dyning
GitHub
commit
9ffeb1e1dc
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@ -0,0 +1,15 @@
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#!/bin/bash
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set -e
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readonly VERSION="3.8"
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version=$(clang-format -version)
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if ! [[ $version == *"$VERSION"* ]]; then
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echo "clang-format version check failed."
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echo "a version contains '$VERSION' is needed, but get '$version'"
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echo "you can install the right version, and make an soft-link to '\$PATH' env"
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exit -1
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fi
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clang-format $@
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@ -18,3 +18,5 @@ output/
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*.idea
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*.log
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.clang-format
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.clang_format.hook
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|
|
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|
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@ -29,7 +29,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
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- 超轻量级中文OCR在线体验地址：https://www.paddlepaddle.org.cn/hub/scene/ocr
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- [**中文OCR模型快速使用**](./doc/doc_ch/quickstart.md)
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- [**中文OCR模型快速使用**](./doc/doc_ch/quickstart.md)
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## 中文OCR模型列表
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@ -50,7 +50,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
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- [基于Python预测引擎推理](./doc/doc_ch/inference.md)
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- 基于C++预测引擎推理(comming soon)
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- [服务部署](./doc/doc_ch/serving.md)
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- 端侧部署(comming soon)
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- [端侧部署](./deploy/lite/readme.md)
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- [数据集](./doc/doc_ch/datasets.md)
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- [FAQ](#FAQ)
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- 效果展示
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|
|
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Binary file not shown.
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After Width: | Height: | Size: 998 KiB |
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@ -0,0 +1,77 @@
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ARM_ABI = arm8
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export ARM_ABI
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include ../Makefile.def
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LITE_ROOT=../../../
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THIRD_PARTY_DIR=${LITE_ROOT}/third_party
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OPENCV_VERSION=opencv4.1.0
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OPENCV_LIBS = ../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgcodecs.a \
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../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_imgproc.a \
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../../../third_party/${OPENCV_VERSION}/arm64-v8a/libs/libopencv_core.a \
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../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtegra_hal.a \
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../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjpeg-turbo.a \
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../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibwebp.a \
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../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibpng.a \
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../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibjasper.a \
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../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/liblibtiff.a \
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../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libIlmImf.a \
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../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libtbb.a \
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../../../third_party/${OPENCV_VERSION}/arm64-v8a/3rdparty/libs/libcpufeatures.a
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OPENCV_INCLUDE = -I../../../third_party/${OPENCV_VERSION}/arm64-v8a/include
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CXX_INCLUDES = $(INCLUDES) ${OPENCV_INCLUDE} -I$(LITE_ROOT)/cxx/include
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CXX_LIBS = ${OPENCV_LIBS} -L$(LITE_ROOT)/cxx/lib/ -lpaddle_light_api_shared $(SYSTEM_LIBS)
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###############################################################
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# How to use one of static libaray: #
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# `libpaddle_api_full_bundled.a` #
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# `libpaddle_api_light_bundled.a` #
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###############################################################
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# Note: default use lite's shared library. #
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###############################################################
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# 1. Comment above line using `libpaddle_light_api_shared.so`
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# 2. Undo comment below line using `libpaddle_api_light_bundled.a`
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#CXX_LIBS = $(LITE_ROOT)/cxx/lib/libpaddle_api_light_bundled.a $(SYSTEM_LIBS)
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ocr_db_crnn: fetch_opencv ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o
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$(CC) $(SYSROOT_LINK) $(CXXFLAGS_LINK) ocr_db_crnn.o crnn_process.o db_post_process.o clipper.o -o ocr_db_crnn $(CXX_LIBS) $(LDFLAGS)
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ocr_db_crnn.o: ocr_db_crnn.cc
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$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o ocr_db_crnn.o -c ocr_db_crnn.cc
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crnn_process.o: fetch_opencv crnn_process.cc
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$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o crnn_process.o -c crnn_process.cc
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db_post_process.o: fetch_clipper fetch_opencv db_post_process.cc
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$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o db_post_process.o -c db_post_process.cc
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clipper.o: fetch_clipper
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$(CC) $(SYSROOT_COMPLILE) $(CXX_DEFINES) $(CXX_INCLUDES) $(CXX_FLAGS) -o clipper.o -c clipper.cpp
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fetch_clipper:
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@test -e clipper.hpp || \
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( echo "Fetch clipper " && \
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wget -c https://paddle-inference-dist.cdn.bcebos.com/PaddleLite/Clipper/clipper.hpp)
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@ test -e clipper.cpp || \
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wget -c https://paddle-inference-dist.cdn.bcebos.com/PaddleLite/Clipper/clipper.cpp
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fetch_opencv:
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@ test -d ${THIRD_PARTY_DIR} || mkdir ${THIRD_PARTY_DIR}
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@ test -e ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz || \
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(echo "fetch opencv libs" && \
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wget -P ${THIRD_PARTY_DIR} https://paddle-inference-dist.bj.bcebos.com/${OPENCV_VERSION}.tar.gz)
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@ test -d ${THIRD_PARTY_DIR}/${OPENCV_VERSION} || \
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tar -zxvf ${THIRD_PARTY_DIR}/${OPENCV_VERSION}.tar.gz -C ${THIRD_PARTY_DIR}
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.PHONY: clean
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clean:
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rm -f ocr_db_crnn.o clipper.o db_post_process.o crnn_process.o
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rm -f ocr_db_crnn
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@ -0,0 +1,4 @@
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max_side_len 960
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det_db_thresh 0.3
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det_db_box_thresh 0.5
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det_db_unclip_ratio 2.0
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@ -0,0 +1,111 @@
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// 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.
