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Commit fe137242 authored by WenmuZhou's avatar WenmuZhou
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Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into tree_doc

parents 53d4eab6 b1623d69
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README_ch.md
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...@@ -54,11 +54,10 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 ...@@ -54,11 +54,10 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
| 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 | | 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 |
| ------------ | --------------- | ----------------|---- | ---------- | -------- | | ------------ | --------------- | ----------------|---- | ---------- | -------- |
| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v1.1_xx |移动端&服务器端|[推理模型](link) / [预训练模型](link)|[推理模型](link) / [预训练模型](link) |[推理模型](link) / [预训练模型](link) | | 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
| 中英文通用OCR模型（155.1M） |ch_ppocr_server_v1.1_xx|服务器端 |[推理模型](link) / [预训练模型](link) |[推理模型](link) / [预训练模型](link) |[推理模型](link) / [预训练模型](link) | | 中英文通用OCR模型（143M） |ch_ppocr_server_v2.0_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
| 中英文超轻量压缩OCR模型（3.5M） | ch_ppocr_mobile_slim_v1.1_xx| 移动端 |[推理模型](link) / [slim模型](link) |[推理模型](link) / [slim模型](link)| [推理模型](link) / [slim模型](link)|
更多模型下载（包括多语言），可以参考[PP-OCR v1.1 系列模型下载](./doc/doc_ch/models_list.md) 更多模型下载（包括多语言），可以参考[PP-OCR v2.0 系列模型下载](./doc/doc_ch/models_list.md)
## 文档教程 ## 文档教程
- [快速安装](./doc/doc_ch/installation.md) - [快速安装](./doc/doc_ch/installation.md)
...@@ -78,9 +77,9 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 ...@@ -78,9 +77,9 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md) - [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
- [基于C++预测引擎推理](./deploy/cpp_infer/readme.md) - [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
- [服务化部署](./deploy/hubserving/readme.md) - [服务化部署](./deploy/hubserving/readme.md)
- [端侧部署](./deploy/lite/readme.md) - [端侧部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)
- [模型量化](./deploy/slim/quantization/README.md) - [模型量化](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/quantization/README.md)
- [模型裁剪](./deploy/slim/prune/README.md) - [模型裁剪](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/prune/README.md)
- [Benchmark](./doc/doc_ch/benchmark.md) - [Benchmark](./doc/doc_ch/benchmark.md)
- 数据集 - 数据集
- [通用中英文OCR数据集](./doc/doc_ch/datasets.md) - [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
...@@ -98,6 +97,9 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力 ...@@ -98,6 +97,9 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
- [许可证书](#许可证书) - [许可证书](#许可证书)
- [贡献代码](#贡献代码) - [贡献代码](#贡献代码)
***注意：动态图端侧部署仍在开发中，目前仅支持动态图训练、python端预测，C++预测，
如果您有需要移动端部署案例或者量化裁剪，请切换到静态图分支；***
<a name="PP-OCR"></a> <a name="PP-OCR"></a>
## PP-OCR Pipline ## PP-OCR Pipline
<div align="center"> <div align="center">
......
README_en.md
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...@@ -62,15 +62,11 @@ Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Andr ...@@ -62,15 +62,11 @@ Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Andr
| Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model | | Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model |
| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| Chinese and English ultra-lightweight OCR model (8.1M) | ch_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | | Chinese and English ultra-lightweight OCR model (8.1M) | ch_ppocr_mobile_v2.0_xx | Mobile & server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
| Chinese and English general OCR model (155.1M) | ch_ppocr_server_v1.1_xx | Server | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | | Chinese and English general OCR model (143M) | ch_ppocr_server_v2.0_xx | Server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_traingit.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
| Chinese and English ultra-lightweight compressed OCR model (3.5M) | ch_ppocr_mobile_slim_v1.1_xx | Mobile | [inference model](link) / [slim model](link) | [inference model](link) / [slim model](link) | [inference model](link) / [slim model](link) |
| French ultra-lightweight OCR model (4.6M) | french_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](link) / [pre-trained model](link) | - | [inference model](link) / [pre-trained model](link) |
| German ultra-lightweight OCR model (4.6M) | german_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link) |
| Korean ultra-lightweight OCR model (5.9M) | korean_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link)|
| Japan ultra-lightweight OCR model (6.2M) | japan_ppocr_mobile_v1.1_xx | Mobile & server | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link) |
For more model downloads (including multiple languages), please refer to [PP-OCR v1.1 series model downloads](./doc/doc_en/models_list_en.md).
