upload lite demo and clang-fomat

.clang_format.hook

+#!/bin/bash
+set -e
+
+readonly VERSION="3.8"
+
+version=$(clang-format -version)
+
+if ! [[ $version == *"$VERSION"* ]]; then
+    echo "clang-format version check failed."
+    echo "a version contains '$VERSION' is needed, but get '$version'"
+    echo "you can install the right version, and make an soft-link to '\$PATH' env"
+    exit -1
+fi
+
+clang-format $@

deploy/lite/Makefile

@@ -40,12 +40,28 @@ CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SY
 
 #CXX_LIBS = $(LITE_ROOT)/cxx/lib/libpaddle_api_light_bundled.a $(SYSTEM_LIBS)
 
-ocr_db_crnn: fetch_opencv ocr_db_crnn.o
-	$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) ocr_db_crnn.o -o ocr_db_crnn  $(CXX_LIBS) $(LDFLAGS)
+ocr_db_crnn: fetch_opencv ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o
+	$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o  -o ocr_db_crnn  $(CXX_LIBS) $(LDFLAGS)
 
 ocr_db_crnn.o: ocr_db_crnn.cc
 	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o ocr_db_crnn.o -c ocr_db_crnn.cc
 
+crnn_process.o: fetch_opencv crnn_process.cc
+	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o crnn_process.o -c crnn_process.cc
+
+db_post_process.o: fetch_clipper fetch_opencv db_post_process.cc
+	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o db_post_process.o -c db_post_process.cc
+
+clipper.o: fetch_clipper
+	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o clipper.o -c clipper.cpp
+
+fetch_clipper:
+	@test -e clipper.hpp || \
+        ( echo "Fetch clipper " && \
+        wget -c https://paddle-inference-dist.cdn.bcebos.com/PaddleLite/Clipper/clipper.hpp)
+	@ test -e clipper.cpp || \
+        wget -c https://paddle-inference-dist.cdn.bcebos.com/PaddleLite/Clipper/clipper.cpp
+
 fetch_opencv:
 	@ test -d ${THIRD_PARTY_DIR} ||  mkdir ${THIRD_PARTY_DIR}
 	@ test -e ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz || \
@@ -57,5 +73,5 @@ fetch_opencv:
 
 .PHONY: clean
 clean:
-	rm -f ocr_db_crnn.o
-	rm -f ocr_db_crnnn
+	rm -f ocr_db_crnn.o clipper.o db_post_process.o crnn_process.o
+	rm -f ocr_db_crnn

deploy/lite/config.txt

+max_side_len  960
+det_db_thresh  0.3
+det_db_box_thresh  0.5
+det_db_unclip_ratio  2.0
\ No newline at end of file

deploy/lite/utils/crnn_process.cpp → deploy/lite/crnn_process.cc

@@ -12,117 +12,110 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <iostream>
-#include <vector>
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
-#include "math.h"
+#include "crnn_process.h"  //NOLINT
+#include <algorithm>
+#include <memory>
+#include <string>
 
-#include <iostream>
-#include <cstring>
-#include <fstream>
+const std::vector<int> rec_image_shape{3, 32, 320};
 
-#define character_type "ch"
-#define max_dict_length 6624
-const std::vector<int> rec_image_shape {3, 32, 320};
-
-
-cv::Mat crnn_resize_norm_img(cv::Mat img, float wh_ratio){
+cv::Mat CrnnResizeNormImg(cv::Mat img, float wh_ratio) {
   int imgC, imgH, imgW;
   imgC = rec_image_shape[0];
   imgW = rec_image_shape[2];
   imgH = rec_image_shape[1];
 
-  if (character_type=="ch")
-    imgW = int(32*wh_ratio);
+  imgW = int(32 * wh_ratio);
 
-  float ratio = float(img.cols)/float(img.rows);
+  float ratio = float(img.cols) / float(img.rows);
   int resize_w, resize_h;
-  if (ceilf(imgH*ratio)>imgW)
+  if (ceilf(imgH * ratio) > imgW)
     resize_w = imgW;
   else
-    resize_w = int(ceilf(imgH*ratio));
+    resize_w = int(ceilf(imgH * ratio));
   cv::Mat resize_img;
-  cv::resize(img, resize_img, cv::Size(resize_w, imgH),0.f, 0.f, cv::INTER_CUBIC);
+  cv::resize(
+      img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f, cv::INTER_CUBIC);
 
   resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);
 
-  for (int h=0; h< resize_img.rows; h++){
-    for (int w=0; w< resize_img.cols; w++){
-      resize_img.at<cv::Vec3f>(h, w)[0] = (resize_img.at<cv::Vec3f>(h, w)[0] - 0.5) *2;
-      resize_img.at<cv::Vec3f>(h, w)[1] = (resize_img.at<cv::Vec3f>(h, w)[1] - 0.5) *2;
-      resize_img.at<cv::Vec3f>(h, w)[2] = (resize_img.at<cv::Vec3f>(h, w)[2] - 0.5) *2;
+  for (int h = 0; h < resize_img.rows; h++) {
+    for (int w = 0; w < resize_img.cols; w++) {
+      resize_img.at<cv::Vec3f>(h, w)[0] =
+          (resize_img.at<cv::Vec3f>(h, w)[0] - 0.5) * 2;
+      resize_img.at<cv::Vec3f>(h, w)[1] =
+          (resize_img.at<cv::Vec3f>(h, w)[1] - 0.5) * 2;
+      resize_img.at<cv::Vec3f>(h, w)[2] =
+          (resize_img.at<cv::Vec3f>(h, w)[2] - 0.5) * 2;
     }
   }
 
   cv::Mat dist;
-  cv::copyMakeBorder(resize_img, dist, 0, 0, 0, int(imgW-resize_w), cv::BORDER_CONSTANT, {0, 0, 0});
+  cv::copyMakeBorder(resize_img,
+                     dist,
+                     0,
+                     0,
+                     0,
+                     int(imgW - resize_w),
+                     cv::BORDER_CONSTANT,
+                     {0, 0, 0});
 
   return dist;
-
 }
 
-cv::Mat crnn_resize_img(cv::Mat img, float wh_ratio){
+cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio) {
   int imgC, imgH, imgW;
   imgC = rec_image_shape[0];
   imgW = rec_image_shape[2];
   imgH = rec_image_shape[1];
 
-  if (character_type=="ch")
-    imgW = int(32*wh_ratio);
+  imgW = int(32 * wh_ratio);
 
-  float ratio = float(img.cols)/float(img.rows);
+  float ratio = float(img.cols) / float(img.rows);
   int resize_w, resize_h;
-  if (ceilf(imgH*ratio)>imgW)
+  if (ceilf(imgH * ratio) > imgW)
     resize_w = imgW;
   else
-    resize_w = int(ceilf(imgH*ratio));
+    resize_w = int(ceilf(imgH * ratio));
   cv::Mat resize_img;
-  cv::resize(img, resize_img, cv::Size(resize_w, imgH),0.f, 0.f, cv::INTER_LINEAR);
+  cv::resize(
+      img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f, cv::INTER_LINEAR);
 
   return resize_img;
 }
 
-std::basic_string<char, std::char_traits<char>, std::allocator<char>> * read_dict(std::string path){
-
-  std::ifstream ifs;
-  std::string charactors[max_dict_length];
-
-  ifs.open(path);
-  if (!ifs.is_open())
-  {
-    std::cout<<"open file "<<path<<"	failed"<<std::endl;
-  }
-  else
-  {
-    std::string con = "";
-    int count = 0;
-    while (ifs)
-    {
-      getline(ifs, charactors[count]);
-      count++;
+std::vector<std::string> ReadDict(std::string path) {
+  std::ifstream in(path);
+  std::string filename;
+  std::string line;
+  std::vector<std::string> m_vec;
+  if (in) {
+    while (getline(in, line)) {
+      m_vec.push_back(line);
     }
+  } else {
+    std::cout << "no such file" << std::endl;
   }
-  return charactors;
+  return m_vec;
 }
 
-cv::Mat get_rotate_crop_image(cv::Mat srcimage, std::vector<std::vector<int>> box){
+cv::Mat GetRotateCropImage(cv::Mat srcimage,
+                           std::vector<std::vector<int>> box) {
   cv::Mat image;
   srcimage.copyTo(image);
   std::vector<std::vector<int>> 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));
+  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);
+  image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
 
