PaddleOCR:https://github.com/PaddlePaddle/PaddleOCR

PaddleOCR是一个非常好用的OCR工具，它有如下特性：

• PP-OCR系列高质量预训练模型，准确的识别效果
  • 超轻量PP-OCRv2系列：检测（3.1M）+ 方向分类器（1.4M）+ 识别（8.5M）= 13.0M
  • 超轻量PP-OCR mobile移动端系列：检测（3.0M）+方向分类器（1.4M）+ 识别（5.0M）= 9.4M
  • 通用PP-OCR server系列：检测（47.1M）+方向分类器（1.4M）+ 识别（94.9M）= 143.4M
  • 支持中英文数字组合识别、竖排文本识别、长文本识别
  • 支持多语言识别：韩语、日语、德语、法语等约80种语言
• PP-Structure文档结构化系统
  • 支持版面分析与表格识别（含Excel导出）
  • 支持关键信息提取任务
  • 支持DocVQA任务
• 丰富易用的OCR相关工具组件
  • 半自动数据标注工具PPOCRLabel：支持快速高效的数据标注
  • 数据合成工具Style-Text：批量合成大量与目标场景类似的图像
• 支持用户自定义训练，提供丰富的预测推理部署方案
• 支持PIP快速安装使用
• 可运行于Linux、Windows、MacOS等多种系统

本文将使用openvino部署PaddleOCR官方提供的检测模型，实现文本检测功能。其效果如下图。

原图：

 检测结果：

目录

一、模型下载

1、下载推理模型

 2、查看模型

 二、Openvino部署

三、部署效果

一、模型下载

这里选择官方提供的中英文超轻量PP-OCRv2检测（DBNet）模型进行部署，DBNet是使用语义分割的方法来检测文本区域，其结构如下图：（论文传送门）

 这里不多介绍原理，可自行阅读论文。

1、下载推理模型

PaddleOCR提供了很多预训练模型，本文选择【中英文超轻量PP-OCRv2模型】中的检测模型进行部署，首先下载模型：

!wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar

解压压缩包，可以得到如下文件：

 2、查看模型

使用netron查看inference.pdmodel结构，如下图，主要关注2点：
（a）模型的输出（关系到后续的后处理）；
（b）输入的维度（设计到后续的预处理）；

 二、Openvino部署

在模型部署阶段，我们只需要完成预处理和后处理的代码即可，预处理过程要和训练一致，查看PaddleOCR对应的配置文件（仅保留预处理和后处理部分）：

PostProcess: name: DBPostProcess thresh: 0.3 box_thresh: 0.6 max_candidates: 1000 unclip_ratio: 1.5 Eval: dataset: name: SimpleDataSet data_dir: ./train_data/icdar2015/text_localization/ label_file_list: - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt transforms: - DecodeImage: # load image img_mode: BGR channel_first: False - DetLabelEncode: # Class handling label - DetResizeForTest: - NormalizeImage: scale: 1./255. mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] order: 'hwc' - ToCHWImage: - KeepKeys: keep_keys: ['image', 'shape', 'polys', 'ignore_tags']

这里需要关注的预处理部分的NormalizeImage 均值和标准差，此外上一步查看模型架构时发现模型的输入维度是[?, 3, 960, 960]，需要在预处理添加resize操作。

后处理部分直接使用PaddleOCR提供的DBPostProcess类即可（需要稍作修改）。

下面给出具体代码及相关命令：

# 命令：python predict.py --model_path {上面导出的inference.pdmodel路径} --image_path {图片路径} # 案例: python predict.py --model_path inference.pdmodel --image_path test.png import cv2 import openvino import argparse import numpy as np import pyclipper from openvino.runtime import Core from shapely.geometry import Polygon def normalize(im, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]): im = im.astype(np.float32, copy=False) / 255.0 im -= mean im /= std return im def resize(im, target_size=608, interp=cv2.INTER_LINEAR): if isinstance(target_size, list) or isinstance(target_size, tuple): w = target_size[0] h = target_size[1] else: w = target_size h = target_size im = cv2.resize(im, (w, h), interpolation=interp) return im 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, 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 = [] for index in range(num_contours): cOntour= contours[index] points, sside = self.get_mini_boxes(contour) if sside score: continue box = self.unclip(points).reshape(-1, 1, 2) box, sside = self.get_mini_boxes(box) if sside 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): ''' 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) return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] def box_score_slow(self, bitmap, contour): ''' 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) return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] def __call__(self, pred, shape_list): pred = pred[:, 0, :, :] segmentation = pred > self.thresh boxes_batch = [] for batch_index in range(pred.shape[0]): src_h, src_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] boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, src_w, src_h) boxes_batch.append({'points': boxes}) return boxes_batch class Predictor: def __init__(self, model_path, target_size=(960, 960), mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]): self.target_size = target_size self.mean = mean self.std = std self.model_path = model_path self.post_process = DBPostProcess(thresh=0.3, box_thresh=0.6, max_candidates=1000, unclip_ratio=1.5, use_dilation=False, score_mode="fast") # 后处理流程参考PaddleOCR def preprocess(self, image): image = resize(image, target_size=self.target_size) image = normalize(image, mean=self.mean, std=self.std) return image def draw_det(self, image, dt_boxes): for box in dt_boxes: box = box.astype(np.int32).reshape((-1, 1, 2)) cv2.polylines(image, [box], True, color=(255, 255, 0), thickness=2) return image def predict(self, image_path): image = cv2.imread(image_path) image_h, image_w, _ = image.shape inputs = self.preprocess(image) input_image = np.expand_dims( inputs.transpose(2, 0, 1), 0 ) ie = Core() model = ie.read_model(model=self.model_path) compiled_model = ie.compile_model(model=model, device_name="CPU") input_layer_ir = next(iter(compiled_model.inputs)) output_layer_ir = next(iter(compiled_model.outputs)) mask = compiled_model([input_image])[output_layer_ir] shape_list = [[image_h, image_w, None, None]] # 对上batch size， batch size为1，所以这里套一个列表 boxes_batch = self.post_process(mask, shape_list) # DBPostProcess， 后处理流程参考PaddleOCR image = self.draw_det(image, boxes_batch[0]['points']) # 绘制box return image def parse_args(): parser = argparse.ArgumentParser(description='Model export.') # params of training parser.add_argument( '--model_path', dest='model_path', help='The path of pdmodel for export', type=str, default=None) parser.add_argument( '--image_path', dest='image_path', help='The path of image to predict.', type=str, default=None) return parser.parse_args() if __name__ == "__main__": args = parse_args() model_path = args.model_path image_path = args.image_path predictor = Predictor(model_path, target_size=(960, 960), mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) image = predictor.predict(image_path) cv2.imwrite("result.png", image)

三、部署效果

原图：

预测结果：