hr-ocr-cet/markdown/new.py

from pathlib import Path
from typing import List, Optional
import cv2
import requests
from dataclasses import dataclass
import json
import time
import base64
from itertools import chain
from tqdm import tqdm

@dataclass
class RequestConfig:
    url: str
    token: str



local_config = RequestConfig(url='http://192.168.199.249:18050/ocr_system/cet', token='')
test_config = RequestConfig(url='http://aihub-test.digitalyili.com/aiSquare/openApi/reasoning-services/rlocrxm/cettest/cet', token='9679c2b3-b90b-4029-a3c7-f347b4d242f7')
sb_config = RequestConfig(url='http://aihub.digitalyili.com/aiSquare/openApi/reasoning-services/rlocrxm/cettest/cet', token='dcae8cc6-0e49-4db8-a2d2-94ef84da3636')

CONFIGS = {
    'local': local_config,
    'test': test_config,
    'sb': sb_config
}

CONFIG_STR = 'local'

IMAGE_TYPE = None

IMAGE_PATH = Path('images/cet6/')


# class MarkdownTable(object):
#     def __init__(self, name):
#         self.name = name
#         self.mdFile = MdUtils(file_name=time.strftime("%m-%d", time.localtime()) + name)
#         self.field_table = ['字段', '正确率']
#         self.true_table = ['图片', '识别结果']
#         self.false_table = ['图片', '识别结果']

#     def add_field_table(self, fields: List):
#         self.field_table.extend(fields)

#     def add_true_table(self, image_and_field: List):
#         self.true_table.extend(image_and_field)

#     def add_false_table(self, image_and_field: List):
#         self.false_table.extend(image_and_field)


class Image:
    def __init__(self, path: Path, rotate):
        self._path = path
        self.rotate = rotate
        self._ocr_result = None
        self.cate = True
        try:
            self.gt_result = self.get_json()
        except Exception as e:
            print(self.json_path)
            raise e

    def __repr__(self):
        return f'path: {self.path}, rotate: {self.rotate}, gt_result: {self.gt_result}, cate: {self.cate}'

    @property
    def path(self):
        return self._path

    @path.setter
    def path(self, path):
        self._path = path

    @property
    def fn(self):
        return self._path.stem

    @property
    def ocr_result(self):
        return self._ocr_result

    @ocr_result.setter
    def ocr_result(self, value):
        self._ocr_result = value

    def get_gt_result(self, key):
        if key in self.gt_result:
            return self.gt_result[key]
        else:
            return None

    @property
    def json_path(self):
        return self.path.parent / f'{self.path.stem}.json'

    def save_image(self, img, rotate=None):
        dire = self.path.parent / (".ro_dire")
        if not dire.exists(): dire.mkdir()
        self.path = dire / f'{self.path.stem}-{rotate+1}.jpg'
        cv2.imwrite(self.path, img)

    def get_base64(self, rotate=None):
        print(self.path)
        img = cv2.imread(str(self.path))
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        if rotate is not None:
            img = cv2.rotate(img, rotate)
            self.save_image(img)
        _, img = cv2.imencode('.jpg', img)
        img_str = base64.b64encode(img).decode('utf-8')
        return img_str

    def get_json(self):
        with open(self.json_path, 'r') as f:
            return json.load(f)


def send_request(base64_str, config_str, image_type=None):
    config = CONFIGS[config_str]
    headers = {
        'Content-Type': 'application/json',
        'Authorization': config.token
    }
    data = {
        'image': base64_str,
    }
    if image_type:
        data['image_type'] = image_type
    response = requests.post(config.url, headers=headers, json=data)
    return response.json()


class Dataset(object):
    def __init__(self, image_path, image_type, config_str, rotate=False):
        self.image_type = image_type
        self.config_str = config_str
        self.image_path = image_path
        self.image_list = []
        for p in chain(*[Path(self.image_path).rglob('*.jpg')]):
            if rotate:
                for rotate in [None, 0, 1, 2]:
                    self.image_list.append(Image(p, rotate))
            else:
                self.image_list.append(Image(p, None))


        self.attrs = ['orientation','name','id','language', 'level', 'exam_time', 'score']

        self.correct = {k: 0 for k in self.attrs}
        self.error = {k: 0 for k in self.attrs}


    def __len__(self):
        return len(self.image_list)


    def _evaluate_one(self, image: Image):
        def _get_predict(r, key):
            if isinstance(r[key], dict):
                return r[key]['text']
            else:
                return r[key]


        base64_str = image.get_base64()
        r = send_request(base64_str, self.config_str, self.image_type)
        print(r)
        err_str = ''

        if r['status'] == '000':
            res = r['result']
            for key in self.attrs:
                if key in res:
                    gt = image.get_gt_result(key)
                    predict = _get_predict(res, key)
                    print(f'gt: {gt}, predict: {predict}')
                    if predict == gt:
                        self.correct[key] += 1
                    else:
                        image.cate = False
                        self.error[key] += 1
                        err_str += f'正确:{gt}<br>返回:{predict}<br>'
            if image.cate:
                image.ocr_result = r['result']
            else:
                image.ocr_result = err_str
        else:
            image.ocr_result = r['msg']
            image.cate = False
            for key in self.attrs:
                self.error[key] += 1

    def evaluate(self):
        for image in tqdm(self.image_list):
            self._evaluate_one(image)


    def accuracy(self):
        print(self.correct.values())
        return sum(list(self.correct.values())) / sum(list(self.correct.values()) + list(self.error.values()))


    def attrs_accuracy(self):
        return {k: self.correct[k] / (self.correct[k] + self.error[k]) for k in self.attrs}


if __name__ == '__main__':
    dataset = Dataset(IMAGE_PATH, IMAGE_TYPE, CONFIG_STR, False)
    print(len(dataset))
    for d in dataset.image_list:
        print(d)

    dataset.evaluate()
    print(dataset.accuracy())