-  for(int i=0; i<points.size(); i++){
+  for (int i = 0; i < points.size(); i++) {
     points[i][0] -= left;
     points[i][1] -= top;
   }
@@ -147,22 +140,18 @@ cv::Mat get_rotate_crop_image(cv::Mat srcimage, std::vector<std::vector<int>> bo
   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);
+  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){
+  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{
+  } else {
     return dst_img;
   }
-
 }
-
-template<class ForwardIterator>
-inline size_t argmax(ForwardIterator first, ForwardIterator last)
-{
-  return std::distance(first, std::max_element(first, last));
-}
\ No newline at end of file

deploy/lite/crnn_process.h

+// 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.
+
+#pragma once
+
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "math.h"  //NOLINT
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+
+cv::Mat CrnnResizeNormImg(cv::Mat img, float wh_ratio);
+
+cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio);
+
+std::vector<std::string> ReadDict(std::string path);
+
+cv::Mat GetRotateCropImage(cv::Mat srcimage, std::vector<std::vector<int>> box);
+
+template <class ForwardIterator>
+inline size_t Argmax(ForwardIterator first, ForwardIterator last) {
+  return std::distance(first, std::max_element(first, last));
+}

deploy/lite/db_post_process.cc

+// 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 "db_post_process.h"  // NOLINT
+#include <algorithm>
+#include <utility>
+
+void GetContourArea(std::vector<std::vector<float>> box,
+                    float unclip_ratio,
+                    float& distance) {
+  int pts_num = 4;
+  float area = 0.0f;
+  float dist = 0.0f;
+  for (int i = 0; i < pts_num; i++) {
+    area += box[i][0] * box[(i + 1) % pts_num][1] -
+            box[i][1] * box[(i + 1) % pts_num][0];
+    dist += sqrtf((box[i][0] - box[(i + 1) % pts_num][0]) *
+                      (box[i][0] - box[(i + 1) % pts_num][0]) +
+                  (box[i][1] - box[(i + 1) % pts_num][1]) *
+                      (box[i][1] - box[(i + 1) % pts_num][1]));
+  }
+  area = fabs(float(area / 2.0));
+
+  distance = area * unclip_ratio / dist;
+}
+
+cv::RotatedRect Unclip(std::vector<std::vector<float>> box,
+                       float unclip_ratio) {
+  float distance = 1.0;
+
+  GetContourArea(box, unclip_ratio, distance);
+
+  ClipperLib::ClipperOffset offset;
+  ClipperLib::Path p;
+  p << ClipperLib::IntPoint(int(box[0][0]), int(box[0][1]))
+    << ClipperLib::IntPoint(int(box[1][0]), int(box[1][1]))
+    << ClipperLib::IntPoint(int(box[2][0]), int(box[2][1]))
+    << ClipperLib::IntPoint(int(box[3][0]), int(box[3][1]));
+  offset.AddPath(p, ClipperLib::jtRound, ClipperLib::etClosedPolygon);
+
+  ClipperLib::Paths soln;
+  offset.Execute(soln, distance);
+  std::vector<cv::Point2f> points;
+
+  for (int j = 0; j < soln.size(); j++) {
+    for (int i = 0; i < soln[soln.size() - 1].size(); i++) {
+      points.emplace_back(soln[j][i].X, soln[j][i].Y);
+    }
+  }
+  cv::RotatedRect res = cv::minAreaRect(points);
+
+  return res;
+}
+
+std::vector<std::vector<float>> Mat2Vector(cv::Mat mat) {
+  std::vector<std::vector<float>> img_vec;
+  std::vector<float> tmp;
+
+  for (int i = 0; i < mat.rows; ++i) {
+    tmp.clear();
+    for (int j = 0; j < mat.cols; ++j) {
+      tmp.push_back(mat.at<float>(i, j));
+    }
+    img_vec.push_back(tmp);
+  }
+  return img_vec;
+}
+
+bool XsortFp32(std::vector<float> a, std::vector<float> b) {
+  if (a[0] != b[0]) return a[0] < b[0];
+  return false;
+}
+
+bool XsortInt(std::vector<int> a, std::vector<int> b) {
+  if (a[0] != b[0]) return a[0] < b[0];
+  return false;
+}
+
+std::vector<std::vector<int>> OrderPointsClockwise(
+    std::vector<std::vector<int>> pts) {
+  std::vector<std::vector<int>> box = pts;
+  std::sort(box.begin(), box.end(), XsortInt);
+
+  std::vector<std::vector<int>> leftmost = {box[0], box[1]};
+  std::vector<std::vector<int>> rightmost = {box[2], box[3]};
+
+  if (leftmost[0][1] > leftmost[1][1]) std::swap(leftmost[0], leftmost[1]);
+
+  if (rightmost[0][1] > rightmost[1][1]) std::swap(rightmost[0], rightmost[1]);
+
+  std::vector<std::vector<int>> rect = {
+      leftmost[0], rightmost[0], rightmost[1], leftmost[1]};
+  return rect;
+}
+
+std::vector<std::vector<float>> GetMiniBoxes(cv::RotatedRect box, float& ssid) {
+  ssid = box.size.width >= box.size.height ? box.size.height : box.size.width;
+
+  cv::Mat points;
+  cv::boxPoints(box, points);
+
+  auto array = Mat2Vector(points);
+  std::sort(array.begin(), array.end(), XsortFp32);
+
+  std::vector<float> idx1 = array[0], idx2 = array[1], idx3 = array[2],
+                     idx4 = array[3];
+  if (array[3][1] <= array[2][1]) {
+    idx2 = array[3];
+    idx3 = array[2];
+  } else {
+    idx2 = array[2];
+    idx3 = array[3];
+  }
+  if (array[1][1] <= array[0][1]) {
+    idx1 = array[1];
+    idx4 = array[0];
+  } else {
+    idx1 = array[0];
+    idx4 = array[1];
+  }
+
+  array[0] = idx1;
+  array[1] = idx2;
+  array[2] = idx3;
+  array[3] = idx4;
+
+  return array;
+}
+
+float BoxScoreFast(std::vector<std::vector<float>> box_array, cv::Mat pred) {
+  auto array = box_array;
+  int width = pred.cols;
+  int height = pred.rows;
+
+  float box_x[4] = {array[0][0], array[1][0], array[2][0], array[3][0]};
+  float box_y[4] = {array[0][1], array[1][1], array[2][1], array[3][1]};
+
+  int xmin = clamp(
+      int(std::floorf(*(std::min_element(box_x, box_x + 4)))), 0, width - 1);
+  int xmax = clamp(
+      int(std::ceilf(*(std::max_element(box_x, box_x + 4)))), 0, width - 1);
+  int ymin = clamp(
+      int(std::floorf(*(std::min_element(box_y, box_y + 4)))), 0, height - 1);
+  int ymax = clamp(
+      int(std::ceilf(*(std::max_element(box_y, box_y + 4)))), 0, height - 1);
+
+  cv::Mat mask;
+  mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
+
+  cv::Point root_point[4];
+  root_point[0] = cv::Point(int(array[0][0]) - xmin, int(array[0][1]) - ymin);
+  root_point[1] = cv::Point(int(array[1][0]) - xmin, int(array[1][1]) - ymin);
+  root_point[2] = cv::Point(int(array[2][0]) - xmin, int(array[2][1]) - ymin);
+  root_point[3] = cv::Point(int(array[3][0]) - xmin, int(array[3][1]) - ymin);
+  const cv::Point* ppt[1] = {root_point};
+  int npt[] = {4};
+  cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
+
+  cv::Mat croppedImg;
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1))
+      .copyTo(croppedImg);
+
+  auto score = cv::mean(croppedImg, mask)[0];
+  return score;
+}
+
+std::vector<std::vector<std::vector<int>>> BoxesFromBitmap(
+    const cv::Mat pred,
+    const cv::Mat bitmap,
+    std::map<std::string, double> Config) {
+  const int min_size = 3;
+  const int max_candidates = 1000;
+  const float box_thresh = float(Config["det_db_box_thresh"]);
+  const float unclip_ratio = float(Config["det_db_unclip_ratio"]);
+
+  int width = bitmap.cols;
+  int height = bitmap.rows;
+
+  std::vector<std::vector<cv::Point>> contours;
+  std::vector<cv::Vec4i> hierarchy;
+
+  cv::findContours(
+      bitmap, contours, hierarchy, cv::RETR_LIST, cv::CHAIN_APPROX_SIMPLE);
+
+  int num_contours =
+      contours.size() >= max_candidates ? max_candidates : contours.size();
+
+  std::vector<std::vector<std::vector<int>>> boxes;
+
+  for (int i = 0; i < num_contours; i++) {
+    float ssid;
+    cv::RotatedRect box = cv::minAreaRect(contours[i]);
+    auto array = GetMiniBoxes(box, ssid);
+
+    auto box_for_unclip = array;
+    // end get_mini_box
+
+    if (ssid < min_size) {
+      continue;
+    }
+
+    float score;
+    score = BoxScoreFast(array, pred);
+    // end box_score_fast
+    if (score < box_thresh) continue;
+
+    // start for unclip
+    cv::RotatedRect points = Unclip(box_for_unclip, unclip_ratio);
+    // end for unclip
+
+    cv::RotatedRect clipbox = points;
+    auto cliparray = GetMiniBoxes(clipbox, ssid);
+
+    if (ssid < min_size + 2) continue;
+
+    int dest_width = pred.cols;
+    int dest_height = pred.rows;
+    std::vector<std::vector<int>> intcliparray;
+
+    for (int num_pt = 0; num_pt < 4; num_pt++) {
+      std::vector<int> a{
+          int(clamp(
+              roundf(cliparray[num_pt][0] / float(width) * float(dest_width)),
+              float(0),
+              float(dest_width))),
+          int(clamp(
+              roundf(cliparray[num_pt][1] / float(height) * float(dest_height)),
+              float(0),
+              float(dest_height)))};
+      intcliparray.push_back(a);
+    }
+    boxes.push_back(intcliparray);
+
+  }  // end for
+  return boxes;
+}
+
+std::vector<std::vector<std::vector<int>>> FilterTagDetRes(
+    std::vector<std::vector<std::vector<int>>> boxes,
+    float ratio_h,
+    float ratio_w,
+    cv::Mat srcimg) {
+  int oriimg_h = srcimg.rows;
+  int oriimg_w = srcimg.cols;
+
+  std::vector<std::vector<std::vector<int>>> root_points;
+  for (int n = 0; n < boxes.size(); n++) {
+    boxes[n] = OrderPointsClockwise(boxes[n]);
+    for (int m = 0; m < boxes[0].size(); m++) {
+      boxes[n][m][0] /= ratio_w;
+      boxes[n][m][1] /= ratio_h;
+
+      boxes[n][m][0] = int(std::min(std::max(boxes[n][m][0], 0), oriimg_w - 1));
+      boxes[n][m][1] = int(std::min(std::max(boxes[n][m][1], 0), oriimg_h - 1));
+    }
+  }
+
+  for (int n = 0; n < boxes.size(); n++) {
+    int rect_width, rect_height;
+    rect_width = int(sqrt(pow(boxes[n][0][0] - boxes[n][1][0], 2) +
+                          pow(boxes[n][0][1] - boxes[n][1][1], 2)));
+    rect_height = int(sqrt(pow(boxes[n][0][0] - boxes[n][3][0], 2) +
+                           pow(boxes[n][0][1] - boxes[n][3][1], 2)));
+    if (rect_width <= 10 || rect_height <= 10) continue;
+    root_points.push_back(boxes[n]);
+  }
+  return root_points;
+}
\ No newline at end of file

