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Jack Yu 38964ee383 | 6 years ago | |
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Font | 7 years ago | |
Prj-Android | 6 years ago | |
Prj-Linux | 6 years ago | |
Prj-PHP | 6 years ago | |
Prj-Win | 6 years ago | |
cache/finemapping | 7 years ago | |
dataset | 6 years ago | |
demo_images | 6 years ago | |
hyperlpr | 6 years ago | |
hyperlpr_py3 | 6 years ago | |
hyperlpr_test | 6 years ago | |
images_rec | 6 years ago | |
model | 6 years ago | |
templates | 7 years ago | |
.gitignore | 6 years ago | |
HyperLPRLite.py | 6 years ago | |
HyperLprGUI.py | 6 years ago | |
LICENSE | 7 years ago | |
README.md | 6 years ago | |
batch.py | 7 years ago | |
benchmark.py | 6 years ago | |
config.json | 6 years ago | |
demo.py | 6 years ago | |
upload.py | 7 years ago | |
wxpy_uploader.py | 7 years ago |
pip install hyperlpr
#导入包
from hyperlpr import *
#导入OpenCV库
import cv2
#读入图片
image = cv2.imread("demo.jpg")
#识别结果
print(HyperLPR_PlateRecogntion(image))
可通过pip一键安装、更新的新的识别模型、倾斜车牌校正算法、定位算法。(2018.08.11)
提交新的端到端识别模型,进一步提高识别准确率(2018.08.03)
增加PHP车牌识别工程@coleflowers (2018.06.20)
添加的新的Python 序列模型-识别率大幅提高(尤其汉字)(2018.3.12)
添加了HyperLPR Lite 仅仅需160 行代码即可实现车牌识别(2018.3.12)
提供精确定位的车牌矩形框(2018.3.12)
增加了端到端模型的cpp实现(Linux)(2018.1.31)
增加字符分割训练代码和字符分割介绍(2018.1.)
更新了Android实现,大幅提高准确率和速度 (骁龙835 (720x1280) ~50ms )(2017.12.27)
添加了IOS版本的实现(感谢xiaojun123456的工作)
添加端到端的序列识别模型识别率大幅度提升,使得无需分割字符即可识别,识别速度提高20% (2017.11.17)
新增的端到端模型可以识别新能源车牌、教练车牌、白色警用车牌、武警车牌 (2017.11.17)
更新Windows版本的Visual Studio 2015 工程(2017.11.15)
增加cpp版本,目前仅支持标准蓝牌(需要依赖OpenCV 3.3) (2017.10.28)
cd cpp_implementation
mkdir build
cd build
cmake ../
sudo make -j
#include "../include/Pipeline.h"
int main(){
pr::PipelinePR prc("model/cascade.xml",
"model/HorizonalFinemapping.prototxt","model/HorizonalFinemapping.caffemodel",
"model/Segmentation.prototxt","model/Segmentation.caffemodel",
"model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel",
"model/SegmentationFree.prototxt","model/SegmentationFree.caffemodel"
);
//定义模型文件
cv::Mat image = cv::imread("/Users/yujinke/ClionProjects/cpp_ocr_demo/test.png");
std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(image,pr::SEGMENTATION_FREE_METHOD);
//使用端到端模型模型进行识别 识别结果将会保存在res里面
for(auto st:res) {
if(st.confidence>0.75) {
std::cout << st.getPlateName() << " " << st.confidence << std::endl;
//输出识别结果 、识别置信度
cv::Rect region = st.getPlateRect();
//获取车牌位置
cv::rectangle(image,cv::Point(region.x,region.y),cv::Point(region.x+region.width,region.y+region.height),cv::Scalar(255,255,0),2);
//画出车牌位置
}
}
cv::imshow("image",image);
cv::waitKey(0);
return 0 ;
}
车牌识别框架开发时使用的数据并不是很多,有意着可以为我们提供相关车牌数据。联系邮箱 jack-yu-business@foxmail.com。
高性能开源中文车牌识别框架
C++ Java C Python CMake other