modules/textdetector/base.py

import base64
import requests
import numpy as np
import cv2
from typing import Union, List, Tuple
from collections import OrderedDict

# import sys,os
# currDir = os.path.dirname(os.path.abspath(__file__))
# rootDir = os.path.dirname( os.path.dirname(currDir) )
# sys.path.append(rootDir)

from utils.textblock import TextBlock
from utils.proj_imgtrans import ProjImgTrans

from utils.registry import Registry
TEXTDETECTORS = Registry('textdetectors')
register_textdetectors = TEXTDETECTORS.register_module

# from ..base import BaseModule, DEFAULT_DEVICE, DEVICE_SELECTOR
from modules.base import BaseModule, DEFAULT_DEVICE, DEVICE_SELECTOR

class TextDetectorBase(BaseModule):

    _postprocess_hooks = OrderedDict()
    _preprocess_hooks = OrderedDict()

    def __init__(self, **params) -> None:
        super().__init__(**params)
        self.name = ''
        for key in TEXTDETECTORS.module_dict:
            if TEXTDETECTORS.module_dict[key] == self.__class__:
                self.name = key
                break

    def _detect(self, img: np.ndarray, proj: ProjImgTrans) -> Tuple[np.ndarray, List[TextBlock]]:
        '''
        The proj context can be accessed via ```proj```
        '''
        raise NotImplementedError

    def setup_detector(self):
        raise NotImplementedError

    def detect(self, img: np.ndarray, proj: ProjImgTrans = None) -> Tuple[np.ndarray, List[TextBlock]]:
        # TODO: allow processing proj entirely in _detect and yield progress
        if not self.all_model_loaded():
            self.load_model()
        
        # All text detectors only support 3 channels input 
        if img.ndim == 3 and img.shape[2] == 4:
            img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)

        mask, blk_list = self._detect(img, proj)
        for blk in blk_list:
            blk.det_model = self.name
        return mask, blk_list