deploy/lite/db_post_process.h

+// 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.
+
+#pragma once
+
+#include <math.h>
+
+#include <iostream>
+#include <map>
+#include <vector>
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include "utils/clipper.hpp"
+
+template <class T>
+T clamp(T x, T min, T max) {
+  if (x > max) return max;
+  if (x < min) return min;
+  return x;
+}
+
+std::vector<std::vector<float>> Mat2Vector(cv::Mat mat);
+
+void GetContourArea(std::vector<std::vector<float>> box,
+                    float unclip_ratio,
+                    float &distance);
+
+cv::RotatedRect Unclip(std::vector<std::vector<float>> box, float unclip_ratio);
+
+std::vector<std::vector<float>> Mat2Vector(cv::Mat mat);
+
+bool XsortFp32(std::vector<float> a, std::vector<float> b);
+
+bool XsortInt(std::vector<int> a, std::vector<int> b);
+
+std::vector<std::vector<int>> OrderPointsClockwise(
+    std::vector<std::vector<int>> pts);
+
+std::vector<std::vector<float>> GetMiniBoxes(cv::RotatedRect box, float &ssid);
+
+float BoxScoreFast(std::vector<std::vector<float>> box_array, cv::Mat pred);
+
+std::vector<std::vector<std::vector<int>>> BoxesFromBitmap(
+    const cv::Mat pred,
+    const cv::Mat bitmap,
+    std::map<std::string, double> Config);
+
+std::vector<std::vector<std::vector<int>>> FilterTagDetRes(
+    std::vector<std::vector<std::vector<int>>> boxes,
+    float ratio_h,
+    float ratio_w,
+    cv::Mat srcimg);

deploy/lite/ocr_db_crnn.cc

-// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+// 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.
@@ -12,32 +12,14 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <iostream>
-#include <vector>
 #include <chrono>
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
 #include "paddle_api.h"  // NOLINT
 
-#include "utils/db_post_process.cpp"
-#include "utils/crnn_process.cpp"
-#include <cstring>
-#include <fstream>
+#include "crnn_process.h"
+#include "db_post_process.h"
 
 using namespace paddle::lite_api;  // NOLINT
-
-struct Object {
-  cv::Rect rec;
-  int class_id;
-  float prob;
-};
-
-int64_t ShapeProduction(const shape_t& shape) {
-  int64_t res = 1;
-  for (auto i : shape) res *= i;
-  return res;
-}
+using namespace std;
 
 // fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
 void neon_mean_scale(const float* din,
@@ -86,14 +68,16 @@ void neon_mean_scale(const float* din,
 }
 
 // resize image to a size multiple of 32 which is required by the network
-cv::Mat resize_img_type0(const cv::Mat img, int max_size_len, float *ratio_h, float *ratio_w){
+cv::Mat DetResizeImg(const cv::Mat img,
+                     int max_size_len,
+                     std::vector<float>& ratio_hw) {
   int w = img.cols;
   int h = img.rows;
 
   float ratio = 1.f;
-  int max_wh = w >=h ? w : h;
-  if (max_wh > max_size_len){
-    if (h > w){
+  int max_wh = w >= h ? w : h;
+  if (max_wh > max_size_len) {
+    if (h > w) {
       ratio = float(max_size_len) / float(h);
     } else {
       ratio = float(max_size_len) / float(w);
@@ -104,36 +88,32 @@ cv::Mat resize_img_type0(const cv::Mat img, int max_size_len, float *ratio_h, fl
   int resize_w = int(float(w) * ratio);
   if (resize_h % 32 == 0)
     resize_h = resize_h;
-  else if (resize_h / 32 < 1)
+  else if (resize_h / 32 < 1 + 1e-5)
     resize_h = 32;
   else
     resize_h = (resize_h / 32 - 1) * 32;
 
   if (resize_w % 32 == 0)
     resize_w = resize_w;
-  else if (resize_w /32 < 1)
+  else if (resize_w / 32 < 1 + 1e-5)
     resize_w = 32;
   else
-    resize_w = (resize_w/32 - 1)*32;
+    resize_w = (resize_w / 32 - 1) * 32;
 
   cv::Mat resize_img;
   cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
 
-  *ratio_h = float(resize_h) / float(h);
-  *ratio_w = float(resize_w) / float(w);
+  ratio_hw.push_back(float(resize_h) / float(h));
+  ratio_hw.push_back(float(resize_w) / float(w));
   return resize_img;
 }
 
-using namespace std;
-
-void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img, std::string rec_model_file){
-
-  MobileConfig config;
-  config.set_model_from_file(rec_model_file);
-
-  std::shared_ptr<PaddlePredictor> predictor_crnn =
-        CreatePaddlePredictor<MobileConfig>(config);
-
+void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes,
+                 cv::Mat img,
+                 std::shared_ptr<PaddlePredictor> predictor_crnn,
+                 std::string dict_path,
+                 std::vector<std::string>& rec_text,
+                 std::vector<float>& rec_text_score) {
   std::vector<float> mean = {0.5f, 0.5f, 0.5f};
   std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
 
@@ -142,56 +122,55 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
   cv::Mat crop_img;
   cv::Mat resize_img;
 
-  std::string dict_path = "./ppocr_keys_v1.txt";
-  auto charactor_dict = read_dict(dict_path);
+  auto charactor_dict = ReadDict(dict_path);
 
-  std::cout << "The predicted text is :" << std::endl;
   int index = 0;
-  for (int i=boxes.size()-1; i >= 0; i--) {
-    crop_img = get_rotate_crop_image(srcimg, boxes[i]);
-
+  for (int i = boxes.size() - 1; i >= 0; i--) {
+    crop_img = GetRotateCropImage(srcimg, boxes[i]);
     float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
 
-    resize_img = crnn_resize_img(crop_img, wh_ratio);
+    resize_img = CrnnResizeImg(crop_img, wh_ratio);
     resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);
 
-    const float *dimg = reinterpret_cast<const float *>(resize_img.data);
+    const float* dimg = reinterpret_cast<const float*>(resize_img.data);
 
-    std::unique_ptr <Tensor> input_tensor0(std::move(predictor_crnn->GetInput(0)));
+    std::unique_ptr<Tensor> input_tensor0(
+        std::move(predictor_crnn->GetInput(0)));
     input_tensor0->Resize({1, 3, resize_img.rows, resize_img.cols});
-    auto *data0 = input_tensor0->mutable_data<float>();
-
-    neon_mean_scale(dimg, data0, resize_img.rows * resize_img.cols, mean, scale);
+    auto* data0 = input_tensor0->mutable_data<float>();
 
