File size: 3,957 Bytes
f6f8d06 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
import numpy as np
import cv2
from typing import Tuple, List
import sys,os
currDir = os.path.dirname(os.path.abspath(__file__))
rootDir = os.path.dirname( os.path.dirname(currDir) )
sys.path.append(rootDir)
if __name__ == "__main__":
from base import register_textdetectors, TextDetectorBase, TextBlock, DEFAULT_DEVICE, DEVICE_SELECTOR, ProjImgTrans
from ctd import CTDModel
else:
from .base import register_textdetectors, TextDetectorBase, TextBlock, DEFAULT_DEVICE, DEVICE_SELECTOR, ProjImgTrans
from .ctd import CTDModel
CTD_ONNX_PATH = 'data/models/comictextdetector.pt.onnx'
CTD_TORCH_PATH = 'data/models/comictextdetector.pt'
def load_ctd_model(model_path, device, detect_size=1024) -> CTDModel:
model = CTDModel(model_path, detect_size=detect_size, device=device)
return model
@register_textdetectors('ctd')
class ComicTextDetector(TextDetectorBase):
params = {
'detect_size': {
'type': 'selector',
'options': [896, 1024, 1152, 1280],
'value': 1280
},
'det_rearrange_max_batches': {
'type': 'selector',
'options': [1, 2, 4, 6, 8, 12, 16, 24, 32],
'value': 4
},
'device': DEVICE_SELECTOR(),
'description': 'ComicTextDetector',
'font size multiplier': 1.,
'font size max': -1,
'font size min': -1,
'mask dilate size': 2
}
_load_model_keys = {'model'}
download_file_list = [{
'url': 'https://github.com/zyddnys/manga-image-translator/releases/download/beta-0.3/',
'files': ['data/models/comictextdetector.pt', 'data/models/comictextdetector.pt.onnx'],
'sha256_pre_calculated': ['1f90fa60aeeb1eb82e2ac1167a66bf139a8a61b8780acd351ead55268540cccb', '1a86ace74961413cbd650002e7bb4dcec4980ffa21b2f19b86933372071d718f'],
'concatenate_url_filename': 2,
}]
device = DEFAULT_DEVICE
detect_size = 1024
def __init__(self, **params) -> None:
super().__init__(**params)
self.model: CTDModel = None
@property
def device(self):
return self.params['device']['value']
@property
def detect_size(self):
return int(self.params['detect_size']['value'])
def _load_model(self):
if self.device != 'cpu':
self.model = load_ctd_model(CTD_TORCH_PATH, self.device, self.detect_size)
else:
self.model = load_ctd_model(CTD_ONNX_PATH, self.device, self.detect_size)
def _detect(self, img: np.ndarray, proj: ProjImgTrans) -> Tuple[np.ndarray, List[TextBlock]]:
_, mask, blk_list = self.model(img)
fnt_rsz = self.get_param_value('font size multiplier')
fnt_max = self.get_param_value('font size max')
fnt_min = self.get_param_value('font size min')
for blk in blk_list:
sz = blk._detected_font_size * fnt_rsz
if fnt_max > 0:
sz = min(fnt_max, sz)
if fnt_min > 0:
sz = max(fnt_min, sz)
blk.font_size = sz
blk._detected_font_size = sz
ksize = self.get_param_value('mask dilate size')
if ksize > 0:
element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * ksize + 1, 2 * ksize + 1),(ksize, ksize))
mask = cv2.dilate(mask, element)
return mask, blk_list
def updateParam(self, param_key: str, param_content):
super().updateParam(param_key, param_content)
device = self.device
if self.model is not None:
if self.model.device != device:
self.model.device = device
if device != 'cpu':
self.model.load_model(CTD_TORCH_PATH)
else:
self.model.load_model(CTD_ONNX_PATH)
self.model.detect_size = self.detect_size
if __name__ == '__main__':
model = load_ctd_model(CTD_ONNX_PATH, 'cpu', 1024)
pass |