add deploy lite demo

.gitignore

@@ -18,3 +18,4 @@ output/
 *.idea
 
 *.log
+.clang-format

deploy/lite/Makefile

+ARM_ABI = arm8
+export ARM_ABI
+
+include ../Makefile.def
+
+LITE_ROOT=../../../
+
+THIRD_PARTY_DIR=${LITE_ROOT}/third_party
+
+OPENCV_VERSION=opencv4.1.0
+
+OPENCV_LIBS = ../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgcodecs.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgproc.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_core.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtegra_hal.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjpeg-turbo.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibwebp.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibpng.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjasper.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibtiff.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libIlmImf.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtbb.a \
+              ../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libcpufeatures.a
+
+OPENCV_INCLUDE = -I../../../third_party/${OPENCV_VERSION}/arm64-v8a/include
+
+CXX_INCLUDES = $(INCLUDES) ${OPENCV_INCLUDE} -I$(LITE_ROOT)/cxx/include
+
+CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SYSTEM_LIBS)
+
+###############################################################
+# How to use one of static libaray:                           #
+#  `libpaddle_api_full_bundled.a`                             #
+#  `libpaddle_api_light_bundled.a`                            #
+###############################################################
+# Note: default use lite's shared library.                    #
+###############################################################
+# 1. Comment above line using `libpaddle_light_api_shared.so`
+# 2. Undo comment below line using `libpaddle_api_light_bundled.a`
+
+#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.o: ocr_db_crnn.cc
+	$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o ocr_db_crnn.o -c ocr_db_crnn.cc
+
+fetch_opencv:
+	@ test -d ${THIRD_PARTY_DIR} ||  mkdir ${THIRD_PARTY_DIR}
+	@ test -e ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz || \
+      (echo "fetch opencv libs" && \
+      wget -P ${THIRD_PARTY_DIR} https://paddle-inference-dist.bj.bcebos.com/${OPENCV_VERSION}.tar.gz)
+	@ test -d ${THIRD_PARTY_DIR}/${OPENCV_VERSION} || \
+      tar -zxvf ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz -C ${THIRD_PARTY_DIR}
+
+
+.PHONY: clean
+clean:
+	rm -f ocr_db_crnn.o
+	rm -f ocr_db_crnnn

