Merge pull request #7693 from BeyondYourself/release/2.6

add a new applaction introduce

applications/快速构建卡证类OCR.md

+# 快速构建卡证类OCR
+
+
+- [快速构建卡证类OCR](#快速构建卡证类ocr)
+  - [1. 金融行业卡证识别应用](#1-金融行业卡证识别应用)
+    - [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)
+    - [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)
+    - [1.3 OCR落地挑战](#13-ocr落地挑战)
+  - [2. 卡证识别技术解析](#2-卡证识别技术解析)
+    - [2.1 卡证分类模型](#21-卡证分类模型)
+    - [2.2 卡证识别模型](#22-卡证识别模型)
+  - [3. OCR技术拆解](#3-ocr技术拆解)
+    - [3.1技术流程](#31技术流程)
+    - [3.2 OCR技术拆解---卡证分类](#32-ocr技术拆解---卡证分类)
+      - [卡证分类：数据、模型准备](#卡证分类数据模型准备)
+      - [卡证分类---修改配置文件](#卡证分类---修改配置文件)
+      - [卡证分类---训练](#卡证分类---训练)
+    - [3.2 OCR技术拆解---卡证识别](#32-ocr技术拆解---卡证识别)
+      - [身份证识别：检测+分类](#身份证识别检测分类)
+      - [数据标注](#数据标注)
+  - [4 . 项目实践](#4--项目实践)
+    - [4.1 环境准备](#41-环境准备)
+    - [4.2 配置文件修改](#42-配置文件修改)
+    - [4.3 代码修改](#43-代码修改)
+      - [4.3.1 数据读取](#431-数据读取)
+      - [4.3.2  head修改](#432--head修改)
+      - [4.3.3 修改loss](#433-修改loss)
+      - [4.3.4 后处理](#434-后处理)
+    - [4.4. 模型启动](#44-模型启动)
+  - [5 总结](#5-总结)
+  - [References](#references)
+
+## 1. 金融行业卡证识别应用
+
+### 1.1 金融行业中的OCR相关技术
+
+* 《“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％。
+
+* 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。
+
+* 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。
+
+
+![](https://ai-studio-static-online.cdn.bcebos.com/8bb381f164c54ea9b4043cf66fc92ffdea8aaf851bab484fa6e19bd2f93f154f)
+
+
+
+### 1.2 金融行业中的卡证识别场景介绍
+
+应用场景：身份证、银行卡、营业执照、驾驶证等。
+
+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。
+
+![](https://ai-studio-static-online.cdn.bcebos.com/981640e17d05487e961162f8576c9e11634ca157f79048d4bd9d3bc21722afe8)
+
+
+
+### 1.3 OCR落地挑战
+
+
+![](https://ai-studio-static-online.cdn.bcebos.com/a5973a8ddeff4bd7ac082f02dc4d0c79de21e721b41641cbb831f23c2cb8fce2)
+
+
+
+
+
+## 2. 卡证识别技术解析
+
+
+![](https://ai-studio-static-online.cdn.bcebos.com/d7f96effc2434a3ca2d4144ff33c50282b830670c892487d8d7dec151921cce7)
+
+
+### 2.1 卡证分类模型
+
+卡证分类：基于PPLCNet
+
+与其他轻量级模型相比在CPU环境下ImageNet数据集上的表现
+
+![](https://ai-studio-static-online.cdn.bcebos.com/cbda3390cb994f98a3c8a9ba88c90c348497763f6c9f4b4797f7d63d84da5f63)
+
+![](https://ai-studio-static-online.cdn.bcebos.com/dedab7b7fd6543aa9e7f625132b24e3ba3f200e361fa468dac615f7814dfb98d)
+
+
+
+* 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。
+
+
+![](https://ai-studio-static-online.cdn.bcebos.com/606d1afaf0d0484a99b1d39895d394b22f24e74591514796859a9ea3a2799b78)
+
+
+
+### 2.2 卡证识别模型
+
+* 检测：DBNet  识别：SVRT
+
+![](https://ai-studio-static-online.cdn.bcebos.com/9a7a4e19edc24310b46620f2ee7430f918223b93d4f14a15a52973c096926bad)
+
+
+* 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
+
+
+![](https://ai-studio-static-online.cdn.bcebos.com/6afdbb77e8db4aef9b169e4e94c5d90a9764cfab4f2c4c04aa9afdf4f54d7680)
+
+
+![](https://ai-studio-static-online.cdn.bcebos.com/c1a7d197847a4f168848c59b8e625d1d5e8066b778144395a8b9382bb85dc364)
+
+
+## 3. OCR技术拆解
+
+### 3.1技术流程
+
+![](https://ai-studio-static-online.cdn.bcebos.com/89ba046177864d8783ced6cb31ba92a66ca2169856a44ee59ac2bb18e44a6c4b)
+
+
+### 3.2 OCR技术拆解---卡证分类
+
+####  卡证分类：数据、模型准备
+
+
+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。
+
+​    注：卡证类数据，建议每个类别数据量在500张以上
