MMOCR 프레임워크에서 한글 사용하기

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with open('dicts_utils/ksx1001.txt', 'r', encoding='utf-8') as f: ksx1001 = f.readline() ksx1001 = ksx1001.split(' ') assert len(ksx1001) == 2350 english = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z',] symbols = ['!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.', '/', ':', ';', '<', '=', '>', '?', '@', '[', '\\', ']', '^', '_', '`', '{', '}', '~',] numbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',] charset = english + symbols + numbers + ksx1001 with open('dicts/korean_english_digits_symbols_space.txt', 'w', encoding='utf-8') as f: for i in range(len(charset)): f.write(charset[i].rstrip('\n')) f.write('\n') f.write(' ')

dictionary = dict( type='Dictionary', #dict_file='{{ fileDirname }}/../../../dicts/english_digits_symbols.txt', dict_file='{{ fileDirname }}/../../../dicts/korean_english_digits_symbols_space.txt', with_start=True, with_end=True, same_start_end=True, with_padding=True, with_unknown=True)

size mismatch for decoder.embedding.weight: copying a param with shape torch.Size([93, 512]) from checkpoint, the shape in current model is torch.Size([2447, 512]). size mismatch for decoder.prediction.weight: copying a param with shape torch.Size([93, 1536]) from checkpoint, the shape in current model is torch.Size([2447, 1536]). size mismatch for decoder.prediction.bias: copying a param with shape torch.Size([93]) from checkpoint, the shape in current model is torch.Size([2447]).

/usr/local/lib/python3.8/dist-packages/mmengine/visualization/utils.py:238: UserWarning: Glyph 48373 (\N{HANGUL SYLLABLE BOG}) missing from current font. s, (width, height) = canvas.print_to_buffer() /usr/local/lib/python3.8/dist-packages/mmengine/visualization/utils.py:238: UserWarning: Glyph 54788 (\N{HANGUL SYLLABLE HYEON}) missing from current font. s, (width, height) = canvas.print_to_buffer() /usr/local/lib/python3.8/dist-packages/mmengine/visualization/utils.py:238: UserWarning: Glyph 51456 (\N{HANGUL SYLLABLE JUN}) missing from current font. s, (width, height) = canvas.print_to_buffer() /usr/local/lib/python3.8/dist-packages/mmengine/visualization/utils.py:238: UserWarning: Glyph 48276 (\N{HANGUL SYLLABLE BEOM}) missing from current font. s, (width, height) = canvas.print_to_buffer() /usr/local/lib/python3.8/dist-packages/mmengine/visualization/utils.py:238: UserWarning: Glyph 54868 (\N{HANGUL SYLLABLE HWA}) missing from current font. s, (width, height) = canvas.print_to_buffer()

# 한글 시각화를 위한 나눔폰트 설치 및 matplotlib 폰트 캐시 제거 RUN wget http://cdn.naver.com/naver/NanumFont/fontfiles/NanumFont_TTF_ALL.zip &&\ unzip NanumFont_TTF_ALL.zip -d NanumFont &&\ rm -f NanumFont_TTF_ALL.zip &&\ mv NanumFont /usr/share/fonts/ &&\ fc-cache -f -v &&\ rm -rf ~/.cache/matplotlib

@VISUALIZERS.register_module() class Visualizer(ManagerMixin): # ... @master_only def draw_texts(self, ... font_families: Union[str, List[str]] = 'sans-serif', ...) -> 'Visualizer': # ... check_type_and_length('font_families', font_families, (str, list), num_text) font_families = value2list(font_families, str, num_text)

visualizer = dict( type='TextRecogLocalVisualizer', name='visualizer', font_families='NanumGothic', vis_backends=vis_backends)

class BaseMMOCRInferencer(BaseInferencer): # ... def visualize(self, inputs: InputsType, preds: PredType, show: bool = False, wait_time: int = 0, draw_pred: bool = True, pred_score_thr: float = 0.3, img_out_dir: str = '') -> List[np.ndarray]: # ... self.visualizer.add_datasample( img_name, img, pred, show=show, wait_time=wait_time, draw_gt=False, draw_pred=draw_pred, pred_score_thr=pred_score_thr, out_file=out_file, ) # ...