|
||||
|
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#include "crnn_process.h" //NOLINT
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#include <algorithm>
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#include <memory>
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#include <string>
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const std::vector<int> rec_image_shape{3, 32, 320};
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cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio) {
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int imgC, imgH, imgW;
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imgC = rec_image_shape[0];
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imgW = rec_image_shape[2];
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imgH = rec_image_shape[1];
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imgW = int(32 * wh_ratio);
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float ratio = float(img.cols) / float(img.rows);
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int resize_w, resize_h;
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if (ceilf(imgH * ratio) > imgW)
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resize_w = imgW;
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else
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resize_w = int(ceilf(imgH * ratio));
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cv::Mat resize_img;
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cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
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cv::INTER_LINEAR);
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return resize_img;
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}
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|
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std::vector<std::string> ReadDict(std::string path) {
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std::ifstream in(path);
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std::string filename;
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std::string line;
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std::vector<std::string> m_vec;
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if (in) {
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while (getline(in, line)) {
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m_vec.push_back(line);
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}
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} else {
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std::cout << "no such file" << std::endl;
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}
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return m_vec;
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}
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cv::Mat GetRotateCropImage(cv::Mat srcimage,
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std::vector<std::vector<int>> box) {
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cv::Mat image;
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srcimage.copyTo(image);
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std::vector<std::vector<int>> points = box;
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int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
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int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
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int left = int(*std::min_element(x_collect, x_collect + 4));
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int right = int(*std::max_element(x_collect, x_collect + 4));
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int top = int(*std::min_element(y_collect, y_collect + 4));
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int bottom = int(*std::max_element(y_collect, y_collect + 4));
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cv::Mat img_crop;
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image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
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for (int i = 0; i < points.size(); i++) {
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points[i][0] -= left;
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points[i][1] -= top;
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}
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int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
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pow(points[0][1] - points[1][1], 2)));
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int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
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pow(points[0][1] - points[3][1], 2)));
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cv::Point2f pts_std[4];
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pts_std[0] = cv::Point2f(0., 0.);
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pts_std[1] = cv::Point2f(img_crop_width, 0.);
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pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
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pts_std[3] = cv::Point2f(0.f, img_crop_height);
