基于PaddleOCR的体检报告识别
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基于PaddleOCR的体检报告识别
一、项目背景与意义
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体检报告识别可以帮助医务服务人员自动识别录入用户征信信息，节约人力成本、提升服务效率，实现降本增效，具有重要实际意义。基于PaddleOCR已在文字识别领域取得优秀成果，本项目基于PaddleOCR实现体检报告检测与识别，对数据进行结构化处理，结合CV+NLP技术达到一定识别精度，未来推广应用场景可以基于识别信息做个性化疾病预测与健康推荐。
二、项目链接
PaddleOCR体检报告识别 - 飞桨AI Studio
三、项目流程
PaddleOCR是百度开源的超轻量级OCR模型库，本文使用其框架进行体检报告识别，本次项目具体流程包括：
- PaddleOCR环境安装与快速预测
- 体检报告检测模型训练det
- 体检报告识别模型训练rec
四、技术介绍
针对PaddleOCR提供的算法模型，本次选择基础模型用于体检报告识别与检测，流程如下：
1.检测：DB算法
文字检测参考这篇：
OCR文字识别技术总结（三）__文本检测算法总结
2.识别：CRNN+CTC
CRNN可参考这篇文章：
CRNN文字识别_GoAI的博客-CSDN博客_crnn
五、数据集介绍
数据位置: data/data159696/report_ex.tar
解压命令 !tar -xf /home/aistudio/data/data159696/report_ex.tar
数据集结构：
/home/aistudio/report_ex
└─ pngs:存放体检照片，以pngs形式结尾
└─ txts: 存放标注坐标信息及包含内容.
└─ json：内容同上 ，存放json格式信息。
数据集txt格式为:
Rect (182.0, 1078.03125, 266.0, 1064.03125) 姓名：张某某
Rect (356.0, 1078.03125, 412.0, 1064.03125) 性别：男
Rect (516.0, 1078.03125, 572.0, 1064.03125) 年龄：40
- 注:本数据坐标是以左下角为原点，利用Paddleocr做检测时需要转换成左上角原点，且本数据坐标需要横纵坐标都乘4.
图片样式：
1.安装环境与测试
1.1 安装项目环境
安装PaddleOCR相关环境
%cd ~
!git clone -b release/2.1 https://github.com/PaddlePaddle/PaddleOCR.git# 安装依赖库
%cd ~/PaddleOCR
!pip install -r requirements.txt -i https://mirror.baidu.com/pypi/simple1.2. 下载预测模型并测试
下载PaddleOCR中文轻量级OCR模型用于测试部分图像识别结果，模型存放在在PaddleOCR/inference目录下。
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! mkdir inference
# 下载超轻量级中文OCR模型的检测模型并解压
! cd inference && wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && rm ch_ppocr_mobile_v2.0_det_infer.tar
# 下载超轻量级中文OCR模型的识别模型并解压
! cd inference && wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && rm ch_ppocr_mobile_v2.0_rec_infer.tar
# 下载超轻量级中文OCR模型的文本方向分类器模型并解压
! cd inference && wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar && rm ch_ppocr_mobile_v2.0_cls_infer.tar1.3测试单张报告数据集并可视化
In [ ]
import matplotlib.pyplot as plt
from PIL import Image
%pylab inline
def show_img(img_path,figsize=(10,10)):
## 显示原图，读取名称为11.jpg的测试图像
img = Image.open(img_path)
plt.figure("test_img", figsize=figsize)
plt.imshow(img)
plt.show()
show_img("../20220623110401-0.png")Populating the interactive namespace from numpy and matplotlib
测试单张图像
调用tools/infer/predict_system.py 完成报告识别，共需要传入三个参数：
- image_dir： 指定要测试的图像
- det_model_dir： 指定轻量检测模型的inference model
- rec_model_dir： 指定轻量识别模型的inference model
- cls_model_dir： 指定轻量方向分类器模型的inference model
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# 快速运行
!python3 ./tools/infer/predict_system.py --image_dir="../20220623110401-0.png" \
--det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer" \
--rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer" \
--cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer"In [27]
# 训练效果
!python3 ./tools/infer/predict_system.py --image_dir="../20220623110401-0.png" \
--det_model_dir="./outputall/db_mv3/best_accuracy" \
--rec_model_dir="./output/rec/best_accuracy" \
--cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer"上述结果说明：输出结果中有两列数据，第一列表示PaddleOCR识别出的文字，第二列表示识别出当前文字的置信度。置信度的数据范围是[0-1]，置信度越接近1表示文本识别对的“信心”越大。 同时，识别结果会可视化在图像中并保存在./inference_results文件夹下，可以通过左边的目录结构选择要打开的文件， 也可以通过如下代码将可视化后的图像显示出来，观察OCR文本识别的效果。
针对上述./inference_results/20220623110401-0.png检测结果展示如下：
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show_img("./inference_results/20220623110401-0.png",figsize=(20,20))
2. 训练文字检测模型
PaddOCR官方检测模型数据集以icdar15为例，本文参照其标注格式进行检测模型的训练、评估与测试，模型以MobienetV3网络为例，可自己更改其他网络。
注：官方icdar15数据集存放在 ~/data/data34815/icdar2015.tar ，后续如有数据格式问题可做参考。官方数据~/train_data/icdar2015/text_localization 有两个文件夹和两个文件，分别是：
~/train_data/icdar2015/text_localization
└─ icdar_c4_train_imgs/ icdar数据集的训练数据
└─ ch4_test_images/ icdar数据集的测试数据
└─ train_icdar2015_label.txt icdar数据集的训练标注
└─ test_icdar2015_label.txt icdar数据集的测试标注
官方提供的标注文件格式为：
" 图像文件名 json.dumps编码的图像标注信息"
ch4_test_images/img_61.jpg [{"transcription": "MASA", "points": [[310, 104], [416, 141], [418, 216], [312, 179]], ...}]
json.dumps编码前的图像标注信息是包含多个字典的list，字典中的pointspointspoints表示文本框的四个点的坐标(x, y)，从左上角的点开始顺时针排列。 transcriptiontranscriptiontranscription表示当前文本框的文字，在文本检测任务中并不需要这个信息。
2.1. 数据准备
首先解压本次体检报告数据到当前目录，解压命令如下：
!tar -xf /home/aistudio/data/data159696/report_ex.tar#查看当前文件夹图片数量
%cd /home/aistudio/report_ex/pngs
!ls -l | grep "^-" | wc -l #一共20011张图片/home/aistudio/report_ex/pngs 20011
本次体检报告数据集txt格式为:
Rect (182.0, 1078.03125, 266.0, 1064.03125) 姓名：张某某
Rect (356.0, 1078.03125, 412.0, 1064.03125) 性别：男
Rect (516.0, 1078.03125, 572.0, 1064.03125) 年龄：40
由于数据格式不同，本项目需要编写转换数据程序构建为PaddleOCR标注文件格式, 由于时间原因，格式代码比较粗糙，读者后续可根据需求自行完善。
以1.部分数据集为例的训练相关代码：
/home/aistudio/report_ex/
└─ train_det_new1_hebing/ report_ex数据集的测试数据
└─ test_det_new1_hebing report_ex数据集的测试数据
/home/aistudio/
└─ train_det_new1_hebing.txt.txt report_ex数据集的训练标注
└─ test_det_new1_hebing.txt.txt report_ex数据集的测试标注
└─ gen_data_det_reg.py 格式转换代码
└─ hebing.py 数据合并
└─ split_data.py 切分训练集与测试集
└─ file.py 拷贝训练集与测试集图片到文件夹
/home/aistudio/PaddleOCR
└─ tools/train.py 训练代码
└─ tools/infer_det.py 推理代码
└─ configs/det/det_mv3_db_all.yml 配置文件
2.2 快速启动训练
下载PaddleOCR主流两种检测模型backbone，MobileNetV3和ResNet50_vd，后续可以根据需求使用PaddleClas中的模型更换backbone。
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# 下载MobileNetV3的预训练模型
!pwd
!wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x0_5_pretrained.tar
! cd pretrain_models/ && tar xf MobileNetV3_large_x0_5_pretrained.tar
# 下载ResNet50的预训练模型
!wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
! cd pretrain_models/ && tar xf ResNet50_vd_ssld_pretrained.tar2.3数据转换
运行转换程序gen_data_det_reg.py，生成det检测和reg识别的txt格式，以实际运行结果为准！
det.txt
20220623110401-0.png [{"transcription":"姓名：张某某","points":[[182.0,4256.125],[266.0,4256.125],[182.0,4312.125],[266.0,4312.125]]}]
20220623110401-0.png [{"transcription":"性别：男","points":[[356.0,4256.125],[412.0,4256.125],[356.0,4312.125],[412.0,4312.125]]}]
20220623110401-0.png [{"transcription":"年龄：40","points":[[516.0,4256.125],[572.0,4256.125],[516.0,4312.125],[572.0,4312.125]]}]
reg.txt
20220623110401-0.png 姓名：张某某
20220623110401-0.png 性别：男
20220623110401-0.png 年龄：40
本次体检报告由于数据量巨大且训练时间长，为了方便大家查看运行效果及调试 ，下列训练用到的数据集会分为 1.部分数据集与 2.全集数据集，脚本命名已写在注释中，按需运行按需打开注释即可。
#1.部分数据集数据转换脚本, 生成det1.txt ,合并后一共一百多张图片， 坐标为横坐标×4 、 纵坐标=图片高度-纵坐标×4 ， reg.txt目前没用到。
#执行报错，会出现IndexError: list index out of range, 只生成2万多条数据，但不影响跑。
%cd /home/aistudio/
# !python ./gen_data_det_reg.py
#2.跑全量数据脚本,生成det_all.txt，可以跑通，但全量数据集太大，还未执行，
# %cd /home/aistudio/
!python ./gen_data_all.py#合并上述生成txt的数据，改为一张照片对应所有坐标合并成一行 ,生成合并后新的txt
#1.部分数据合并 det1.txt，生成det_new_hebing.txt
# !python hebing.py
#2.全量数据合并 det_all.txt，生成det_new_hebing_all.txt
!python hebing_all.py2.4数据集划分
将检测数据det.txt、识别数据reg.txt 划分训练集和验证集 , 生成train_det.txt 、 test_det.txt、 train_reg.txt 、 test_reg.txt四个文件
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#1.划分部分数据集用于训练，将det.txt拆成train_det_new1_hebing.txt和test_det_new1_hebing.txt，注意生成的训练测试集txt名字要跟训练时相同
# !python split_data.py
#2.划分全量数据集,将det_new_hebing_all.txt拆成 train_det_hebing_all.txt和test_det_hebing_all.txt
!python split_data_all.py20011
2.5拷贝数据集图片
#注：拷贝上述训练及验证集图片到对应路径
#编辑file.py打开对应注释，重复执行两次！！！ 一次train 、一次test , 生成上述txt对应的图片文件夹包含图片。
#1.部分数据图片拷贝到文件夹,拷贝train_det_new1.txt和test_det_new1.txt包含的图片拷贝到新文件夹./report_ex/train_det_new1和report_ex/test_det_new1，用于测试
# !python file.py
#2.全量数据图片拷贝到文件夹，train_det_hebing_all.txt和test_det_hebing_all.txt包含的图片拷贝到新文件夹./report_ex/train_det_hebing_all和./report_ex/test_det_hebing_all
!python file_all.py2.6.检测模型训练
本次选择backbone为MobileNetV3、Resnet50的db算法的检测模型.通过-c 选择训练使用配置文件configs/det/det_db_mv3.yml配置文件，-o参数在不需要修改yml文件的情况下，改变训练的参数
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# 官方训练backbone为MobileNetV3的db算法的检测模型，此部分只做参考，不用执行！
# !python3 PaddleOCR/tools/train.py -c PaddleOCR/configs/det/det_mv3_db.yml -o \
# Global.eval_batch_step="[0,500]" \
# Global.load_static_weights=true \
# Global.pretrained_model='PaddleOCR/pretrain_models/MobileNetV3_large_x0_5_pretrained' \
# Train.dataset.data_dir='PaddleOCR/train_data/text_localization/' \
# Train.dataset.label_file_list=['PaddleOCR/train_data/text_localization/train_icdar2015_label.txt'] \
# Eval.dataset.data_dir='PaddleOCR/train_data/text_localization/' \
# Eval.dataset.label_file_list=['PaddleOCR/train_data/text_localization/test_icdar2015_label.txt']#每次启动运行结果提示缺少包执行此条安装相关环境
!pip install lmdb