@VISUALIZERS.register_module() class TextRecogLocalVisualizer(BaseLocalVisualizer): # ... def add_datasample(self, name: str, image: np.ndarray, data_sample: Optional['TextRecogDataSample'] = None, draw_gt: bool = True, draw_pred: bool = True, show: bool = False, wait_time: int = 0, pred_score_thr: float = None, out_file: Optional[str] = None, step=0) -> None: """Visualize datasample and save to all backends. - If GT and prediction are plotted at the same time, they are displayed in a stitched image where the left image is the ground truth and the right image is the prediction. .... """ # ... cat_images = [image] if draw_gt and data_sample is not None and 'gt_text' in data_sample: gt_text = data_sample.gt_text.item cat_images.append(self._draw_instances(image, gt_text)) if (draw_pred and data_sample is not None and 'pred_text' in data_sample): pred_text = data_sample.pred_text.item cat_images.append(self._draw_instances(image, pred_text)) cat_images = self._cat_image(cat_images, axis=0) # ... if out_file is not None: mmcv.imwrite(cat_images[..., ::-1], out_file) def _draw_instances(self, image: np.ndarray, text: str) -> np.ndarray: # ... self.draw_texts( text, np.array([width / 2, height / 2]), colors=self.gt_color, font_sizes=font_size, vertical_alignments='center', horizontal_alignments='center') # ...

@VISUALIZERS.register_module() class Visualizer(ManagerMixin): # ... @master_only def draw_texts(self, ... font_families: Union[str, List[str]] = 'sans-serif', ...) -> 'Visualizer': # ... check_type_and_length('font_families', font_families, (str, list), num_text) font_families = value2list(font_families, str, num_text)

@VISUALIZERS.register_module() class Visualizer(ManagerMixin): # ... @master_only def draw_texts(self, ... font_families: Union[str, List[str]] = 'sans-serif', ...) -> 'Visualizer': # ... check_type_and_length('font_families', font_families, (str, list), num_text) font_families = value2list(font_families, str, num_text)

@master_only def draw_texts( self, ... font_families: Union[str, List[str]] = 'NanumGothicCoding', ...) -> 'Visualizer':

visualizer = dict( type='TextRecogLocalVisualizer', name='visualizer', vis_backends=vis_backends)

visualizer = dict( type='TextRecogLocalVisualizer', name='visualizer', vis_backends=vis_backends, font_families='NanumGothic')

@VISUALIZERS.register_module() class TextRecogLocalVisualizer(BaseLocalVisualizer): # ... def _draw_instances(self, image: np.ndarray, text: str) -> np.ndarray: """Draw text on image. Args: image (np.ndarray): The image to draw. text (str): The text to draw. Returns: np.ndarray: The image with text drawn. """ height, width = image.shape[:2] empty_img = np.full_like(image, 255) self.set_image(empty_img) font_size = 0.5 * width / (len(text) + 1) self.draw_texts( text, np.array([width / 2, height / 2]), colors=self.gt_color, font_sizes=font_size, vertical_alignments='center', horizontal_alignments='center', font_families=self.font_families) text_image = self.get_image() return text_image

@VISUALIZERS.register_module() class BaseLocalVisualizer(Visualizer): # ... # ... def get_labels_image(self, image: np.ndarray, labels: Union[np.ndarray, torch.Tensor], bboxes: Union[np.ndarray, torch.Tensor], colors: Union[str, Sequence[str]] = 'k', font_size: Union[int, float] = 10, auto_font_size: bool = False) -> np.ndarray: # ... self.draw_texts( labels, (bboxes[:, :2] + bboxes[:, 2:]) / 2, vertical_alignments='center', horizontal_alignments='center', colors='k', font_sizes=font_size) # ... # ...

