From bcd56d0054cd251d9280cc43d10348981e52b783 Mon Sep 17 00:00:00 2001 From: andyjpaddle Date: Tue, 28 Jun 2022 12:31:14 +0000 Subject: [PATCH] update doc for case --- applications/imgs/det.png | Bin 66713 -> 0 bytes applications/imgs/rec.png | Bin 13765 -> 0 bytes applications/imgs/sys_res.png | Bin 291470 -> 0 bytes ...73\346\225\260\350\257\206\345\210\253.md" | 86 ++++++++++-------- 4 files changed, 49 insertions(+), 37 deletions(-) delete mode 100644 applications/imgs/det.png delete mode 100644 applications/imgs/rec.png delete mode 100644 applications/imgs/sys_res.png diff --git a/applications/imgs/det.png b/applications/imgs/det.png deleted file mode 100644 index 6f21ee170ed272e6ae9c3ef475f60855a064cbbc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 66713 zcmYhj2Rzk({4aivWABhXLnv8C_Exe&^fj`HlgQp%va%1d9m+^{$lg0Lv-jRBd*Ao@ z{qBAI{~nb(j^lIQpVxSf*Q+oMwHHMAwD=GRgh)y8nI;4RRRn*(#>EDI-WQQv0iQ6P 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z&#skqsu1JT)v45OnBkT@Mly6S;m4z0t~HQyZCNw3}mU+h?Q&x(Cf<^`WAECQy}C`8`=zo715M@61j z93=1jG>@JC-FGd?vU+m<4<-KCV1Ykn`v2;5_V4bT|Bv + + +将下载或训练完成的模型放置在对应目录下即可完成模型推理。 + +#### 4.3.6 模型导出推理 训练完成后,可以将训练模型转换成inference模型。inference 模型会额外保存模型的结构信息,在预测部署、加速推理上性能优越,灵活方便,适合于实际系统集成。 -#### 4.3.6.1 模型导出 +##### 4.3.6.1 模型导出 导出命令如下: ```python @@ -312,7 +318,7 @@ python tools/export_model.py \ ``` -#### 4.3.6.2 模型推理 +##### 4.3.6.2 模型推理 导出模型后,可以使用如下命令进行推理预测: ```python @@ -320,10 +326,10 @@ python tools/export_model.py \ python tools/infer/predict_det.py --image_dir="train_data/icdar2015/text_localization/test/1.jpg" --det_model_dir="./inference/det_ppocrv3/Student" ``` -# 5. 文字识别 +## 5. 文字识别 文本识别的任务是识别出图像中的文字内容,一般输入来自于文本检测得到的文本框截取出的图像文字区域。文本识别一般可以根据待识别文本形状分为规则文本识别和不规则文本识别两大类。规则文本主要指印刷字体、扫描文本等,文本大致处在水平线位置;不规则文本往往不在水平位置,存在弯曲、遮挡、模糊等问题。不规则文本场景具有很大的挑战性,也是目前文本识别领域的主要研究方向。本项目基于PP-OCRv3算法进行优化。 -## 5.1 PP-OCRv3识别算法介绍 +### 5.1 PP-OCRv3识别算法介绍 PP-OCRv3的识别模块是基于文本识别算法[SVTR](https://arxiv.org/abs/2205.00159)优化。SVTR不再采用RNN结构,通过引入Transformers结构更加有效地挖掘文本行图像的上下文信息,从而提升文本识别能力。如下图所示,PP-OCRv3采用了6个优化策略。 ![](https://ai-studio-static-online.cdn.bcebos.com/d4f5344b5b854d50be738671598a89a45689c6704c4d481fb904dd7cf72f2a1a) @@ -337,7 +343,7 @@ PP-OCRv3的识别模块是基于文本识别算法[SVTR](https://arxiv.org/abs/2 详细优化策略描述请参考[PP-OCRv3优化策略](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/doc/doc_ch/PP-OCRv3_introduction.md#3-%E8%AF%86%E5%88%AB%E4%BC%98%E5%8C%96) -## 5.2 数据准备 +### 5.2 数据准备 [计量设备屏幕字符识别数据集](https://aistudio.baidu.com/aistudio/datasetdetail/128714)数据来源于实际项目中各种计量设备的数显屏,以及在网上搜集的一些其他数显屏,包含训练集19912张,测试集4099张。 ```python @@ -368,10 +374,10 @@ def get_one_image(train): get_one_image(train) ``` -![rec_png](imgs/rec.png) +![rec_png](https://ai-studio-static-online.cdn.bcebos.com/3de0d475c69746d0a184029001ef07c85fd68816d66d4beaa10e6ef60030f9b4) -## 5.3 模型训练 -### 下载预训练模型 +### 5.3 模型训练 +#### 下载预训练模型 下载我们需要的PP-OCRv3识别预训练模型,更多选择请自行选择其他的[文字识别模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/doc/doc_ch/models_list.md#2-%E6%96%87%E6%9C%AC%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B) ```python @@ -381,7 +387,7 @@ wget -P ./pretrained_models/ https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch tar -xf ./pretrained_models/ch_PP-OCRv3_rec_train.tar -C pretrained_models ``` -### 修改配置文件 +#### 修改配置文件 我们使用configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml,主要修改训练轮数和学习率参相关参数,设置预训练模型路径,设置数据集路径。 