mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-06-03 21:53:39 +08:00
308 lines
9.2 KiB
C++
308 lines
9.2 KiB
C++
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// Copyright (c) 2020 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 <include/postprocess_op.h>
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namespace PaddleOCR {
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void PostProcessor::GetContourArea(float **box, float unclip_ratio,
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float &distance) {
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int pts_num = 4;
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float area = 0.0f;
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float dist = 0.0f;
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for (int i = 0; i < pts_num; i++) {
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area += box[i][0] * box[(i + 1) % pts_num][1] -
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box[i][1] * box[(i + 1) % pts_num][0];
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dist += sqrtf((box[i][0] - box[(i + 1) % pts_num][0]) *
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(box[i][0] - box[(i + 1) % pts_num][0]) +
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(box[i][1] - box[(i + 1) % pts_num][1]) *
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(box[i][1] - box[(i + 1) % pts_num][1]));
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}
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area = fabs(float(area / 2.0));
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distance = area * unclip_ratio / dist;
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}
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cv::RotatedRect PostProcessor::unclip(float **box) {
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float unclip_ratio = 2.0;
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float distance = 1.0;
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GetContourArea(box, unclip_ratio, distance);
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ClipperLib::ClipperOffset offset;
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ClipperLib::Path p;
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p << ClipperLib::IntPoint(int(box[0][0]), int(box[0][1]))
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<< ClipperLib::IntPoint(int(box[1][0]), int(box[1][1]))
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<< ClipperLib::IntPoint(int(box[2][0]), int(box[2][1]))
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<< ClipperLib::IntPoint(int(box[3][0]), int(box[3][1]));
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offset.AddPath(p, ClipperLib::jtRound, ClipperLib::etClosedPolygon);
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ClipperLib::Paths soln;
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offset.Execute(soln, distance);
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std::vector<cv::Point2f> points;
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for (int j = 0; j < soln.size(); j++) {
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for (int i = 0; i < soln[soln.size() - 1].size(); i++) {
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points.emplace_back(soln[j][i].X, soln[j][i].Y);
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}
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}
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cv::RotatedRect res = cv::minAreaRect(points);
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return res;
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}
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float **PostProcessor::Mat2Vec(cv::Mat mat) {
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auto **array = new float *[mat.rows];
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for (int i = 0; i < mat.rows; ++i)
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array[i] = new float[mat.cols];
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for (int i = 0; i < mat.rows; ++i) {
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for (int j = 0; j < mat.cols; ++j) {
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array[i][j] = mat.at<float>(i, j);
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}
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}
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return array;
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}
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void PostProcessor::quickSort(float **s, int l, int r) {
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if (l < r) {
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int i = l, j = r;
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float x = s[l][0];
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float *xp = s[l];
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while (i < j) {
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while (i < j && s[j][0] >= x)
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j--;
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if (i < j)
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std::swap(s[i++], s[j]);
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while (i < j && s[i][0] < x)
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i++;
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if (i < j)
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std::swap(s[j--], s[i]);
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}
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s[i] = xp;
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quickSort(s, l, i - 1);
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quickSort(s, i + 1, r);
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}
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}
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void PostProcessor::quickSort_vector(std::vector<std::vector<int>> &box, int l,
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int r, int axis) {
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if (l < r) {
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int i = l, j = r;
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int x = box[l][axis];
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std::vector<int> xp(box[l]);
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while (i < j) {
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while (i < j && box[j][axis] >= x)
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j--;
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if (i < j)
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std::swap(box[i++], box[j]);
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while (i < j && box[i][axis] < x)
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i++;
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if (i < j)
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std::swap(box[j--], box[i]);
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}
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box[i] = xp;
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quickSort_vector(box, l, i - 1, axis);
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quickSort_vector(box, i + 1, r, axis);
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}
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}
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std::vector<std::vector<int>>
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PostProcessor::order_points_clockwise(std::vector<std::vector<int>> pts) {
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std::vector<std::vector<int>> box = pts;
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quickSort_vector(box, 0, int(box.size() - 1), 0);
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std::vector<std::vector<int>> leftmost = {box[0], box[1]};
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std::vector<std::vector<int>> rightmost = {box[2], box[3]};
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if (leftmost[0][1] > leftmost[1][1])
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std::swap(leftmost[0], leftmost[1]);
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if (rightmost[0][1] > rightmost[1][1])
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std::swap(rightmost[0], rightmost[1]);
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std::vector<std::vector<int>> rect = {leftmost[0], rightmost[0], rightmost[1],
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leftmost[1]};
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return rect;
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}
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float **PostProcessor::get_mini_boxes(cv::RotatedRect box, float &ssid) {
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ssid = box.size.width >= box.size.height ? box.size.height : box.size.width;
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cv::Mat points;
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cv::boxPoints(box, points);
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// sorted box points
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auto array = Mat2Vec(points);
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quickSort(array, 0, 3);
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float *idx1 = array[0], *idx2 = array[1], *idx3 = array[2], *idx4 = array[3];
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if (array[3][1] <= array[2][1]) {
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idx2 = array[3];
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idx3 = array[2];
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} else {
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idx2 = array[2];
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idx3 = array[3];
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}
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if (array[1][1] <= array[0][1]) {
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idx1 = array[1];
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idx4 = array[0];
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} else {
