Merge pull request #7693 from BeyondYourself/release/2.6
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# 快速构建卡证类OCR
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- [快速构建卡证类OCR](#快速构建卡证类ocr)
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- [1. 金融行业卡证识别应用](#1-金融行业卡证识别应用)
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- [1.1 金融行业中的OCR相关技术](#11-金融行业中的ocr相关技术)
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- [1.2 金融行业中的卡证识别场景介绍](#12-金融行业中的卡证识别场景介绍)
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- [1.3 OCR落地挑战](#13-ocr落地挑战)
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- [2. 卡证识别技术解析](#2-卡证识别技术解析)
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- [2.1 卡证分类模型](#21-卡证分类模型)
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- [2.2 卡证识别模型](#22-卡证识别模型)
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- [3. OCR技术拆解](#3-ocr技术拆解)
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- [3.1技术流程](#31技术流程)
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- [3.2 OCR技术拆解---卡证分类](#32-ocr技术拆解---卡证分类)
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- [卡证分类：数据、模型准备](#卡证分类数据模型准备)
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- [卡证分类---修改配置文件](#卡证分类---修改配置文件)
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- [卡证分类---训练](#卡证分类---训练)
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- [3.2 OCR技术拆解---卡证识别](#32-ocr技术拆解---卡证识别)
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- [身份证识别：检测+分类](#身份证识别检测分类)
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- [数据标注](#数据标注)
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- [4 . 项目实践](#4--项目实践)
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- [4.1 环境准备](#41-环境准备)
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- [4.2 配置文件修改](#42-配置文件修改)
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- [4.3 代码修改](#43-代码修改)
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- [4.3.1 数据读取](#431-数据读取)
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- [4.3.2 head修改](#432--head修改)
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- [4.3.3 修改loss](#433-修改loss)
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- [4.3.4 后处理](#434-后处理)
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- [4.4. 模型启动](#44-模型启动)
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- [5 总结](#5-总结)
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- [References](#references)
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## 1. 金融行业卡证识别应用
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### 1.1 金融行业中的OCR相关技术
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* 《“十四五”数字经济发展规划》指出，2020年我国数字经济核心产业增加值占GDP比重达7.8％，随着数字经济迈向全面扩展，到2025年该比例将提升至10％。
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* 在过去数年的跨越发展与积累沉淀中，数字金融、金融科技已在对金融业的重塑与再造中充分印证了其自身价值。
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* 以智能为目标，提升金融数字化水平，实现业务流程自动化，降低人力成本。
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### 1.2 金融行业中的卡证识别场景介绍
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应用场景：身份证、银行卡、营业执照、驾驶证等。
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应用难点：由于数据的采集来源多样，以及实际采集数据各种噪声：反光、褶皱、模糊、倾斜等各种问题干扰。
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### 1.3 OCR落地挑战
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## 2. 卡证识别技术解析
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### 2.1 卡证分类模型
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卡证分类：基于PPLCNet
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与其他轻量级模型相比在CPU环境下ImageNet数据集上的表现
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* 模型来自模型库PaddleClas，它是一个图像识别和图像分类任务的工具集，助力使用者训练出更好的视觉模型和应用落地。
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### 2.2 卡证识别模型
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* 检测：DBNet 识别：SVRT
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* PPOCRv3在文本检测、识别进行了一系列改进优化，在保证精度的同时提升预测效率
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## 3. OCR技术拆解
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### 3.1技术流程
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### 3.2 OCR技术拆解---卡证分类
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#### 卡证分类：数据、模型准备
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A 使用爬虫获取无标注数据，将相同类别的放在同一文件夹下，文件名从0开始命名。具体格式如下图所示。
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注：卡证类数据，建议每个类别数据量在500张以上
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B 一行命令生成标签文件
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```
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tree -r -i -f | grep -E "jpg|JPG|jpeg|JPEG|png|PNG|webp" | awk -F "/" '{print $0" "$2}' > train_list.txt
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```
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C [下载预训练模型 ](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
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#### 卡证分类---修改配置文件
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配置文件主要修改三个部分：
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全局参数：预训练模型路径/训练轮次/图像尺寸
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模型结构：分类数
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数据处理：训练/评估数据路径
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#### 卡证分类---训练
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指定配置文件启动训练：
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```
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!python /home/aistudio/work/PaddleClas/tools/train.py -c /home/aistudio/work/PaddleClas/ppcls/configs/PULC/text_image_orientation/PPLCNet_x1_0.yaml
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```
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注：日志中显示了训练结果和评估结果（训练时可以设置固定轮数评估一次）
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### 3.2 OCR技术拆解---卡证识别
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卡证识别（以身份证检测为例）
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存在的困难及问题：
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* 在自然场景下，由于各种拍摄设备以及光线、角度不同等影响导致实际得到的证件影像千差万别。
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* 如何快速提取需要的关键信息
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* 多行的文本信息，检测结果如何正确拼接
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* OCR技术拆解---OCR工具库
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PaddleOCR是一个丰富、领先且实用的OCR工具库，助力开发者训练出更好的模型并应用落地
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身份证识别：用现有的方法识别
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#### 身份证识别：检测+分类
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> 方法：基于现有的dbnet检测模型，加入分类方法。检测同时进行分类，从一定程度上优化识别流程
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#### 数据标注
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使用PaddleOCRLable进行快速标注
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* 修改PPOCRLabel.py，将下图中的kie参数设置为True
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* 数据标注踩坑分享
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注：两者只有标注有差别，训练参数数据集都相同
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## 4 . 项目实践