!pip install pyclipper
!pip install Levenshtein
!pip install imgaug全量数据训练
由于数据量较大且训练时间较长，针对上述两种不同数据集大小，本次训练分别列出全量及部分数据集训练代码，可按需选择。
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#1.合并后全量数据集+MobileNetV3检测模型训练
%cd /home/aistudio/
!python3 PaddleOCR/tools/train.py -c PaddleOCR/configs/det/det_mv3_db_all.yml -o \
Global.eval_batch_step="[0,300]" \
Global.load_static_weights=true \
Global.checkpoints='./outputall/db_mv3/best_accuracy' \
Global.pretrained_model='PaddleOCR/pretrain_models/MobileNetV3_large_x0_5_pretrained' \
Train.loader.batch_size_per_card=32 \
Train.dataset.data_dir='./report_ex/train_det_hebing_all' \
Train.dataset.label_file_list=['./train_det_hebing_all.txt'] \
Eval.dataset.data_dir='./report_ex/test_det_hebing_all' \
Eval.dataset.label_file_list=['./test_det_hebing_all.txt']/home/aistudio /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations def convert_to_list(value, n, name, dtype=np.int): [2022/08/10 15:52:35] root INFO: Architecture : [2022/08/10 15:52:35] root INFO: Backbone : [2022/08/10 15:52:35] root INFO: model_name : large [2022/08/10 15:52:35] root INFO: name : MobileNetV3 [2022/08/10 15:52:35] root INFO: scale : 0.5 [2022/08/10 15:52:35] root INFO: Head : [2022/08/10 15:52:35] root INFO: k : 50 [2022/08/10 15:52:35] root INFO: name : DBHead [2022/08/10 15:52:35] root INFO: Neck : [2022/08/10 15:52:35] root INFO: name : DBFPN [2022/08/10 15:52:35] root INFO: out_channels : 256 [2022/08/10 15:52:35] root INFO: Transform : None [2022/08/10 15:52:35] root INFO: algorithm : DB [2022/08/10 15:52:35] root INFO: model_type : det [2022/08/10 15:52:35] root INFO: Eval : [2022/08/10 15:52:35] root INFO: dataset : [2022/08/10 15:52:35] root INFO: data_dir : ./report_ex/test_det_hebing_all [2022/08/10 15:52:35] root INFO: label_file_list : ['./test_det_hebing_all.txt'] [2022/08/10 15:52:35] root INFO: name : SimpleDataSet [2022/08/10 15:52:35] root INFO: transforms : [2022/08/10 15:52:35] root INFO: DecodeImage : [2022/08/10 15:52:35] root INFO: channel_first : False [2022/08/10 15:52:35] root INFO: img_mode : BGR [2022/08/10 15:52:35] root INFO: DetLabelEncode : None [2022/08/10 15:52:35] root INFO: DetResizeForTest : [2022/08/10 15:52:35] root INFO: image_shape : [736, 1280] [2022/08/10 15:52:35] root INFO: NormalizeImage : [2022/08/10 15:52:35] root INFO: mean : [0.485, 0.456, 0.406] [2022/08/10 15:52:35] root INFO: order : hwc [2022/08/10 15:52:35] root INFO: scale : 1./255. [2022/08/10 15:52:35] root INFO: std : [0.229, 0.224, 0.225] [2022/08/10 15:52:35] root INFO: ToCHWImage : None [2022/08/10 15:52:35] root INFO: KeepKeys : [2022/08/10 15:52:35] root INFO: keep_keys : ['image', 'shape', 'polys', 'ignore_tags'] [2022/08/10 15:52:35] root INFO: loader : [2022/08/10 15:52:35] root INFO: batch_size_per_card : 1 [2022/08/10 15:52:35] root INFO: drop_last : False [2022/08/10 15:52:35] root INFO: num_workers : 8 [2022/08/10 15:52:35] root INFO: shuffle : False [2022/08/10 15:52:35] root INFO: use_shared_memory : False [2022/08/10 15:52:35] root INFO: Global : [2022/08/10 15:52:35] root INFO: cal_metric_during_train : False [2022/08/10 15:52:35] root INFO: checkpoints : ./outputall/db_mv3/best_accuracy [2022/08/10 15:52:35] root INFO: debug : False [2022/08/10 15:52:35] root INFO: distributed : False [2022/08/10 15:52:35] root INFO: epoch_num : 1200 [2022/08/10 15:52:35] root INFO: eval_batch_step : [0, 500] [2022/08/10 15:52:35] root INFO: infer_img : ./20220623110401-0.png [2022/08/10 15:52:35] root INFO: load_static_weights : True [2022/08/10 15:52:35] root INFO: log_smooth_window : 20 [2022/08/10 15:52:35] root INFO: pretrained_model : PaddleOCR/pretrain_models/MobileNetV3_large_x0_5_pretrained [2022/08/10 15:52:35] root INFO: print_batch_step : 10 [2022/08/10 15:52:35] root INFO: save_epoch_step : 1200 [2022/08/10 15:52:35] root INFO: save_inference_dir : None [2022/08/10 15:52:35] root INFO: save_model_dir : /home/aistudio/outputall/db_mv3/ [2022/08/10 15:52:35] root INFO: save_res_path : ./outputall/det_db/predicts_db.txt [2022/08/10 15:52:35] root INFO: use_gpu : True [2022/08/10 15:52:35] root INFO: use_visualdl : False [2022/08/10 15:52:35] root INFO: Loss : [2022/08/10 15:52:35] root INFO: alpha : 5 [2022/08/10 15:52:35] root INFO: balance_loss : True [2022/08/10 15:52:35] root INFO: beta : 10 [2022/08/10 15:52:35] root INFO: main_loss_type : DiceLoss [2022/08/10 15:52:35] root INFO: name : DBLoss [2022/08/10 15:52:35] root INFO: ohem_ratio : 3 [2022/08/10 15:52:35] root INFO: Metric : [2022/08/10 15:52:35] root INFO: main_indicator : hmean [2022/08/10 15:52:35] root INFO: name : DetMetric [2022/08/10 15:52:35] root INFO: Optimizer : [2022/08/10 15:52:35] root INFO: beta1 : 0.9 [2022/08/10 15:52:35] root INFO: beta2 : 0.999 [2022/08/10 15:52:35] root INFO: lr : [2022/08/10 15:52:35] root INFO: learning_rate : 0.001 [2022/08/10 15:52:35] root INFO: name : Adam [2022/08/10 15:52:35] root INFO: regularizer : [2022/08/10 15:52:35] root INFO: factor : 0 [2022/08/10 15:52:35] root INFO: name : L2 [2022/08/10 15:52:35] root INFO: PostProcess : [2022/08/10 15:52:35] root INFO: box_thresh : 0.6 [2022/08/10 15:52:35] root INFO: max_candidates : 1000 [2022/08/10 15:52:35] root INFO: name : DBPostProcess [2022/08/10 15:52:35] root INFO: thresh : 0.3 [2022/08/10 15:52:35] root INFO: unclip_ratio : 1.5 [2022/08/10 15:52:35] root INFO: Train : [2022/08/10 15:52:35] root INFO: dataset : [2022/08/10 15:52:35] root INFO: data_dir : ./report_ex/train_det_hebing_all [2022/08/10 15:52:35] root INFO: label_file_list : ['./train_det_hebing_all.txt'] [2022/08/10 15:52:35] root INFO: name : SimpleDataSet [2022/08/10 15:52:35] root INFO: ratio_list : [1.0] [2022/08/10 15:52:35] root INFO: transforms : [2022/08/10 15:52:35] root INFO: DecodeImage : [2022/08/10 15:52:35] root INFO: channel_first : False [2022/08/10 15:52:35] root INFO: img_mode : BGR [2022/08/10 15:52:35] root INFO: DetLabelEncode : None [2022/08/10 15:52:35] root INFO: IaaAugment : [2022/08/10 15:52:35] root INFO: augmenter_args : [2022/08/10 15:52:35] root INFO: args : [2022/08/10 15:52:35] root INFO: p : 0.5 [2022/08/10 15:52:35] root INFO: type : Fliplr [2022/08/10 15:52:35] root INFO: args : [2022/08/10 15:52:35] root INFO: rotate : [-10, 10] [2022/08/10 15:52:35] root INFO: type : Affine [2022/08/10 15:52:35] root INFO: args : [2022/08/10 15:52:35] root INFO: size : [0.5, 3] [2022/08/10 15:52:35] root INFO: type : Resize [2022/08/10 15:52:35] root INFO: EastRandomCropData : [2022/08/10 15:52:35] root INFO: keep_ratio : True [2022/08/10 15:52:35] root INFO: max_tries : 50 [2022/08/10 15:52:35] root INFO: size : [640, 640] [2022/08/10 15:52:35] root INFO: MakeBorderMap : [2022/08/10 15:52:35] root INFO: shrink_ratio : 0.4 [2022/08/10 15:52:35] root INFO: thresh_max : 0.7 [2022/08/10 15:52:35] root INFO: thresh_min : 0.3 [2022/08/10 15:52:35] root INFO: MakeShrinkMap : [2022/08/10 15:52:35] root INFO: min_text_size : 8 [2022/08/10 15:52:35] root INFO: shrink_ratio : 0.4 [2022/08/10 15:52:35] root INFO: NormalizeImage : [2022/08/10 15:52:35] root INFO: mean : [0.485, 0.456, 0.406] [2022/08/10 15:52:35] root INFO: order : hwc [2022/08/10 15:52:35] root INFO: scale : 1./255. [2022/08/10 15:52:35] root INFO: std : [0.229, 0.224, 0.225] [2022/08/10 15:52:35] root INFO: ToCHWImage : None [2022/08/10 15:52:35] root INFO: KeepKeys : [2022/08/10 15:52:35] root INFO: keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] [2022/08/10 15:52:35] root INFO: loader : [2022/08/10 15:52:35] root INFO: batch_size_per_card : 32 [2022/08/10 15:52:35] root INFO: drop_last : False [2022/08/10 15:52:35] root INFO: num_workers : 8 [2022/08/10 15:52:35] root INFO: shuffle : True [2022/08/10 15:52:35] root INFO: use_shared_memory : False [2022/08/10 15:52:35] root INFO: train with paddle 2.0.2 and device CUDAPlace(0) [2022/08/10 15:52:35] root INFO: Initialize indexs of datasets:['./train_det_hebing_all.txt'] [2022/08/10 15:52:35] root INFO: Initialize indexs of datasets:['./test_det_hebing_all.txt']
