From ca2b680c1ac35c26caf99e3b00df6346c96b3949 Mon Sep 17 00:00:00 2001 From: yanghuiyu <760997646@qq.com> Date: Thu, 2 Jul 2020 17:03:11 +0800 Subject: [PATCH] add dbnet --- README.md | 104 ++++++++++++++++++++++++++++++++++++ config.py | 54 ------------------- dbnet/.DS_Store | Bin 0 -> 8196 bytes dbnet/model/dbnet_lite.onnx | Bin 1711657 -> 0 bytes model.py | 0 utils.py | 0 6 files changed, 104 insertions(+), 54 deletions(-) create mode 100644 README.md delete mode 100755 config.py create mode 100644 dbnet/.DS_Store delete mode 100644 dbnet/model/dbnet_lite.onnx mode change 100755 => 100644 model.py mode change 100755 => 100644 utils.py diff --git a/README.md b/README.md new file mode 100644 index 0000000..269d28c --- /dev/null +++ b/README.md @@ -0,0 +1,104 @@ +## 本项目基于[chineseocr](https://github.com/chineseocr/chineseocr) 与[psenet](https://github.com/WenmuZhou/PSENet.pytorch) 实现中文自然场景文字检测及识别 + +# 环境 +- pytorch 1.2.0 + +- python3 + +- linux/macos/windows +--- +- windows环境配置参考热心网友的文章[Python构建快速高效的中文文字识别OCR](https://blog.csdn.net/lly1122334/article/details/104752851) 👍 +- windows环境运行需要手动编译psenet/pse目录下的pse.cpp为DLL,为了方便新增支持python3.5和python3.6的pyd文件,在windows平台运行 +的时候需要根据自己环境的python版本更改对应的pyd文件为pse.pyd即可 + +- Docker 环境 + + 1. 可以直接在项目根目录下面运行`docker build -t my/chineseocr .` 构建运行环境的镜像,也可以 + + 使用其他已经构建好的镜像`docker run -dit -p 8080:8080 -v /mnt/d/data/:/data --name chineseocr vitzy/chineseocr_lite`。 + + 2. 可通过`docker attach --sig-proxy=false ` 或者`docker exec -it /bin/bash`进入容器,然后`git clone https://github.com/ouyanghuiyu/chineseocr_lite`拉取本项目代码到`/data` + 3. cd 到`chineseocr_lite`下进行安装:`pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt` + 4. 启动 web `python3 app.py 8080`, 在浏览器中打开` http://127.0.0.1:8080/ocr`。 +## PSENET 编译 +``` Bash +cd psenet/pse +rm -rf pse.so +make +``` + +# 实现功能 +- [x] 提供轻量的backone检测模型psenet(8.5M),crnn_lstm_lite(9.5M) 和行文本方向分类网络(1.5M) +- [x] 任意方向文字检测,识别时判断行文本方向 +- [x] crnn\crnn_lite lstm\dense识别(ocr-dense和ocr-lstm是搬运[chineseocr](https://github.com/chineseocr/chineseocr)的) +- [x] 支持竖排文本识别 +- [x] ncnn 实现 (支持lstm) nihui大佬实现的[crnn_lstm推理](https://github.com/ouyanghuiyu/chineseocr_lite/pull/41) 具体操作详解: [详细记录超轻量中文OCR LSTM模型ncnn实现](https://zhuanlan.zhihu.com/p/113338890?utm_source=qq&utm_medium=social&utm_oi=645149500650557440) +- [x] 提供竖排文字样例以及字体库(旋转90度的字体) +- [x] dbnet ncnn 实现 + +# 2020.07.02更新 +- 提供dbnet模型,dbnet.onnx(3.7M) dbnet_lite.onnx(1.7M) + + +# 2020.03.16更新 +- psenet ncnn核扩展实现,有效解决粘连文本检测问题,详见[ncnn ocr一条龙](https://github.com/ouyanghuiyu/chineseocr_lite/tree/master/ncnn_project/ocr) +- nihui大佬实现的[crnn_lstm推理](https://github.com/ouyanghuiyu/chineseocr_lite/pull/41) 具体操作详解: [详细记录超轻量中文OCR LSTM模型ncnn实现](https://zhuanlan.zhihu.com/p/113338890?utm_source=qq&utm_medium=social&utm_oi=645149500650557440) + +# 2020.03.12更新 +- 升级crnn_lite_lstm_dw.pth模型crnn_lite_lstm_dw_v2.pth , 精度更高 + + + +## 竖排字体样式: + + +## 竖排生成的竖排文本样例: + + + + + + +## web服务启动 +``` Bash +cd chineseocr_lite## 进入chineseocr目录 +python app.py 8080 ##8080端口号,可以设置任意端口 +``` +## 访问服务 +http://127.0.0.1:8080/ocr + + +## Flask-RESTful API demo +`cd chineseocr_lite && python flask_app.py` +```text +- 请求url: http://ip:port/api/v1/ocr +- 请求方式:POST +- 请求参数 + - imgString:图片转base64后的字符串, str +- 返回实例 +{ + "code": "SUCCESS", + "text": "不配图我总觉得不舒服不完整不专业" +} +``` +## 识别结果展示 + + + + + + + + +## ncnn检测识别展示(x86 cpu 单进程) + + + + + +## 参考 +1. ncnn https://github.com/Tencent/ncnn +2. crnn https://github.com/meijieru/crnn.pytorch.git +3. chineseocr https://github.com/chineseocr/chineseocr +4. Psenet https://github.com/WenmuZhou/PSENet.pytorch +5. 语言模型实现 https://github.com/lukhy/masr diff --git a/config.py b/config.py deleted file mode 100755 index 1c779e2..0000000 --- a/config.py +++ /dev/null @@ -1,54 +0,0 @@ -import os - -filt_path = os.path.abspath(__file__) -father_path = os.path.abspath(os.path.dirname(filt_path) + os.path.sep + ".") - -GPU_ID = 0 - -# psenet相关 -pse_long_size = 960 # 图片长边 -dbnet_short_size = 960 -# det_model_type = "pse_mobilenetv2" -det_model_type = "dbnet" -pse_scale = 1 - -if det_model_type == "pse_mobilenetv2": - model_path = os.path.join(father_path, "models/psenet_lite_mbv2.pth") - -elif det_model_type == "dbnet": - model_path = os.path.join(father_path, "models/dbnet_lite.onnx") - -# crnn相关 -nh = 256 -crnn_type = "full_lstm" - -crnn_vertical_model_path = os.path.join(father_path, "models/crnn_dw_lstm_vertical.pth") - -if crnn_type == "lite_lstm": - LSTMFLAG = True - crnn_model_path = os.path.join(father_path, "models/crnn_lite_lstm_dw_v2.pth") -elif crnn_type == "lite_dense": - LSTMFLAG = False - crnn_model_path = os.path.join(father_path, "models/crnn_lite_dense_dw.pth") -elif crnn_type == "full_lstm": - LSTMFLAG = True - crnn_model_path = os.path.join(father_path, "models/ocr-lstm.pth") -elif crnn_type == "full_dense": - LSTMFLAG = False - crnn_model_path = os.path.join(father_path, "models/ocr-dense.pth") - -# crnn_model_path = os.path.join(father_path,"models/ocr-lstm.pth") - -# from crnn.keys import alphabet -from crnn.keys import alphabetChinese as alphabet - -# angle_class相关 -lable_map_dict = {0: "hengdao", 1: "hengzhen", 2: "shudao", 3: "shuzhen"} # hengdao: 文本行横向倒立 其他类似 -rotae_map_dict = {"hengdao": 180, "hengzhen": 0, "shudao": 180, "shuzhen": 0} # 文本行需要旋转的角度 -angle_type = "shufflenetv2_05" -# angle_type = "resnet18" -angle_model_path = os.path.join(father_path, "models/{}.pth".format(angle_type)) - -TIMEOUT = 30 - -version = 'api/v1' diff --git a/dbnet/.DS_Store b/dbnet/.DS_Store new file mode 100644 index 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