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AIStudio项目链接：[快速构建卡证类OCR](https://aistudio.baidu.com/aistudio/projectdetail/4459116)
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### 4.1 环境准备
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1）拉取[paddleocr](https://github.com/PaddlePaddle/PaddleOCR)项目，如果从github上拉取速度慢可以选择从gitee上获取。
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```
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!git clone https://github.com/PaddlePaddle/PaddleOCR.git -b release/2.6 /home/aistudio/work/
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```
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2）获取并解压预训练模型，如果要使用其他模型可以从模型库里自主选择合适模型。
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```
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!wget -P work/pre_trained/ https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar
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!tar -vxf /home/aistudio/work/pre_trained/ch_PP-OCRv3_det_distill_train.tar -C /home/aistudio/work/pre_trained
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```
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3） 安装必要依赖
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```
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!pip install -r /home/aistudio/work/requirements.txt
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```
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### 4.2 配置文件修改
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修改配置文件 *work/configs/det/detmv3db.yml*
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具体修改说明如下：
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注：在上述的配置文件的Global变量中需要添加以下两个参数：
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label_list 为标签表
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num_classes 为分类数
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上述两个参数根据实际的情况配置即可
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其中lable_list内容如下例所示，***建议第一个参数设置为 background，不要设置为实际要提取的关键信息种类***：
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配置文件中的其他设置说明
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### 4.3 代码修改
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#### 4.3.1 数据读取
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* 修改 PaddleOCR/ppocr/data/imaug/label_ops.py中的DetLabelEncode
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```python
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class DetLabelEncode(object):
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# 修改检测标签的编码处，新增了参数分类数：num_classes，重写初始化方法，以及分类标签的读取
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def __init__(self, label_list, num_classes=8, **kwargs):
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self.num_classes = num_classes
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self.label_list = []
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if label_list:
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if isinstance(label_list, str):
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with open(label_list, 'r+', encoding='utf-8') as f:
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for line in f.readlines():
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self.label_list.append(line.replace("\n", ""))
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else:
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self.label_list = label_list
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else:
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assert ' please check label_list whether it is none or config is right'
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if num_classes != len(self.label_list): # 校验分类数和标签的一致性
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assert 'label_list length is not equal to the num_classes'
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def __call__(self, data):
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label = data['label']
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label = json.loads(label)
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nBox = len(label)
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boxes, txts, txt_tags, classes = [], [], [], []
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for bno in range(0, nBox):
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box = label[bno]['points']
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txt = label[bno]['key_cls'] # 此处将kie中的参数作为分类读取
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boxes.append(box)
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txts.append(txt)
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if txt in ['*', '###']:
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txt_tags.append(True)
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if self.num_classes > 1:
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classes.append(-2)
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else:
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txt_tags.append(False)
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if self.num_classes > 1: # 将KIE内容的key标签作为分类标签使用
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classes.append(int(self.label_list.index(txt)))
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if len(boxes) == 0:
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return None
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boxes = self.expand_points_num(boxes)
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boxes = np.array(boxes, dtype=np.float32)
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txt_tags = np.array(txt_tags, dtype=np.bool)
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classes = classes
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data['polys'] = boxes
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data['texts'] = txts
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data['ignore_tags'] = txt_tags
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if self.num_classes > 1:
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data['classes'] = classes
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return data
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```
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* 修改 PaddleOCR/ppocr/data/imaug/make_shrink_map.py中的MakeShrinkMap类。这里需要注意的是，如果我们设置的label_list中的第一个参数为要检测的信息那么会得到如下的mask,
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举例说明：
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这是检测的mask图，图中有四个mask那么实际对应的分类应该是4类
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label_list中第一个为关键分类，则得到的分类Mask实际如下，与上图相比，少了一个box：
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```python
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class MakeShrinkMap(object):
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r'''
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Making binary mask from detection data with ICDAR format.
