# 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