+![](https://ai-studio-static-online.cdn.bcebos.com/6f875b6e695e4fe5aedf427beb0d4ce8064ad7cc33c44faaad59d3eb9732639d)
+
+
+B  一行命令生成标签文件
+
+```
+tree -r -i -f | grep -E "jpg|JPG|jpeg|JPEG|png|PNG|webp" | awk -F "/" '{print $0" "$2}' > train_list.txt
+```
+
+C [下载预训练模型 ](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
+
+
+
+####  卡证分类---修改配置文件
+
+
+配置文件主要修改三个部分：
+
+  全局参数：预训练模型路径/训练轮次/图像尺寸
+
+  模型结构：分类数
+
+  数据处理：训练/评估数据路径
+
+
+  ![](https://ai-studio-static-online.cdn.bcebos.com/e0dc05039c7444c5ab1260ff550a408748df8d4cfe864223adf390e51058dbd5)
+
+#### 卡证分类---训练
+
+
+指定配置文件启动训练：
+
+```
+!python /home/aistudio/work/PaddleClas/tools/train.py -c   /home/aistudio/work/PaddleClas/ppcls/configs/PULC/text_image_orientation/PPLCNet_x1_0.yaml
+```
+![](https://ai-studio-static-online.cdn.bcebos.com/06af09bde845449ba0a676410f4daa1cdc3983ac95034bdbbafac3b7fd94042f)
+
+​    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）
+
+
+### 3.2 OCR技术拆解---卡证识别
+
+卡证识别（以身份证检测为例）
+存在的困难及问题：
+
+  * 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。
+
+  * 如何快速提取需要的关键信息
+
+  * 多行的文本信息，检测结果如何正确拼接
+
+  ![](https://ai-studio-static-online.cdn.bcebos.com/4f8f5533a2914e0a821f4a639677843c32ec1f08a1b1488d94c0b8bfb6e72d2d)
+
+
+
+* OCR技术拆解---OCR工具库
+
+    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
+
+![](https://ai-studio-static-online.cdn.bcebos.com/16c5e16d53b8428c95129cac4f5520204d869910247943e494d854227632e882)
+
+
+身份证识别：用现有的方法识别
+
+![](https://ai-studio-static-online.cdn.bcebos.com/12d402e6a06d482a88f979e0ebdfb39f4d3fc8b80517499689ec607ddb04fbf3)
+
+
+
+
+####  身份证识别：检测+分类
+
+>   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
+
+![](https://ai-studio-static-online.cdn.bcebos.com/e1e798c87472477fa0bfca0da12bb0c180845a3e167a4761b0d26ff4330a5ccb)
+
+
+![](https://ai-studio-static-online.cdn.bcebos.com/23a5a19c746441309864586e467f995ec8a551a3661640e493fc4d77520309cd)
+
+#### 数据标注
+
+使用PaddleOCRLable进行快速标注
+
+![](https://ai-studio-static-online.cdn.bcebos.com/a73180425fa14f919ce52d9bf70246c3995acea1831843cca6c17d871b8f5d95)
+
+
+* 修改PPOCRLabel.py，将下图中的kie参数设置为True
+
+
+![](https://ai-studio-static-online.cdn.bcebos.com/d445cf4d850e4063b9a7fc6a075c12204cf912ff23ec471fa2e268b661b3d693)
+
+
+* 数据标注踩坑分享
+
+![](https://ai-studio-static-online.cdn.bcebos.com/89f42eccd600439fa9e28c97ccb663726e4e54ce3a854825b4c3b7d554ea21df)
+
+​    注：两者只有标注有差别，训练参数数据集都相同
+
+## 4 . 项目实践
+
+AIStudio项目链接：[快速构建卡证类OCR](https://aistudio.baidu.com/aistudio/projectdetail/4459116)
+
+### 4.1 环境准备
+
+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。
+```
+!git clone https://github.com/PaddlePaddle/PaddleOCR.git  -b release/2.6  /home/aistudio/work/
+```
+
+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。
+```
+!wget -P work/pre_trained/   https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar 
+!tar -vxf /home/aistudio/work/pre_trained/ch_PP-OCRv3_det_distill_train.tar -C /home/aistudio/work/pre_trained
+```
+3） 安装必要依赖
+```
+!pip install -r /home/aistudio/work/requirements.txt
+```
+
+### 4.2 配置文件修改
+
+修改配置文件 *work/configs/det/detmv3db.yml*
+
+具体修改说明如下：
+
+![](https://ai-studio-static-online.cdn.bcebos.com/fcdf517af5a6466294d72db7450209378d8efd9b77764e329d3f2aff3579a20c)
+
+  注：在上述的配置文件的Global变量中需要添加以下两个参数：
+
+​      label_list 为标签表
+​     num_classes 为分类数
+​     上述两个参数根据实际的情况配置即可
+
+
