mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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d3845eb4e1
* avoid mem copy for cpp benchmark * set CMAKE_BUILD_TYPE to Release * Add SegmentationDiff * change pointer to reference * fixed bug * cast uint8 to int32 * Add diff compare for OCR * Add diff compare for OCR * rm ppocr pipeline * Add yolov5 diff compare * Add yolov5 diff compare * deal with comments * deal with comments * fixed bug * fixed bug
126 lines
4.8 KiB
C++
Executable File
126 lines
4.8 KiB
C++
Executable File
// 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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#pragma once
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#include <opencv2/opencv.hpp>
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#include <set>
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#include <vector>
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#include "fastdeploy/core/fd_tensor.h"
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#include "fastdeploy/utils/utils.h"
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#include "fastdeploy/vision/common/result.h"
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// #include "unsupported/Eigen/CXX11/Tensor"
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#include "fastdeploy/function/reduce.h"
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#include "fastdeploy/function/softmax.h"
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#include "fastdeploy/function/transpose.h"
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#include "fastdeploy/vision/common/processors/mat.h"
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namespace fastdeploy {
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namespace vision {
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namespace utils {
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// topk sometimes is a very small value
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// so this implementation is simple but I don't think it will
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// cost too much time
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// Also there may be cause problem since we suppose the minimum value is
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// -99999999
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// Do not use this function on array which topk contains value less than
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// -99999999
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template <typename T>
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std::vector<int32_t> TopKIndices(const T* array, int array_size, int topk) {
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topk = std::min(array_size, topk);
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std::vector<int32_t> res(topk);
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std::set<int32_t> searched;
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for (int32_t i = 0; i < topk; ++i) {
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T min = static_cast<T>(-99999999);
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for (int32_t j = 0; j < array_size; ++j) {
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if (searched.find(j) != searched.end()) {
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continue;
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}
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if (*(array + j) > min) {
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res[i] = j;
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min = *(array + j);
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}
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}
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searched.insert(res[i]);
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}
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return res;
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}
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void NMS(DetectionResult* output, float iou_threshold = 0.5,
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std::vector<int>* index = nullptr);
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void NMS(FaceDetectionResult* result, float iou_threshold = 0.5);
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/// Sort DetectionResult/FaceDetectionResult by score
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FASTDEPLOY_DECL void SortDetectionResult(DetectionResult* result);
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FASTDEPLOY_DECL void SortDetectionResult(FaceDetectionResult* result);
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/// Lex Sort DetectionResult by x(w) & y(h) axis
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FASTDEPLOY_DECL void LexSortDetectionResultByXY(DetectionResult* result);
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/// Lex Sort OCRDet Result by x(w) & y(h) axis
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FASTDEPLOY_DECL void LexSortOCRDetResultByXY(
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std::vector<std::array<int, 8>>* result);
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/// L2 Norm / cosine similarity (for face recognition, ...)
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FASTDEPLOY_DECL std::vector<float>
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L2Normalize(const std::vector<float>& values);
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FASTDEPLOY_DECL float CosineSimilarity(const std::vector<float>& a,
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const std::vector<float>& b,
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bool normalized = true);
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/** \brief Do face align for model with five points.
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*
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* \param[in] image The original image
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* \param[in] result FaceDetectionResult
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* \param[in] std_landmarks Standard face template
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* \param[in] output_size The size of output mat
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*/
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FASTDEPLOY_DECL std::vector<cv::Mat> AlignFaceWithFivePoints(
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cv::Mat& image, FaceDetectionResult& result,
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std::vector<std::array<float, 2>> std_landmarks = {{38.2946f, 51.6963f},
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{73.5318f, 51.5014f},
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{56.0252f, 71.7366f},
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{41.5493f, 92.3655f},
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{70.7299f, 92.2041f}},
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std::array<int, 2> output_size = {112, 112});
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bool CropImageByBox(Mat& src_im, Mat* dst_im, const std::vector<float>& box,
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std::vector<float>* center, std::vector<float>* scale,
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const float expandratio = 0.3);
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/**
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* Function: for keypoint detection model, fine positioning of keypoints in
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* postprocess
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* Parameters:
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* heatmap: model inference results for keypoint detection models
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* dim: shape information of the inference result
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* coords: coordinates after refined positioning
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* px: px = int(coords[ch * 2] + 0.5) , refer to API detection::GetFinalPredictions
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* py: px = int(coords[ch * 2 + 1] + 0.5), refer to API detection::GetFinalPredictions
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* index: index information of heatmap pixels
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* ch: channel
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* Paper reference: DARK postpocessing, Zhang et al.
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* Distribution-Aware Coordinate Representation for Human Pose Estimation (CVPR
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* 2020).
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*/
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void DarkParse(const std::vector<float>& heatmap, const std::vector<int>& dim,
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std::vector<float>* coords, const int px, const int py,
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const int index, const int ch);
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} // namespace utils
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} // namespace vision
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} // namespace fastdeploy
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