For more model downloads (including multiple languages), please refer to [PP-OCR v2.0 series model downloads](./doc/doc_en/models_list_en.md).
For a new language request, please refer to [Guideline for new language_requests](#language_requests). For a new language request, please refer to [Guideline for new language_requests](#language_requests).
...@@ -92,9 +88,9 @@ For a new language request, please refer to [Guideline for new language_requests ...@@ -92,9 +88,9 @@ For a new language request, please refer to [Guideline for new language_requests
- [Python Inference](./doc/doc_en/inference_en.md) - [Python Inference](./doc/doc_en/inference_en.md)
- [C++ Inference](./deploy/cpp_infer/readme_en.md) - [C++ Inference](./deploy/cpp_infer/readme_en.md)
- [Serving](./deploy/hubserving/readme_en.md) - [Serving](./deploy/hubserving/readme_en.md)
- [Mobile](./deploy/lite/readme_en.md) - [Mobile](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme_en.md)
- [Model Quantization](./deploy/slim/quantization/README_en.md) - [Model Quantization](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/quantization/README_en.md)
- [Model Compression](./deploy/slim/prune/README_en.md) - [Model Compression](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/slim/prune/README_en.md)
- [Benchmark](./doc/doc_en/benchmark_en.md) - [Benchmark](./doc/doc_en/benchmark_en.md)
- Data Annotation and Synthesis - Data Annotation and Synthesis
- [Semi-automatic Annotation Tool](./PPOCRLabel/README_en.md) - [Semi-automatic Annotation Tool](./PPOCRLabel/README_en.md)
...@@ -112,6 +108,12 @@ For a new language request, please refer to [Guideline for new language_requests ...@@ -112,6 +108,12 @@ For a new language request, please refer to [Guideline for new language_requests
- [License](#LICENSE) - [License](#LICENSE)
- [Contribution](#CONTRIBUTION) - [Contribution](#CONTRIBUTION)
***Note: The dynamic graphs branch is still under development.
Currently, only dynamic graph training, python-end prediction, and C++ prediction are supported.
If you need mobile-end deployment cases or quantitative demo,
please use the static graph branch.***
<a name="PP-OCR-Pipeline"></a> <a name="PP-OCR-Pipeline"></a>
## PP-OCR Pipeline ## PP-OCR Pipeline
......
configs/rec/rec_icdar15_train.yml 0 → 100644
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Global:
use_gpu: true
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/ic15/
save_epoch_step: 3
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words_en/word_10.png
# for data or label process
character_dict_path: ppocr/utils/ic15_dict.txt
character_type: ch
max_text_length: 25
infer_mode: False
use_space_char: False
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
learning_rate: 0.0005
regularizer:
name: 'L2'
factor: 0
Architecture:
model_type: rec
algorithm: CRNN
Transform:
Backbone:
name: ResNet
layers: 34
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 256
Head:
name: CTCHead
fc_decay: 0
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/
label_file_list: ["./train_data/train_list.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [3, 32, 100]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 256
drop_last: True
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/
label_file_list: ["./train_data/train_list.txt"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [3, 32, 100]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 4
