【模型部署】PaddleOCR模型openvino部署(一)

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)_第1张图片

 检测结果:

【模型部署】PaddleOCR模型openvino部署(一)_第2张图片


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)_第3张图片

 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)_第4张图片

 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)_第5张图片

 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 < self.min_size:
                continue
            points = 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:
                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)
        return np.array(boxes, dtype=np.int16), 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):
        '''
        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:

【模型部署】PaddleOCR模型openvino部署(一)_第6张图片

预测结果:

【模型部署】PaddleOCR模型openvino部署(一)_第7张图片

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