| 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 |