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cv::Point2f pointsf[4];
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pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
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pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
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pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
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pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
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cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
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|
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cv::Mat dst_img;
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cv::warpPerspective(img_crop, dst_img, M,
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cv::Size(img_crop_width, img_crop_height),
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cv::BORDER_REPLICATE);
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|
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if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
|
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cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
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cv::transpose(dst_img, srcCopy);
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cv::flip(srcCopy, srcCopy, 0);
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return srcCopy;
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} else {
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return dst_img;
|
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}
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}
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|
|
@ -0,0 +1,38 @@
|
|||
// 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
|
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#include "opencv2/core.hpp"
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#include "opencv2/imgcodecs.hpp"
|
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#include "opencv2/imgproc.hpp"
|
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|
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cv::Mat CrnnResizeImg(cv::Mat img, float wh_ratio);
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|
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std::vector<std::string> ReadDict(std::string path);
|
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|
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cv::Mat GetRotateCropImage(cv::Mat srcimage, std::vector<std::vector<int>> box);
|
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|
||||
template <class ForwardIterator>
|
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inline size_t Argmax(ForwardIterator first, ForwardIterator last) {
|
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return std::distance(first, std::max_element(first, last));
|
||||
}
|
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|
|
@ -0,0 +1,279 @@
|
|||
// 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] -
|
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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 = std::max(box.size.width, box.size.height);
|
||||
|
||||
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;
|
||||
}
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
// 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 "clipper.hpp"
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
#include "opencv2/imgproc.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);
|
||||
|
|
@ -0,0 +1,364 @@
|
|||
// 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 "paddle_api.h" // NOLINT
|
||||
#include <chrono>
|
||||
|
||||
#include "crnn_process.h"
|
||||
#include "db_post_process.h"
|
||||
|
||||
using namespace paddle::lite_api; // NOLINT
|
||||
using namespace std;
|
||||
|
||||
// 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 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) {
|
||||
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 + 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 + 1e-5)
|
||||
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_hw.push_back(float(resize_h) / float(h));
|
||||
ratio_hw.push_back(float(resize_w) / float(w));
|
||||
return resize_img;
|
||||
}
|
||||
|
||||
void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
|
||||
std::shared_ptr<PaddlePredictor> predictor_crnn,
|
||||
std::vector<std::string> &rec_text,
|
||||
std::vector<float> &rec_text_score,
|
||||
std::vector<std::string> charactor_dict) {
|
||||
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;
|
||||
|
||||
int index = 0;
|
||||
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 = 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);
|
||||
|
||||
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<int64>();
|
||||
|
||||
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]); n += 1) {
|
||||
pred_idx.push_back(int(rec_idx[n]));
|
||||
}
|
||||
|
||||
if (pred_idx.size() < 1e-3)
|
||||
continue;
|
||||
|
||||
index += 1;
|
||||
std::string pred_txt = "";
|
||||
for (int n = 0; n < pred_idx.size(); 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>();
|
||||
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;