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Typically following the process of class `MakeICDARData`.
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'''
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def __init__(self, min_text_size=8, shrink_ratio=0.4, num_classes=8, **kwargs):
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self.min_text_size = min_text_size
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self.shrink_ratio = shrink_ratio
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self.num_classes = num_classes # 添加了分类
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def __call__(self, data):
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image = data['image']
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text_polys = data['polys']
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ignore_tags = data['ignore_tags']
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if self.num_classes > 1:
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classes = data['classes']
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h, w = image.shape[:2]
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text_polys, ignore_tags = self.validate_polygons(text_polys,
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ignore_tags, h, w)
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gt = np.zeros((h, w), dtype=np.float32)
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mask = np.ones((h, w), dtype=np.float32)
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gt_class = np.zeros((h, w), dtype=np.float32) # 新增分类
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for i in range(len(text_polys)):
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polygon = text_polys[i]
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height = max(polygon[:, 1]) - min(polygon[:, 1])
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width = max(polygon[:, 0]) - min(polygon[:, 0])
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if ignore_tags[i] or min(height, width) < self.min_text_size:
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cv2.fillPoly(mask,
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polygon.astype(np.int32)[np.newaxis, :, :], 0)
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ignore_tags[i] = True
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else:
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polygon_shape = Polygon(polygon)
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subject = [tuple(l) for l in polygon]
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padding = pyclipper.PyclipperOffset()
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padding.AddPath(subject, pyclipper.JT_ROUND,
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pyclipper.ET_CLOSEDPOLYGON)
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shrinked = []
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# Increase the shrink ratio every time we get multiple polygon returned back
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possible_ratios = np.arange(self.shrink_ratio, 1,
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self.shrink_ratio)
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np.append(possible_ratios, 1)
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for ratio in possible_ratios:
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distance = polygon_shape.area * (
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1 - np.power(ratio, 2)) / polygon_shape.length
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shrinked = padding.Execute(-distance)
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if len(shrinked) == 1:
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break
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if shrinked == []:
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cv2.fillPoly(mask,
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polygon.astype(np.int32)[np.newaxis, :, :], 0)
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ignore_tags[i] = True
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continue
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for each_shirnk in shrinked:
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shirnk = np.array(each_shirnk).reshape(-1, 2)
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cv2.fillPoly(gt, [shirnk.astype(np.int32)], 1)
