import os import os.path as osp from typing import Tuple, List import torch import numpy as np import cv2 from .base import register_textdetectors, TextDetectorBase, TextBlock, DEVICE_SELECTOR from utils.textblock import mit_merge_textlines, sort_regions, examine_textblk, sort_pnts from utils.imgproc_utils import xywh2xyxypoly from utils.proj_imgtrans import ProjImgTrans MODEL_DIR = 'data/models' CKPT_LIST = [] def update_ckpt_list(): if not osp.exists(MODEL_DIR): return global CKPT_LIST CKPT_LIST.clear() for p in os.listdir(MODEL_DIR): if p.startswith('ysgyolo') or p.startswith('ultralyticsyolo'): CKPT_LIST.append(osp.join(MODEL_DIR, p).replace('\\', '/')) update_ckpt_list() @register_textdetectors('ysgyolo') class YSGYoloDetector(TextDetectorBase): params = { 'model path': { 'type': 'selector', 'options': CKPT_LIST, 'value': 'data/models/ysgyolo_1.2_OS1.0.pt', 'editable': True, 'flush_btn': True, 'path_selector': True, 'path_filter': '*.pt *.ckpt *.pth *.safetensors', 'size': 'median', 'display_name': '模型路径' }, 'merge text lines': { 'display_name': '合并文本行', 'type': 'checkbox', 'value': True }, 'confidence threshold': { 'display_name': '置信度阈值', 'type': 'line_editor', 'value': 0.3 }, 'IoU threshold': { 'display_name': 'IoU阈值', 'type': 'line_editor', 'value': 0.5 }, 'font size multiplier': { 'display_name': '字号乘数', 'type': 'line_editor', 'value': 1. }, 'font size max': { 'display_name': '最大字号', 'type': 'line_editor', 'value': -1 }, 'font size min': { 'display_name': '最小字号', 'type': 'line_editor', 'value': -1 }, 'detect size': { 'display_name': '检测尺寸', 'type': 'line_editor', 'value': 1024 }, 'device': { **DEVICE_SELECTOR(), 'display_name': '设备' }, 'label': { 'value': { 'balloon': True, 'qipao': True, 'shuqing': True, 'changfangtiao': True, 'hengxie': True, 'other': True }, 'type': 'check_group', 'display_name': '标签' }, 'source text is vertical': { 'display_name': '竖排文本', 'type': 'checkbox', 'value': True }, 'mask dilate size': { 'display_name': '掩码扩张尺寸', 'type': 'line_editor', 'value': 2 } } _load_model_keys = {'model'} def __init__(self, **params) -> None: super().__init__(**params) update_ckpt_list() def _load_model(self): model_path = self.get_param_value('model path') if not osp.exists(model_path): global CKPT_LIST df_model_path = model_path for p in CKPT_LIST: if osp.exists(p): df_model_path = p break self.logger.warning(f'{model_path} does not exist, try fall back to default value {df_model_path}') model_path = df_model_path if 'rtdetr' in os.path.basename(model_path): from ultralytics import RTDETR as MODEL else: from ultralytics import YOLO as MODEL if not hasattr(self, 'model') or self.model is None: self.model = MODEL(model_path).to(device=self.get_param_value('device')) def get_valid_labels(self): return [k for k, v in self.params['label']['value'].items() if v] @property def is_ysg(self): return osp.basename(self.get_param_value('model path').startswith('ysg')) def _detect(self, img: np.ndarray, proj: ProjImgTrans = None) -> Tuple[np.ndarray, List[TextBlock]]: result = self.model.predict( source=img, save=False, show=False, verbose=False, conf=self.get_param_value('confidence threshold'), iou=self.get_param_value('IoU threshold'), agnostic_nms=True )[0] valid_labels = set(self.get_valid_labels()) valid_ids = [idx for idx, name in result.names.items() if name in valid_labels] mask = np.zeros_like(img[..., 0]) if not valid_ids: return [], mask im_h, im_w = img.shape[:2] detected_items = [] # Process standard boxes dets = result.boxes if dets is not None and len(dets.cls) > 0: for i in range(len(dets.cls)): cls_idx = int(dets.cls[i]) if cls_idx in valid_ids: label_name = result.names[cls_idx] xyxy = dets.xyxy[i].cpu().numpy() x1, y1, x2, y2 = xyxy.astype(int) cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1) pts = xywh2xyxypoly(np.array([[x1, y1, x2 - x1, y2 - y1]])).reshape(4, 2).tolist() detected_items.append({'pts': pts, 'label': label_name}) # Process oriented boxes dets = result.obb if dets is not None and len(dets.cls) > 0: for i in range(len(dets.cls)): cls_idx = int(dets.cls[i]) if cls_idx in valid_ids: label_name = result.names[cls_idx] pts = dets.xyxyxyxy[i].cpu().numpy().astype(int) cv2.fillPoly(mask, [pts], 255) detected_items.append({'pts': pts.tolist(), 'label': label_name}) blk_list = [] if self.get_param_value('merge text lines'): pts_only_list = [item['pts'] for item in detected_items] blk_list = mit_merge_textlines(pts_only_list, width=im_w, height=im_h) else: for item in detected_items: pts_sorted, is_vertical = sort_pnts(item['pts']) blk = TextBlock(lines=[pts_sorted], src_is_vertical=is_vertical, label=item['label']) blk.vertical = is_vertical blk.adjust_bbox() examine_textblk(blk, im_w, im_h) blk_list.append(blk) blk_list = sort_regions(blk_list) 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) if param_key == 'model path': if hasattr(self, 'model'): del self.model def flush(self, param_key: str): if param_key == 'model path': update_ckpt_list() global CKPT_LIST return CKPT_LIST