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*.iml
.gradle
/local.properties
/.idea/*
.DS_Store
/build
/captures
.externalNativeBuild
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# 如何快速测试
### 1. 安装最新版本的Android Studio
可以从https://developer.android.com/studio 下载。本Demo使用是4.0版本Android Studio编写。
### 2. 按照NDK 20 以上版本
Demo测试的时候使用的是NDK 20b版本，20版本以上均可以支持编译成功。
如果您是初学者，可以用以下方式安装和测试NDK编译环境。
点击 File -> New ->New Project， 新建 "Native C++" project
### 3. 导入项目
点击 File->New->Import Project...， 然后跟着Android Studio的引导导入
# 获得更多支持
前往[端计算模型生成平台EasyEdge](https://ai.baidu.com/easyedge/app/open_source_demo?referrerUrl=paddlelite)，获得更多开发支持：
- Demo APP：可使用手机扫码安装，方便手机端快速体验文字识别
- SDK：模型被封装为适配不同芯片硬件和操作系统SDK，包括完善的接口，方便进行二次开发
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import java.security.MessageDigest
apply plugin: 'com.android.application'
android {
compileSdkVersion 29
defaultConfig {
applicationId "com.baidu.paddle.lite.demo.ocr"
minSdkVersion 23
targetSdkVersion 29
versionCode 1
versionName "1.0"
testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"
externalNativeBuild {
cmake {
cppFlags "-std=c++11 -frtti -fexceptions -Wno-format"
arguments '-DANDROID_PLATFORM=android-23', '-DANDROID_STL=c++_shared' ,"-DANDROID_ARM_NEON=TRUE"
}
}
ndk {
// abiFilters "arm64-v8a", "armeabi-v7a"
abiFilters "arm64-v8a", "armeabi-v7a"
ldLibs "jnigraphics"
}
}
buildTypes {
release {
minifyEnabled false
proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
}
}
externalNativeBuild {
cmake {
path "src/main/cpp/CMakeLists.txt"
version "3.10.2"
}
}
}
dependencies {
implementation fileTree(include: ['*.jar'], dir: 'libs')
implementation 'androidx.appcompat:appcompat:1.1.0'
implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
testImplementation 'junit:junit:4.12'
androidTestImplementation 'com.android.support.test:runner:1.0.2'
androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
}
def archives = [
[
'src' : 'https://paddlelite-demo.bj.bcebos.com/libs/android/paddle_lite_libs_v2_6_1.tar.gz',
'dest': 'PaddleLite'
],
[
'src' : 'https://paddlelite-demo.bj.bcebos.com/libs/android/opencv-4.2.0-android-sdk.tar.gz',
'dest': 'OpenCV'
],
[
'src' : 'https://paddleocr.bj.bcebos.com/deploy/lite/ocr_v1_for_cpu.tar.gz',
'dest' : 'src/main/assets/models/ocr_v1_for_cpu'
]
]
task downloadAndExtractArchives(type: DefaultTask) {
doFirst {
println "Downloading and extracting archives including libs and models"
}
doLast {
// Prepare cache folder for archives
String cachePath = "cache"
if (!file("${cachePath}").exists()) {
mkdir "${cachePath}"
}
archives.eachWithIndex { archive, index ->
MessageDigest messageDigest = MessageDigest.getInstance('MD5')
messageDigest.update(archive.src.bytes)
String cacheName = new BigInteger(1, messageDigest.digest()).toString(32)
// Download the target archive if not exists
boolean copyFiles = !file("${archive.dest}").exists()
if (!file("${cachePath}/${cacheName}.tar.gz").exists()) {
ant.get(src: archive.src, dest: file("${cachePath}/${cacheName}.tar.gz"))
copyFiles = true; // force to copy files from the latest archive files
}
// Extract the target archive if its dest path does not exists
if (copyFiles) {
copy {
from tarTree("${cachePath}/${cacheName}.tar.gz")
into "${archive.dest}"
}
}
}
}
}
preBuild.dependsOn downloadAndExtractArchives
\ No newline at end of file
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# Add project specific ProGuard rules here.