+    neon_mean_scale(
+        dimg, data0, resize_img.rows * resize_img.cols, mean, scale);
     //// Run CRNN predictor
     predictor_crnn->Run();
 
     // Get output and run postprocess
     std::unique_ptr<const Tensor> output_tensor0(
-            std::move(predictor_crnn->GetOutput(0)));
-    auto *rec_idx = output_tensor0->data<int>();
+        std::move(predictor_crnn->GetOutput(0)));
+    auto* rec_idx = output_tensor0->data<int>();
 
     auto rec_idx_lod = output_tensor0->lod();
     auto shape_out = output_tensor0->shape();
 
     std::vector<int> pred_idx;
-    for (int n = int(rec_idx_lod[0][0]); n < int(rec_idx_lod[0][1] * 2); n += 2) {
+    for (int n = int(rec_idx_lod[0][0]); n < int(rec_idx_lod[0][1] * 2);
+         n += 2) {
       pred_idx.push_back(int(rec_idx[n]));
     }
 
-    if (pred_idx.size() < 1e-3)
-      continue;
-    std::cout << std::endl;
+    if (pred_idx.size() < 1e-3) continue;
 
     index += 1;
-    std::cout << index << "\t";
+    std::string pred_txt = "";
     for (int n = 0; n < pred_idx.size(); n++) {
-      std::cout << charactor_dict[pred_idx[n]];
+      pred_txt += charactor_dict[pred_idx[n]];
     }
+    rec_text.push_back(pred_txt);
 
     ////get score
-    std::unique_ptr<const Tensor> output_tensor1(std::move(predictor_crnn->GetOutput(1)));
-    auto *predict_batch = output_tensor1->data<float>();
+    std::unique_ptr<const Tensor> output_tensor1(
+        std::move(predictor_crnn->GetOutput(1)));
+    auto* predict_batch = output_tensor1->data<float>();
     auto predict_shape = output_tensor1->shape();
 
     auto predict_lod = output_tensor1->lod();
@@ -203,38 +182,34 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
     float max_value = 0.0f;
 
     for (int n = predict_lod[0][0]; n < predict_lod[0][1] - 1; n++) {
-      argmax_idx = int(argmax(&predict_batch[n * predict_shape[1]], &predict_batch[(n + 1) * predict_shape[1]]));
-      max_value = float(
-              *std::max_element(&predict_batch[n * predict_shape[1]], &predict_batch[(n + 1) * predict_shape[1]]));
+      argmax_idx = int(Argmax(&predict_batch[n * predict_shape[1]],
+                              &predict_batch[(n + 1) * predict_shape[1]]));
+      max_value =
+          float(*std::max_element(&predict_batch[n * predict_shape[1]],
+                                  &predict_batch[(n + 1) * predict_shape[1]]));
 
       if (blank - 1 - argmax_idx > 1e-5) {
         score += max_value;
         count += 1;
       }
-
     }
     score /= count;
-    std::cout << "\tscore: " << score << std::endl;
+    rec_text_score.push_back(score);
   }
 }
 
-std::vector<std::vector<std::vector<int>>> RunDetModel(std::string model_file, cv::Mat img) {
-  // Set MobileConfig
-  MobileConfig config;
-  config.set_model_from_file(model_file);
-
-  std::shared_ptr<PaddlePredictor> predictor =
-      CreatePaddlePredictor<MobileConfig>(config);
-
+std::vector<std::vector<std::vector<int>>> RunDetModel(
+    std::shared_ptr<PaddlePredictor> predictor,
+    cv::Mat img,
+    std::map<std::string, double> Config) {
   // Read img
-  int max_side_len = 960;
-  float ratio_h{};
-  float ratio_w{};
+  int max_side_len = int(Config["max_side_len"]);
 
   cv::Mat srcimg;
   img.copyTo(srcimg);
 
-  img = resize_img_type0(img, max_side_len, &ratio_h, &ratio_w);
+  std::vector<float> ratio_hw;
+  img = DetResizeImg(img, max_side_len, ratio_hw);
   cv::Mat img_fp;
   img.convertTo(img_fp, CV_32FC3, 1.0 / 255.f);
 
@@ -244,7 +219,7 @@ std::vector<std::vector<std::vector<int>>> RunDetModel(std::string model_file, c
   auto* data0 = input_tensor0->mutable_data<float>();
 
   std::vector<float> mean = {0.485f, 0.456f, 0.406f};
-  std::vector<float> scale = {1/0.229f, 1/0.224f, 1/0.225f};
+  std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
   const float* dimg = reinterpret_cast<const float*>(img_fp.data);
   neon_mean_scale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
 
@@ -252,7 +227,8 @@ std::vector<std::vector<std::vector<int>>> RunDetModel(std::string model_file, c
   predictor->Run();
 
   // Get output and post process
-  std::unique_ptr<const Tensor> output_tensor(std::move(predictor->GetOutput(0)));
+  std::unique_ptr<const Tensor> output_tensor(
+      std::move(predictor->GetOutput(0)));
   auto* outptr = output_tensor->data<float>();
   auto shape_out = output_tensor->shape();
 
@@ -266,68 +242,135 @@ std::vector<std::vector<std::vector<int>>> RunDetModel(std::string model_file, c
   float pred[shape_out[2]][shape_out[3]];
   unsigned char cbuf[shape_out[2]][shape_out[3]];
 
-  for (int i=0; i< int(shape_out[2]*shape_out[3]); i++){
-    pred[int(i/int(shape_out[3]))][int(i%shape_out[3])] = float(outptr[i]);
-    cbuf[int(i/int(shape_out[3]))][int(i%shape_out[3])] = (unsigned char) ((outptr[i])*255);
+  for (int i = 0; i < int(shape_out[2] * shape_out[3]); i++) {
+    pred[int(i / int(shape_out[3]))][int(i % shape_out[3])] = float(outptr[i]);
+    cbuf[int(i / int(shape_out[3]))][int(i % shape_out[3])] =
+        (unsigned char)((outptr[i]) * 255);
   }
 
   cv::Mat cbuf_map(shape_out[2], shape_out[3], CV_8UC1, (unsigned char*)cbuf);
-  cv::Mat pred_map(shape_out[2], shape_out[3], CV_32F, (float *)pred);
+  cv::Mat pred_map(shape_out[2], shape_out[3], CV_32F, (float*)pred);
 
-  const double threshold = 0.3*255;
+  const double threshold = double(Config["det_db_thresh"]) * 255;
   const double maxvalue = 255;
   cv::Mat bit_map;
   cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
 
-  auto boxes = boxes_from_bitmap(pred_map, bit_map);
+  auto boxes = BoxesFromBitmap(pred_map, bit_map, Config);
 
-  std::vector<std::vector<std::vector<int>>> filter_boxes = filter_tag_det_res(boxes, ratio_h, ratio_w, srcimg);
+  std::vector<std::vector<std::vector<int>>> filter_boxes =
+      FilterTagDetRes(boxes, ratio_hw[0], ratio_hw[1], srcimg);
 
-  //// visualization
-  cv::Point rook_points[filter_boxes.size()][4];
-  for (int n=0; n<filter_boxes.size(); n++){
-    for (int m=0; m< filter_boxes[0].size(); m++){
-      rook_points[n][m] = cv::Point(int(filter_boxes[n][m][0]), int(filter_boxes[n][m][1]));
+  return filter_boxes;
+}
+
+std::shared_ptr<PaddlePredictor> loadModel(std::string model_file) {
+  MobileConfig config;
+  config.set_model_from_file(model_file);
+
+  std::shared_ptr<PaddlePredictor> predictor =
+      CreatePaddlePredictor<MobileConfig>(config);
+  return predictor;
+}
+
+cv::Mat Visualization(cv::Mat srcimg,
+                      std::vector<std::vector<std::vector<int>>> boxes) {
+  cv::Point rook_points[boxes.size()][4];
+  for (int n = 0; n < boxes.size(); n++) {
+    for (int m = 0; m < boxes[0].size(); m++) {
+      rook_points[n][m] = cv::Point(int(boxes[n][m][0]), int(boxes[n][m][1]));
     }
   }
-
   cv::Mat img_vis;
   srcimg.copyTo(img_vis);
-  for (int n=0; n<boxes.size(); n++){
-    const cv::Point* ppt[1] = { rook_points[n] };
-    int npt[] = { 4 };
-    cv::polylines(img_vis, ppt, npt,1,1,CV_RGB(0,255,0),2,8,0);
+  for (int n = 0; n < boxes.size(); n++) {
+    const cv::Point* ppt[1] = {rook_points[n]};
+    int npt[] = {4};
+    cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
   }
 
