diff --git "a/applications/\345\277\253\351\200\237\346\236\204\345\273\272\345\215\241\350\257\201\347\261\273OCR.md" "b/applications/\345\277\253\351\200\237\346\236\204\345\273\272\345\215\241\350\257\201\347\261\273OCR.md" new file mode 100644 index 0000000000000000000000000000000000000000..ab9ddf1bbd20e538f0b1e3d43e9a5af5383487b4 --- /dev/null +++ "b/applications/\345\277\253\351\200\237\346\236\204\345\273\272\345\215\241\350\257\201\347\261\273OCR.md" @@ -0,0 +1,782 @@ +# 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