# You can control the set of applied configuration files using the
# proguardFiles setting in build.gradle.
#
# For more details, see
# http://developer.android.com/guide/developing/tools/proguard.html
# If your project uses WebView with JS, uncomment the following
# and specify the fully qualified class name to the JavaScript interface
# class:
#-keepclassmembers class fqcn.of.javascript.interface.for.webview {
# public *;
#}
# Uncomment this to preserve the line number information for
# debugging stack traces.
#-keepattributes SourceFile,LineNumberTable
# If you keep the line number information, uncomment this to
# hide the original source file name.
#-renamesourcefileattribute SourceFile
deploy/android_demo/app/src/androidTest/java/com/baidu/paddle/lite/demo/ocr/ExampleInstrumentedTest.java deleted 100644 → 0
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package com.baidu.paddle.lite.demo.ocr;
import android.content.Context;
import android.support.test.InstrumentationRegistry;
import android.support.test.runner.AndroidJUnit4;
import org.junit.Test;
import org.junit.runner.RunWith;
import static org.junit.Assert.*;
/**
* Instrumented test, which will execute on an Android device.
*
* @see <a href="http://d.android.com/tools/testing">Testing documentation</a>
*/
@RunWith(AndroidJUnit4.class)
public class ExampleInstrumentedTest {
@Test
public void useAppContext() {
// Context of the app under test.
Context appContext = InstrumentationRegistry.getTargetContext();
assertEquals("com.baidu.paddle.lite.demo", appContext.getPackageName());
}
}
deploy/android_demo/app/src/main/AndroidManifest.xml deleted 100644 → 0
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<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
package="com.baidu.paddle.lite.demo.ocr">
<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
<uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/>
<uses-permission android:name="android.permission.CAMERA"/>
<application
android:allowBackup="true"
android:icon="@mipmap/ic_launcher"
android:label="@string/app_name"
android:roundIcon="@mipmap/ic_launcher_round"
android:supportsRtl="true"
android:theme="@style/AppTheme">
<!-- to test MiniActivity, change this to com.baidu.paddle.lite.demo.ocr.MiniActivity -->
<activity android:name="com.baidu.paddle.lite.demo.ocr.MainActivity">
<intent-filter>
<action android:name="android.intent.action.MAIN"/>
<category android:name="android.intent.category.LAUNCHER"/>
</intent-filter>
</activity>
<activity
android:name="com.baidu.paddle.lite.demo.ocr.SettingsActivity"
android:label="Settings">
</activity>
<provider
android:name="androidx.core.content.FileProvider"
android:authorities="com.baidu.paddle.lite.demo.ocr.fileprovider"
android:exported="false"
android:grantUriPermissions="true">
<meta-data
android:name="android.support.FILE_PROVIDER_PATHS"
android:resource="@xml/file_paths"></meta-data>
</provider>
</application>
</manifest>
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/CMakeLists.txt deleted 100644 → 0
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# For more information about using CMake with Android Studio, read the
# documentation: https://d.android.com/studio/projects/add-native-code.html
# Sets the minimum version of CMake required to build the native library.
cmake_minimum_required(VERSION 3.4.1)
# Creates and names a library, sets it as either STATIC or SHARED, and provides
# the relative paths to its source code. You can define multiple libraries, and
# CMake builds them for you. Gradle automatically packages shared libraries with
# your APK.
set(PaddleLite_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../../PaddleLite")
include_directories(${PaddleLite_DIR}/cxx/include)
set(OpenCV_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../../OpenCV/sdk/native/jni")
message(STATUS "opencv dir: ${OpenCV_DIR}")
find_package(OpenCV REQUIRED)
message(STATUS "OpenCV libraries: ${OpenCV_LIBS}")
include_directories(${OpenCV_INCLUDE_DIRS})
aux_source_directory(. SOURCES)
set(CMAKE_CXX_FLAGS
"${CMAKE_CXX_FLAGS} -ffast-math -Ofast -Os"
)
set(CMAKE_CXX_FLAGS
"${CMAKE_CXX_FLAGS} -fvisibility=hidden -fvisibility-inlines-hidden -fdata-sections -ffunction-sections"
)
set(CMAKE_SHARED_LINKER_FLAGS
"${CMAKE_SHARED_LINKER_FLAGS} -Wl,--gc-sections -Wl,-z,nocopyreloc")