+![](https://ai-studio-static-online.cdn.bcebos.com/0b056be24f374812b61abf43305774767ae122c8479242f98aa0799b7bfc81d4)
+
+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***：
+
+![](https://ai-studio-static-online.cdn.bcebos.com/9fc78bbcdf754898b9b2c7f000ddf562afac786482ab4f2ab063e2242faa542a)
+
+配置文件中的其他设置说明
+
+![](https://ai-studio-static-online.cdn.bcebos.com/c7fc5e631dd44bc8b714630f4e49d9155a831d9e56c64e2482ded87081d0db22)
+
+![](https://ai-studio-static-online.cdn.bcebos.com/8d1022ac25d9474daa4fb236235bd58760039d58ad46414f841559d68e0d057f)
+
+![](https://ai-studio-static-online.cdn.bcebos.com/ee927ad9ebd442bb96f163a7ebbf4bc95e6bedee97324a51887cf82de0851fd3)
+
+
+
+
+### 4.3 代码修改
+
+
+#### 4.3.1 数据读取
+
+
+
+* 修改 PaddleOCR/ppocr/data/imaug/label_ops.py中的DetLabelEncode
+
+
+```python
+class DetLabelEncode(object):
+   
+    # 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
+
+    def __init__(self, label_list, num_classes=8, **kwargs):
+        self.num_classes = num_classes
+        self.label_list = []
+        if label_list:
+            if isinstance(label_list, str):
+                with open(label_list, 'r+', encoding='utf-8') as f:
+                    for line in f.readlines():
+                        self.label_list.append(line.replace("\n", ""))
+            else:
+                self.label_list = label_list
+        else:
+            assert ' please check label_list whether it is none or config is right'
+
+        if num_classes != len(self.label_list): # 校验分类数和标签的一致性
+            assert 'label_list length is not equal to the num_classes'
+
+    def __call__(self, data):
+        label = data['label']
+        label = json.loads(label)
+        nBox = len(label)
+        boxes, txts, txt_tags, classes = [], [], [], []
+        for bno in range(0, nBox):
+            box = label[bno]['points']
+            txt = label[bno]['key_cls']  # 此处将kie中的参数作为分类读取
+            boxes.append(box)
+            txts.append(txt)
+
+            if txt in ['*', '###']:
+                txt_tags.append(True)
+                if self.num_classes > 1:
+                    classes.append(-2)
+            else:
+                txt_tags.append(False)
+                if self.num_classes > 1:  # 将KIE内容的key标签作为分类标签使用
+                    classes.append(int(self.label_list.index(txt)))
+
+        if len(boxes) == 0:
+            
+            return None
+        boxes = self.expand_points_num(boxes)
+        boxes = np.array(boxes, dtype=np.float32)
+        txt_tags = np.array(txt_tags, dtype=np.bool)
+        classes = classes
+        data['polys'] = boxes
+        data['texts'] = txts
+        data['ignore_tags'] = txt_tags
+        if self.num_classes > 1:
+            data['classes'] = classes
+        return data
+```
+
+* 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,
+
+举例说明：
+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
+
+![](https://ai-studio-static-online.cdn.bcebos.com/42d2188d3d6b498880952e12c3ceae1efabf135f8d9f4c31823f09ebe02ba9d2)
+
+
+
+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：
+
+![](https://ai-studio-static-online.cdn.bcebos.com/864604967256461aa7c5d32cd240645e9f4c70af773341d5911f22d5a3e87b5f)
+
+
+
+```python
+class MakeShrinkMap(object):
+    r'''
+    Making binary mask from detection data with ICDAR format.