In [ ]
#2.合并全量数据集+Resnet检测模型训练
%cd /home/aistudio/
!python3 PaddleOCR/tools/train.py -c PaddleOCR/configs/det/det_mv3_db_all_resnet.yml -o \
Global.eval_batch_step="[0,500]" \
Global.load_static_weights=true \
Global.checkpoints='/home/aistudio/outputall/db_resnet/best_accuracy' \
Global.pretrained_model='PaddleOCR/pretrain_models/ResNet50_vd_ssld_pretrained' \
Train.loader.batch_size_per_card=16 \
Train.dataset.data_dir='./report_ex/train_det_hebing_all' \
Train.dataset.label_file_list=['./train_det_hebing_all.txt'] \
Eval.dataset.data_dir='./report_ex/test_det_hebing_all' \
Eval.dataset.label_file_list=['./test_det_hebing_all.txt']/home/aistudio /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations def convert_to_list(value, n, name, dtype=np.int): [2022/08/11 13:35:54] root INFO: Architecture : [2022/08/11 13:35:54] root INFO: Backbone : [2022/08/11 13:35:54] root INFO: model_name : large [2022/08/11 13:35:54] root INFO: name : ResNet [2022/08/11 13:35:54] root INFO: scale : 0.5 [2022/08/11 13:35:54] root INFO: Head : [2022/08/11 13:35:54] root INFO: k : 50 [2022/08/11 13:35:54] root INFO: name : DBHead [2022/08/11 13:35:54] root INFO: Neck : [2022/08/11 13:35:54] root INFO: name : DBFPN [2022/08/11 13:35:54] root INFO: out_channels : 256 [2022/08/11 13:35:54] root INFO: Transform : None [2022/08/11 13:35:54] root INFO: algorithm : DB [2022/08/11 13:35:54] root INFO: model_type : det [2022/08/11 13:35:54] root INFO: Eval : [2022/08/11 13:35:54] root INFO: dataset : [2022/08/11 13:35:54] root INFO: data_dir : ./report_ex/test_det_hebing_all [2022/08/11 13:35:54] root INFO: label_file_list : ['./test_det_hebing_all.txt'] [2022/08/11 13:35:54] root INFO: name : SimpleDataSet [2022/08/11 13:35:54] root INFO: transforms : [2022/08/11 13:35:54] root INFO: DecodeImage : [2022/08/11 13:35:54] root INFO: channel_first : False [2022/08/11 13:35:54] root INFO: img_mode : BGR [2022/08/11 13:35:54] root INFO: DetLabelEncode : None [2022/08/11 13:35:54] root INFO: DetResizeForTest : [2022/08/11 13:35:54] root INFO: image_shape : [736, 1280] [2022/08/11 13:35:54] root INFO: NormalizeImage : [2022/08/11 13:35:54] root INFO: mean : [0.485, 0.456, 0.406] [2022/08/11 13:35:54] root INFO: order : hwc [2022/08/11 13:35:54] root INFO: scale : 1./255. [2022/08/11 13:35:54] root INFO: std : [0.229, 0.224, 0.225] [2022/08/11 13:35:54] root INFO: ToCHWImage : None [2022/08/11 13:35:54] root INFO: KeepKeys : [2022/08/11 13:35:54] root INFO: keep_keys : ['image', 'shape', 'polys', 'ignore_tags'] [2022/08/11 13:35:54] root INFO: loader : [2022/08/11 13:35:54] root INFO: batch_size_per_card : 1 [2022/08/11 13:35:54] root INFO: drop_last : False [2022/08/11 13:35:54] root INFO: num_workers : 8 [2022/08/11 13:35:54] root INFO: shuffle : False [2022/08/11 13:35:54] root INFO: use_shared_memory : False [2022/08/11 13:35:54] root INFO: Global : [2022/08/11 13:35:54] root INFO: cal_metric_during_train : False [2022/08/11 13:35:54] root INFO: checkpoints : /home/aistudio/outputall/db_resnet//best_accuracy [2022/08/11 13:35:54] root INFO: debug : False [2022/08/11 13:35:54] root INFO: distributed : False [2022/08/11 13:35:54] root INFO: epoch_num : 1200 [2022/08/11 13:35:54] root INFO: eval_batch_step : [0, 500] [2022/08/11 13:35:54] root INFO: infer_img : ./20220623110401-0.png [2022/08/11 13:35:54] root INFO: load_static_weights : True [2022/08/11 13:35:54] root INFO: log_smooth_window : 20 [2022/08/11 13:35:54] root INFO: pretrained_model : PaddleOCR/pretrain_models/ResNet50_vd_ssld_pretrained [2022/08/11 13:35:54] root INFO: print_batch_step : 10 [2022/08/11 13:35:54] root INFO: save_epoch_step : 1200 [2022/08/11 13:35:54] root INFO: save_inference_dir : None [2022/08/11 13:35:54] root INFO: save_model_dir : /home/aistudio/outputall/db_resnet/ [2022/08/11 13:35:54] root INFO: save_res_path : ./outputall_resnet/det_db/predicts_db.txt [2022/08/11 13:35:54] root INFO: use_gpu : True [2022/08/11 13:35:54] root INFO: use_visualdl : False [2022/08/11 13:35:54] root INFO: Loss : [2022/08/11 13:35:54] root INFO: alpha : 5 [2022/08/11 13:35:54] root INFO: balance_loss : True [2022/08/11 13:35:54] root INFO: beta : 10 [2022/08/11 13:35:54] root INFO: main_loss_type : DiceLoss [2022/08/11 13:35:54] root INFO: name : DBLoss [2022/08/11 13:35:54] root INFO: ohem_ratio : 3 [2022/08/11 13:35:54] root INFO: Metric : [2022/08/11 13:35:54] root INFO: main_indicator : hmean [2022/08/11 13:35:54] root INFO: name : DetMetric [2022/08/11 13:35:54] root INFO: Optimizer : [2022/08/11 13:35:54] root INFO: beta1 : 0.9 [2022/08/11 13:35:54] root INFO: beta2 : 0.999 [2022/08/11 13:35:54] root INFO: lr : [2022/08/11 13:35:54] root INFO: learning_rate : 0.001 [2022/08/11 13:35:54] root INFO: name : Adam [2022/08/11 13:35:54] root INFO: regularizer : [2022/08/11 13:35:54] root INFO: factor : 0 [2022/08/11 13:35:54] root INFO: name : L2 [2022/08/11 13:35:54] root INFO: PostProcess : [2022/08/11 13:35:54] root INFO: box_thresh : 0.6 [2022/08/11 13:35:54] root INFO: max_candidates : 1000 [2022/08/11 13:35:54] root INFO: name : DBPostProcess [2022/08/11 13:35:54] root INFO: thresh : 0.3 [2022/08/11 13:35:54] root INFO: unclip_ratio : 1.5 [2022/08/11 13:35:54] root INFO: Train : [2022/08/11 13:35:54] root INFO: dataset : [2022/08/11 13:35:54] root INFO: data_dir : ./report_ex/train_det_hebing_all [2022/08/11 13:35:54] root INFO: label_file_list : ['./train_det_hebing_all.txt'] [2022/08/11 13:35:54] root INFO: name : SimpleDataSet [2022/08/11 13:35:54] root INFO: ratio_list : [1.0] [2022/08/11 13:35:54] root INFO: transforms : [2022/08/11 13:35:54] root INFO: DecodeImage : [2022/08/11 13:35:54] root INFO: channel_first : False [2022/08/11 13:35:54] root INFO: img_mode : BGR [2022/08/11 13:35:54] root INFO: DetLabelEncode : None [2022/08/11 13:35:54] root INFO: IaaAugment : [2022/08/11 13:35:54] root INFO: augmenter_args : [2022/08/11 13:35:54] root INFO: args : [2022/08/11 13:35:54] root INFO: p : 0.5 [2022/08/11 13:35:54] root INFO: type : Fliplr [2022/08/11 13:35:54] root INFO: args : [2022/08/11 13:35:54] root INFO: rotate : [-10, 10] [2022/08/11 13:35:54] root INFO: type : Affine [2022/08/11 13:35:54] root INFO: args : [2022/08/11 13:35:54] root INFO: size : [0.5, 3] [2022/08/11 13:35:54] root INFO: type : Resize [2022/08/11 13:35:54] root INFO: EastRandomCropData : [2022/08/11 13:35:54] root INFO: keep_ratio : True [2022/08/11 13:35:54] root INFO: max_tries : 50 [2022/08/11 13:35:54] root INFO: size : [640, 640] [2022/08/11 13:35:54] root INFO: MakeBorderMap : [2022/08/11 13:35:54] root INFO: shrink_ratio : 0.4 [2022/08/11 13:35:54] root INFO: thresh_max : 0.7 [2022/08/11 13:35:54] root INFO: thresh_min : 0.3 [2022/08/11 13:35:54] root INFO: MakeShrinkMap : [2022/08/11 13:35:54] root INFO: min_text_size : 8 [2022/08/11 13:35:54] root INFO: shrink_ratio : 0.4 [2022/08/11 13:35:54] root INFO: NormalizeImage : [2022/08/11 13:35:54] root INFO: mean : [0.485, 0.456, 0.406] [2022/08/11 13:35:54] root INFO: order : hwc [2022/08/11 13:35:54] root INFO: scale : 1./255. [2022/08/11 13:35:54] root INFO: std : [0.229, 0.224, 0.225] [2022/08/11 13:35:54] root INFO: ToCHWImage : None [2022/08/11 13:35:54] root INFO: KeepKeys : [2022/08/11 13:35:54] root INFO: keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] [2022/08/11 13:35:54] root INFO: loader : [2022/08/11 13:35:54] root INFO: batch_size_per_card : 16 [2022/08/11 13:35:54] root INFO: drop_last : False [2022/08/11 13:35:54] root INFO: num_workers : 8 [2022/08/11 13:35:54] root INFO: shuffle : True [2022/08/11 13:35:54] root INFO: use_shared_memory : False [2022/08/11 13:35:54] root INFO: train with paddle 2.0.2 and device CUDAPlace(0) [2022/08/11 13:35:54] root INFO: Initialize indexs of datasets:['./train_det_hebing_all.txt'] [2022/08/11 13:35:54] root INFO: Initialize indexs of datasets:['./test_det_hebing_all.txt'] W0811 13:35:54.603739 1610 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1 W0811 13:35:54.608341 1610 device_context.cc:372] device: 0, cuDNN Version: 7.6.