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if self.num_classes > 1: # 绘制分类的mask
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cv2.fillPoly(gt_class, polygon.astype(np.int32)[np.newaxis, :, :], classes[i])
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data['shrink_map'] = gt
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if self.num_classes > 1:
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data['class_mask'] = gt_class
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data['shrink_mask'] = mask
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return data
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```
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由于在训练数据中会对数据进行resize设置，yml中的操作为：EastRandomCropData，所以需要修改PaddleOCR/ppocr/data/imaug/random_crop_data.py中的EastRandomCropData
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```python
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class EastRandomCropData(object):
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def __init__(self,
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size=(640, 640),
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max_tries=10,
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min_crop_side_ratio=0.1,
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keep_ratio=True,
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num_classes=8,
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**kwargs):
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self.size = size
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self.max_tries = max_tries
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self.min_crop_side_ratio = min_crop_side_ratio
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self.keep_ratio = keep_ratio
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self.num_classes = num_classes
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def __call__(self, data):
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img = data['image']
|
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text_polys = data['polys']
|
||||
ignore_tags = data['ignore_tags']
|
||||
texts = data['texts']
|
||||
if self.num_classes > 1:
|
||||
classes = data['classes']
|
||||
all_care_polys = [
|
||||
text_polys[i] for i, tag in enumerate(ignore_tags) if not tag
|
||||
]
|
||||
# 计算crop区域
|
||||
crop_x, crop_y, crop_w, crop_h = crop_area(
|
||||
img, all_care_polys, self.min_crop_side_ratio, self.max_tries)
|
||||
# crop 图片 保持比例填充
|
||||
scale_w = self.size[0] / crop_w
|
||||
scale_h = self.size[1] / crop_h
|
||||
scale = min(scale_w, scale_h)
|
||||
h = int(crop_h * scale)
|
||||
w = int(crop_w * scale)
|
||||
if self.keep_ratio:
|
||||
padimg = np.zeros((self.size[1], self.size[0], img.shape[2]),
|
||||
img.dtype)
|
||||
padimg[:h, :w] = cv2.resize(
|
||||
img[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w], (w, h))
|
||||
img = padimg
|
||||
else:
|
||||
img = cv2.resize(
|
||||
img[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w],
|
||||
tuple(self.size))
|
||||
# crop 文本框
|
||||
text_polys_crop = []
|
||||
ignore_tags_crop = []
|
||||
texts_crop = []
|
||||
classes_crop = []
|
||||
for poly, text, tag,class_index in zip(text_polys, texts, ignore_tags,classes):
|
||||
poly = ((poly - (crop_x, crop_y)) * scale).tolist()
|
||||
if not is_poly_outside_rect(poly, 0, 0, w, h):
|
||||
text_polys_crop.append(poly)
|
||||
ignore_tags_crop.append(tag)
|
||||
texts_crop.append(text)
|
||||
if self.num_classes > 1:
|
||||
classes_crop.append(class_index)
|
||||
data['image'] = img
|
||||
data['polys'] = np.array(text_polys_crop)
|
||||
data['ignore_tags'] = ignore_tags_crop
|
||||
data['texts'] = texts_crop
|
||||
if self.num_classes > 1:
|
||||
data['classes'] = classes_crop
|
||||
return data
|
||||
```
|
||||
|
||||
#### 4.3.2 head修改
|
||||
|
||||
|
||||
|
||||
主要修改 ppocr/modeling/heads/det_db_head.py，将Head类中的最后一层的输出修改为实际的分类数，同时在DBHead中新增分类的head。
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#### 4.3.3 修改loss
|
||||
|
||||
|
||||
修改PaddleOCR/ppocr/losses/det_db_loss.py中的DBLoss类，分类采用交叉熵损失函数进行计算。
|
||||
|
||||
|
||||
|
||||
|
||||
#### 4.3.4 后处理
|
||||
|
||||
|
||||
|
||||
由于涉及到eval以及后续推理能否正常使用，我们需要修改后处理的相关代码，修改位置 PaddleOCR/ppocr/postprocess/db_postprocess.py中的DBPostProcess类
|
||||
|
||||
|
||||
```python
|
||||
class DBPostProcess(object):
|
||||
"""
|
||||