+    Typically following the process of class `MakeICDARData`.
+    '''
+
+    def __init__(self, min_text_size=8, shrink_ratio=0.4, num_classes=8, **kwargs):
+        self.min_text_size = min_text_size
+        self.shrink_ratio = shrink_ratio
+        self.num_classes = num_classes  #  添加了分类
+
+    def __call__(self, data):
+        image = data['image']
+        text_polys = data['polys']
+        ignore_tags = data['ignore_tags']
+        if self.num_classes > 1:
+            classes = data['classes']
+
+        h, w = image.shape[:2]
+        text_polys, ignore_tags = self.validate_polygons(text_polys,
+                                                         ignore_tags, h, w)
+        gt = np.zeros((h, w), dtype=np.float32)
+        mask = np.ones((h, w), dtype=np.float32)
+        gt_class = np.zeros((h, w), dtype=np.float32)  # 新增分类
+        for i in range(len(text_polys)):
+            polygon = text_polys[i]
+            height = max(polygon[:, 1]) - min(polygon[:, 1])
+            width = max(polygon[:, 0]) - min(polygon[:, 0])
+            if ignore_tags[i] or min(height, width) < self.min_text_size:
+                cv2.fillPoly(mask,
+                             polygon.astype(np.int32)[np.newaxis, :, :], 0)
+                ignore_tags[i] = True
+            else:
+                polygon_shape = Polygon(polygon)
+                subject = [tuple(l) for l in polygon]
+                padding = pyclipper.PyclipperOffset()
+                padding.AddPath(subject, pyclipper.JT_ROUND,
+                                pyclipper.ET_CLOSEDPOLYGON)
+                shrinked = []
+
+                # Increase the shrink ratio every time we get multiple polygon returned back
+                possible_ratios = np.arange(self.shrink_ratio, 1,
+                                            self.shrink_ratio)
+                np.append(possible_ratios, 1)
+                for ratio in possible_ratios:
+                    distance = polygon_shape.area * (
+                        1 - np.power(ratio, 2)) / polygon_shape.length
+                    shrinked = padding.Execute(-distance)
+                    if len(shrinked) == 1:
+                        break
+
+                if shrinked == []:
+                    cv2.fillPoly(mask,
+                                 polygon.astype(np.int32)[np.newaxis, :, :], 0)
+                    ignore_tags[i] = True
+                    continue
+
+                for each_shirnk in shrinked:
+                    shirnk = np.array(each_shirnk).reshape(-1, 2)
+                    cv2.fillPoly(gt, [shirnk.astype(np.int32)], 1)
+                    if self.num_classes > 1:  # 绘制分类的mask
+                        cv2.fillPoly(gt_class, polygon.astype(np.int32)[np.newaxis, :, :], classes[i])
+
+
+        data['shrink_map'] = gt
+       
+        if self.num_classes > 1:
+            data['class_mask'] = gt_class
+        
+        data['shrink_mask'] = mask
+        return data
+```
+
+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
+
+
+```python
+class EastRandomCropData(object):
+    def __init__(self,
+                 size=(640, 640),
+                 max_tries=10,
+                 min_crop_side_ratio=0.1,
+                 keep_ratio=True,
+                 num_classes=8,
+                 **kwargs):
+        self.size = size
+        self.max_tries = max_tries
+        self.min_crop_side_ratio = min_crop_side_ratio
+        self.keep_ratio = keep_ratio
+        self.num_classes = num_classes
+
+    def __call__(self, data):
+        img = data['image']
+        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。
+
+![](https://ai-studio-static-online.cdn.bcebos.com/0e25da2ccded4af19e95c85c3d3287ab4d53e31a4eed4607b6a4cb637c43f6d3)
+
+
+
+#### 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。
+
+![](https://ai-studio-static-online.cdn.bcebos.com/dc10a070018d4d27946c26ec24a2a85bc3f16422f4964f72a9b63c6170d954e1)
+
+
+#### 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