部分数据集训练
#3.合并后部分数据训练模版，下面显示输出是用这个训练的结果
%cd /home/aistudio/
!python3 PaddleOCR/tools/train.py -c PaddleOCR/configs/det/det_mv3_db.yml -o \
Global.eval_batch_step="[0,50]" \
Global.load_static_weights=true \
Global.pretrained_model='PaddleOCR/pretrain_models/MobileNetV3_large_x0_5_pretrained' \
Train.loader.batch_size_per_card=16 \
Train.dataset.data_dir='./report_ex/train_det_new1_hebing' \
Train.dataset.label_file_list=['./train_det_new1_hebing.txt'] \
Eval.dataset.data_dir='./report_ex/test_det_new1_hebing' \
Eval.dataset.label_file_list=['./test_det_new1_hebing.txt']
#3.合并后全集训练模版，可能要调batch_size_per_card大小 ,执行打开注释，注释其他，
# %cd /home/aistudio/
# !python3 PaddleOCR/tools/train.py -c PaddleOCR/configs/det/det_mv3_db.yml -o \
# Global.eval_batch_step="[0,10]" \
# Global.load_static_weights=true \
# Global.pretrained_model='PaddleOCR/pretrain_models/MobileNetV3_large_x0_5_pretrained' \
# Train.loader.batch_size_per_card=32 \
# Train.dataset.data_dir='./report_ex/train_det_hebing_all' \
# Train.dataset.label_file_list=['./train_det_hebing_all.txt'] \
# Eval.dataset.data_dir='./report_ex/test_det_hebing_all' \
# Eval.dataset.label_file_list=['./test_det_hebing_all.txt']
/home/aistudio /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations def convert_to_list(value, n, name, dtype=np.int): [2022/08/09 09:38:47] root INFO: Architecture : [2022/08/09 09:38:47] root INFO: Backbone : [2022/08/09 09:38:47] root INFO: model_name : large [2022/08/09 09:38:47] root INFO: name : MobileNetV3 [2022/08/09 09:38:47] root INFO: scale : 0.5 [2022/08/09 09:38:47] root INFO: Head : [2022/08/09 09:38:47] root INFO: k : 50 [2022/08/09 09:38:47] root INFO: name : DBHead [2022/08/09 09:38:47] root INFO: Neck : [2022/08/09 09:38:47] root INFO: name : DBFPN [2022/08/09 09:38:47] root INFO: out_channels : 256 [2022/08/09 09:38:47] root INFO: Transform : None [2022/08/09 09:38:47] root INFO: algorithm : DB [2022/08/09 09:38:47] root INFO: model_type : det [2022/08/09 09:38:47] root INFO: Eval : [2022/08/09 09:38:47] root INFO: dataset : [2022/08/09 09:38:47] root INFO: data_dir : ./report_ex/test_det_new1_hebing [2022/08/09 09:38:47] root INFO: label_file_list : ['./test_det_new1_hebing.txt'] [2022/08/09 09:38:47] root INFO: name : SimpleDataSet [2022/08/09 09:38:47] root INFO: transforms : [2022/08/09 09:38:47] root INFO: DecodeImage : [2022/08/09 09:38:47] root INFO: channel_first : False [2022/08/09 09:38:47] root INFO: img_mode : BGR [2022/08/09 09:38:47] root INFO: DetLabelEncode : None [2022/08/09 09:38:47] root INFO: DetResizeForTest : [2022/08/09 09:38:47] root INFO: image_shape : [736, 1280] [2022/08/09 09:38:47] root INFO: NormalizeImage : [2022/08/09 09:38:47] root INFO: mean : [0.485, 0.456, 0.406] [2022/08/09 09:38:47] root INFO: order : hwc [2022/08/09 09:38:47] root INFO: scale : 1./255. [2022/08/09 09:38:47] root INFO: std : [0.229, 0.224, 0.225] [2022/08/09 09:38:47] root INFO: ToCHWImage : None [2022/08/09 09:38:47] root INFO: KeepKeys : [2022/08/09 09:38:47] root INFO: keep_keys : ['image', 'shape', 'polys', 'ignore_tags'] [2022/08/09 09:38:47] root INFO: loader : [2022/08/09 09:38:47] root INFO: batch_size_per_card : 1 [2022/08/09 09:38:47] root INFO: drop_last : False [2022/08/09 09:38:47] root INFO: num_workers : 8 [2022/08/09 09:38:47] root INFO: shuffle : False [2022/08/09 09:38:47] root INFO: use_shared_memory : False [2022/08/09 09:38:47] root INFO: Global : [2022/08/09 09:38:47] root INFO: cal_metric_during_train : False [2022/08/09 09:38:47] root INFO: checkpoints : None [2022/08/09 09:38:47] root INFO: debug : False [2022/08/09 09:38:47] root INFO: distributed : False [2022/08/09 09:38:47] root INFO: epoch_num : 1200 [2022/08/09 09:38:47] root INFO: eval_batch_step : [0, 50] [2022/08/09 09:38:47] root INFO: infer_img : ./20220623110401-0.png [2022/08/09 09:38:47] root INFO: load_static_weights : True [2022/08/09 09:38:47] root INFO: log_smooth_window : 20 [2022/08/09 09:38:47] root INFO: pretrained_model : PaddleOCR/pretrain_models/MobileNetV3_large_x0_5_pretrained [2022/08/09 09:38:47] root INFO: print_batch_step : 10 [2022/08/09 09:38:47] root INFO: save_epoch_step : 1200 [2022/08/09 09:38:47] root INFO: save_inference_dir : None [2022/08/09 09:38:47] root INFO: save_model_dir : ./output1/db_mv3/ [2022/08/09 09:38:47] root INFO: save_res_path : ./output1/det_db/predicts_db.txt [2022/08/09 09:38:47] root INFO: use_gpu : True [2022/08/09 09:38:47] root INFO: use_visualdl : False [2022/08/09 09:38:47] root INFO: Loss : [2022/08/09 09:38:47] root INFO: alpha : 5 [2022/08/09 09:38:47] root INFO: balance_loss : True [2022/08/09 09:38:47] root INFO: beta : 10 [2022/08/09 09:38:47] root INFO: main_loss_type : DiceLoss [2022/08/09 09:38:47] root INFO: name : DBLoss [2022/08/09 09:38:47] root INFO: ohem_ratio : 3 [2022/08/09 09:38:47] root INFO: Metric : [2022/08/09 09:38:47] root INFO: main_indicator : hmean [2022/08/09 09:38:47] root INFO: name : DetMetric [2022/08/09 09:38:47] root INFO: Optimizer : [2022/08/09 09:38:47] root INFO: beta1 : 0.9 [2022/08/09 09:38:47] root INFO: beta2 : 0.999 [2022/08/09 09:38:47] root INFO: lr : [2022/08/09 09:38:47] root INFO: learning_rate : 0.001 [2022/08/09 09:38:47] root INFO: name : Adam [2022/08/09 09:38:47] root INFO: regularizer : [2022/08/09 09:38:47] root INFO: factor : 0 [2022/08/09 09:38:47] root INFO: name : L2 [2022/08/09 09:38:47] root INFO: PostProcess : [2022/08/09 09:38:47] root INFO: box_thresh : 0.6 [2022/08/09 09:38:47] root INFO: max_candidates : 1000 [2022/08/09 09:38:47] root INFO: name : DBPostProcess [2022/08/09 09:38:47] root INFO: thresh : 0.3 [2022/08/09 09:38:47] root INFO: unclip_ratio : 1.5 [2022/08/09 09:38:47] root INFO: Train : [2022/08/09 09:38:47] root INFO: dataset : [2022/08/09 09:38:47] root INFO: data_dir : ./report_ex/train_det_new1_hebing [2022/08/09 09:38:47] root INFO: label_file_list : ['./train_det_new1_hebing.txt'] [2022/08/09 09:38:47] root INFO: name : SimpleDataSet [2022/08/09 09:38:47] root INFO: ratio_list : [1.0] [2022/08/09 09:38:47] root INFO: transforms : [2022/08/09 09:38:47] root INFO: DecodeImage : [2022/08/09 09:38:47] root INFO: channel_first : False [2022/08/09 09:38:47] root INFO: img_mode : BGR [2022/08/09 09:38:47] root INFO: DetLabelEncode : None [2022/08/09 09:38:47] root INFO: IaaAugment : [2022/08/09 09:38:47] root INFO: augmenter_args : [2022/08/09 09:38:47] root INFO: args : [2022/08/09 09:38:47] root INFO: p : 0.5 [2022/08/09 09:38:47] root INFO: type : Fliplr [2022/08/09 09:38:47] root INFO: args : [2022/08/09 09:38:47] root INFO: rotate : [-10, 10] [2022/08/09 09:38:47] root INFO: type : Affine [2022/08/09 09:38:47] root INFO: args : [2022/08/09 09:38:47] root INFO: size : [0.5, 3] [2022/08/09 09:38:47] root INFO: type : Resize [2022/08/09 09:38:47] root INFO: EastRandomCropData : [2022/08/09 09:38:47] root INFO: keep_ratio : True [2022/08/09 09:38:47] root INFO: max_tries : 50 [2022/08/09 09:38:47] root INFO: size : [640, 640] [2022/08/09 09:38:47] root INFO: MakeBorderMap : [2022/08/09 09:38:47] root INFO: shrink_ratio : 0.4 [2022/08/09 09:38:47] root INFO: thresh_max : 0.7 [2022/08/09 09:38:47] root INFO: thresh_min : 0.3 [2022/08/09 09:38:47] root INFO: MakeShrinkMap : [2022/08/09 09:38:47] root INFO: min_text_size : 8 [2022/08/09 09:38:47] root INFO: shrink_ratio : 0.4 [2022/08/09 09:38:47] root INFO: NormalizeImage : [2022/08/09 09:38:47] root INFO: mean : [0.485, 0.456, 0.406] [2022/08/09 09:38:47] root INFO: order : hwc [2022/08/09 09:38:47] root INFO: scale : 1./255. [2022/08/09 09:38:47] root INFO: std : [0.229, 0.224, 0.225] [2022/08/09 09:38:47] root INFO: ToCHWImage : None [2022/08/09 09:38:47] root INFO: KeepKeys : [2022/08/09 09:38:47] root INFO: keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] [2022/08/09 09:38:47] root INFO: loader : [2022/08/09 09:38:47] root INFO: batch_size_per_card : 16 [2022/08/09 09:38:47] root INFO: drop_last : False [2022/08/09 09:38:47] root INFO: num_workers : 8 [2022/08/09 09:38:47] root INFO: shuffle : True [2022/08/09 09:38:47] root INFO: use_shared_memory : False [2022/08/09 09:38:47] root INFO: train with paddle 2.0.2 and device CUDAPlace(0) [2022/08/09 09:38:47] root INFO: Initialize indexs of datasets:['./train_det_new1_hebing.txt'] [2022/08/09 09:38:47] root INFO: Initialize indexs of datasets:['./test_det_new1_hebing.txt'] W0809 09:38:47.257441 10327 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1 W0809 09:38:47.261169 10327 device_context.cc:372] device: 0, cuDNN Version: 7.6. [2022/08/09 09:38:50] root INFO: load pretrained model from ['PaddleOCR/pretrain_models/MobileNetV3_large_x0_5_pretrained'] [2022/08/09 09:38:50] root INFO: train dataloader has 9 iters [2022/08/09 09:38:50] root INFO: valid dataloader has 16 iters [2022/08/09 09:38:50] root INFO: During the training process, after the 0th iteration, an evaluation is run every 50 iterations [2022/08/09 09:38:50] root INFO: Initialize indexs of datasets:['./train_det_new1_hebing.txt'] [2022/08/09 09:39:51] root INFO: epoch: [1/1200], iter: 8, lr: 0.001000, loss: 7.751804, loss_shrink_maps: 4.606901, loss_threshold_maps: 2.225027, loss_binary_maps: 0.919876, reader_cost: 5.55701 s, batch_cost: 6.10987 s, samples: 140, ips: 2.29137 [2022/08/09 09:39:52] root INFO: save model in ./output1/db_mv3/latest [2022/08/09 09:39:52] root INFO: Initialize indexs of datasets:['./train_det_new1_hebing.txt'] [2022/08/09 09:40:55] root INFO: epoch: [2/1200], iter: 10, lr: 0.001000, loss: 7.377272, loss_shrink_maps: 4.554792, loss_threshold_maps: 1.911745, loss_binary_maps: 0.910735, reader_cost: 6.11302 s, batch_cost: 6.33029 s, samples: 32, ips: 0.50551 [2022/08/09 09:40:58] root INFO: epoch: [2/1200], iter: 17, lr: 0.001000, loss: 6.610305, loss_shrink_maps: 4.466334, loss_threshold_maps: 1.255741, loss_binary_maps: 0.897122, reader_cost: 0.07724 s, batch_cost: 0.30940 s, samples: 108, ips: 34.90611 [2022/08/09 09:40:59] root INFO: save model in ./output1/db_mv3/latest [2022/08/09 09:40:59] root INFO: Initialize indexs of datasets:['./train_det_new1_hebing.txt'] ^C main proc 11724 exit, kill process group 10327 main proc 11723 exit, kill process group 10327