|
||||
rec_text_score.push_back(score);
|
||||
}
|
||||
}
|
||||
|
||||
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 = int(Config["max_side_len"]);
|
||||
|
||||
cv::Mat srcimg;
|
||||
img.copyTo(srcimg);
|
||||
|
||||
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);
|
||||
|
||||
// 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();
|
||||
|
||||
// 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[i] = float(outptr[i]);
|
||||
cbuf[i] = (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 = 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 = BoxesFromBitmap(pred_map, bit_map, Config);
|
||||
|
||||
std::vector<std::vector<std::vector<int>>> filter_boxes =
|
||||
FilterTagDetRes(boxes, ratio_hw[0], ratio_hw[1], srcimg);
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
cv::imwrite("./vis.jpg", img_vis);
|
||||
std::cout << "The detection visualized image saved in ./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 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 < 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);
|
||||
|
||||
auto charactor_dict = ReadDict(dict_path);
|
||||
|
||||
cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
|
||||
auto boxes = RunDetModel(det_predictor, srcimg, Config);
|
||||
|
||||
std::vector<std::string> rec_text;
|
||||
std::vector<float> rec_text_score;
|
||||
RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
|
||||
charactor_dict);
|
||||
|
||||
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;
|
||||
}
|
||||
|
|
@ -0,0 +1,214 @@
|
|||
# PaddleOCR 端侧模型部署
|
||||
|
||||
本教程将介绍在移动端部署PaddleOCR超轻量中文检测、识别模型的详细步骤。
|
||||
|
||||
|
||||
## 1. 准备环境
|
||||
|
||||
### 运行准备
|
||||
- 电脑（编译Paddle-Lite）
|
||||
- 安卓手机（armv7或armv8）
|
||||
|
||||
### 1.1 准备交叉编译环境
|
||||
交叉编译环境用于编译[Paddle-Lite](https://github.com/PaddlePaddle/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 准备预测库
|
||||
|
||||
预测库有两种获取方式：
|
||||
- 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
|
||||
git checkout 2.6.1
|
||||
./lite/tools/build_android.sh --arch=armv8 --with_cv=ON --with_extra=ON
|
||||
```
|
||||
|
||||
注意：编译Paddle-Lite获得预测库时，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)。
|
||||
|
||||
直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8/`文件夹，通过编译Paddle-Lite得到的预测库位于
|
||||
`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。
|
||||
预测库的文件目录如下：
|
||||
```
|
||||
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inference模型进行优化，优化后的模型更轻量，模型运行速度更快。
|
||||
|
||||
下述表格中提供了优化好的超轻量中文模型：
|
||||
|
||||
|模型简介|检测模型|识别模型|Paddle-Lite版本|
|
||||
|-|-|-|-|
|
||||
|超轻量级中文OCR opt优化模型|[下载地址](https://paddleocr.bj.bcebos.com/deploy/lite/ch_det_mv3_db_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/deploy/lite/ch_rec_mv3_crnn_opt.nb)|2.6.1|
|
||||
|
||||
如果直接使用上述表格中的模型进行部署，可略过下述步骤，直接阅读 [2.2节](###2.2与手机联调)。
|
||||
|
||||
如果要部署的模型不在上述表格中，则需要按照如下步骤获得优化后的模型。
|
||||
|
||||
模型优化需要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：
|
||||
```
|
||||
# 如果准备环境时已经clone了Paddle-Lite，则不用重新clone Paddle-Lite
|
||||
git clone https://github.com/PaddlePaddle/Paddle-Lite.git
|
||||
cd Paddle-Lite
|
||||
git checkout 2.6.1
|
||||
# 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。
|
||||
|
||||
注意：使用paddle-lite部署时，需要使用opt工具优化后的模型。 opt 转换的输入模型是paddle保存的inference模型
|
||||
|
||||
### 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，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。 adb安装方式如下：
|
||||
|
||||
3.1. MAC电脑安装ADB:
|
||||
```
|
||||
brew cask install android-platform-tools
|
||||
```
|
||||
3.2. Linux安装ADB
|
||||
```
|
||||
sudo apt update
|
||||
sudo apt install -y wget adb
|
||||
```
|
||||
3.3. Window安装ADB
|
||||
|
||||
win上安装需要去谷歌的安卓平台下载adb软件包进行安装：[链接](https://developer.android.com/studio)
|
||||
|
||||
打开终端，手机连接电脑，在终端中输入
|
||||
```
|
||||
adb devices
|
||||
```
|
||||
如果有device输出，则表示安装成功。
|
||||
```
|
||||
List of devices attached
|
||||
744be294 device
|
||||
```
|
||||
|
||||
4. 准备优化后的模型、预测库文件、测试图像和使用的字典文件。
|
||||
在预测库`inference_lite_lib.android.armv8/demo/cxx/`下新建一个`ocr/`文件夹，
|
||||
将PaddleOCR repo中`PaddleOCR/deploy/lite/` 下的除`readme.md`所有文件放在新建的ocr文件夹下。在`ocr`文件夹下新建一个`debug`文件夹，
|
||||
将C++预测库so文件复制到debug文件夹下。
|
||||
```
|
||||
# 进入OCR demo的工作目录
|
||||
cd demo/cxx/ocr/
|
||||
# 将C++预测动态库so文件复制到debug文件夹中
|
||||
cp ../../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
|
||||
```
|
||||
准备测试图像，以`PaddleOCR/doc/imgs/11.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/
|
||||
| |--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
|
||||
| |--11.jpg 待测试图像
|
||||
| |--ppocr_keys_v1.txt 字典文件
|
||||
| |--libpaddle_light_api_shared.so C++预测库文件
|
||||
| |--config.txt DB-CRNN超参数配置
|
||||
|-- config.txt DB-CRNN超参数配置
|
||||
|-- crnn_process.cc 识别模型CRNN的预处理和后处理文件
|
||||
|-- crnn_process.h
|
||||
|-- db_post_process.cc 检测模型DB的后处理文件
|
||||
|-- db_post_process.h
|
||||
|-- Makefile 编译文件
|
||||
|-- ocr_db_crnn.cc C++预测源文件
|
||||
|
||||
```
|
||||
|
||||
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:
|
||||
# ./ocr_db_crnn 检测模型文件 识别模型文件 测试图像路径
|
||||
make
|
||||
# 将编译的可执行文件移动到debug文件夹中
|
||||
mv ocr_db_crnn ./debug/
|
||||
# 将debug文件夹push到手机上
|
||||
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 ./11.jpg ppocr_keys_v1.txt
|
||||
```
|
||||
|
||||
如果对代码做了修改，则需要重新编译并push到手机上。
|
||||
|
||||
运行效果如下：
|
||||
|
||||
Loading…
Reference in New Issue