deploy/lite/ocr_db_crnn.cc

+// Copyright (c) 2019 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 <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>
+
+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;
+}
+
+// fill tensor with mean and scale and trans layout: nhwc -> nchw, neon speed up
+void neon_mean_scale(const float* din,
+                     float* dout,
+                     int size,
+                     const std::vector<float> mean,
+                     const std::vector<float> scale) {
+  if (mean.size() != 3 || scale.size() != 3) {
+    std::cerr << "[ERROR] mean or scale size must equal to 3\n";
+    exit(1);
+  }
+  float32x4_t vmean0 = vdupq_n_f32(mean[0]);
+  float32x4_t vmean1 = vdupq_n_f32(mean[1]);
+  float32x4_t vmean2 = vdupq_n_f32(mean[2]);
+  float32x4_t vscale0 = vdupq_n_f32(scale[0]);
+  float32x4_t vscale1 = vdupq_n_f32(scale[1]);
+  float32x4_t vscale2 = vdupq_n_f32(scale[2]);
+
+  float* dout_c0 = dout;
+  float* dout_c1 = dout + size;
+  float* dout_c2 = dout + size * 2;
+
+  int i = 0;
+  for (; i < size - 3; i += 4) {
+    float32x4x3_t vin3 = vld3q_f32(din);
+    float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0);
+    float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1);
+    float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2);
+    float32x4_t vs0 = vmulq_f32(vsub0, vscale0);
+    float32x4_t vs1 = vmulq_f32(vsub1, vscale1);
+    float32x4_t vs2 = vmulq_f32(vsub2, vscale2);
+    vst1q_f32(dout_c0, vs0);
+    vst1q_f32(dout_c1, vs1);
+    vst1q_f32(dout_c2, vs2);
+
+    din += 12;
+    dout_c0 += 4;
+    dout_c1 += 4;
+    dout_c2 += 4;
+  }
+  for (; i < size; i++) {
+    *(dout_c0++) = (*(din++) - mean[0]) * scale[0];
+    *(dout_c1++) = (*(din++) - mean[1]) * scale[1];
+    *(dout_c2++) = (*(din++) - mean[2]) * scale[2];
+  }
+}
+
+// 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){
+  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){
+      ratio = float(max_size_len) / float(h);
+    } else {
+      ratio = float(max_size_len) / float(w);
+    }
+  }
+
+  int resize_h = int(float(h) * ratio);
+  int resize_w = int(float(w) * ratio);
+  if (resize_h % 32 == 0)
+    resize_h = resize_h;
+  else if (resize_h / 32 < 1)
+    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)
+    resize_w = 32;
+  else
+    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);
+  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);
+
+  std::vector<float> mean = {0.5f, 0.5f, 0.5f};
+  std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
+
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+  cv::Mat crop_img;
+  cv::Mat resize_img;
+
+  std::string dict_path = "./ppocr_keys_v1.txt";
+  auto charactor_dict = read_dict(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]);
+
+    float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
+
+    resize_img = crnn_resize_img(crop_img, wh_ratio);
+    resize_img.convertTo(resize_img, CV_32FC3, 1 / 255.f);
+
+    const float *dimg = reinterpret_cast<const float *>(resize_img.data);
+
+    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);
+
+    //// 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>();
+
+    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) {
+      pred_idx.push_back(int(rec_idx[n]));
+    }
+
+    if (pred_idx.size() < 1e-3)
+      continue;
+    std::cout << std::endl;
+
+    index += 1;
+    std::cout << index << "\t";
+    for (int n = 0; n < pred_idx.size(); n++) {
+      std::cout << charactor_dict[pred_idx[n]];
+    }
+
+    ////get score
+    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();
+
+    int argmax_idx;
+    int blank = predict_shape[1];
+    float score = 0.f;
+    int count = 0;
+    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]]));
+
+      if (blank - 1 - argmax_idx > 1e-5) {
+        score += max_value;
+        count += 1;
+      }
+
+    }
+    score /= count;
+    std::cout << "\tscore: " << score << std::endl;
+  }
+}
+
+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);
+
+  // Read img
+  int max_side_len = 960;
+  float ratio_h{};
+  float ratio_w{};
+
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+
+  img = resize_img_type0(img, max_side_len, &ratio_h, &ratio_w);
+  cv::Mat img_fp;
+  img.convertTo(img_fp, CV_32FC3, 1.0 / 255.f);
+
+  // Prepare input data from image
+  std::unique_ptr<Tensor> input_tensor0(std::move(predictor->GetInput(0)));
+  input_tensor0->Resize({1, 3, img_fp.rows, img_fp.cols});
+  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};
+  const float* dimg = reinterpret_cast<const float*>(img_fp.data);
+  neon_mean_scale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
+
+  // Run predictor
+  predictor->Run();
+
+  // Get output and post process
+  std::unique_ptr<const Tensor> output_tensor(std::move(predictor->GetOutput(0)));
+  auto* outptr = output_tensor->data<float>();
+  auto shape_out = output_tensor->shape();
+
+  int64_t out_numl = 1;
+  double sum = 0;
+  for (auto i : shape_out) {
+    out_numl *= i;
+  }
+
+  // Save output
+  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);
+  }
+
+  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);
+
+  const double threshold = 0.3*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);
+
+  std::vector<std::vector<std::vector<int>>> filter_boxes = filter_tag_det_res(boxes, ratio_h, ratio_w, 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]));
+    }
+  }
+
+  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);
+  }
+
+  cv::imwrite("./imgs_vis/vis.jpg", img_vis);
+  std::cout << "The detection visualized image saved in ./imgs_vis/" <<std::endl;
+
+  return filter_boxes;
+}
+
+
+int main(int argc, char** argv) {
+  if (argc < 4) {
+    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];
+
+  auto start = std::chrono::system_clock::now();
+
+  cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
+  auto boxes = RunDetModel(det_model_file, srcimg);
+
+  RunRecModel(boxes, srcimg, rec_model_file);
+
+  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
+            << "秒" << std::endl;
+
+  return 0;
+}
+