2.7测试检测效果
训练过程中的检测模型保存在'./output/det_db/'中，模型保存的位置通过yml配置文件的Global.save_model_dir参数设置。
使用训练好的模型测试单张图像的检测效果。
In [ ]
# %cd PaddleOCR
#部分数据结果
# !python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="../20220623110401-0.png" Global.pretrained_model="/home/aistudio/output1/db_mv3/best_accuracy"
#全集数据结果，全集只训练一个epoch
!python3 tools/infer_det.py -c configs/det/det_mv3_db_all.yml -o Global.infer_img="../20220623110401-0.png" Global.pretrained_model="/home/aistudio/outputall/db_mv3/best_accuracy"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations def convert_to_list(value, n, name, dtype=np.int): [2022/08/17 22:49:37] root INFO: Architecture : [2022/08/17 22:49:37] root INFO: Backbone : [2022/08/17 22:49:37] root INFO: model_name : large [2022/08/17 22:49:37] root INFO: name : MobileNetV3 [2022/08/17 22:49:37] root INFO: scale : 0.5 [2022/08/17 22:49:37] root INFO: Head : [2022/08/17 22:49:37] root INFO: k : 50 [2022/08/17 22:49:37] root INFO: name : DBHead [2022/08/17 22:49:37] root INFO: Neck : [2022/08/17 22:49:37] root INFO: name : DBFPN [2022/08/17 22:49:37] root INFO: out_channels : 256 [2022/08/17 22:49:37] root INFO: Transform : None [2022/08/17 22:49:37] root INFO: algorithm : DB [2022/08/17 22:49:37] root INFO: model_type : det [2022/08/17 22:49:37] root INFO: Eval : [2022/08/17 22:49:37] root INFO: dataset : [2022/08/17 22:49:37] root INFO: data_dir : ./train_data/icdar2015/text_localization/ [2022/08/17 22:49:37] root INFO: label_file_list : ['./train_data/icdar2015/text_localization/test_icdar2015_label.txt'] [2022/08/17 22:49:37] root INFO: name : SimpleDataSet [2022/08/17 22:49:37] root INFO: transforms : [2022/08/17 22:49:37] root INFO: DecodeImage : [2022/08/17 22:49:37] root INFO: channel_first : False [2022/08/17 22:49:37] root INFO: img_mode : BGR [2022/08/17 22:49:37] root INFO: DetLabelEncode : None [2022/08/17 22:49:37] root INFO: DetResizeForTest : [2022/08/17 22:49:37] root INFO: image_shape : [736, 1280] [2022/08/17 22:49:37] root INFO: NormalizeImage : [2022/08/17 22:49:37] root INFO: mean : [0.485, 0.456, 0.406] [2022/08/17 22:49:37] root INFO: order : hwc [2022/08/17 22:49:37] root INFO: scale : 1./255. [2022/08/17 22:49:37] root INFO: std : [0.229, 0.224, 0.225] [2022/08/17 22:49:37] root INFO: ToCHWImage : None [2022/08/17 22:49:37] root INFO: KeepKeys : [2022/08/17 22:49:37] root INFO: keep_keys : ['image', 'shape', 'polys', 'ignore_tags'] [2022/08/17 22:49:37] root INFO: loader : [2022/08/17 22:49:37] root INFO: batch_size_per_card : 1 [2022/08/17 22:49:37] root INFO: drop_last : False [2022/08/17 22:49:37] root INFO: num_workers : 8 [2022/08/17 22:49:37] root INFO: shuffle : False [2022/08/17 22:49:37] root INFO: use_shared_memory : False [2022/08/17 22:49:37] root INFO: Global : [2022/08/17 22:49:37] root INFO: cal_metric_during_train : False [2022/08/17 22:49:37] root INFO: checkpoints : None [2022/08/17 22:49:37] root INFO: debug : False [2022/08/17 22:49:37] root INFO: distributed : False [2022/08/17 22:49:37] root INFO: epoch_num : 1200 [2022/08/17 22:49:37] root INFO: eval_batch_step : [0, 2000] [2022/08/17 22:49:37] root INFO: infer_img : ../20220623110401-0.png [2022/08/17 22:49:37] root INFO: log_smooth_window : 20 [2022/08/17 22:49:37] root INFO: pretrained_model : /home/aistudio/outputall/db_mv3/best_accuracy [2022/08/17 22:49:37] root INFO: print_batch_step : 10 [2022/08/17 22:49:37] root INFO: save_epoch_step : 1200 [2022/08/17 22:49:37] root INFO: save_inference_dir : None [2022/08/17 22:49:37] root INFO: save_model_dir : /home/aistudio/outputall/db_mv3/ [2022/08/17 22:49:37] root INFO: save_res_path : ./outputall/det_db/predicts_db.txt [2022/08/17 22:49:37] root INFO: use_gpu : True [2022/08/17 22:49:37] root INFO: use_visualdl : False [2022/08/17 22:49:37] root INFO: Loss : [2022/08/17 22:49:37] root INFO: alpha : 5 [2022/08/17 22:49:37] root INFO: balance_loss : True [2022/08/17 22:49:37] root INFO: beta : 10 [2022/08/17 22:49:37] root INFO: main_loss_type : DiceLoss [2022/08/17 22:49:37] root INFO: name : DBLoss [2022/08/17 22:49:37] root INFO: ohem_ratio : 3 [2022/08/17 22:49:37] root INFO: Metric : [2022/08/17 22:49:37] root INFO: main_indicator : hmean [2022/08/17 22:49:37] root INFO: name : DetMetric [2022/08/17 22:49:37] root INFO: Optimizer : [2022/08/17 22:49:37] root INFO: beta1 : 0.9 [2022/08/17 22:49:37] root INFO: beta2 : 0.999 [2022/08/17 22:49:37] root INFO: lr : [2022/08/17 22:49:37] root INFO: learning_rate : 0.001 [2022/08/17 22:49:37] root INFO: name : Adam [2022/08/17 22:49:37] root INFO: regularizer : [2022/08/17 22:49:37] root INFO: factor : 0 [2022/08/17 22:49:37] root INFO: name : L2 [2022/08/17 22:49:37] root INFO: PostProcess : [2022/08/17 22:49:37] root INFO: box_thresh : 0.6 [2022/08/17 22:49:37] root INFO: max_candidates : 1000 [2022/08/17 22:49:37] root INFO: name : DBPostProcess [2022/08/17 22:49:37] root INFO: thresh : 0.3 [2022/08/17 22:49:37] root INFO: unclip_ratio : 1.5 [2022/08/17 22:49:37] root INFO: Train : [2022/08/17 22:49:37] root INFO: dataset : [2022/08/17 22:49:37] root INFO: data_dir : ./train_data/icdar2015/text_localization/ [2022/08/17 22:49:37] root INFO: label_file_list : ['./train_data/icdar2015/text_localization/train_icdar2015_label.txt'] [2022/08/17 22:49:37] root INFO: name : SimpleDataSet [2022/08/17 22:49:37] root INFO: ratio_list : [1.0] [2022/08/17 22:49:37] root INFO: transforms : [2022/08/17 22:49:37] root INFO: DecodeImage : [2022/08/17 22:49:37] root INFO: channel_first : False [2022/08/17 22:49:37] root INFO: img_mode : BGR [2022/08/17 22:49:37] root INFO: DetLabelEncode : None [2022/08/17 22:49:37] root INFO: IaaAugment : [2022/08/17 22:49:37] root INFO: augmenter_args : [2022/08/17 22:49:37] root INFO: args : [2022/08/17 22:49:37] root INFO: p : 0.5 [2022/08/17 22:49:37] root INFO: type : Fliplr [2022/08/17 22:49:37] root INFO: args : [2022/08/17 22:49:37] root INFO: rotate : [-10, 10] [2022/08/17 22:49:37] root INFO: type : Affine [2022/08/17 22:49:37] root INFO: args : [2022/08/17 22:49:37] root INFO: size : [0.5, 3] [2022/08/17 22:49:37] root INFO: type : Resize [2022/08/17 22:49:37] root INFO: EastRandomCropData : [2022/08/17 22:49:37] root INFO: keep_ratio : True [2022/08/17 22:49:37] root INFO: max_tries : 50 [2022/08/17 22:49:37] root INFO: size : [640, 640] [2022/08/17 22:49:37] root INFO: MakeBorderMap : [2022/08/17 22:49:37] root INFO: shrink_ratio : 0.4 [2022/08/17 22:49:37] root INFO: thresh_max : 0.7 [2022/08/17 22:49:37] root INFO: thresh_min : 0.3 [2022/08/17 22:49:37] root INFO: MakeShrinkMap : [2022/08/17 22:49:37] root INFO: min_text_size : 8 [2022/08/17 22:49:37] root INFO: shrink_ratio : 0.4 [2022/08/17 22:49:37] root INFO: NormalizeImage : [2022/08/17 22:49:37] root INFO: mean : [0.485, 0.456, 0.406] [2022/08/17 22:49:37] root INFO: order : hwc [2022/08/17 22:49:37] root INFO: scale : 1./255. [2022/08/17 22:49:37] root INFO: std : [0.229, 0.224, 0.225] [2022/08/17 22:49:37] root INFO: ToCHWImage : None [2022/08/17 22:49:37] root INFO: KeepKeys : [2022/08/17 22:49:37] root INFO: keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] [2022/08/17 22:49:37] root INFO: loader : [2022/08/17 22:49:37] root INFO: batch_size_per_card : 64 [2022/08/17 22:49:37] root INFO: drop_last : False [2022/08/17 22:49:37] root INFO: num_workers : 8 [2022/08/17 22:49:37] root INFO: shuffle : True [2022/08/17 22:49:37] root INFO: use_shared_memory : False [2022/08/17 22:49:37] root INFO: train with paddle 2.0.2 and device CUDAPlace(0) W0817 22:49:37.830164 5900 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1 W0817 22:49:37.835045 5900 device_context.cc:372] device: 0, cuDNN Version: 7.6. [2022/08/17 22:49:40] root INFO: load pretrained model from ['/home/aistudio/outputall/db_mv3/best_accuracy'] [2022/08/17 22:49:40] root INFO: infer_img: ../20220623110401-0.png [2022/08/17 22:49:41] root INFO: The detected Image saved in ./outputall/det_db/det_results/20220623110401-0.png [2022/08/17 22:49:41] root INFO: success!
In [ ]
# %cd PaddleOCR/
# !python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="../20220623110401-0.png" Global.checkpoints="./output/db_mv3/best_accuracy"使用训练好的模型，测试文件夹下所有图像的检测效果,路径按需更改！下同。
In [ ]
#!python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.checkpoints="./output/db_mv3/best_accuracy"
3. 训练文字识别模型
3.1. 数据准备
首先请将训练图片放入同一个文件夹（trainimages），并用一个txt文件（recgttrain.txt）记录图片路径和标签。
本项目识别使用的数据集： train_reg.txt 、test_reg.txt ，注意： 默认请将图片路径和图片标签用 \t 分割。
- txt训练集
" 图像文件名 图像标注信息 "
20220623110401-0.png 姓名：张某某
训练集及测试集文件结构如下：
|- train_reg.txt
|- report_ex/
|- train_reg
|- word_001.png
|- word_002.jpg
| ...
|- test_reg
|- word_001.png
|- word_002.jpg
| ...