-  cv::imwrite("./imgs_vis/vis.jpg", img_vis);
-  std::cout << "The detection visualized image saved in ./imgs_vis/" <<std::endl;
+  cv::imwrite("./imgs/vis.jpg", img_vis);
+  std::cout << "The detection visualized image saved in ./imgs/vis.jpg"
+            << std::endl;
+  return img_vis;
+}
+
+std::vector<std::string> split(const std::string& str,
+                               const std::string& delim) {
+  std::vector<std::string> res;
+  if ("" == str) return res;
+  char* strs = new char[str.length() + 1];
+  std::strcpy(strs, str.c_str());
+
+  char* d = new char[delim.length() + 1];
+  std::strcpy(d, delim.c_str());
+
+  char* p = std::strtok(strs, d);
+  while (p) {
+    string s = p;
+    res.push_back(s);
+    p = std::strtok(NULL, d);
+  }
 
-  return filter_boxes;
+  return res;
 }
 
+std::map<std::string, double> LoadConfigTxt(std::string config_path) {
+  auto config = ReadDict(config_path);
+
+  std::map<std::string, double> dict;
+  for (int i = 0; i < config.size(); i++) {
+    std::vector<std::string> res = split(config[i], " ");
+    dict[res[0]] = stod(res[1]);
+  }
+  return dict;
+}
 
 int main(int argc, char** argv) {
-  if (argc < 4) {
-    std::cerr << "[ERROR] usage: " << argv[0] << " det_model_file rec_model_file image_path\n";
+  if (argc < 5) {
+    std::cerr << "[ERROR] usage: " << argv[0]
+              << " det_model_file rec_model_file image_path\n";
     exit(1);
   }
   std::string det_model_file = argv[1];
   std::string rec_model_file = argv[2];
   std::string img_path = argv[3];
+  std::string dict_path = argv[4];
+
+  //// load config from txt file
+  auto Config = LoadConfigTxt("./config.txt");
 
   auto start = std::chrono::system_clock::now();
 
+  auto det_predictor = loadModel(det_model_file);
+  auto rec_predictor = loadModel(rec_model_file);
+
   cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
-  auto boxes = RunDetModel(det_model_file, srcimg);
+  auto boxes = RunDetModel(det_predictor, srcimg, Config);
 
-  RunRecModel(boxes, srcimg, rec_model_file);
+  std::vector<std::string> rec_text;
+  std::vector<float> rec_text_score;
+  RunRecModel(
+      boxes, srcimg, rec_predictor, dict_path, rec_text, rec_text_score);
 
-  auto end   = std::chrono::system_clock::now();
-  auto duration = std::chrono::duration_cast<std::chrono::microseconds>(end - start);
-  std::cout <<  "花费了"
-            << double(duration.count()) * std::chrono::microseconds::period::num /std::chrono::microseconds::period::den
+  auto end = std::chrono::system_clock::now();
+  auto duration =
+      std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+
+  //// visualization
+  auto img_vis = Visualization(srcimg, boxes);
+
+  //// print recognized text
+  for (int i = 0; i < rec_text.size(); i++) {
+    std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
+              << std::endl;
+  }
+
+  std::cout << "花费了"
+            << double(duration.count()) *
+                   std::chrono::microseconds::period::num /
+                   std::chrono::microseconds::period::den
             << "秒" << std::endl;
 
   return 0;
 }
-

deploy/lite/readme.md

@@ -128,7 +128,7 @@ wget  https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar
 ### 2.2 与手机联调
 