deploy/lite/readme.md

+# PaddleOCR 移动端部署
+
+本教程介绍如何在移动端部署PaddleOCR超轻量中文检测、识别模型。
+
+## 运行准备
+- 电脑（编译Paddle-Lite）
+- 安卓手机（armv7或armv8）
+
+
+## 1. 准备环境
+
+### 1.1 准备交叉编译环境
+交叉编译环境用于编译Paddle-Lite和PaddleOCR的C++ demo。
+支持多种开发环境，不同开发环境的编译流程请参考对应文档。：
+1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#docker)
+2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#android)
+3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/user_guides/source_compile.html#id13)
+4. [Windows](https://paddle-lite.readthedocs.io/zh/latest/demo_guides/x86.html#windows)
+
+
+### 1.2 准备预编译库
+
+预编译库有两种获取方式：
+- 1. 直接下载，下载[链接](https://paddle-lite.readthedocs.io/zh/latest/user_guides/release_lib.html#android-toolchain-gcc).
+    注意选择with_extra=ON，with_cv=ON的下载链接。
+- 2. 编译Paddle-Lite得到，Paddle-Lite的编译方式如下：
+```
+git clone https://github.com/PaddlePaddle/Paddle-Lite.git
+cd Paddle-Lite
+./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON
+```
+
+注意：编译Paddle-Lite获得预编译库时，需要打开--with_cv=ON --with_extra=ON两个选项，--arch表示arm版本，这里指定为armv8，
+更多编译命令
+介绍请参考[链接](https://paddle-lite.readthedocs.io/zh/latest/user_guides/Compile/Android.html#id2)。
+
+直接下载预编译库并解压后，可以得到'inference_lite_lib.android.armv8/'文件夹，通过编译Paddle-Lite得到的预编译库位于
+'Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/'文件夹下。
+预编译库的文件目录如下：
+```
+inference_lite_lib.android.armv8/
+|-- cxx                           C++ 预测库和头文件
+|   |-- include                                C++ 头文件
+|   |   |-- paddle_api.h
+|   |   |-- paddle_image_preprocess.h
+|   |   |-- paddle_lite_factory_helper.h
+|   |   |-- paddle_place.h
+|   |   |-- paddle_use_kernels.h
+|   |   |-- paddle_use_ops.h
+|   |   `-- paddle_use_passes.h
+|   `-- lib                                    C++预测库
+|       |-- libpaddle_api_light_bundled.a             C++静态库
+|       `-- libpaddle_light_api_shared.so             C++动态库
+|-- java                          Java预测库
+|   |-- jar
+|   |   `-- PaddlePredictor.jar
+|   |-- so
+|   |   `-- libpaddle_lite_jni.so
+|   `-- src
+|-- demo                          C++和Java示例代码
+|   |-- cxx                                  C++  预测库demo
+|   `-- java                                 Java 预测库demo
+```
+
+## 2 开始运行
+
+### 2.1 模型优化
+
+Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括量化、子图融合、混合调度、Kernel优选等方法，使用Paddle_lite的opt工具可以自动
+对模inference型进行优化，优化后的模型更轻量，模型运行速度更快。
+
+模型优化需要使用Paddle-Lite的opt可执行文件，可以通过编译Paddle-Lite源码获得，编译步骤如下：
+```
+# 如果准备环境中已经clone了Paddle-Lite，则不用重新clone Paddle-Lite
+git clone https://github.com/PaddlePaddle/Paddle-Lite.git
+cd Paddle-Lite
+# 启动编译
+./lite/tools/build.sh build_optimize_tool
+```
+
+编译完成后，opt文件位于'build.opt/lite/api/'下，可通过如下方式查看opt的运行选项和使用方式；
+```
+cd build.opt/lite/api/
+./opt
+```
+
+|选项|说明|
+|:-:|:-:|
+|--model_dir|待优化的PaddlePaddle模型（非combined形式）的路径|
+|--model_file|待优化的PaddlePaddle模型（combined形式）的网络结构文件路径|
+|--param_file|待优化的PaddlePaddle模型（combined形式）的权重文件路径|
+|--optimize_out_type|输出模型类型，目前支持两种类型：protobuf和naive_buffer，其中naive_buffer是一种更轻量级的序列化/反序列化实现。若您需要在mobile端执行模型预测，请将此选项设置为naive_buffer。默认为protobuf|
+|--optimize_out|优化模型的输出路径|
+|--valid_targets|指定模型可执行的backend，默认为arm。目前可支持x86、arm、opencl、npu、xpu，可以同时指定多个backend(以空格分隔)，Model Optimize Tool将会自动选择最佳方式。如果需要支持华为NPU（Kirin 810/990 Soc搭载的达芬奇架构NPU），应当设置为npu, arm|
+|--record_tailoring_info|当使用 根据模型裁剪库文件 功能时，则设置该选项为true，以记录优化后模型含有的kernel和OP信息，默认为false|
+
+--model_dir适用于待优化的模型是非combined方式，PaddleOCR的inference模型是combined方式，即模型结构和模型参数使用单独一个文件存储。
+
+下面以PaddleOCR的超轻量中文模型为例，介绍使用编译好的opt文件完成inference模型到Paddle-Lite优化模型的转换。
+
+```
+# 下载PaddleOCR的超轻量文inference模型，并解压
+wget  https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar
+wget  https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar
+
+# 转换检测模型
+./opt --model_file=./ch_det_mv3_db/model --param_file=./ch_det_mv3_db/params --optimize_out_type=naive_buffer --optimize_out=./ch_det_mv3_db_opt --valid_targets=arm
+
+# 转换识别模型