另外,batch_size可根据自己机器显存大小进行调整。 具体修改如下几个地方: ``` epoch_num: 100 # 训练epoch数 @@ -439,7 +445,7 @@ python tools/eval.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml -o |---|---------------------------|---| | 0 | PP-OCRv3中英文超轻量识别预训练模型直接预测 |70.4%| -### 开始训练 +#### 开始训练 我们使用上面修改好的配置文件configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml,预训练模型,数据集路径,学习率,训练轮数等都已经设置完毕后,可以使用下面命令开始训练。 ```python @@ -460,9 +466,15 @@ python tools/eval.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml -o | 0 | PP-OCRv3中英文超轻量识别预训练模型直接预测 |70.4%| | 1 | PP-OCRv3中英文超轻量识别预训练模型finetune |82.2%| -## 5.4 模型导出推理 +如需获取已训练模型,请扫码填写问卷,加入PaddleOCR官方交流群获取全部OCR垂类模型下载链接、《动手学OCR》电子书等全套OCR学习资料🎁 +

+将下载或训练完成的模型放置在对应目录下即可完成模型推理。 + +### 5.4 模型导出推理 训练完成后,可以将训练模型转换成inference模型。inference 模型会额外保存模型的结构信息,在预测部署、加速推理上性能优越,灵活方便,适合于实际系统集成。 -### 模型导出 +#### 模型导出 导出命令如下: ```python @@ -470,7 +482,7 @@ python tools/eval.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml -o python tools/export_model.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml -o Global.pretrained_model="./output/ch_PP-OCR_v3_rec/best_accuracy" Global.save_inference_dir="./inference/rec_ppocrv3/" ``` -### 模型推理 +#### 模型推理 导出模型后,可以使用如下命令进行推理预测 ```python @@ -478,7 +490,7 @@ python tools/export_model.py -c configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillatio python tools/infer/predict_rec.py --image_dir="train_data/ic15_data/test/1_crop_0.jpg" --rec_model_dir="./inference/rec_ppocrv3/Student" ``` -# 6. 系统串联 +## 6. 系统串联 我们将上面训练好的检测和识别模型进行系统串联测试,命令如下: ```python @@ -500,9 +512,9 @@ plt.imshow(img) plt.show() ``` -![sys_res_png](imgs/sys_res.png) +![sys_res_png](https://ai-studio-static-online.cdn.bcebos.com/901ab741cb46441ebec510b37e63b9d8d1b7c95f63cc4e5e8757f35179ae6373) -## 6.1 后处理 +### 6.1 后处理 如果需要获取key-value信息,可以基于启发式的规则,将识别结果与关键字库进行匹配;如果匹配上了,则取该字段为key, 后面一个字段为value。 ```python @@ -523,7 +535,7 @@ def postprocess(rec_res): key_value = postprocess(filter_rec_res) ``` -# 7. PaddleServing部署 +## 7. PaddleServing部署 首先需要安装PaddleServing部署相关的环境 ```python @@ -532,7 +544,7 @@ python -m pip install paddle_serving_client python -m pip install paddle-serving-app ``` -## 7.1 转化检测模型 +### 7.1 转化检测模型 ```python cd deploy/pdserving/ @@ -543,7 +555,7 @@ python -m paddle_serving_client.convert --dirname ../../inference/det_ppocrv3/St --serving_client ./ppocr_det_v3_client/ ``` -## 7.2 转化识别模型 +### 7.2 转化识别模型 ```python python -m paddle_serving_client.convert --dirname ../../inference/rec_ppocrv3/Student \ @@ -554,7 +566,7 @@ python -m paddle_serving_client.convert --dirname ../../inference/rec_ppocrv3/St ``` -## 7.3 启动服务 +### 7.3 启动服务 首先可以将后处理代码加入到web_service.py中,具体修改如下: ``` # 代码153行后面增加下面代码 @@ -582,7 +594,7 @@ res = {"result": str(key_value)} python web_service.py 2>&1 >log.txt ``` -## 7.4 发送请求 +### 7.4 发送请求 然后再开启一个新的终端,运行下面的客户端代码 ```python -- GitLab