 首先需要进行一些准备工作。
- 1. 准备一台arm8的安卓手机，如果编译的预测库和opt文件是armv7，则需要arm7的手机。
+ 1. 准备一台arm8的安卓手机，如果编译的预测库和opt文件是armv7，则需要arm7的手机，并修改Makefile中`ARM_ABI = arm7`。
  2. 打开手机的USB调试选项，选择文件传输模式，连接电脑。
  3. 电脑上安装adb工具，用于调试。在电脑终端中输入`adb devices`，如果有类似以下输出，则表示adb安装成功。
 ```
@@ -148,12 +148,12 @@ wget  https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar
  ```
  准备测试图像，以`PaddleOCR/doc/imgs/12.jpg`为例，将测试的图像复制到`demo/cxx/ocr/debug/`文件夹下。
  准备字典文件，中文超轻量模型的字典文件是`PaddleOCR/ppocr/utils/ppocr_keys_v1.txt`，将其复制到`demo/cxx/ocr/debug/`文件夹下。
- 
+
  执行完成后，ocr文件夹下将有如下文件格式：
 
 ```
 demo/cxx/ocr/
-|-- debug/                              
+|-- debug/  
 |   |--ch_det_mv3_db_opt.nb             优化后的检测模型文件
 |   |--ch_rec_mv3_crnn_opt.nb           优化后的识别模型文件
 |   |--12.jpg                           待测试图像
@@ -171,7 +171,7 @@ demo/cxx/ocr/
  5. 启动调试
 
  上述步骤完成后就可以使用adb将文件push到手机上运行，步骤如下：
- 
+
  ```
  # 执行编译，得到可执行文件ocr_db_crnn
  # ocr_db_crnn可执行文件的使用方式为:
@@ -188,4 +188,3 @@ demo/cxx/ocr/
  ```
 
  如果对代码做了修改，则需要重新编译并push到手机上。
- 

deploy/lite/utils/clipper.hpp

-/*******************************************************************************
-*                                                                              *
-* Author    :  Angus Johnson                                                   *
-* Version   :  6.4.2                                                           *
-* Date      :  27 February 2017                                                *
-* Website   :  http://www.angusj.com                                           *
-* Copyright :  Angus Johnson 2010-2017                                         *
-*                                                                              *
-* License:                                                                     *
-* Use, modification & distribution is subject to Boost Software License Ver 1. *
-* http://www.boost.org/LICENSE_1_0.txt                                         *
-*                                                                              *
-* Attributions:                                                                *
-* The code in this library is an extension of Bala Vatti's clipping algorithm: *
-* "A generic solution to polygon clipping"                                     *
-* Communications of the ACM, Vol 35, Issue 7 (July 1992) pp 56-63.             *
-* http://portal.acm.org/citation.cfm?id=129906                                 *
-*                                                                              *
-* Computer graphics and geometric modeling: implementation and algorithms      *
-* By Max K. Agoston                                                            *
-* Springer; 1 edition (January 4, 2005)                                        *
-* http://books.google.com/books?q=vatti+clipping+agoston                       *
-*                                                                              *
-* See also:                                                                    *
-* "Polygon Offsetting by Computing Winding Numbers"                            *
-* Paper no. DETC2005-85513 pp. 565-575                                         *
-* ASME 2005 International Design Engineering Technical Conferences             *
-* and Computers and Information in Engineering Conference (IDETC/CIE2005)      *
-* September 24-28, 2005 , Long Beach, California, USA                          *
-* http://www.me.berkeley.edu/~mcmains/pubs/DAC05OffsetPolygon.pdf              *
-*                                                                              *
-*******************************************************************************/
-
-#ifndef clipper_hpp
-#define clipper_hpp
-
-#define CLIPPER_VERSION "6.4.2"
-
-//use_int32: When enabled 32bit ints are used instead of 64bit ints. This
-//improve performance but coordinate values are limited to the range +/- 46340
-//#define use_int32
-
-//use_xyz: adds a Z member to IntPoint. Adds a minor cost to perfomance.
-//#define use_xyz
-
-//use_lines: Enables line clipping. Adds a very minor cost to performance.
-#define use_lines
-  
-//use_deprecated: Enables temporary support for the obsolete functions
-//#define use_deprecated  
-
-#include <vector>
-#include <list>
-#include <set>
-#include <stdexcept>
-#include <cstring>
-#include <cstdlib>
-#include <ostream>
-#include <functional>
-#include <queue>
-
-namespace ClipperLib {
-
-enum ClipType { ctIntersection, ctUnion, ctDifference, ctXor };
-enum PolyType { ptSubject, ptClip };
-//By far the most widely used winding rules for polygon filling are
-//EvenOdd & NonZero (GDI, GDI+, XLib, OpenGL, Cairo, AGG, Quartz, SVG, Gr32)
-//Others rules include Positive, Negative and ABS_GTR_EQ_TWO (only in OpenGL)
-//see http://glprogramming.com/red/chapter11.html
-enum PolyFillType { pftEvenOdd, pftNonZero, pftPositive, pftNegative };
-
-#ifdef use_int32
-  typedef int cInt;
-  static cInt const loRange = 0x7FFF;
-  static cInt const hiRange = 0x7FFF;
-#else
-  typedef signed long long cInt;
-  static cInt const loRange = 0x3FFFFFFF;
-  static cInt const hiRange = 0x3FFFFFFFFFFFFFFFLL;
-  typedef signed long long long64;     //used by Int128 class
-  typedef unsigned long long ulong64;
-
-#endif
-
-struct IntPoint {
-  cInt X;
-  cInt Y;
-#ifdef use_xyz
-  cInt Z;
-  IntPoint(cInt x = 0, cInt y = 0, cInt z = 0): X(x), Y(y), Z(z) {};
-#else
-  IntPoint(cInt x = 0, cInt y = 0): X(x), Y(y) {};
-#endif
-
-  friend inline bool operator== (const IntPoint& a, const IntPoint& b)
-  {
-    return a.X == b.X && a.Y == b.Y;
-  }
-  friend inline bool operator!= (const IntPoint& a, const IntPoint& b)
-  {
-    return a.X != b.X  || a.Y != b.Y; 
-  }
-};
-//------------------------------------------------------------------------------
-
-typedef std::vector< IntPoint > Path;
-typedef std::vector< Path > Paths;
-
-inline Path& operator <<(Path& poly, const IntPoint& p) {poly.push_back(p); return poly;}
-inline Paths& operator <<(Paths& polys, const Path& p) {polys.push_back(p); return polys;}
-
-std::ostream& operator <<(std::ostream &s, const IntPoint &p);
-std::ostream& operator <<(std::ostream &s, const Path &p);
-std::ostream& operator <<(std::ostream &s, const Paths &p);
-
-struct DoublePoint
-{
-  double X;
-  double Y;
-  DoublePoint(double x = 0, double y = 0) : X(x), Y(y) {}
-  DoublePoint(IntPoint ip) : X((double)ip.X), Y((double)ip.Y) {}
-};
-//------------------------------------------------------------------------------
-
-#ifdef use_xyz
-typedef void (*ZFillCallback)(IntPoint& e1bot, IntPoint& e1top, IntPoint& e2bot, IntPoint& e2top, IntPoint& pt);
-#endif
-
-enum InitOptions {ioReverseSolution = 1, ioStrictlySimple = 2, ioPreserveCollinear = 4};
-enum JoinType {jtSquare, jtRound, jtMiter};
-enum EndType {etClosedPolygon, etClosedLine, etOpenButt, etOpenSquare, etOpenRound};
-
-class PolyNode;
-typedef std::vector< PolyNode* > PolyNodes;
-
-class PolyNode 
-{ 
-public:
-    PolyNode();
-    virtual ~PolyNode(){};
-    Path Contour;
-    PolyNodes Childs;
-    PolyNode* Parent;
-    PolyNode* GetNext() const;
-    bool IsHole() const;
-    bool IsOpen() const;
-    int ChildCount() const;
-private:
-    //PolyNode& operator =(PolyNode& other); 
-    unsigned Index; //node index in Parent.Childs
-    bool m_IsOpen;
-    JoinType m_jointype;
-    EndType m_endtype;
-    PolyNode* GetNextSiblingUp() const;
-    void AddChild(PolyNode& child);
-    friend class Clipper; //to access Index
-    friend class ClipperOffset; 
-};
-
-class PolyTree: public PolyNode
-{ 
-public:
-    ~PolyTree(){ Clear(); };
-    PolyNode* GetFirst() const;
-    void Clear();
-    int Total() const;
-private:
-  //PolyTree& operator =(PolyTree& other);
-  PolyNodes AllNodes;
-    friend class Clipper; //to access AllNodes
-};
-
-bool Orientation(const Path &poly);
-double Area(const Path &poly);
-int PointInPolygon(const IntPoint &pt, const Path &path);
-
-void SimplifyPolygon(const Path &in_poly, Paths &out_polys, PolyFillType fillType = pftEvenOdd);
-void SimplifyPolygons(const Paths &in_polys, Paths &out_polys, PolyFillType fillType = pftEvenOdd);
-void SimplifyPolygons(Paths &polys, PolyFillType fillType = pftEvenOdd);
-
-void CleanPolygon(const Path& in_poly, Path& out_poly, double distance = 1.415);
-void CleanPolygon(Path& poly, double distance = 1.415);
-void CleanPolygons(const Paths& in_polys, Paths& out_polys, double distance = 1.415);
-void CleanPolygons(Paths& polys, double distance = 1.415);
-
-void MinkowskiSum(const Path& pattern, const Path& path, Paths& solution, bool pathIsClosed);
-void MinkowskiSum(const Path& pattern, const Paths& paths, Paths& solution, bool pathIsClosed);
-void MinkowskiDiff(const Path& poly1, const Path& poly2, Paths& solution);
-
-void PolyTreeToPaths(const PolyTree& polytree, Paths& paths);