The post process for Differentiable Binarization (DB).
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
thresh=0.3,
|
||||
box_thresh=0.7,
|
||||
max_candidates=1000,
|
||||
unclip_ratio=2.0,
|
||||
use_dilation=False,
|
||||
score_mode="fast",
|
||||
**kwargs):
|
||||
self.thresh = thresh
|
||||
self.box_thresh = box_thresh
|
||||
self.max_candidates = max_candidates
|
||||
self.unclip_ratio = unclip_ratio
|
||||
self.min_size = 3
|
||||
self.score_mode = score_mode
|
||||
assert score_mode in [
|
||||
"slow", "fast"
|
||||
], "Score mode must be in [slow, fast] but got: {}".format(score_mode)
|
||||
|
||||
self.dilation_kernel = None if not use_dilation else np.array(
|
||||
[[1, 1], [1, 1]])
|
||||
|
||||
def boxes_from_bitmap(self, pred, _bitmap, classes, dest_width, dest_height):
|
||||
"""
|
||||
_bitmap: single map with shape (1, H, W),
|
||||
whose values are binarized as {0, 1}
|
||||
"""
|
||||
|
||||
bitmap = _bitmap
|
||||
height, width = bitmap.shape
|
||||
|
||||
outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST,
|
||||
cv2.CHAIN_APPROX_SIMPLE)
|
||||
if len(outs) == 3:
|
||||
img, contours, _ = outs[0], outs[1], outs[2]
|
||||
elif len(outs) == 2:
|
||||
contours, _ = outs[0], outs[1]
|
||||
|
||||
num_contours = min(len(contours), self.max_candidates)
|
||||
|
||||
boxes = []
|
||||
scores = []
|
||||
class_indexes = []
|
||||
class_scores = []
|
||||
for index in range(num_contours):
|
||||
contour = contours[index]
|
||||
points, sside = self.get_mini_boxes(contour)
|
||||
if sside < self.min_size:
|
||||
continue
|
||||
points = np.array(points)
|
||||
if self.score_mode == "fast":
|
||||
score, class_index, class_score = self.box_score_fast(pred, points.reshape(-1, 2), classes)
|
||||
else:
|
||||
score, class_index, class_score = self.box_score_slow(pred, contour, classes)
|
||||
if self.box_thresh > score:
|
||||
continue
|
||||
|
||||
box = self.unclip(points).reshape(-1, 1, 2)
|
||||
box, sside = self.get_mini_boxes(box)
|
||||
if sside < self.min_size + 2:
|
||||
continue
|
||||
box = np.array(box)
|
||||
|
||||
box[:, 0] = np.clip(
|
||||
np.round(box[:, 0] / width * dest_width), 0, dest_width)
|
||||
box[:, 1] = np.clip(
|
||||
np.round(box[:, 1] / height * dest_height), 0, dest_height)
|
||||
|
||||
boxes.append(box.astype(np.int16))
|
||||
scores.append(score)
|
||||
|
||||
class_indexes.append(class_index)
|
||||
class_scores.append(class_score)
|
||||
|
||||
if classes is None:
|
||||
return np.array(boxes, dtype=np.int16), scores
|
||||
else:
|
||||
return np.array(boxes, dtype=np.int16), scores, class_indexes, class_scores
|
||||
|
||||
def unclip(self, box):
|
||||
unclip_ratio = self.unclip_ratio
|
||||
poly = Polygon(box)
|
||||
distance = poly.area * unclip_ratio / poly.length
|
||||
offset = pyclipper.PyclipperOffset()
|
||||
offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
|
||||
expanded = np.array(offset.Execute(distance))
|
||||
return expanded
|
||||
|
||||
def get_mini_boxes(self, contour):
|
||||
bounding_box = cv2.minAreaRect(contour)
|
||||
points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
|
||||
|
||||
index_1, index_2, index_3, index_4 = 0, 1, 2, 3
|
||||
if points[1][1] > points[0][1]:
|
||||
index_1 = 0
|
||||
index_4 = 1
|
||||
else:
|
||||
index_1 = 1
|
||||
index_4 = 0
|
||||
if points[3][1] > points[2][1]:
|
||||
index_2 = 2
|
||||
index_3 = 3
|
||||
else:
|
||||
index_2 = 3
|
||||
index_3 = 2
|
||||
|
||||
box = [
|
||||
points[index_1], points[index_2], points[index_3], points[index_4]
|
||||
]
|
||||
return box, min(bounding_box[1])
|
||||
|
||||
def box_score_fast(self, bitmap, _box, classes):
|
||||
'''
|
||||
box_score_fast: use bbox mean score as the mean score
|
||||
'''
|
||||
h, w = bitmap.shape[:2]
|
||||
box = _box.copy()
|
||||
xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
|
||||
xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
|
||||
ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
|
||||
ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
|
||||
|
||||
mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
|
||||
box[:, 0] = box[:, 0] - xmin
|
||||
box[:, 1] = box[:, 1] - ymin
|
||||
cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
|
||||
|
||||
if classes is None:
|
||||
return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0], None, None
|
||||
else:
|
||||
k = 999
|
||||
class_mask = np.full((ymax - ymin + 1, xmax - xmin + 1), k, dtype=np.int32)
|