add_library(
# Sets the name of the library.
Native
# Sets the library as a shared library.
SHARED
# Provides a relative path to your source file(s).
${SOURCES})
find_library(
# Sets the name of the path variable.
log-lib
# Specifies the name of the NDK library that you want CMake to locate.
log)
add_library(
# Sets the name of the library.
paddle_light_api_shared
# Sets the library as a shared library.
SHARED
# Provides a relative path to your source file(s).
IMPORTED)
set_target_properties(
# Specifies the target library.
paddle_light_api_shared
# Specifies the parameter you want to define.
PROPERTIES
IMPORTED_LOCATION
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libpaddle_light_api_shared.so
# Provides the path to the library you want to import.
)
# Specifies libraries CMake should link to your target library. You can link
# multiple libraries, such as libraries you define in this build script,
# prebuilt third-party libraries, or system libraries.
target_link_libraries(
# Specifies the target library.
Native
paddle_light_api_shared
${OpenCV_LIBS}
GLESv2
EGL
jnigraphics
${log-lib}
)
add_custom_command(
TARGET Native
POST_BUILD
COMMAND
${CMAKE_COMMAND} -E copy
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libc++_shared.so
${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libc++_shared.so)
add_custom_command(
TARGET Native
POST_BUILD
COMMAND
${CMAKE_COMMAND} -E copy
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libpaddle_light_api_shared.so
${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libpaddle_light_api_shared.so)
add_custom_command(
TARGET Native
POST_BUILD
COMMAND
${CMAKE_COMMAND} -E copy
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai.so
${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai.so)
add_custom_command(
TARGET Native
POST_BUILD
COMMAND
${CMAKE_COMMAND} -E copy
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai_ir.so
${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai_ir.so)
add_custom_command(
TARGET Native
POST_BUILD
COMMAND
${CMAKE_COMMAND} -E copy
${PaddleLite_DIR}/cxx/libs/${ANDROID_ABI}/libhiai_ir_build.so
${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/libhiai_ir_build.so)
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/common.h deleted 100644 → 0
View file @ 53d4eab6
//
// Created by fu on 4/25/18.
//
#pragma once
#import <vector>
#import <numeric>
#ifdef __ANDROID__
#include <android/log.h>
#define LOG_TAG "OCR_NDK"
#define LOGI(...) \
__android_log_print(ANDROID_LOG_INFO, LOG_TAG, __VA_ARGS__)
#define LOGW(...) \
__android_log_print(ANDROID_LOG_WARN, LOG_TAG, __VA_ARGS__)
#define LOGE(...) \
__android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__)
#else
#include <stdio.h>
#define LOGI(format, ...) \
fprintf(stdout, "[" LOG_TAG "]" format "\n", ##__VA_ARGS__)
#define LOGW(format, ...) \
fprintf(stdout, "[" LOG_TAG "]" format "\n", ##__VA_ARGS__)
#define LOGE(format, ...) \
fprintf(stderr, "[" LOG_TAG "]Error: " format "\n", ##__VA_ARGS__)
#endif
enum RETURN_CODE {
RETURN_OK = 0
};
enum NET_TYPE{
NET_OCR = 900100,
NET_OCR_INTERNAL = 991008
};
template <typename T>
inline T product(const std::vector<T> &vec) {
if (vec.empty()){
return 0;
}
return std::accumulate(vec.begin(), vec.end(), 1, std::multiplies<T>());
}
deploy/android_demo/app/src/main/cpp/native.cpp deleted 100644 → 0
View file @ 53d4eab6
//
// Created by fujiayi on 2020/7/5.