-void ClosedPathsFromPolyTree(const PolyTree& polytree, Paths& paths);
-void OpenPathsFromPolyTree(PolyTree& polytree, Paths& paths);
-
-void ReversePath(Path& p);
-void ReversePaths(Paths& p);
-
-struct IntRect { cInt left; cInt top; cInt right; cInt bottom; };
-
-//enums that are used internally ...
-enum EdgeSide { esLeft = 1, esRight = 2};
-
-//forward declarations (for stuff used internally) ...
-struct TEdge;
-struct IntersectNode;
-struct LocalMinimum;
-struct OutPt;
-struct OutRec;
-struct Join;
-
-typedef std::vector < OutRec* > PolyOutList;
-typedef std::vector < TEdge* > EdgeList;
-typedef std::vector < Join* > JoinList;
-typedef std::vector < IntersectNode* > IntersectList;
-
-//------------------------------------------------------------------------------
-
-//ClipperBase is the ancestor to the Clipper class. It should not be
-//instantiated directly. This class simply abstracts the conversion of sets of
-//polygon coordinates into edge objects that are stored in a LocalMinima list.
-class ClipperBase
-{
-public:
-  ClipperBase();
-  virtual ~ClipperBase();
-  virtual bool AddPath(const Path &pg, PolyType PolyTyp, bool Closed);
-  bool AddPaths(const Paths &ppg, PolyType PolyTyp, bool Closed);
-  virtual void Clear();
-  IntRect GetBounds();
-  bool PreserveCollinear() {return m_PreserveCollinear;};
-  void PreserveCollinear(bool value) {m_PreserveCollinear = value;};
-protected:
-  void DisposeLocalMinimaList();
-  TEdge* AddBoundsToLML(TEdge *e, bool IsClosed);
-  virtual void Reset();
-  TEdge* ProcessBound(TEdge* E, bool IsClockwise);
-  void InsertScanbeam(const cInt Y);
-  bool PopScanbeam(cInt &Y);
-  bool LocalMinimaPending();
-  bool PopLocalMinima(cInt Y, const LocalMinimum *&locMin);
-  OutRec* CreateOutRec();
-  void DisposeAllOutRecs();
-  void DisposeOutRec(PolyOutList::size_type index);
-  void SwapPositionsInAEL(TEdge *edge1, TEdge *edge2);
-  void DeleteFromAEL(TEdge *e);
-  void UpdateEdgeIntoAEL(TEdge *&e);
-
-  typedef std::vector<LocalMinimum> MinimaList;
-  MinimaList::iterator m_CurrentLM;
-  MinimaList           m_MinimaList;
-
-  bool              m_UseFullRange;
-  EdgeList          m_edges;
-  bool              m_PreserveCollinear;
-  bool              m_HasOpenPaths;
-  PolyOutList       m_PolyOuts;
-  TEdge           *m_ActiveEdges;
-
-  typedef std::priority_queue<cInt> ScanbeamList;
-  ScanbeamList     m_Scanbeam;
-};
-//------------------------------------------------------------------------------
-
-class Clipper : public virtual ClipperBase
-{
-public:
-  Clipper(int initOptions = 0);
-  bool Execute(ClipType clipType,
-      Paths &solution,
-      PolyFillType fillType = pftEvenOdd);
-  bool Execute(ClipType clipType,
-      Paths &solution,
-      PolyFillType subjFillType,
-      PolyFillType clipFillType);
-  bool Execute(ClipType clipType,
-      PolyTree &polytree,
-      PolyFillType fillType = pftEvenOdd);
-  bool Execute(ClipType clipType,
-      PolyTree &polytree,
-      PolyFillType subjFillType,
-      PolyFillType clipFillType);
-  bool ReverseSolution() { return m_ReverseOutput; };
-  void ReverseSolution(bool value) {m_ReverseOutput = value;};
-  bool StrictlySimple() {return m_StrictSimple;};
-  void StrictlySimple(bool value) {m_StrictSimple = value;};
-  //set the callback function for z value filling on intersections (otherwise Z is 0)
-#ifdef use_xyz
-  void ZFillFunction(ZFillCallback zFillFunc);
-#endif
-protected:
-  virtual bool ExecuteInternal();
-private:
-  JoinList         m_Joins;
-  JoinList         m_GhostJoins;
-  IntersectList    m_IntersectList;
-  ClipType         m_ClipType;
-  typedef std::list<cInt> MaximaList;
-  MaximaList       m_Maxima;
-  TEdge           *m_SortedEdges;
-  bool             m_ExecuteLocked;
-  PolyFillType     m_ClipFillType;
-  PolyFillType     m_SubjFillType;
-  bool             m_ReverseOutput;
-  bool             m_UsingPolyTree; 
-  bool             m_StrictSimple;
-#ifdef use_xyz
-  ZFillCallback   m_ZFill; //custom callback 
-#endif
-  void SetWindingCount(TEdge& edge);
-  bool IsEvenOddFillType(const TEdge& edge) const;
-  bool IsEvenOddAltFillType(const TEdge& edge) const;
-  void InsertLocalMinimaIntoAEL(const cInt botY);
-  void InsertEdgeIntoAEL(TEdge *edge, TEdge* startEdge);
-  void AddEdgeToSEL(TEdge *edge);
-  bool PopEdgeFromSEL(TEdge *&edge);
-  void CopyAELToSEL();
-  void DeleteFromSEL(TEdge *e);
-  void SwapPositionsInSEL(TEdge *edge1, TEdge *edge2);
-  bool IsContributing(const TEdge& edge) const;
-  bool IsTopHorz(const cInt XPos);
-  void DoMaxima(TEdge *e);
-  void ProcessHorizontals();
-  void ProcessHorizontal(TEdge *horzEdge);
-  void AddLocalMaxPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
-  OutPt* AddLocalMinPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
-  OutRec* GetOutRec(int idx);
-  void AppendPolygon(TEdge *e1, TEdge *e2);
-  void IntersectEdges(TEdge *e1, TEdge *e2, IntPoint &pt);
-  OutPt* AddOutPt(TEdge *e, const IntPoint &pt);
-  OutPt* GetLastOutPt(TEdge *e);
-  bool ProcessIntersections(const cInt topY);
-  void BuildIntersectList(const cInt topY);
-  void ProcessIntersectList();
-  void ProcessEdgesAtTopOfScanbeam(const cInt topY);
-  void BuildResult(Paths& polys);
-  void BuildResult2(PolyTree& polytree);
-  void SetHoleState(TEdge *e, OutRec *outrec);
-  void DisposeIntersectNodes();
-  bool FixupIntersectionOrder();
-  void FixupOutPolygon(OutRec &outrec);
-  void FixupOutPolyline(OutRec &outrec);
-  bool IsHole(TEdge *e);
-  bool FindOwnerFromSplitRecs(OutRec &outRec, OutRec *&currOrfl);
-  void FixHoleLinkage(OutRec &outrec);
-  void AddJoin(OutPt *op1, OutPt *op2, const IntPoint offPt);
-  void ClearJoins();
-  void ClearGhostJoins();
-  void AddGhostJoin(OutPt *op, const IntPoint offPt);
-  bool JoinPoints(Join *j, OutRec* outRec1, OutRec* outRec2);
-  void JoinCommonEdges();
-  void DoSimplePolygons();
-  void FixupFirstLefts1(OutRec* OldOutRec, OutRec* NewOutRec);
-  void FixupFirstLefts2(OutRec* InnerOutRec, OutRec* OuterOutRec);
-  void FixupFirstLefts3(OutRec* OldOutRec, OutRec* NewOutRec);
-#ifdef use_xyz
-  void SetZ(IntPoint& pt, TEdge& e1, TEdge& e2);
-#endif
-};
-//------------------------------------------------------------------------------
-
-class ClipperOffset 
-{
-public:
-  ClipperOffset(double miterLimit = 2.0, double roundPrecision = 0.25);
-  ~ClipperOffset();
-  void AddPath(const Path& path, JoinType joinType, EndType endType);
-  void AddPaths(const Paths& paths, JoinType joinType, EndType endType);
-  void Execute(Paths& solution, double delta);
-  void Execute(PolyTree& solution, double delta);
-  void Clear();
-  double MiterLimit;
-  double ArcTolerance;
-private:
-  Paths m_destPolys;
-  Path m_srcPoly;
-  Path m_destPoly;
-  std::vector<DoublePoint> m_normals;
-  double m_delta, m_sinA, m_sin, m_cos;
-  double m_miterLim, m_StepsPerRad;
-  IntPoint m_lowest;
-  PolyNode m_polyNodes;
-
-  void FixOrientations();
-  void DoOffset(double delta);
-  void OffsetPoint(int j, int& k, JoinType jointype);
-  void DoSquare(int j, int k);
-  void DoMiter(int j, int k, double r);
-  void DoRound(int j, int k);
-};
-//------------------------------------------------------------------------------
-
-class clipperException : public std::exception
-{
-  public:
-    clipperException(const char* description): m_descr(description) {}
-    virtual ~clipperException() throw() {}
-    virtual const char* what() const throw() {return m_descr.c_str();}
-  private:
-    std::string m_descr;
-};
-//------------------------------------------------------------------------------
-
-} //ClipperLib namespace
-
-#endif //clipper_hpp
-
-

deploy/lite/utils/db_post_process.cpp

-// 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 <iostream>
-#include <vector>
-#include <math.h>
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
-#include "clipper.hpp"
-#include "clipper.cpp"
-
-
-void getcontourarea(float ** box, float unclip_ratio, float & distance){
-  int pts_num=4;
-  float area = 0.0f;
-  float dist = 0.0f;
-  for (int i=0; i<pts_num; i++){
-    area += box[i][0] * box[(i+1)%pts_num][1] - box[i][1] * box[(i + 1) % pts_num][0];
-    dist += sqrtf( (box[i][0] - box[(i + 1) % pts_num][0]) * (box[i][0] - box[(i + 1) % pts_num][0]) + (box[i][1] - box[(i + 1) % pts_num][1]) * (box[i][1] - box[(i + 1) % pts_num][1]) );
-  }
-  area = fabs(float(area/2.0));
-
-  distance = area * unclip_ratio / dist;
-
-}
-
-cv::RotatedRect unclip(float ** box){
-  float unclip_ratio = 2.0;
-  float distance = 1.0;
-
-  getcontourarea(box, unclip_ratio, distance);
-
-  ClipperLib::ClipperOffset offset;
-  ClipperLib::Path p;
-  p << ClipperLib::IntPoint(int(box[0][0]), int(box[0][1])) << ClipperLib::IntPoint(int(box[1][0]), int(box[1][1])) <<
-    ClipperLib::IntPoint(int(box[2][0]), int(box[2][1])) << ClipperLib::IntPoint(int(box[3][0]), int(box[3][1]));