+./opt --model_file=./ch_rec_mv3_crnn/model --param_file=./ch_rec_mv3_crnn/params --optimize_out_type=naive_buffer --optimize_out=./ch_rec_mv3_crnn_opt --valid_targets=arm
+```
+
+转换成功后，当前目录下会多出ch_det_mv3_db_opt.nb, ch_rec_mv3_crnn_opt.nb结尾的文件，即是转换成功的模型文件。
+
+
+### 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。
+ 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，则表示安装成功。
+```
+    List of devices attached
+    744be294    device
+```
+
+ 4. 准备预测库、模型和预测文件，在预测库inference_lite_lib.android.armv8/demo/cxx/下新建一个ocr/文件夹，并将转换后的nb模型、
+ PaddleOCR repo中PaddleOCR/deploy/lite/ 下的所有文件放在新建的ocr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/                      新建debug文件夹存放模型文件
+|   |--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
+|-- utils/  
+|   |-- clipper.cpp             Clipper库的cpp文件
+|   |-- clipper.hpp             Clipper库的hpp文件
+|   |-- crnn_process.cpp        识别模型CRNN的预处理和后处理cpp文件
+|   |-- db_post_process.cpp     检测模型DB的后处理cpp文件
+|-- Makefile                    编译文件
+|-- ocr_db_crnn.cc              C++预测文件
+```
+
+ 5. 编译C++预测文件，准备测试图像，准备字典文件
+ ```
+ cd demo/cxx/ocr/
+ # 执行编译
+ make
+ # 将编译的可执行文件移动到debug文件夹中
+ mv ocr_db_crnn ./debug/
+ ```
+ 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，将PaddleOCR/ppocr/utils/ppocr_keys_v1.txt复制到demo/cxx/ocr/debug/文件夹下。
+ 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：
+ ```
+ adb push debug /data/local/tmp/
+ adb shell
+ cd /data/local/tmp/debug
+ export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH
+ ./ocr_db_crnn ch_det_mv3_db_opt.nb  ch_rec_mv3_crnn_opt.nb ./12.jpg
+ ```
+ 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/crnn_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 "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include "math.h"
+
+#include <iostream>
+#include <cstring>
+#include <fstream>
+
+#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){
+  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);
+
+  float ratio = float(img.cols)/float(img.rows);
+  int resize_w, resize_h;
+  if (ceilf(imgH*ratio)>imgW)
+    resize_w = imgW;
+  else
+    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);
+
+  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;
+    }
+  }
+
+  cv::Mat dist;
+  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){
+  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);
+
+  float ratio = float(img.cols)/float(img.rows);
+  int resize_w, resize_h;
+  if (ceilf(imgH*ratio)>imgW)
+    resize_w = imgW;
+  else
+    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);
+
+  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++;
+    }
+  }
+  return charactors;
+}
+
+cv::Mat get_rotate_crop_image(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));
+
+  cv::Mat img_crop;
+  image(cv::Rect(left, top, right-left, bottom-top)).copyTo(img_crop);
+
+  for(int i=0; i<points.size(); i++){
+    points[i][0] -= left;
+    points[i][1] -= top;
+  }
+
+  int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
+                                pow(points[0][1] - points[1][1], 2)));
+  int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
+                                 pow(points[0][1] - points[3][1], 2)));
+
+  cv::Point2f pts_std[4];
+  pts_std[0] = cv::Point2f(0., 0.);
+  pts_std[1] = cv::Point2f(img_crop_width, 0.);
+  pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
+  pts_std[3] = cv::Point2f(0.f, img_crop_height);
+
+  cv::Point2f pointsf[4];
+  pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
+  pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
+  pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
+  pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
+
+  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);
+
+  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{
+    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/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();
+//}