上一篇博客【模型部署】PaddleOCR模型openvino部署（一）介绍了PaddleOCR检测模型DBNet的部署方法，本篇将介绍文本方向分类、文本识别的 部署方法，同时将检测、方向分类、文本识别模型串联起来，给出完整的部署流程。

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

部署深度学习模型的时候，我们主要完成预处理和后处理部分的代码即可，对于检测、分类、识别3个模型也是同样的，我们只需要完成预处理和后处理即可。

模型部署效果如下（左为原图，右为检测结果）：

 识别结果如下：

目录

一、检测模型部署

二、方向分类模型部署

1、下载分类模型

2、解压压缩包

3、查看模型

 4、部署

三、识别模型部署

1、下载模型

2、解压压缩包

 3、查看模型结构

 4、部署

四、检测+分类+识别串行部署

五、参考

一、检测模型部署

检测模型部署参考：【模型部署】PaddleOCR模型openvino部署（一），本文不再过多介绍。

二、方向分类模型部署

1、下载分类模型

!wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar

2、解压压缩包

解压得到的是飞桨的静态图模型，如下图：

3、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）；

 4、部署

openvino部署代码如下：

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 ClsPostProcess(object):
    """ Convert between text-label and text-index """

    def __init__(self, label_list=['0', '180'], threshold=0.9):
        super(ClsPostProcess, self).__init__()
        self.label_list = label_list
        self.threshold = threshold

    def __call__(self, preds, image=None):
        pred_idxs = preds.argmax(axis=1)
        assert pred_idxs.shape[0] == 1, "batch size must be 1, but got {}.".format(pred_idxs.shape[0])
        direction = self.label_list[pred_idxs[0]]
        if direction == '180' and preds[0, 1] > self.threshold:
            image = cv2.rotate(image, 1)
        return image
        

class ClsPredictor:
    def __init__(self, model_path, target_size=(100, 32), mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], threshold=0.9):
        self.target_size = target_size
        self.mean = mean
        self.std = std
        self.model_path = model_path
        self.post_process = ClsPostProcess(threshold=threshold)
        
    def preprocess(self, image):
        image = resize(image, target_size=self.target_size)
        image = normalize(image, mean=self.mean, std=self.std)
        return image
        
    def predict(self, image):
        if isinstance(image, str):
            image = cv2.imread(image)
        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))
        
        preds = compiled_model([input_image])[output_layer_ir]
        image = self.post_process(preds, image)
        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="ch_ppocr_mobile_v2.0_cls_infer/inference.pdmodel")
    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

    cls_predictor = ClsPredictor(model_path, target_size=(100, 32), mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], threshold=0.7)
    image = cls_predictor.predict(image_path)
    cv2.imwrite('cls_result.png', image)
    cv2.imshow('1', image)
    cv2.waitKey(0)

三、识别模型部署

1、下载模型

!wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar

2、解压压缩包

 3、查看模型结构

可以看到识别模型的输入和方向分类的是一样的（因为方向分类后直接识别），输出的维度为[?, 25, 6625]，这里的？表示batch size，25表示识别的字符长度，6625是字符类别个数（识别模型有个对应的字典，字典内字符数量应该和字符类别一致）。

 4、部署

需要先准备好模型对应的字典，代码如下：

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 BaseRecLabelDecode(object):
    """ Convert between text-label and text-index """

    def __init__(self, character_dict_path=None, use_space_char=False):
        self.beg_str = "sos"
        self.end_str = "eos"

        self.character_str = []
        if character_dict_path is None:
            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
            dict_character = list(self.character_str)
        else:
            with open(character_dict_path, "rb") as fin:
                lines = fin.readlines()
                for line in lines:
                    line = line.decode('utf-8').strip("\n").strip("\r\n")
                    self.character_str.append(line)
            if use_space_char:
                self.character_str.append(" ")
            dict_character = list(self.character_str)

        dict_character = self.add_special_char(dict_character)
        self.dict = {}
        for i, char in enumerate(dict_character):
            self.dict[char] = i
        self.character = dict_character

    def add_special_char(self, dict_character):
        return dict_character

    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
        """ convert text-index into text-label. """
        result_list = []
        ignored_tokens = self.get_ignored_tokens()
        batch_size = len(text_index)
        for batch_idx in range(batch_size):
            char_list = []
            conf_list = []
            for idx in range(len(text_index[batch_idx])):
                if text_index[batch_idx][idx] in ignored_tokens:
                    continue
                if is_remove_duplicate:
                    # only for predict
                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
                            batch_idx][idx]:
                        continue

                char_list.append(self.character[int(text_index[batch_idx][
                    idx])])
                if text_prob is not None:
                    conf_list.append(text_prob[batch_idx][idx])
                else:
                    conf_list.append(1)
            text = ''.join(char_list)
            result_list.append((text, np.mean(conf_list)))
        return result_list

    def get_ignored_tokens(self):
        return [0]  # for ctc blank


class CTCLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """

    def __init__(self, character_dict_path=None, use_space_char=False,
                 **kwargs):
        super(CTCLabelDecode, self).__init__(character_dict_path,
                                             use_space_char)

    def __call__(self, preds, label=None, *args, **kwargs):
        if isinstance(preds, (tuple, list)):
            preds = preds[-1]
        preds_idx = preds.argmax(axis=2)
        preds_prob = preds.max(axis=2)
        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
        if label is None:
            return text
        label = self.decode(label)
        return text, label