-  offset.AddPath(p, ClipperLib::jtRound, ClipperLib::etClosedPolygon);
-
-  ClipperLib::Paths soln;
-  offset.Execute(soln, distance);
-  std::vector<cv::Point2f> points;
-
-  for (int j=0; j<soln.size(); j++){
-    for (int i=0; i< soln[soln.size()-1].size(); i++){
-      points.emplace_back(soln[j][i].X, soln[j][i].Y);
-    }
-  }
-  cv::RotatedRect res = cv::minAreaRect(points);
-
-  return res;
-}
-
-float ** Mat2Vec(cv::Mat mat)
-{
-  auto **array = new float*[mat.rows];
-  for (int i = 0; i<mat.rows; ++i)
-    array[i] = new float[mat.cols];
-  for (int i = 0; i < mat.rows; ++i)
-  {
-    for (int j = 0; j < mat.cols; ++j)
-    {
-      array[i][j] = mat.at<float>(i, j);
-    }
-  }
-
-  return array;
-}
-
-void quickSort(float ** s, int l, int r)
-{
-  if (l < r)
-  {
-    int i = l, j = r;
-    float x = s[l][0];
-    float * xp = s[l];
-    while (i < j)
-    {
-      while(i < j && s[j][0]>= x)
-        j--;
-      if(i < j)
-        std::swap(s[i++], s[j]);
-      while(i < j && s[i][0]< x)
-        i++;
-      if(i < j)
-        std::swap(s[j--], s[i]);
-    }
-    s[i] = xp;
-    quickSort(s, l, i - 1);
-    quickSort(s, i + 1, r);
-  }
-}
-
-void quickSort_vector(std::vector<std::vector<int>> & box, int l, int r, int axis){
-  if (l < r){
-    int i = l, j = r;
-    int x = box[l][axis];
-    std::vector<int> xp (box[l]);
-    while (i < j)
-    {
-      while(i < j && box[j][axis]>= x)
-        j--;
-      if(i < j)
-        std::swap(box[i++], box[j]);
-      while(i < j && box[i][axis]< x)
-        i++;
-      if(i < j)
-        std::swap(box[j--], box[i]);
-    }
-    box[i] = xp;
-    quickSort_vector(box, l, i - 1, axis);
-    quickSort_vector(box, i + 1, r, axis);
-  }
-}
-
-std::vector<std::vector<int>> order_points_clockwise(std::vector<std::vector<int>> pts){
-  std::vector<std::vector<int>> box = pts;
-  quickSort_vector(box, 0, int(box.size()-1), 0);
-  std::vector<std::vector<int>> leftmost = {box[0], box[1]};
-  std::vector<std::vector<int>> rightmost = {box[2], box[3]};
-
-  if (leftmost[0][1]>leftmost[1][1])
-    std::swap(leftmost[0], leftmost[1]);
-
-  if (rightmost[0][1]> rightmost[1][1])
-    std::swap(rightmost[0], rightmost[1]);
-
-  std::vector<std::vector<int>> rect = {leftmost[0], rightmost[0], rightmost[1], leftmost[1]};
-  return rect;
-}
-
-float ** get_mini_boxes(cv::RotatedRect box, float & ssid){
-  ssid = box.size.width>=box.size.height?box.size.height:box.size.width;
-
-  cv::Mat points;
-  cv::boxPoints(box, points);
-  // sorted box points
-  auto array = Mat2Vec(points);
-  quickSort(array, 0, 3);
-
-  float * idx1=array[0], *idx2=array[1], *idx3=array[2], *idx4=array[3];
-  if (array[3][1]<=array[2][1]) {
-    idx2 = array[3];
-    idx3 = array[2];
-  }
-  else{
-    idx2 = array[2];
-    idx3 = array[3];
-  }
-  if (array[1][1]<=array[0][1]){
-    idx1 = array[1];
-    idx4 = array[0];
-  }
-  else{
-    idx1 = array[0];
-    idx4 = array[1];
-  }
-
-  array[0] = idx1;
-  array[1] = idx2;
-  array[2] = idx3;
-  array[3] = idx4;
-
-  return array;
-}
-
-template<class T>
-T clamp(T x, T min, T max)
-{
-  if (x > max)
-    return max;
-  if (x < min)
-    return min;
-  return x;
-}
-float clampf(float x, float min, float max){
-  if (x > max)
-    return max;
-  if (x < min)
-    return min;
-  return x;
-}
-
-
-float box_score_fast(float ** box_array, cv::Mat pred){
-  auto array=box_array;
-  int width = pred.cols;
-  int height = pred.rows;
-
-  float box_x[4]={array[0][0], array[1][0], array[2][0], array[3][0]};
-  float box_y[4]={array[0][1], array[1][1], array[2][1], array[3][1]};
-
-  int xmin = clamp(int(std::floorf(*(std::min_element(box_x, box_x+4)))), 0, width - 1);
-  int xmax = clamp(int(std::ceilf(*(std::max_element(box_x, box_x+4)))), 0, width - 1);
-  int ymin = clamp(int(std::floorf(*(std::min_element(box_y, box_y+4)))), 0, height - 1);
-  int ymax = clamp(int(std::ceilf(*(std::max_element(box_y, box_y+4)))), 0, height - 1);
-
-  cv::Mat mask;
-  mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
-
-  cv::Point root_point[4];
-  root_point[0] = cv::Point(int(array[0][0])-xmin, int(array[0][1])-ymin);
-  root_point[1] = cv::Point(int(array[1][0])-xmin, int(array[1][1])-ymin);
-  root_point[2] = cv::Point(int(array[2][0])-xmin, int(array[2][1])-ymin);
-  root_point[3] = cv::Point(int(array[3][0])-xmin, int(array[3][1])-ymin);
-  const cv::Point* ppt[1] = {root_point};
-  int npt[] = {4};
-  cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
-
-  cv::Mat croppedImg;
-  pred(cv::Rect(xmin, ymin, xmax-xmin+1,ymax-ymin+1)).copyTo(croppedImg);
-
-  auto score = cv::mean(croppedImg, mask)[0];
-  return score;
-}
-
-
-std::vector<std::vector<std::vector<int>>> boxes_from_bitmap(const cv::Mat pred, const cv::Mat bitmap) {
-  const int min_size=3;
-  const int max_candidates = 1000;
-  const float box_thresh=0.5;
-
-  int width = bitmap.cols;
-  int height = bitmap.rows;
-
-  std::vector<std::vector<cv::Point>> contours;
-  std::vector<cv::Vec4i> hierarchy;
-
-  cv::findContours(bitmap, contours, hierarchy, cv::RETR_LIST, cv::CHAIN_APPROX_SIMPLE);
-
-  int num_contours = contours.size() >= max_candidates ? max_candidates : contours.size();
-
-  std::vector<std::vector<std::vector<int>>> boxes;
-
-  for (int _i = 0; _i < num_contours; _i++) {
-    float ssid;
-    cv::RotatedRect box = cv::minAreaRect(contours[_i]);
-    auto array = get_mini_boxes(box, ssid);
-
-    auto box_for_unclip = array;
-    //end get_mini_box
-
-    if (ssid< min_size) {
-      continue;
-    }
-
-    float score;
-    score = box_score_fast(array, pred);
-    //end box_score_fast
-    if (score < box_thresh)
-      continue;
-
-
-    // start for unclip
-    cv::RotatedRect points = unclip(box_for_unclip);
-    // end for unclip
-
-    cv::RotatedRect clipbox = points;
-    auto cliparray = get_mini_boxes(clipbox, ssid);
-
-    if (ssid < min_size+2) continue;
-
-    int dest_width=pred.cols;
-    int dest_height=pred.rows;
-    std::vector<std::vector<int>> intcliparray;
-
-    for (int num_pt=0; num_pt<4; num_pt++){
-      std::vector<int> a  { int( clampf(roundf(cliparray[num_pt][0]/float(width)*float(dest_width)), 0, float(dest_width)) ),
-                            int( clampf(roundf(cliparray[num_pt][1]/float(height)*float(dest_height)), 0, float(dest_height)) )};
-      intcliparray.push_back(a);
-    }
-    boxes.push_back(intcliparray);
-
-  }//end for
-  return boxes;
-}
-
-int _max(int a, int b){
-  return a>=b?a:b;
-}
-
-int _min(int a, int b){
-  return a>=b?b:a;
-}
-
-std::vector<std::vector<std::vector<int>>>  filter_tag_det_res(std::vector<std::vector<std::vector<int>>> boxes,
-        float ratio_h, float ratio_w, cv::Mat srcimg){
-  int oriimg_h = srcimg.rows;
-  int oriimg_w = srcimg.cols;
-
-  std::vector<std::vector<std::vector<int>>> root_points;
-  for (int n=0; n<boxes.size(); n++){
-    boxes[n] = order_points_clockwise(boxes[n]);
-    for (int m=0; m< boxes[0].size(); m++){
-      boxes[n][m][0] /= ratio_w;
-      boxes[n][m][1] /= ratio_h;
-
-      boxes[n][m][0] = int(_min(_max(boxes[n][m][0], 0), oriimg_w-1));
-      boxes[n][m][1] = int(_min(_max(boxes[n][m][1], 0), oriimg_h-1));
-    }
-  }
-
-  for(int n=0; n<boxes.size(); n++){
-  int rect_width, rect_height;
-  rect_width = int(sqrt(pow(boxes[n][0][0] - boxes[n][1][0], 2) + pow(boxes[n][0][1] - boxes[n][1][1], 2)));
-  rect_height = int(sqrt(pow(boxes[n][0][0] - boxes[n][3][0], 2) + pow(boxes[n][0][1] - boxes[n][3][1], 2)));
-  if (rect_width <=10 || rect_height<=10)
-    continue;
-  root_points.push_back(boxes[n]);
-  }
-  return root_points;
-}
-
-/*
-using namespace std;
-// read data from txt file
-cv::Mat readtxt2(std::string path, int imgw, int imgh, int imgc) {
-  std::cout << "read data file from txt file! " << std::endl;
-  ifstream in(path);
-  string line;
-  int count = 0;
-  int i = 0, j = 0;
-  std::vector<float> img_mean = {0.485, 0.456, 0.406};
-  std::vector<float> img_std = {0.229, 0.224, 0.225};
-
-  float trainData[imgh][imgw*imgc];
-
-  while (getline(in, line)) {
-    stringstream ss(line);
-    double x;
-    while (ss >> x) {
-//      trainData[i][j] = float(x) * img_std[j % 3] + img_mean[j % 3];
-      trainData[i][j] = float(x);
-      j++;
-    }
-    i++;
-    j = 0;
-  }
-
-  cv::Mat pred_map(imgh, imgw*imgc, CV_32FC1, (float *) trainData);
-  cv::Mat reshape_img = pred_map.reshape(imgc, imgh);
-  return reshape_img;
-}
- */
-//using namespace std;
-//
-//void writetxt(vector<vector<float>> data, std::string save_path){
-//
-//  ofstream fout(save_path);
-//
-//  for (int i = 0; i < data.size(); i++) {
-//    for (int j=0; j< data[0].size(); j++){
-//      fout << data[i][j] << " ";
-//    }
-//    fout << endl;
-//  }
-//  fout << endl;
-//  fout.close();
-//}