### 3.2. 快速启动训练
本节文字识别网络以 CRNN 识别模型为例，网络模型使用PaddleOCR主流两种识别模型backbone，MobileNetV3和ResNet50_vd ：
In [ ]
# 下载ResNet50的预训练模型
%cd PaddleOCR/
!wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar
! cd pretrain_models/ && tar xf rec_mv3_none_bilstm_ctc_v2.0_train.tar/home/aistudio/PaddleOCR --2022-08-05 14:11:03-- https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar Resolving paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)... 100.67.200.6 Connecting to paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)|100.67.200.6|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 51200000 (49M) [application/x-tar] Saving to: ‘./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train.tar.1’ rec_mv3_none_bilstm 100%[===================>] 48.83M 119MB/s in 0.4s 2022-08-05 14:11:03 (119 MB/s) - ‘./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train.tar.1’ saved [51200000/51200000]
In [12]
#rec.py为按坐标截取图片脚本，将原来一张图片的所有标注截取成多张，生成new_pngs图像文件夹并生成对应rec.txt，用于识别模型
%cd /home/aistudio/
!python ./rec.pyIn [ ]
#查看当前文件夹图片数量
%cd ./new_pngs
!ls -l | grep "^-" | wc -l #一共1490577张图片/home/aistudio/new_pngs 1492727
In [ ]
#切分训练与测试数据集
%cd /home/aistudio/
!python ./rec_split_data.py/home/aistudio 2150
In [ ]
#拷贝训练集与测试集对应图片到文件夹用于文字识别训练，执行方法同上,需要执行两次，一次 train ,一次test
!python rec_file.py3.文字识别训练
本次文字识别训练因为服务器内存原因，只跑小部分数据集，模型只做基础演示，后续可调节参数或更换网络模型进行训练。
In [ ]
%cd PaddleOCR/
!python3 ./tools/train.py -c ./configs/rec/rec_icdar15_train.yml -o \
Global.eval_batch_step="[0,100]" \
Global.save_epoch_step=500 \
Global.pretrained_model='./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train/best_accuracy' \
Train.dataset.data_dir='../report_ex/train_rec' \
Train.dataset.label_file_list=['../train_rec.txt'] \
Eval.dataset.data_dir='../report_ex/test_rec' \
Eval.dataset.label_file_list=['../test_rec.txt'] \
Optimizer.lr.learning_rate=0.001[Errno 2] No such file or directory: 'PaddleOCR//' /home/aistudio/PaddleOCR /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations def convert_to_list(value, n, name, dtype=np.int): [2022/08/09 08:47:02] root INFO: Architecture : [2022/08/09 08:47:02] root INFO: Backbone : [2022/08/09 08:47:02] root INFO: model_name : large [2022/08/09 08:47:02] root INFO: name : MobileNetV3 [2022/08/09 08:47:02] root INFO: scale : 0.5 [2022/08/09 08:47:02] root INFO: Head : [2022/08/09 08:47:02] root INFO: fc_decay : 0 [2022/08/09 08:47:02] root INFO: name : CTCHead [2022/08/09 08:47:02] root INFO: Neck : [2022/08/09 08:47:02] root INFO: encoder_type : rnn [2022/08/09 08:47:02] root INFO: hidden_size : 96 [2022/08/09 08:47:02] root INFO: name : SequenceEncoder [2022/08/09 08:47:02] root INFO: Transform : None [2022/08/09 08:47:02] root INFO: algorithm : CRNN [2022/08/09 08:47:02] root INFO: model_type : rec [2022/08/09 08:47:02] root INFO: Eval : [2022/08/09 08:47:02] root INFO: dataset : [2022/08/09 08:47:02] root INFO: data_dir : ../report_ex/test_rec [2022/08/09 08:47:02] root INFO: label_file_list : ['../test_rec.txt'] [2022/08/09 08:47:02] root INFO: name : SimpleDataSet [2022/08/09 08:47:02] root INFO: transforms : [2022/08/09 08:47:02] root INFO: DecodeImage : [2022/08/09 08:47:02] root INFO: channel_first : False [2022/08/09 08:47:02] root INFO: img_mode : BGR [2022/08/09 08:47:02] root INFO: CTCLabelEncode : None [2022/08/09 08:47:02] root INFO: RecResizeImg : [2022/08/09 08:47:02] root INFO: image_shape : [3, 32, 100] [2022/08/09 08:47:02] root INFO: KeepKeys : [2022/08/09 08:47:02] root INFO: keep_keys : ['image', 'label', 'length'] [2022/08/09 08:47:02] root INFO: loader : [2022/08/09 08:47:02] root INFO: batch_size_per_card : 256 [2022/08/09 08:47:02] root INFO: drop_last : False [2022/08/09 08:47:02] root INFO: num_workers : 4 [2022/08/09 08:47:02] root INFO: shuffle : False [2022/08/09 08:47:02] root INFO: use_shared_memory : False [2022/08/09 08:47:02] root INFO: Global : [2022/08/09 08:47:02] root INFO: cal_metric_during_train : True [2022/08/09 08:47:02] root INFO: character_dict_path : ../label_list.txt [2022/08/09 08:47:02] root INFO: character_type : ch [2022/08/09 08:47:02] root INFO: checkpoints : None [2022/08/09 08:47:02] root INFO: debug : False [2022/08/09 08:47:02] root INFO: distributed : False [2022/08/09 08:47:02] root INFO: epoch_num : 100 [2022/08/09 08:47:02] root INFO: eval_batch_step : [0, 100] [2022/08/09 08:47:02] root INFO: infer_img : ./20220623110401-0.png [2022/08/09 08:47:02] root INFO: infer_mode : False [2022/08/09 08:47:02] root INFO: log_smooth_window : 20 [2022/08/09 08:47:02] root INFO: max_text_length : 25 [2022/08/09 08:47:02] root INFO: pretrained_model : ./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train/best_accuracy [2022/08/09 08:47:02] root INFO: print_batch_step : 100 [2022/08/09 08:47:02] root INFO: save_epoch_step : 100 [2022/08/09 08:47:02] root INFO: save_inference_dir : None [2022/08/09 08:47:02] root INFO: save_model_dir : ./output/rec/ [2022/08/09 08:47:02] root INFO: save_res_path : ./output/rec/predicts.txt [2022/08/09 08:47:02] root INFO: use_gpu : True [2022/08/09 08:47:02] root INFO: use_space_char : False [2022/08/09 08:47:02] root INFO: use_visualdl : False [2022/08/09 08:47:02] root INFO: Loss : [2022/08/09 08:47:02] root INFO: name : CTCLoss [2022/08/09 08:47:02] root INFO: Metric : [2022/08/09 08:47:02] root INFO: main_indicator : acc [2022/08/09 08:47:02] root INFO: name : RecMetric [2022/08/09 08:47:02] root INFO: Optimizer : [2022/08/09 08:47:02] root INFO: beta1 : 0.9 [2022/08/09 08:47:02] root INFO: beta2 : 0.999 [2022/08/09 08:47:02] root INFO: lr : [2022/08/09 08:47:02] root INFO: learning_rate : 0.001 [2022/08/09 08:47:02] root INFO: name : Adam [2022/08/09 08:47:02] root INFO: regularizer : [2022/08/09 08:47:02] root INFO: factor : 0 [2022/08/09 08:47:02] root INFO: name : L2 [2022/08/09 08:47:02] root INFO: PostProcess : [2022/08/09 08:47:02] root INFO: name : CTCLabelDecode [2022/08/09 08:47:02] root INFO: Train : [2022/08/09 08:47:02] root INFO: dataset : [2022/08/09 08:47:02] root INFO: data_dir : ../report_ex/train_rec [2022/08/09 08:47:02] root INFO: label_file_list : ['../train_rec.txt'] [2022/08/09 08:47:02] root INFO: name : SimpleDataSet [2022/08/09 08:47:02] root INFO: transforms : [2022/08/09 08:47:02] root INFO: DecodeImage : [2022/08/09 08:47:02] root INFO: channel_first : False [2022/08/09 08:47:02] root INFO: img_mode : BGR [2022/08/09 08:47:02] root INFO: CTCLabelEncode : None [2022/08/09 08:47:02] root INFO: RecResizeImg : [2022/08/09 08:47:02] root INFO: image_shape : [3, 32, 100] [2022/08/09 08:47:02] root INFO: KeepKeys : [2022/08/09 08:47:02] root INFO: keep_keys : ['image', 'label', 'length'] [2022/08/09 08:47:02] root INFO: loader : [2022/08/09 08:47:02] root INFO: batch_size_per_card : 256 [2022/08/09 08:47:02] root INFO: drop_last : True [2022/08/09 08:47:02] root INFO: num_workers : 8 [2022/08/09 08:47:02] root INFO: shuffle : True [2022/08/09 08:47:02] root INFO: use_shared_memory : False [2022/08/09 08:47:02] root INFO: train with paddle 2.0.2 and device CUDAPlace(0) [2022/08/09 08:47:02] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:47:02] root INFO: Initialize indexs of datasets:['../test_rec.txt'] W0809 08:47:02.545296 3246 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1 W0809 08:47:02.548940 3246 device_context.cc:372] device: 0, cuDNN Version: 7.6. 2022-08-09 08:47:05,035 - INFO - If regularizer of a Parameter has been set by 'paddle.ParamAttr' or 'static.WeightNormParamAttr' already. The weight_decay[L2Decay, regularization_coeff=0.000000] in Optimizer will not take effect, and it will only be applied to other Parameters! [2022/08/09 08:47:05] root INFO: load pretrained model from ['./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train/best_accuracy'] [2022/08/09 08:47:05] root INFO: train dataloader has 7 iters [2022/08/09 08:47:05] root INFO: valid dataloader has 1 iters [2022/08/09 08:47:05] root INFO: During the training process, after the 0th iteration, an evaluation is run every 100 iterations eval model:: 100%|████████████████████████████████| 1/1 [00:00<00:00, 1.64it/s] [2022/08/09 08:50:29] root INFO: cur metric, acc: 0.5062761506276151, norm_edit_dis: 0.7929171366686749, fps: 3181.1431744832903 [2022/08/09 08:50:30] root INFO: save best model is to ./output/rec/best_accuracy [2022/08/09 08:50:30] root INFO: best metric, acc: 0.5062761506276151, norm_edit_dis: 0.7929171366686749, fps: 3181.1431744832903, best_epoch: 86 [2022/08/09 08:50:30] root INFO: epoch: [86/100], iter: 601, lr: 0.001000, loss: 2.796403, acc: 0.519531, norm_edit_dis: 0.831250, reader_cost: 0.00002 s, batch_cost: 0.00133 s, samples: 256, ips: 1925.17934 [2022/08/09 08:50:30] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:30] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:50:32] root INFO: epoch: [87/100], iter: 608, lr: 0.001000, loss: 2.669565, acc: 0.542969, norm_edit_dis: 0.835502, reader_cost: 0.00819 s, batch_cost: 0.01664 s, samples: 1792, ips: 1076.79762 [2022/08/09 08:50:33] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:33] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:50:35] root INFO: epoch: [88/100], iter: 615, lr: 0.001000, loss: 2.684035, acc: 0.552734, norm_edit_dis: 0.843117, reader_cost: 0.00747 s, batch_cost: 0.01662 s, samples: 1792, ips: 1078.17574 [2022/08/09 08:50:35] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:35] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:50:37] root INFO: epoch: [89/100], iter: 622, lr: 0.001000, loss: 2.511953, acc: 0.554688, norm_edit_dis: 0.844917, reader_cost: 0.00842 s, batch_cost: 0.01714 s, samples: 1792, ips: 1045.68432 [2022/08/09 08:50:38] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:38] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:50:40] root INFO: epoch: [90/100], iter: 629, lr: 0.001000, loss: 2.481447, acc: 0.562500, norm_edit_dis: 0.847279, reader_cost: 0.00735 s, batch_cost: 0.01727 s, samples: 1792, ips: 1037.49143 [2022/08/09 08:50:40] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:40] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:50:42] root INFO: epoch: [91/100], iter: 636, lr: 0.001000, loss: 2.463750, acc: 0.580078, norm_edit_dis: 0.857158, reader_cost: 0.00820 s, batch_cost: 0.01770 s, samples: 1792, ips: 1012.58188 [2022/08/09 08:50:43] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:43] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:50:45] root