||||
|
||||
cv2.fillPoly(class_mask, box.reshape(1, -1, 2).astype(np.int32), 0)
|
||||
classes = classes[ymin:ymax + 1, xmin:xmax + 1]
|
||||
|
||||
new_classes = classes + class_mask
|
||||
a = new_classes.reshape(-1)
|
||||
b = np.where(a >= k)
|
||||
classes = np.delete(a, b[0].tolist())
|
||||
|
||||
class_index = np.argmax(np.bincount(classes))
|
||||
class_score = np.sum(classes == class_index) / len(classes)
|
||||
|
||||
return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0], class_index, class_score
|
||||
|
||||
def box_score_slow(self, bitmap, contour, classes):
|
||||
"""
|
||||
box_score_slow: use polyon mean score as the mean score
|
||||
"""
|
||||
h, w = bitmap.shape[:2]
|
||||
contour = contour.copy()
|
||||
contour = np.reshape(contour, (-1, 2))
|
||||
|
||||
xmin = np.clip(np.min(contour[:, 0]), 0, w - 1)
|
||||
xmax = np.clip(np.max(contour[:, 0]), 0, w - 1)
|
||||
ymin = np.clip(np.min(contour[:, 1]), 0, h - 1)
|
||||
ymax = np.clip(np.max(contour[:, 1]), 0, h - 1)
|
||||
|
||||
mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
|
||||
|
||||
contour[:, 0] = contour[:, 0] - xmin
|
||||
contour[:, 1] = contour[:, 1] - ymin
|
||||
|
||||
cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1)
|
||||
|
||||
if classes is None:
|
||||
return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0], None, None
|
||||
else:
|
||||
k = 999
|
||||
class_mask = np.full((ymax - ymin + 1, xmax - xmin + 1), k, dtype=np.int32)
|
||||
|
||||
cv2.fillPoly(class_mask, contour.reshape(1, -1, 2).astype(np.int32), 0)
|
||||
classes = classes[ymin:ymax + 1, xmin:xmax + 1]
|
||||
|
||||
new_classes = classes + class_mask
|
||||
a = new_classes.reshape(-1)
|
||||
b = np.where(a >= k)
|
||||
classes = np.delete(a, b[0].tolist())
|
||||
|
||||
class_index = np.argmax(np.bincount(classes))
|
||||
class_score = np.sum(classes == class_index) / len(classes)
|
||||
|
||||
return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0], class_index, class_score
|
||||
|
||||
def __call__(self, outs_dict, shape_list):
|
||||
pred = outs_dict['maps']
|
||||
if isinstance(pred, paddle.Tensor):
|
||||
pred = pred.numpy()
|
||||
pred = pred[:, 0, :, :]
|
||||
segmentation = pred > self.thresh
|
||||
|
||||
if "classes" in outs_dict:
|
||||
classes = outs_dict['classes']
|
||||
if isinstance(classes, paddle.Tensor):
|
||||
classes = classes.numpy()
|
||||
classes = classes[:, 0, :, :]
|
||||
|
||||
else:
|
||||
classes = None
|
||||
|
||||
boxes_batch = []
|
||||
for batch_index in range(pred.shape[0]):
|
||||
src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
|
||||
if self.dilation_kernel is not None:
|
||||
mask = cv2.dilate(
|
||||
np.array(segmentation[batch_index]).astype(np.uint8),
|
||||
self.dilation_kernel)
|
||||
else:
|
||||
mask = segmentation[batch_index]
|
||||
|
||||
if classes is None:
|
||||
boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, None,
|
||||
src_w, src_h)
|
||||
boxes_batch.append({'points': boxes})
|
||||
else:
|
||||
boxes, scores, class_indexes, class_scores = self.boxes_from_bitmap(pred[batch_index], mask,
|
||||
classes[batch_index],
|
||||
src_w, src_h)
|
||||
boxes_batch.append({'points': boxes, "classes": class_indexes, "class_scores": class_scores})
|
||||
|
||||
return boxes_batch
|
||||
```
|
||||
|
||||
### 4.4. 模型启动
|
||||
|
||||
在完成上述步骤后我们就可以正常启动训练
|
||||
|
||||
```
|
||||
!python /home/aistudio/work/PaddleOCR/tools/train.py -c /home/aistudio/work/PaddleOCR/configs/det/det_mv3_db.yml
|
||||
```
|
||||
|
||||
其他命令：
|
||||
```
|
||||
!python /home/aistudio/work/PaddleOCR/tools/eval.py -c /home/aistudio/work/PaddleOCR/configs/det/det_mv3_db.yml
|
||||
!python /home/aistudio/work/PaddleOCR/tools/infer_det.py -c /home/aistudio/work/PaddleOCR/configs/det/det_mv3_db.yml
|
||||
```
|
||||
模型推理
|
||||
```
|
||||
!python /home/aistudio/work/PaddleOCR/tools/infer/predict_det.py --image_dir="/home/aistudio/work/test_img/" --det_model_dir="/home/aistudio/work/PaddleOCR/output/infer"
|
||||
```
|
||||
|
||||
## 5 总结
|
||||
|
||||
1. 分类+检测在一定程度上能够缩短用时，具体的模型选取要根据业务场景恰当选择。
|
||||
2. 数据标注需要多次进行测试调整标注方法，一般进行检测模型微调，需要标注至少上百张。
|
||||
3. 设置合理的batch_size以及resize大小，同时注意lr设置。
|
||||
|
||||
|
||||
## References
|
||||
|
||||
1 https://github.com/PaddlePaddle/PaddleOCR
|
||||
|
||||
2 https://github.com/PaddlePaddle/PaddleClas
|
||||
|
||||
3 https://blog.csdn.net/YY007H/article/details/124491217
|
||||
Loading…
Reference in New Issue