or width is dynamic
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if (i >= 2 && shape[i] <= 0) {
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is_dynamic_input_ = true;
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break;
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}
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}
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return true;
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}
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bool UltraFace::Preprocess(
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Mat* mat, FDTensor* output,
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std::map<std::string, std::array<float, 2>>* im_info) {
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// ultraface's preprocess steps
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// 1. resize
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// 2. BGR->RGB
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// 3. HWC->CHW
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int resize_w = size[0];
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int resize_h = size[1];
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if (resize_h != mat->Height() || resize_w != mat->Width()) {
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Resize::Run(mat, resize_w, resize_h);
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}
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BGR2RGB::Run(mat);
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// Compute `result = mat * alpha + beta` directly by channel
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// Reference: detect_imgs_onnx.py#L73
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std::vector<float> alpha = {1.0f / 128.0f, 1.0f / 128.0f, 1.0f / 128.0f};
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std::vector<float> beta = {-127.0f * (1.0f / 128.0f),
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-127.0f * (1.0f / 128.0f),
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-127.0f * (1.0f / 128.0f)}; // RGB;
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Convert::Run(mat, alpha, beta);
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// Record output shape of preprocessed image
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(*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
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static_cast<float>(mat->Width())};
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HWC2CHW::Run(mat);
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Cast::Run(mat, "float");
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mat->ShareWithTensor(output);
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output->shape.insert(output->shape.begin(), 1); // reshape to n, h, w, c
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return true;
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}
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bool UltraFace::Postprocess(
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std::vector<FDTensor>& infer_result, FaceDetectionResult* result,
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const std::map<std::string, std::array<float, 2>>& im_info,
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float conf_threshold, float nms_iou_threshold) {
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// ultraface has 2 output tensors, scores & boxes
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FDASSERT(
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(infer_result.size() == 2),
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"The default number of output tensor must be 2 according to ultraface.");
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FDTensor& scores_tensor = infer_result.at(0); // (1,4420,2)
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FDTensor& boxes_tensor = infer_result.at(1); // (1,4420,4)
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FDASSERT((scores_tensor.shape[0] == 1), "Only support batch =1 now.");
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FDASSERT((boxes_tensor.shape[0] == 1), "Only support batch =1 now.");
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if (scores_tensor.dtype != FDDataType::FP32) {
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FDERROR << "Only support post process with float32 data." << std::endl;
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return false;
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}
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if (boxes_tensor.dtype != FDDataType::FP32) {
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FDERROR << "Only support post process with float32 data." << std::endl;
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return false;
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}
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result->Clear();
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// must be setup landmarks_per_face before reserve.
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// ultraface detector does not detect landmarks by default.
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result->landmarks_per_face = 0;
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result->Reserve(boxes_tensor.shape[1]);
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float* scores_ptr = static_cast<float*>(scores_tensor.Data());
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float* boxes_ptr = static_cast<float*>(boxes_tensor.Data());
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const size_t num_bboxes = boxes_tensor.shape[1]; // e.g 4420
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// fetch original image shape
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auto iter_ipt = im_info.find("input_shape");
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FDASSERT((iter_ipt != im_info.end()),
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"Cannot find input_shape from im_info.");
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float ipt_h = iter_ipt->second[0];
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float ipt_w = iter_ipt->second[1];
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// decode bounding boxes
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for (size_t i = 0; i < num_bboxes; ++i) {
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float confidence = scores_ptr[2 * i + 1];
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// filter boxes by conf_threshold
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if (confidence <= conf_threshold) {
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continue;
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}
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float x1 = boxes_ptr[4 * i + 0] * ipt_w;
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float y1 = boxes_ptr[4 * i + 1] * ipt_h;
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float x2 = boxes_ptr[4 * i + 2] * ipt_w;
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float y2 = boxes_ptr[4 * i + 3] * ipt_h;
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result->boxes.emplace_back(std::array<float, 4>{x1, y1, x2, y2});
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result->scores.push_back(confidence);
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}
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if (result->boxes.size() == 0) {
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return true;
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}
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utils::NMS(result, nms_iou_threshold);
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// scale and clip box
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for (size_t i = 0; i < result->boxes.size(); ++i) {
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result->boxes[i][0] = std::max(result->boxes[i][0], 0.0f);
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result->boxes[i][1] = std::max(result->boxes[i][1], 0.0f);
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result->boxes[i][2] = std::max(result->boxes[i][2], 0.0f);
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result->boxes[i][3] = std::max(result->boxes[i][3], 0.0f);
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result->boxes[i][0] = std::min(result->boxes[i][0], ipt_w - 1.0f);
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result->boxes[i][1] = std::min(result->boxes[i][1], ipt_h - 1.0f);
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result->boxes[i][2] = std::min(result->boxes[i][2], ipt_w - 1.0f);
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result->boxes[i][3] = std::min(result->boxes[i][3], ipt_h - 1.0f);
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}
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return true;
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}
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bool UltraFace::Predict(cv::Mat* im, FaceDetectionResult* result,
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float conf_threshold, float nms_iou_threshold) {
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Mat mat(*im);
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std::vector<FDTensor> input_tensors(1);
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std::map<std::string, std::array<float, 2>> im_info;
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// Record the shape of image and the shape of preprocessed image
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im_info["input_shape"] = {static_cast<float>(mat.Height()),
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static_cast<float>(mat.Width())};
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im_info["output_shape"] = {static_cast<float>(mat.Height()),
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static_cast<float>(mat.Width())};
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if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
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FDERROR << "Failed to preprocess input image." << std::endl;
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return false;
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}
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input_tensors[0].name = InputInfoOfRuntime(0).name;
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std::vector<FDTensor> output_tensors;
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if (!Infer(input_tensors, &output_tensors)) {
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FDERROR << "Failed to inference." << std::endl;
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return false;
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}
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if (!Postprocess(output_tensors, result, im_info, conf_threshold,
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nms_iou_threshold)) {
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FDERROR << "Failed to post process." << std::endl;
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return false;
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}
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return true;
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}
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} // namespace facedet
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} // namespace vision
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} // namespace fastdeploy
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