//
#include "native.h"
#include "ocr_ppredictor.h"
#include <string>
#include <algorithm>
#include <paddle_api.h>
static paddle::lite_api::PowerMode str_to_cpu_mode(const std::string &cpu_mode);
extern "C"
JNIEXPORT jlong JNICALL
Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_init(JNIEnv *env, jobject thiz,
jstring j_det_model_path,
jstring j_rec_model_path,
jint j_thread_num,
jstring j_cpu_mode) {
std::string det_model_path = jstring_to_cpp_string(env, j_det_model_path);
std::string rec_model_path = jstring_to_cpp_string(env, j_rec_model_path);
int thread_num = j_thread_num;
std::string cpu_mode = jstring_to_cpp_string(env, j_cpu_mode);
ppredictor::OCR_Config conf;
conf.thread_num = thread_num;
conf.mode = str_to_cpu_mode(cpu_mode);
ppredictor::OCR_PPredictor *orc_predictor = new ppredictor::OCR_PPredictor{conf};
orc_predictor->init_from_file(det_model_path, rec_model_path);
return reinterpret_cast<jlong>(orc_predictor);
}
/**
* "LITE_POWER_HIGH" convert to paddle::lite_api::LITE_POWER_HIGH
* @param cpu_mode
* @return
*/
static paddle::lite_api::PowerMode str_to_cpu_mode(const std::string &cpu_mode) {
static std::map<std::string, paddle::lite_api::PowerMode> cpu_mode_map{
{"LITE_POWER_HIGH", paddle::lite_api::LITE_POWER_HIGH},
{"LITE_POWER_LOW", paddle::lite_api::LITE_POWER_HIGH},
{"LITE_POWER_FULL", paddle::lite_api::LITE_POWER_FULL},
{"LITE_POWER_NO_BIND", paddle::lite_api::LITE_POWER_NO_BIND},
{"LITE_POWER_RAND_HIGH", paddle::lite_api::LITE_POWER_RAND_HIGH},
{"LITE_POWER_RAND_LOW", paddle::lite_api::LITE_POWER_RAND_LOW}
};
std::string upper_key;
std::transform(cpu_mode.cbegin(), cpu_mode.cend(), upper_key.begin(), ::toupper);
auto index = cpu_mode_map.find(upper_key);
if (index == cpu_mode_map.end()) {
LOGE("cpu_mode not found %s", upper_key.c_str());
return paddle::lite_api::LITE_POWER_HIGH;
} else {
return index->second;
}
}
extern "C"
JNIEXPORT jfloatArray JNICALL
Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_forward(JNIEnv *env, jobject thiz,
jlong java_pointer, jfloatArray buf,
jfloatArray ddims,
jobject original_image) {
LOGI("begin to run native forward");
if (java_pointer == 0) {
LOGE("JAVA pointer is NULL");
return cpp_array_to_jfloatarray(env, nullptr, 0);
}
cv::Mat origin = bitmap_to_cv_mat(env, original_image);
if (origin.size == 0) {
LOGE("origin bitmap cannot convert to CV Mat");
return cpp_array_to_jfloatarray(env, nullptr, 0);
}
ppredictor::OCR_PPredictor *ppredictor = (ppredictor::OCR_PPredictor *) java_pointer;
std::vector<float> dims_float_arr = jfloatarray_to_float_vector(env, ddims);
std::vector<int64_t> dims_arr;
dims_arr.resize(dims_float_arr.size());
std::copy(dims_float_arr.cbegin(), dims_float_arr.cend(), dims_arr.begin());
// 这里值有点大，就不调用jfloatarray_to_float_vector了
int64_t buf_len = (int64_t) env->GetArrayLength(buf);
jfloat *buf_data = env->GetFloatArrayElements(buf, JNI_FALSE);
float *data = (jfloat *) buf_data;
std::vector<ppredictor::OCRPredictResult> results = ppredictor->infer_ocr(dims_arr, data,
buf_len,
NET_OCR, origin);
LOGI("infer_ocr finished with boxes %ld", results.size());
// 这里将std::vector<ppredictor::OCRPredictResult> 序列化成 float数组，传输到java层再反序列化
std::vector<float> float_arr;
for (const ppredictor::OCRPredictResult &r :results) {
float_arr.push_back(r.points.size());
float_arr.push_back(r.word_index.size());
float_arr.push_back(r.score);
for (const std::vector<int> &point : r.points) {
float_arr.push_back(point.at(0));
float_arr.push_back(point.at(1));
}
for (int index: r.word_index) {
float_arr.push_back(index);
}
}
return cpp_array_to_jfloatarray(env, float_arr.data(), float_arr.size());
}
extern "C"
JNIEXPORT void JNICALL
Java_com_baidu_paddle_lite_demo_ocr_OCRPredictorNative_release(JNIEnv *env, jobject thiz,
jlong java_pointer){
if (java_pointer == 0) {
LOGE("JAVA pointer is NULL");
return;
}
ppredictor::OCR_PPredictor *ppredictor = (ppredictor::OCR_PPredictor *) java_pointer;
delete ppredictor;
}
\ No newline at end of file
deploy/android_demo/app/src/main/cpp/native.h deleted 100644 → 0
View file @ 53d4eab6
//
// Created by fujiayi on 2020/7/5.
