更换文档检测模型
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185
paddle_detection/deploy/lite/src/preprocess_op.cc
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185
paddle_detection/deploy/lite/src/preprocess_op.cc
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// Copyright (c) 2021 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 <string>
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#include <thread>
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#include <vector>
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#include "include/preprocess_op.h"
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namespace PaddleDetection {
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void InitInfo::Run(cv::Mat* im, ImageBlob* data) {
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data->im_shape_ = {static_cast<float>(im->rows),
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static_cast<float>(im->cols)};
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data->scale_factor_ = {1., 1.};
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data->in_net_shape_ = {static_cast<float>(im->rows),
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static_cast<float>(im->cols)};
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}
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void NormalizeImage::Run(cv::Mat* im, ImageBlob* data) {
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double e = 1.0;
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if (is_scale_) {
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e *= 1./255.0;
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}
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(*im).convertTo(*im, CV_32FC3, e);
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for (int h = 0; h < im->rows; h++) {
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for (int w = 0; w < im->cols; w++) {
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im->at<cv::Vec3f>(h, w)[0] =
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(im->at<cv::Vec3f>(h, w)[0] - mean_[0]) / scale_[0];
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im->at<cv::Vec3f>(h, w)[1] =
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(im->at<cv::Vec3f>(h, w)[1] - mean_[1]) / scale_[1];
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im->at<cv::Vec3f>(h, w)[2] =
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(im->at<cv::Vec3f>(h, w)[2] - mean_[2]) / scale_[2];
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}
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}
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}
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void Permute::Run(cv::Mat* im, ImageBlob* data) {
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(*im).convertTo(*im, CV_32FC3);
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int rh = im->rows;
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int rw = im->cols;
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int rc = im->channels();
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(data->im_data_).resize(rc * rh * rw);
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float* base = (data->im_data_).data();
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for (int i = 0; i < rc; ++i) {
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cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i);
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}
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}
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void Resize::Run(cv::Mat* im, ImageBlob* data) {
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auto resize_scale = GenerateScale(*im);
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data->im_shape_ = {static_cast<float>(im->cols * resize_scale.first),
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static_cast<float>(im->rows * resize_scale.second)};
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data->in_net_shape_ = {static_cast<float>(im->cols * resize_scale.first),
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static_cast<float>(im->rows * resize_scale.second)};
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cv::resize(
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*im, *im, cv::Size(), resize_scale.first, resize_scale.second, interp_);
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data->im_shape_ = {
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static_cast<float>(im->rows), static_cast<float>(im->cols),
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};
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data->scale_factor_ = {
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resize_scale.second, resize_scale.first,
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};
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}
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std::pair<float, float> Resize::GenerateScale(const cv::Mat& im) {
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std::pair<float, float> resize_scale;
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int origin_w = im.cols;
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int origin_h = im.rows;
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if (keep_ratio_) {
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int im_size_max = std::max(origin_w, origin_h);
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int im_size_min = std::min(origin_w, origin_h);
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int target_size_max =
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*std::max_element(target_size_.begin(), target_size_.end());
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int target_size_min =
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*std::min_element(target_size_.begin(), target_size_.end());
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float scale_min =
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static_cast<float>(target_size_min) / static_cast<float>(im_size_min);
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float scale_max =
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static_cast<float>(target_size_max) / static_cast<float>(im_size_max);
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float scale_ratio = std::min(scale_min, scale_max);
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resize_scale = {scale_ratio, scale_ratio};
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} else {
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resize_scale.first =
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static_cast<float>(target_size_[1]) / static_cast<float>(origin_w);
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resize_scale.second =
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static_cast<float>(target_size_[0]) / static_cast<float>(origin_h);
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}
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return resize_scale;
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}
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void PadStride::Run(cv::Mat* im, ImageBlob* data) {
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if (stride_ <= 0) {
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return;
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}
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int rc = im->channels();
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int rh = im->rows;
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int rw = im->cols;
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int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_;
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int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_;
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cv::copyMakeBorder(
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*im, *im, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT, cv::Scalar(0));
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data->in_net_shape_ = {
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static_cast<float>(im->rows), static_cast<float>(im->cols),
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};
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}
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void TopDownEvalAffine::Run(cv::Mat* im, ImageBlob* data) {
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cv::resize(*im, *im, cv::Size(trainsize_[0], trainsize_[1]), 0, 0, interp_);
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// todo: Simd::ResizeBilinear();
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data->in_net_shape_ = {
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static_cast<float>(trainsize_[1]), static_cast<float>(trainsize_[0]),
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};
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}
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// Preprocessor op running order
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const std::vector<std::string> Preprocessor::RUN_ORDER = {"InitInfo",
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"TopDownEvalAffine",
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"Resize",
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"NormalizeImage",
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"PadStride",
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"Permute"};
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void Preprocessor::Run(cv::Mat* im, ImageBlob* data) {
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for (const auto& name : RUN_ORDER) {
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if (ops_.find(name) != ops_.end()) {
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ops_[name]->Run(im, data);
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}
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}
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}
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void CropImg(cv::Mat& img,
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cv::Mat& crop_img,
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std::vector<int>& area,
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std::vector<float>& center,
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std::vector<float>& scale,
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float expandratio) {
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int crop_x1 = std::max(0, area[0]);
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int crop_y1 = std::max(0, area[1]);
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int crop_x2 = std::min(img.cols - 1, area[2]);
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int crop_y2 = std::min(img.rows - 1, area[3]);
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int center_x = (crop_x1 + crop_x2) / 2.;
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int center_y = (crop_y1 + crop_y2) / 2.;
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int half_h = (crop_y2 - crop_y1) / 2.;
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int half_w = (crop_x2 - crop_x1) / 2.;
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if (half_h * 3 > half_w * 4) {
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half_w = static_cast<int>(half_h * 0.75);
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} else {
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half_h = static_cast<int>(half_w * 4 / 3);
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}
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crop_x1 =
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std::max(0, center_x - static_cast<int>(half_w * (1 + expandratio)));
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crop_y1 =
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std::max(0, center_y - static_cast<int>(half_h * (1 + expandratio)));
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crop_x2 = std::min(img.cols - 1,
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static_cast<int>(center_x + half_w * (1 + expandratio)));
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crop_y2 = std::min(img.rows - 1,
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static_cast<int>(center_y + half_h * (1 + expandratio)));
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crop_img =
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img(cv::Range(crop_y1, crop_y2 + 1), cv::Range(crop_x1, crop_x2 + 1));
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center.clear();
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center.emplace_back((crop_x1 + crop_x2) / 2);
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center.emplace_back((crop_y1 + crop_y2) / 2);
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scale.clear();
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scale.emplace_back((crop_x2 - crop_x1));
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scale.emplace_back((crop_y2 - crop_y1));
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}
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} // namespace PaddleDetection
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