INFO: epoch: [92/100], iter: 643, lr: 0.001000, loss: 2.466842, acc: 0.595703, norm_edit_dis: 0.862211, reader_cost: 0.00826 s, batch_cost: 0.01762 s, samples: 1792, ips: 1017.25276 [2022/08/09 08:50:45] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:45] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:50:47] root INFO: epoch: [93/100], iter: 650, lr: 0.001000, loss: 2.466842, acc: 0.611328, norm_edit_dis: 0.868967, reader_cost: 0.00813 s, batch_cost: 0.01704 s, samples: 1792, ips: 1051.46489 [2022/08/09 08:50:48] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:48] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:50:50] root INFO: epoch: [94/100], iter: 657, lr: 0.001000, loss: 2.242416, acc: 0.632812, norm_edit_dis: 0.876017, reader_cost: 0.00886 s, batch_cost: 0.01761 s, samples: 1792, ips: 1017.45601 [2022/08/09 08:50:51] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:51] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:50:52] root INFO: epoch: [95/100], iter: 664, lr: 0.001000, loss: 2.196207, acc: 0.640625, norm_edit_dis: 0.881983, reader_cost: 0.00792 s, batch_cost: 0.01659 s, samples: 1792, ips: 1080.27781 [2022/08/09 08:50:53] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:53] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:50:55] root INFO: epoch: [96/100], iter: 671, lr: 0.001000, loss: 2.113156, acc: 0.648438, norm_edit_dis: 0.882381, reader_cost: 0.00960 s, batch_cost: 0.01858 s, samples: 1792, ips: 964.26967 [2022/08/09 08:50:56] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:56] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:50:58] root INFO: epoch: [97/100], iter: 678, lr: 0.001000, loss: 2.080866, acc: 0.656250, norm_edit_dis: 0.883170, reader_cost: 0.00756 s, batch_cost: 0.01682 s, samples: 1792, ips: 1065.37528 [2022/08/09 08:50:58] root INFO: save model in ./output/rec/latest [2022/08/09 08:50:58] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:51:00] root INFO: epoch: [98/100], iter: 685, lr: 0.001000, loss: 2.028392, acc: 0.660156, norm_edit_dis: 0.885557, reader_cost: 0.00843 s, batch_cost: 0.01686 s, samples: 1792, ips: 1062.57177 [2022/08/09 08:51:01] root INFO: save model in ./output/rec/latest [2022/08/09 08:51:01] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:51:03] root INFO: epoch: [99/100], iter: 692, lr: 0.001000, loss: 1.941369, acc: 0.671875, norm_edit_dis: 0.890019, reader_cost: 0.00725 s, batch_cost: 0.01653 s, samples: 1792, ips: 1084.06991 [2022/08/09 08:51:03] root INFO: save model in ./output/rec/latest [2022/08/09 08:51:03] root INFO: Initialize indexs of datasets:['../train_rec.txt'] [2022/08/09 08:51:05] root INFO: epoch: [100/100], iter: 699, lr: 0.001000, loss: 1.861010, acc: 0.691406, norm_edit_dis: 0.898171, reader_cost: 0.00835 s, batch_cost: 0.01703 s, samples: 1792, ips: 1052.16714 [2022/08/09 08:51:06] root INFO: save model in ./output/rec/latest [2022/08/09 08:51:06] root INFO: save model in ./output/rec/iter_epoch_100 [2022/08/09 08:51:06] root INFO: best metric, acc: 0.5062761506276151, norm_edit_dis: 0.7929171366686749, fps: 3181.1431744832903, best_epoch: 86
3.2测试识别效果
测试单张图像的识别结果
# !python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints=./pretrain_models/rec_mv3_none_bilstm_ctc_v2.0_train/best_accuracy Global.infer_img=../20220623110401-0.png
!python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints=./output/rec/best_accuracy Global.infer_img=../report_ex/test_rec/20220623110401-1001_0119.png/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
def convert_to_list(value, n, name, dtype=np.int):
[2022/08/17 23:45:05] root INFO: Architecture :
[2022/08/17 23:45:05] root INFO: Backbone :
[2022/08/17 23:45:05] root INFO: model_name : large
[2022/08/17 23:45:05] root INFO: name : MobileNetV3
[2022/08/17 23:45:05] root INFO: scale : 0.5
[2022/08/17 23:45:05] root INFO: Head :
[2022/08/17 23:45:05] root INFO: fc_decay : 0
[2022/08/17 23:45:05] root INFO: name : CTCHead
[2022/08/17 23:45:05] root INFO: Neck :
[2022/08/17 23:45:05] root INFO: encoder_type : rnn
[2022/08/17 23:45:05] root INFO: hidden_size : 96
[2022/08/17 23:45:05] root INFO: name : SequenceEncoder
[2022/08/17 23:45:05] root INFO: Transform : None
[2022/08/17 23:45:05] root INFO: algorithm : CRNN
[2022/08/17 23:45:05] root INFO: model_type : rec
[2022/08/17 23:45:05] root INFO: Eval :
[2022/08/17 23:45:05] root INFO: dataset :
[2022/08/17 23:45:05] root INFO: data_dir : ./train_data/
[2022/08/17 23:45:05] root INFO: label_file_list : ['./train_data/val_list.txt']
[2022/08/17 23:45:05] root INFO: name : SimpleDataSet
[2022/08/17 23:45:05] root INFO: transforms :
[2022/08/17 23:45:05] root INFO: DecodeImage :
[2022/08/17 23:45:05] root INFO: channel_first : False
[2022/08/17 23:45:05] root INFO: img_mode : BGR
[2022/08/17 23:45:05] root INFO: CTCLabelEncode : None
[2022/08/17 23:45:05] root INFO: RecResizeImg :
[2022/08/17 23:45:05] root INFO: image_shape : [3, 32, 100]
[2022/08/17 23:45:05] root INFO: KeepKeys :
[2022/08/17 23:45:05] root INFO: keep_keys : ['image', 'label', 'length']
[2022/08/17 23:45:05] root INFO: loader :
[2022/08/17 23:45:05] root INFO: batch_size_per_card : 256
[2022/08/17 23:45:05] root INFO: drop_last : False
[2022/08/17 23:45:05] root INFO: num_workers : 4
[2022/08/17 23:45:05] root INFO: shuffle : False
[2022/08/17 23:45:05] root INFO: use_shared_memory : False
[2022/08/17 23:45:05] root INFO: Global :
[2022/08/17 23:45:05] root INFO: cal_metric_during_train : True
[2022/08/17 23:45:05] root INFO: character_dict_path : ../label_list.txt
[2022/08/17 23:45:05] root INFO: character_type : ch
[2022/08/17 23:45:05] root INFO: checkpoints : ./output/rec/best_accuracy
[2022/08/17 23:45:05] root INFO: debug : False
[2022/08/17 23:45:05] root INFO: distributed : False
[2022/08/17 23:45:05] root INFO: epoch_num : 100
[2022/08/17 23:45:05] root INFO: eval_batch_step : [0, 2000]
[2022/08/17 23:45:05] root INFO: infer_img : ../report_ex/test_rec/20220623110401-1001_0119.png
[2022/08/17 23:45:05] root INFO: infer_mode : False
[2022/08/17 23:45:05] root INFO: log_smooth_window : 20
[2022/08/17 23:45:05] root INFO: max_text_length : 25
[2022/08/17 23:45:05] root INFO: pretrained_model : None
[2022/08/17 23:45:05] root INFO: print_batch_step : 100
[2022/08/17 23:45:05] root INFO: save_epoch_step : 3
[2022/08/17 23:45:05] root INFO: save_inference_dir : None
[2022/08/17 23:45:05] root INFO: save_model_dir : ./output/rec/
[2022/08/17 23:45:05] root INFO: save_res_path : ./output/rec/predicts.txt
[2022/08/17 23:45:05] root INFO: use_gpu : True
[2022/08/17 23:45:05] root INFO: use_space_char : False
[2022/08/17 23:45:05] root INFO: use_visualdl : False
[2022/08/17 23:45:05] root INFO: Loss :
[2022/08/17 23:45:05] root INFO: name : CTCLoss
[2022/08/17 23:45:05] root INFO: Metric :
[2022/08/17 23:45:05] root INFO: main_indicator : acc
[2022/08/17 23:45:05] root INFO: name : RecMetric
[2022/08/17 23:45:05] root INFO: Optimizer :
[2022/08/17 23:45:05] root INFO: beta1 : 0.9
[2022/08/17 23:45:05] root INFO: beta2 : 0.999
[2022/08/17 23:45:05] root INFO: lr :
[2022/08/17 23:45:05] root INFO: learning_rate : 0.0005
[2022/08/17 23:45:05] root INFO: name : Adam
[2022/08/17 23:45:05] root INFO: regularizer :
[2022/08/17 23:45:05] root INFO: factor : 0
[2022/08/17 23:45:05] root INFO: name : L2
[2022/08/17 23:45:05] root INFO: PostProcess :
[2022/08/17 23:45:05] root INFO: name : CTCLabelDecode
[2022/08/17 23:45:05] root INFO: Train :
[2022/08/17 23:45:05] root INFO: dataset :
[2022/08/17 23:45:05] root INFO: data_dir : ./train_data/
[2022/08/17 23:45:05] root INFO: label_file_list : ['./train_data/train_list.txt']
[2022/08/17 23:45:05] root INFO: name : SimpleDataSet
[2022/08/17 23:45:05] root INFO: transforms :
[2022/08/17 23:45:05] root INFO: DecodeImage :
[2022/08/17 23:45:05] root INFO: channel_first : False
[2022/08/17 23:45:05] root INFO: img_mode : BGR
[2022/08/17 23:45:05] root INFO: CTCLabelEncode : None
[2022/08/17 23:45:05] root INFO: RecResizeImg :
[2022/08/17 23:45:05] root INFO: image_shape : [3, 32, 100]
[2022/08/17 23:45:05] root INFO: KeepKeys :
[2022/08/17 23:45:05] root INFO: keep_keys : ['image', 'label', 'length']
[2022/08/17 23:45:05] root INFO: loader :
[2022/08/17 23:45:05] root INFO: batch_size_per_card : 256
[2022/08/17 23:45:05] root INFO: drop_last : True
[2022/08/17 23:45:05] root INFO: num_workers : 8
[2022/08/17 23:45:05] root INFO: shuffle : True
[2022/08/17 23:45:05] root INFO: use_shared_memory : False
[2022/08/17 23:45:05] root INFO: train with paddle 2.0.2 and device CUDAPlace(0)
W0817 23:45:05.465901 12119 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1
W0817 23:45:05.470815 12119 device_context.cc:372] device: 0, cuDNN Version: 7.6.
[2022/08/17 23:45:08] root INFO: resume from ./output/rec/best_accuracy
[2022/08/17 23:45:08] root INFO: infer_img: ../report_ex/test_rec/20220623110401-1001_0119.png
[2022/08/17 23:45:08] root INFO: result: ('阴性', 0.89460135)
[2022/08/17 23:45:08] root INFO: success!
In [ ]
import matplotlib.pyplot as plt
from PIL import Image
%pylab inline
def show_img(img_path,figsize=(10,10)):
## 显示原图，读取名称为11.jpg的测试图像
img = Image.open(img_path)
plt.figure("test_img", figsize=figsize)
plt.imshow(img)
plt.show()
show_img("../report_ex/test_rec/20220623110401-1001_0119.png")Populating the interactive namespace from numpy and matplotlib
测试文件夹下所有图像的文字识别效果,根据自身需要自行测试
In [ ]
!python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints=./output/rec_CRNN/best_accuracy Global.infer_img=./doc/imgs_words_en/4.导出inference模型
In [ ]
!cp ./data/data164761/best_accuracy.pdopt ./outputall/rec
!cp ./data/data164761/best_accuracy.pdparams ./outputall/recIn [ ]
#1.导出检测模型
%cd PaddleOCR/
!python tools/export_model.py -c configs/det/det_mv3_db_all.yml \
-o Global.pretrained_model="../outputall/db_mv3/best_accuracy" \
Global.save_inference_dir="./my_det_model"/home/aistudio/PaddleOCR /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations def convert_to_list(value, n, name, dtype=np.int): W0817 22:11:30.547513 1528 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1 W0817 22:11:30.552330 1528 device_context.cc:372] device: 0, cuDNN Version: 7.6. [2022/08/17 22:11:33] root INFO: load pretrained model from ['../outputall/db_mv3/best_accuracy'] [2022/08/17 22:11:34] root INFO: inference model is saved to ./my_det_model/inference
In [ ]
#2.