//
#pragma once
#include <jni.h>
#include <string>
#include <vector>
#include <android/bitmap.h>
#include <opencv2/opencv.hpp>
#include "common.h"
inline std::string jstring_to_cpp_string(JNIEnv *env, jstring jstr) {
// In java, a unicode char will be encoded using 2 bytes (utf16).
// so jstring will contain characters utf16. std::string in c++ is
// essentially a string of bytes, not characters, so if we want to
// pass jstring from JNI to c++, we have convert utf16 to bytes.
if (!jstr) {
return "";
}
const jclass stringClass = env->GetObjectClass(jstr);
const jmethodID getBytes =
env->GetMethodID(stringClass, "getBytes", "(Ljava/lang/String;)[B");
const jbyteArray stringJbytes = (jbyteArray) env->CallObjectMethod(
jstr, getBytes, env->NewStringUTF("UTF-8"));
size_t length = (size_t) env->GetArrayLength(stringJbytes);
jbyte *pBytes = env->GetByteArrayElements(stringJbytes, NULL);
std::string ret = std::string(reinterpret_cast<char *>(pBytes), length);
env->ReleaseByteArrayElements(stringJbytes, pBytes, JNI_ABORT);
env->DeleteLocalRef(stringJbytes);
env->DeleteLocalRef(stringClass);
return ret;
}
inline jstring cpp_string_to_jstring(JNIEnv *env, std::string str) {
auto *data = str.c_str();
jclass strClass = env->FindClass("java/lang/String");
jmethodID strClassInitMethodID =
env->GetMethodID(strClass, "<init>", "([BLjava/lang/String;)V");
jbyteArray bytes = env->NewByteArray(strlen(data));
env->SetByteArrayRegion(bytes, 0, strlen(data),
reinterpret_cast<const jbyte *>(data));
jstring encoding = env->NewStringUTF("UTF-8");
jstring res = (jstring) (
env->NewObject(strClass, strClassInitMethodID, bytes, encoding));
env->DeleteLocalRef(strClass);
env->DeleteLocalRef(encoding);
env->DeleteLocalRef(bytes);
return res;
}
inline jfloatArray cpp_array_to_jfloatarray(JNIEnv *env, const float *buf,
int64_t len) {
if (len == 0) {
return env->NewFloatArray(0);
}
jfloatArray result = env->NewFloatArray(len);
env->SetFloatArrayRegion(result, 0, len, buf);
return result;
}
inline jintArray cpp_array_to_jintarray(JNIEnv *env, const int *buf,
int64_t len) {
jintArray result = env->NewIntArray(len);
env->SetIntArrayRegion(result, 0, len, buf);
return result;
}
inline jbyteArray cpp_array_to_jbytearray(JNIEnv *env, const int8_t *buf,
int64_t len) {
jbyteArray result = env->NewByteArray(len);
env->SetByteArrayRegion(result, 0, len, buf);
return result;
}
inline jlongArray int64_vector_to_jlongarray(JNIEnv *env,
const std::vector<int64_t> &vec) {
jlongArray result = env->NewLongArray(vec.size());
jlong *buf = new jlong[vec.size()];
for (size_t i = 0; i < vec.size(); ++i) {
buf[i] = (jlong) vec[i];
}
env->SetLongArrayRegion(result, 0, vec.size(), buf);
delete[] buf;
return result;
}
inline std::vector<int64_t> jlongarray_to_int64_vector(JNIEnv *env,
jlongArray data) {
int data_size = env->GetArrayLength(data);
jlong *data_ptr = env->GetLongArrayElements(data, nullptr);
std::vector<int64_t> data_vec(data_ptr, data_ptr + data_size);
env->ReleaseLongArrayElements(data, data_ptr, 0);
return data_vec;
}
inline std::vector<float> jfloatarray_to_float_vector(JNIEnv *env,
jfloatArray data) {