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idx1 = array[0];
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idx4 = array[1];
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}
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array[0] = idx1;
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array[1] = idx2;
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array[2] = idx3;
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array[3] = idx4;
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return array;
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}
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float PostProcessor::box_score_fast(float **box_array, cv::Mat pred) {
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auto array = box_array;
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int width = pred.cols;
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int height = pred.rows;
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float box_x[4] = {array[0][0], array[1][0], array[2][0], array[3][0]};
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float box_y[4] = {array[0][1], array[1][1], array[2][1], array[3][1]};
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int xmin = clamp(int(std::floor(*(std::min_element(box_x, box_x + 4)))), 0,
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width - 1);
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int xmax = clamp(int(std::ceil(*(std::max_element(box_x, box_x + 4)))), 0,
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width - 1);
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int ymin = clamp(int(std::floor(*(std::min_element(box_y, box_y + 4)))), 0,
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height - 1);
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int ymax = clamp(int(std::ceil(*(std::max_element(box_y, box_y + 4)))), 0,
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height - 1);
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cv::Mat mask;
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mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
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cv::Point root_point[4];
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root_point[0] = cv::Point(int(array[0][0]) - xmin, int(array[0][1]) - ymin);
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root_point[1] = cv::Point(int(array[1][0]) - xmin, int(array[1][1]) - ymin);
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root_point[2] = cv::Point(int(array[2][0]) - xmin, int(array[2][1]) - ymin);
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root_point[3] = cv::Point(int(array[3][0]) - xmin, int(array[3][1]) - ymin);
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const cv::Point *ppt[1] = {root_point};
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int npt[] = {4};
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cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
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cv::Mat croppedImg;
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pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1))
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.copyTo(croppedImg);
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auto score = cv::mean(croppedImg, mask)[0];
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return score;
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}
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std::vector<std::vector<std::vector<int>>>
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PostProcessor::boxes_from_bitmap(const cv::Mat pred, const cv::Mat bitmap) {
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const int min_size = 3;
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const int max_candidates = 1000;
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const float box_thresh = 0.5;
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int width = bitmap.cols;
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int height = bitmap.rows;
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std::vector<std::vector<cv::Point>> contours;
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std::vector<cv::Vec4i> hierarchy;
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cv::findContours(bitmap, contours, hierarchy, cv::RETR_LIST,
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cv::CHAIN_APPROX_SIMPLE);
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int num_contours =
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contours.size() >= max_candidates ? max_candidates : contours.size();
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std::vector<std::vector<std::vector<int>>> boxes;
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for (int _i = 0; _i < num_contours; _i++) {
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float ssid;
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cv::RotatedRect box = cv::minAreaRect(contours[_i]);
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auto array = get_mini_boxes(box, ssid);
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auto box_for_unclip = array;
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// end get_mini_box
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if (ssid < min_size) {
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continue;
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}
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float score;
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score = box_score_fast(array, pred);
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// end box_score_fast
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if (score < box_thresh)
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continue;
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// start for unclip
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cv::RotatedRect points = unclip(box_for_unclip);
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// end for unclip
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cv::RotatedRect clipbox = points;
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auto cliparray = get_mini_boxes(clipbox, ssid);
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if (ssid < min_size + 2)
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continue;
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int dest_width = pred.cols;
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int dest_height = pred.rows;
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std::vector<std::vector<int>> intcliparray;
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for (int num_pt = 0; num_pt < 4; num_pt++) {
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std::vector<int> a{int(clampf(roundf(cliparray[num_pt][0] / float(width) *
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float(dest_width)),
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0, float(dest_width))),
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int(clampf(roundf(cliparray[num_pt][1] /
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float(height) * float(dest_height)),
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0, float(dest_height)))};
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intcliparray.push_back(a);
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}
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boxes.push_back(intcliparray);
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} // end for
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return boxes;
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}
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std::vector<std::vector<std::vector<int>>> PostProcessor::filter_tag_det_res(
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std::vector<std::vector<std::vector<int>>> boxes, float ratio_h,
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float ratio_w, cv::Mat srcimg) {
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int oriimg_h = srcimg.rows;
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int oriimg_w = srcimg.cols;
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std::vector<std::vector<std::vector<int>>> root_points;
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for (int n = 0; n < boxes.size(); n++) {
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boxes[n] = order_points_clockwise(boxes[n]);
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for (int m = 0; m < boxes[0].size(); m++) {
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boxes[n][m][0] /= ratio_w;
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boxes[n][m][1] /= ratio_h;
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boxes[n][m][0] = int(_min(_max(boxes[n][m][0], 0), oriimg_w - 1));
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boxes[n][m][1] = int(_min(_max(boxes[n][m][1], 0), oriimg_h - 1));
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}
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}
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for (int n = 0; n < boxes.size(); n++) {
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int rect_width, rect_height;
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rect_width = int(sqrt(pow(boxes[n][0][0] - boxes[n][1][0], 2) +
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pow(boxes[n][0][1] - boxes[n][1][1], 2)));
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rect_height = int(sqrt(pow(boxes[n][0][0] - boxes[n][3][0], 2) +
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pow(boxes[n][0][1] - boxes[n][3][1], 2)));
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if (rect_width <= 10 || rect_height <= 10)
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continue;
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root_points.push_back(boxes[n]);
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}
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return root_points;
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}
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} // namespace PaddleOCR
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