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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：

【模型部署】PaddleOCR模型openvino部署（一）-风君子博客

 检测结果：

【模型部署】PaddleOCR模型openvino部署（一）-风君子博客

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）

【模型部署】PaddleOCR模型openvino部署（一）-风君子博客

 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：

【模型部署】PaddleOCR模型openvino部署（一）-风君子博客

 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）；

【模型部署】PaddleOCR模型openvino部署（一）-风君子博客

 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: DBPostProcessthresh: 0.3box_thresh: 0.6max_candidates: 1000unclip_ratio: 1.5Eval:dataset:name: SimpleDataSetdata_dir: ./train_data/icdar2015/text_localization/label_file_list:- ./train_data/icdar2015/text_localization/test_icdar2015_label.txttransforms:- DecodeImage: # load imageimg_mode: BGRchannel_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.pngimport cv2
import openvino
import argparse
import numpy as np
import pyclipper
from openvino.runtime import Core
from shapely.geometry import Polygondef normalize(im, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):im = im.astype(np.float32, copy=False) / 255.0im -= meanim /= stdreturn imdef 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_sizeh = target_sizeim = cv2.resize(im, (w, h), interpolation=interp)return imclass 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 = threshself.box_thresh = box_threshself.max_candidates = max_candidatesself.unclip_ratio = unclip_ratioself.min_size = 3self.score_mode = score_modeassert 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 = _bitmapheight, width = bitmap.shapeouts = 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 < self.min_size:continuepoints = np.array(points)if self.score_mode == "fast":score = self.box_score_fast(pred, points.reshape(-1, 2))else:score = self.box_score_slow(pred, contour)if self.box_thresh > score:continuebox = self.unclip(points).reshape(-1, 1, 2)box, sside = self.get_mini_boxes(box)if sside < self.min_size + 2:continuebox = 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)return np.array(boxes, dtype=np.int16), scoresdef unclip(self, box):unclip_ratio = self.unclip_ratiopoly = Polygon(box)distance = poly.area * unclip_ratio / poly.lengthoffset = pyclipper.PyclipperOffset()offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)expanded = np.array(offset.Execute(distance))return expandeddef 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, 3if points[1][1] > points[0][1]:index_1 = 0index_4 = 1else:index_1 = 1index_4 = 0if points[3][1] > points[2][1]:index_2 = 2index_3 = 3else:index_2 = 3index_3 = 2box = [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] - xminbox[:, 1] = box[:, 1] - ymincv2.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] - xmincontour[:, 1] = contour[:, 1] - ymincv2.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.threshboxes_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_batchclass 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_sizeself.mean = meanself.std = stdself.model_path = model_pathself.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PaddleOCRdef preprocess(self, image):image = resize(image, target_size=self.target_size)image = normalize(image, mean=self.mean, std=self.std)return imagedef 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 imagedef predict(self, image_path):image = cv2.imread(image_path)image_h, image_w, _ = image.shapeinputs = 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PaddleOCRimage = self.draw_det(image, boxes_batch[0]['points']) # 绘制boxreturn imagedef parse_args():parser = argparse.ArgumentParser(description='Model export.')# params of trainingparser.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_pathimage_path = args.image_pathpredictor = 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：

【模型部署】PaddleOCR模型openvino部署（一）-风君子博客

预测结果：

【模型部署】PaddleOCR模型openvino部署（一）-风君子博客