int data_size = env->GetArrayLength(data);
jfloat *data_ptr = env->GetFloatArrayElements(data, nullptr);
std::vector<float> data_vec(data_ptr, data_ptr + data_size);
env->ReleaseFloatArrayElements(data, data_ptr, 0);
return data_vec;
}
inline cv::Mat bitmap_to_cv_mat(JNIEnv *env, jobject bitmap) {
AndroidBitmapInfo info;
int result = AndroidBitmap_getInfo(env, bitmap, &info);
if (result != ANDROID_BITMAP_RESULT_SUCCESS) {
LOGE("AndroidBitmap_getInfo failed, result: %d", result);
return cv::Mat{};
}
if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
LOGE("Bitmap format is not RGBA_8888 !");
return cv::Mat{};
}
unsigned char *srcData = NULL;
AndroidBitmap_lockPixels(env, bitmap, (void **) &srcData);
cv::Mat mat = cv::Mat::zeros(info.height, info.width, CV_8UC4);
memcpy(mat.data, srcData, info.height * info.width * 4);
AndroidBitmap_unlockPixels(env, bitmap);
cv::cvtColor(mat, mat, cv::COLOR_RGBA2BGR);
/**
if (!cv::imwrite("/sdcard/1/copy.jpg", mat)){
LOGE("Write image failed " );
}
*/
return mat;
}
deploy/android_demo/app/src/main/cpp/ocr_clipper.cpp deleted 100644 → 0
View file @ 53d4eab6
This diff is collapsed.
deploy/android_demo/app/src/main/cpp/ocr_clipper.hpp deleted 100644 → 0
View file @ 53d4eab6
/*******************************************************************************
* *
* 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/android_demo/app/src/main/cpp/ocr_crnn_process.cpp deleted 100644 → 0
View file @ 53d4eab6
// 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 "ocr_crnn_process.h"
#include <iostream>
#include <vector>
#include <iostream>
#include <cstring>
#include <fstream>
#include <cmath>
const std::string CHARACTER_TYPE = "ch";
const int MAX_DICT_LENGTH = 6624;
const std::vector<int> REC_IMAGE_SHAPE = {3, 32, 320};
static cv::Mat crnn_resize_norm_img(cv::Mat img, float wh_ratio) {
int imgC = REC_IMAGE_SHAPE[0];
int imgW = REC_IMAGE_SHAPE[2];
int 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 = 0;
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(const cv::Mat &img, float wh_ratio) {
int imgC = REC_IMAGE_SHAPE[0];
int imgW = REC_IMAGE_SHAPE[2];
int 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 = 0;
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));
return resize_img;
}
cv::Mat get_rotate_crop_image(const cv::Mat &srcimage, const std::vector<std::vector<int>> &box) {
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;
srcimage(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;
*/
cv::transpose(dst_img, dst_img);
cv::flip(dst_img, dst_img, 0);
return dst_img;
} else {
return dst_img;
}
}
deploy/android_demo/app/src/main/cpp/ocr_crnn_process.h deleted 100644 → 0
View file @ 53d4eab6
//
// Created by fujiayi on 2020/7/3.
//
#pragma once
#include <vector>
#include <opencv2/opencv.hpp>
#include "common.h"
extern const std::vector<int> REC_IMAGE_SHAPE;
cv::Mat get_rotate_crop_image(const cv::Mat &srcimage, const std::vector<std::vector<int>> &box);
cv::Mat crnn_resize_img(const cv::Mat &img, float wh_ratio);
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
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