%cd PaddleOCR/
!python tools/export_model.py -c configs/rec/ch_PP-OCRv3_rec_distillation.yml \
-o Global.pretrained_model="../outputall/rec/best_accuracy" \
Global.save_inference_dir="./my_rec_model"#3.导出上述代码生成的识别模型
%cd PaddleOCR/
!python tools/export_model.py -c configs/rec/rec_icdar15_train.yml \
-o Global.pretrained_model="./output/rec/best_accuracy" \
Global.save_inference_dir="./my_zj_rec_model"/home/aistudio/PaddleOCR /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations def convert_to_list(value, n, name, dtype=np.int): W0820 10:01:46.662283 1446 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1 W0820 10:01:46.666363 1446 device_context.cc:372] device: 0, cuDNN Version: 7.6. [2022/08/20 10:01:49] root INFO: load pretrained model from ['./output/rec/best_accuracy'] [2022/08/20 10:01:50] root INFO: inference model is saved to ./my_zj_rec_model/inference
# 检测+识别
!pwd
%cd PaddleOCR
# !python3 ./tools/infer/predict_system.py --image_dir="../report_ex/test_rec/20220623110401-1006_0123.png" \
!python3 ./tools/infer/predict_system.py --image_dir="../report_ex/pngs/20220623110401-101.png" \
--det_model_dir="./my_det_model/" \ #使用上述代码生成的检测模型
--rec_model_dir="./my_zj_rec_model/" \ #使用上述代码生成的识别模型
#--rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer" \ #使用官方识别模型/home/aistudio/PaddleOCR [Errno 2] No such file or directory: 'PaddleOCR' /home/aistudio/PaddleOCR /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations def convert_to_list(value, n, name, dtype=np.int): [2022/08/20 09:51:36] root INFO: dt_boxes num : 117, elapse : 1.619128942489624 [2022/08/20 09:51:36] root INFO: rec_res num : 117, elapse : 0.24797725677490234 [2022/08/20 09:51:36] root INFO: Predict time of ../report_ex/pngs/20220623110401-101.png: 2.001s [2022/08/20 09:51:36] root INFO: 性别：男, 0.994 [2022/08/20 09:51:36] root INFO: 姓名：张某某, 0.995 [2022/08/20 09:51:36] root INFO: 年龄：40, 0.997 [2022/08/20 09:51:36] root INFO: 尿液其他检查, 0.947 [2022/08/20 09:51:36] root INFO: 检查日期：2022-6-1, 0.997 [2022/08/20 09:51:36] root INFO: 检查医生：徐金凤, 0.981 [2022/08/20 09:51:36] root INFO: 本周氏蛋白定, 0.977 [2022/08/20 09:51:36] root INFO: 阴性, 0.996 [2022/08/20 09:51:36] root INFO: 阴性, 0.992 [2022/08/20 09:51:36] root INFO: 乳魔尿, 0.812 [2022/08/20 09:51:36] root INFO: 阳性, 0.999 [2022/08/20 09:51:36] root INFO: 阴性, 0.995 [2022/08/20 09:51:36] root INFO: U/L, 0.827 [2022/08/20 09:51:36] root INFO: 阴性, 0.995 [2022/08/20 09:51:36] root INFO: 阴性, 0.990 [2022/08/20 09:51:36] root INFO: 58., 0.862 [2022/08/20 09:51:36] root INFO: 3325U/, 0.821 [2022/08/20 09:51:36] root INFO: 绒毛膜促性腺, 0.946 [2022/08/20 09:51:36] root INFO: 尿液淀粉酶测, 0.973 [2022/08/20 09:51:36] root INFO: 阴性, 0.995 [2022/08/20 09:51:36] root INFO: RBC, 0.995 [2022/08/20 09:51:36] root INFO: RBC<3，, 0.874 [2022/08/20 09:51:36] root INFO: 尿含铁血黄素, 0.848 [2022/08/20 09:51:36] root INFO: 阴性, 0.980 [2022/08/20 09:51:36] root INFO: 检查小结, 0.996 [2022/08/20 09:51:36] root INFO: 安全用户电影地方所有你的, 1.000 [2022/08/20 09:51:36] root INFO: 娄便检查, 0.776 [2022/08/20 09:51:36] root INFO: 检查日期：2022-6-1, 0.996 [2022/08/20 09:51:36] root INFO: 检查医生：王超, 0.978 [2022/08/20 09:51:36] root INFO: 粪便常规, 0.830 [2022/08/20 09:51:36] root INFO: RBC, 0.997 [2022/08/20 09:51:36] root INFO: RBC：0, 0.898 [2022/08/20 09:51:36] root INFO: 寄生虫检查, 0.992 [2022/08/20 09:51:36] root INFO: O/H, 0.977 [2022/08/20 09:51:36] root INFO: O/HP, 0.939 [2022/08/20 09:51:36] root INFO: 大便动力, 0.998 [2022/08/20 09:51:36] root INFO: 便潜血检查, 0.971 [2022/08/20 09:51:36] root INFO: 阳性, 0.988 [2022/08/20 09:51:36] root INFO: 阴性, 0.813 [2022/08/20 09:51:36] root INFO: 动力阴性, 0.856 [2022/08/20 09:51:36] root INFO: 阴性, 0.997 [2022/08/20 09:51:36] root INFO: 检查小结, 0.995 [2022/08/20 09:51:36] root INFO: 这个任何不断产品如何今年不是根据, 0.926 [2022/08/20 09:51:36] root INFO: 血液学检测基他, 0.947 [2022/08/20 09:51:36] root INFO: 检查日期：2022-6-1, 0.998 [2022/08/20 09:51:36] root INFO: 检查医生：金桂珍, 0.985 [2022/08/20 09:51:36] root INFO: 项目名称, 0.999 [2022/08/20 09:51:36] root INFO: 检查结果, 0.996 [2022/08/20 09:51:36] root INFO: 参考标识, 0.971 [2022/08/20 09:51:36] root INFO: 单位, 0.999 [2022/08/20 09:51:36] root INFO: 参考范围, 0.996 [2022/08/20 09:51:36] root INFO: 网织红细胞计数, 0.998 [2022/08/20 09:51:36] root INFO: 0.6, 0.956 [2022/08/20 09:51:36] root INFO: （0.5-1.5）0, 0.845 [2022/08/20 09:51:36] root INFO: 12.4, 0.995 [2022/08/20 09:51:36] root INFO: mm/hour, 0.997 [2022/08/20 09:51:36] root INFO: 血沉, 0.980 [2022/08/20 09:51:36] root INFO: 0-15, 0.986 [2022/08/20 09:51:36] root INFO: 阴性, 0.984 [2022/08/20 09:51:36] root INFO: 阴性, 0.991 [2022/08/20 09:51:36] root INFO: 嗜酸球计数, 0.938 [2022/08/20 09:51:36] root INFO: 8, 0.715 [2022/08/20 09:51:36] root INFO: 1.5, 0.980 [2022/08/20 09:51:36] root INFO: （0.5-5）。, 0.841 [2022/08/20 09:51:36] root INFO: 检查小结, 0.994 [2022/08/20 09:51:36] root INFO: 生化室项目一览表, 0.985 [2022/08/20 09:51:36] root INFO: 检查日期：2022-6-1, 0.998 [2022/08/20 09:51:36] root INFO: 检查医生：张海燕, 0.995 [2022/08/20 09:51:36] root INFO: 检查结果, 0.986 [2022/08/20 09:51:36] root INFO: 参考标识, 0.974 [2022/08/20 09:51:36] root INFO: 单位, 0.999 [2022/08/20 09:51:36] root INFO: 项目名称, 0.984 [2022/08/20 09:51:36] root INFO: 参考范围, 0.945 [2022/08/20 09:51:36] root INFO: 131.2, 0.995 [2022/08/20 09:51:36] root INFO: 肌酸激酶, 0.967 [2022/08/20 09:51:36] root INFO: U/L, 0.853 [2022/08/20 09:51:36] root INFO: 15.00~173.00U/l, 0.927 [2022/08/20 09:51:36] root INFO: 5.8, 0.994 [2022/08/20 09:51:36] root INFO: 快速血糖, 0.998 [2022/08/20 09:51:36] root INFO: mmovL, 0.822 [2022/08/20 09:51:36] root INFO: 4.22~6.11mmoVl, 0.920 [2022/08/20 09:51:36] root INFO: 5.3, 0.895 [2022/08/20 09:51:36] root INFO: mmovL, 0.822 [2022/08/20 09:51:36] root INFO: 快速钾, 0.895 [2022/08/20 09:51:36] root INFO: 个, 0.984 [2022/08/20 09:51:36] root INFO: 3.50~5.10 mmov/L, 0.943 [2022/08/20 09:51:36] root INFO: 84.1, 0.993 [2022/08/20 09:51:36] root INFO: umol/L, 0.755 [2022/08/20 09:51:36] root INFO: 44.00~133.00 umol/L, 0.962 [2022/08/20 09:51:36] root INFO: 阳性, 0.998 [2022/08/20 09:51:36] root INFO: 肌红蛋白, 0.965 [2022/08/20 09:51:36] root INFO: 阴性, 0.995 [2022/08/20 09:51:36] root INFO: 30.2, 0.996 [2022/08/20 09:51:36] root INFO: U/L, 0.930 [2022/08/20 09:51:36] root INFO: 0.00~46.00 U/L, 0.907 [2022/08/20 09:51:36] root INFO: 快速谷内转氨酶, 0.951 [2022/08/20 09:51:36] root INFO: 0.00~300 mg/L, 0.964 [2022/08/20 09:51:36] root INFO: 118.0, 0.993 [2022/08/20 09:51:36] root INFO: 脂蛋日（a）, 0.883 [2022/08/20 09:51:36] root INFO: mg/L, 0.848 [2022/08/20 09:51:36] root INFO: 224.7, 0.982 [2022/08/20 09:51:36] root INFO: 20.00~220.00mmoV/L, 0.955 [2022/08/20 09:51:36] root INFO: mmovt, 0.774 [2022/08/20 09:51:36] root INFO: 血淀粉酶, 0.906 [2022/08/20 09:51:36] root INFO: 0.4, 0.977 [2022/08/20 09:51:36] root INFO: mmovL, 0.822 [2022/08/20 09:51:36] root INFO: 0.21~0.78 mmoVL, 0.963 [2022/08/20 09:51:36] root INFO: 极低密度脂蛋白胆固醇, 0.992 [2022/08/20 09:51:36] root INFO: 468.2, 0.998 [2022/08/20 09:51:36] root INFO: pmovL, 0.814 [2022/08/20 09:51:36] root INFO: 339.0~1107.0pmol/L, 0.952 [2022/08/20 09:51:36] root INFO: 血清维生素B12, 0.995 [2022/08/20 09:51:36] root INFO: 检查小结, 0.988 [2022/08/20 09:51:36] root INFO: 电脑他的建设进行客户历史自己, 0.999 [2022/08/20 09:51:36] root INFO: XX医院体检中心体检报告, 0.980
5.项目总结：
通过本次体检报告识别项目，使我对PaddleOCR有了进一步了解，整体项目分为检测+识别部分，最终识别精度可达到90%以上，针对部分文字和符号存在一定错误，后续可以尝试更多模型算法并引入NLP对识别结果进行文字纠错，进一步提升精度，在后续模型推广方面，可以进一步用于移动端或服务器端模型部署，达到真正用于企业需求效果。
参考：https://github.com/PaddlePaddle/PaddleOCR