@VISUALIZERS.register_module() class KIELocalVisualizer(BaseLocalVisualizer): # ... def _draw_instances( self, image: np.ndarray, bbox_labels: Union[np.ndarray, torch.Tensor], bboxes: Union[np.ndarray, torch.Tensor], polygons: Sequence[np.ndarray], edge_labels: Union[np.ndarray, torch.Tensor], texts: Sequence[str], class_names: Dict, is_openset: bool = False, arrow_colors: str = 'g', ) -> np.ndarray: # ... classes_image = np.full(empty_shape, 255, dtype=np.uint8) bbox_classes = [class_names[int(i)]['name'] for i in bbox_labels] classes_image = self.get_labels_image(classes_image, bbox_classes, bboxes) # ... if is_openset: edge_image = np.full(empty_shape, 255, dtype=np.uint8) edge_image = self._draw_edge_label(edge_image, edge_labels, bboxes, texts, arrow_colors) cat_image.append(edge_image) return self._cat_image(cat_image, axis=1) # ... def _draw_edge_label(self, image: np.ndarray, edge_labels: Union[np.ndarray, torch.Tensor], bboxes: Union[np.ndarray, torch.Tensor], texts: Sequence[str], arrow_colors: str = 'g') -> np.ndarray: # ... if key_texts: self.draw_texts( key_texts, (bboxes[key_index, :2] + bboxes[key_index, 2:]) / 2, colors='k', horizontal_alignments='center', vertical_alignments='center') if val_texts: self.draw_texts( val_texts, (bboxes[val_index, :2] + bboxes[val_index, 2:]) / 2, colors='k', horizontal_alignments='center', vertical_alignments='center') # ... # ...

@VISUALIZERS.register_module() class TextRecogLocalVisualizer(BaseLocalVisualizer): """MMOCR Text Detection Local Visualizer. Args: name (str): Name of the instance. Defaults to 'visualizer'. image (np.ndarray, optional): The origin image to draw. The format should be RGB. Defaults to None. vis_backends (list, optional): Visual backend config list. Defaults to None. save_dir (str, optional): Save file dir for all storage backends. If it is None, the backend storage will not save any data. gt_color (str or tuple[int, int, int]): Colors of GT text. The tuple of color should be in RGB order. Or using an abbreviation of color, such as `'g'` for `'green'`. Defaults to 'g'. pred_color (str or tuple[int, int, int]): Colors of Predicted text. The tuple of color should be in RGB order. Or using an abbreviation of color, such as `'r'` for `'red'`. Defaults to 'r'. """ def __init__(self, name: str = 'visualizer', image: Optional[np.ndarray] = None, vis_backends: Optional[Dict] = None, save_dir: Optional[str] = None, gt_color: Optional[Union[str, Tuple[int, int, int]]] = 'g', pred_color: Optional[Union[str, Tuple[int, int, int]]] = 'r') -> None: super().__init__( name=name, image=image, vis_backends=vis_backends, save_dir=save_dir) self.gt_color = gt_color self.pred_color = pred_color

@VISUALIZERS.register_module() class TextRecogLocalVisualizer(BaseLocalVisualizer): # ... def __init__(self, name: str = 'visualizer', image: Optional[np.ndarray] = None, vis_backends: Optional[Dict] = None, save_dir: Optional[str] = None, gt_color: Optional[Union[str, Tuple[int, int, int]]] = 'g', pred_color: Optional[Union[str, Tuple[int, int, int]]] = 'r', **kwargs) -> None: super().__init__( name=name, image=image, vis_backends=vis_backends, save_dir=save_dir, **kwargs) self.gt_color = gt_color self.pred_color = pred_color

@VISUALIZERS.register_module() class BaseLocalVisualizer(Visualizer): # ... def __init__(self, name: str = 'visualizer', font_families: Union[str, List[str]] = 'sans-serif', **kwargs) -> None: super().__init__(name=name, **kwargs) self.font_families = font_families