    def add_special_char(self, dict_character):
        dict_character = ['blank'] + dict_character
        return dict_character


class RecPredictor:
    def __init__(self, model_path, character_dict_path, target_size=(100, 32), mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], use_space_char=False):
        self.target_size = target_size
        self.mean = mean
        self.std = std
        self.model_path = model_path
        self.post_process = CTCLabelDecode(character_dict_path=character_dict_path, use_space_char=use_space_char)
        
    def preprocess(self, image):
        image = resize(image, target_size=self.target_size)
        #cv2.imshow('rec', image)
        #cv2.waitKey(0)
        image = normalize(image, mean=self.mean, std=self.std)
        return image
        
    def predict(self, image):
        if isinstance(image, str):
            image = cv2.imread(image)
        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))
        
        preds = compiled_model([input_image])[output_layer_ir]
        text = self.post_process(preds)
        return text
        

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)
    parser.add_argument(
        '--use_space_char',
        dest='use_space_char',
        help='Whether use space char.',
        type=bool,
        default=False)
    parser.add_argument(
        '--character_dict_path',
        dest='character_dict_path',
        help='The path of character dict.',
        type=str,
        default="ppocr_keys_v1.txt")
    return parser.parse_args()
        

if __name__ == "__main__":
    args = parse_args()
    model_path = args.model_path
    image_path = args.image_path
    use_space_char = args.use_space_char
    character_dict_path = args.character_dict_path
    
    rec_predictor = RecPredictor(model_path, character_dict_path=character_dict_path, target_size=(100, 32), mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], use_space_char=use_space_char)
    text = rec_predictor.predict(image_path)
    print(text)

四、检测+分类+识别串行部署

有了3个模型各自的部署代码，将其串起来，如下（全部代码见文末）：

import cv2
import openvino
import argparse
import numpy as np
import pyclipper
from openvino.runtime import Core
from shapely.geometry import Polygon
from ppocr_cls import ClsPredictor
from ppocr_det import DetPredictor
from ppocr_rec import RecPredictor
from PIL import Image, ImageDraw, ImageFont


class PaddleOCR:
    def __init__(self, det_model_path, rec_model_path, character_dict_path, cls_model_path=None, use_space_char=False, det_image_size=[960, 960], rec_image_size=[100, 32], mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
        self.det_predictor = DetPredictor(det_model_path, target_size=det_image_size, mean=mean, std=std)
        self.cls_predictor = ClsPredictor(cls_model_path, target_size=rec_image_size, mean=mean, std=std) if cls_model_path else None
        self.rec_predictor = RecPredictor(rec_model_path, character_dict_path=character_dict_path, target_size=rec_image_size, mean=mean, std=std, use_space_char=use_space_char)

    def predict(self, image_path):
        image = cv2.imread(image_path)
        raw_image = image.copy()
        boxes_batch = self.det_predictor.predict(image_path)
        draw_image = self.det_predictor.draw_det(image, boxes_batch[0]['points'])
        texts = []
        for box in boxes_batch[0]['points']:
            box = box.astype(np.int32)
            left, top = box[0, 0], box[0, 1]
            right, bottom = box[2, 0], box[2, 1]
            sub_image = raw_image[top:bottom, left:right, :]
            if self.cls_predictor is not None:
                sub_image = self.cls_predictor.predict(sub_image)
            text = self.rec_predictor.predict(sub_image)
            texts.append(text)
        return draw_image, texts
            

def parse_args():
    parser = argparse.ArgumentParser(description='Model export.')
    # params of training
    parser.add_argument(
        '--det_model_path',
        dest='det_model_path',
        help='The path of detection pdmodel for export',
        type=str,
        default='ch_PP-OCRv2_det_infer/inference.pdmodel')
    parser.add_argument(
        '--rec_model_path',
        dest='rec_model_path',
        help='The path of recognition pdmodel for export',
        type=str,
        default="ch_PP-OCRv2_rec_infer/inference.pdmodel")
    parser.add_argument(
        '--cls_model_path',
        dest='cls_model_path',
        help='The path of direction class pdmodel for export',
        type=str,
        default="ch_ppocr_mobile_v2.0_cls_infer/inference.pdmodel")
    parser.add_argument(
        '--image_path',
        dest='image_path',
        help='The path of image to predict.',
        type=str,
        default=None)
    parser.add_argument(
        '--save_path',
        dest='save_path',
        help='The image save path.',
        type=str,
        default="result.png")
    parser.add_argument(
        '--use_space_char',
        dest='use_space_char',
        help='Whether use space char.',
        type=bool,
        default=True)
    parser.add_argument(
        '--character_dict_path',
        dest='character_dict_path',
        help='The path of character dict.',
        type=str,
        default="ppocr_keys_v1.txt")
    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()
    
    predictor = PaddleOCR(det_model_path=args.det_model_path, rec_model_path=args.rec_model_path, character_dict_path=args.character_dict_path, cls_model_path=args.cls_model_path, use_space_char=args.use_space_char)
    draw_image, texts = predictor.predict(args.image_path)
    cv2.imwrite(args.save_path, draw_image)
    print(texts)

五、参考

1、本文全部代码连接：pp-ocrv2pythonopenvino部署代码-深度学习文档类资源-CSDN下载

2、参考连接：PaddlePaddle+openvino】PP-OCRv2部署

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