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import json, os, sys, time, io
import os.path as osp
from PIL import Image
import PIL
import cv2
import numpy as np
# see utils\io_utils.py
def imread(imgpath, read_type=cv2.IMREAD_COLOR, max_retry_limit=5, retry_interval=0.1):
if not osp.exists(imgpath):
return None
num_tries = 0
while True:
try:
img = Image.open(imgpath)
if read_type == cv2.IMREAD_GRAYSCALE:
img = img.convert('L')
img = np.array(img)
if read_type != cv2.IMREAD_GRAYSCALE:
if img.ndim == 3 and img.shape[-1] == 1:
img = img[..., :2]
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
if img.ndim == 3 and img.shape[-1] == 4:
if np.all(img[..., -1] == 255):
img = np.ascontiguousarray(img[..., :3])
break
except PIL.UnidentifiedImageError as e:
# IMG I/O thread might not finished yet
num_tries += 1
if max_retry_limit is not None and num_tries >= max_retry_limit:
return None
time.sleep(retry_interval)
return img
def chunks(lst, n):
"""Yield successive n-sized chunks from lst."""
for i in range(0, len(lst), n):
yield lst[i:i + n]
if __name__ == '__main__':
from modules.textdetector.ctd.inference import TextDetector as CTDModel
from modules.ocr.mit48px import Model48pxOCR
CTD_ONNX_PATH = 'data/models/comictextdetector.pt.onnx'
device = 'cpu'
detect_size = 1280
ctd_model = CTDModel(CTD_ONNX_PATH, detect_size=detect_size, device=device)
OCR48PXMODEL_PATH = 'data/models/ocr_ar_48px.ckpt'
ocr_model = Model48pxOCR(OCR48PXMODEL_PATH, device)
img = imread('E:/huggingface/BallonsTranslator/assets/kcc-0010.jpg')
# 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 = ctd_model(img)
fnt_rsz = 1.0
fnt_max = -1
fnt_min = -1
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 = 2
if ksize > 0:
element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * ksize + 1, 2 * ksize + 1),(ksize, ksize)) # 创建一个椭圆形的结构元素（kernel），用于后续的形态学操作 # 元素的尺寸 # (ksize, ksize) ：椭圆的锚点（中心点）
mask = cv2.dilate(mask, element) # 对 mask 图像进行膨胀操作（dilate），使用上面创建的椭圆结构元素。膨胀操作可以让白色区域（通常是前景或目标区域）变大，常用于去除小的黑洞、连接断开的区域等。
for blk in blk_list:
blk.det_model = 'ctd'
need_save_mask = True
detect_counter = 0
detect_counter += 1
# self.ocr.run_ocr(img, blk_list)
for blk in blk_list:
blk.text = []
split_textblk = False
seg_func = None
model_text_height = 48
model_maxwidth = 8100
from utils.textblock import collect_textblock_regions
chunk_size = 16
regions, textblk_lst_indices = collect_textblock_regions(img, blk_list, model_text_height, model_maxwidth, split_textblk, seg_func)
ocr_model(blk_list, regions, textblk_lst_indices, chunk_size=chunk_size)
img_draw = img.copy()
# from qtpy.QtWidgets import QApplication
# from qtpy.QtGui import QIcon, QFontDatabase, QGuiApplication, QFont, QFontMetrics
# ui\mainwindow.py
for blk in blk_list:
text = blk.get_text()
for line in blk.lines:
img_draw = cv2.rectangle(img_draw, line[0], line[3], (0, 0, 255), 2) # 在一行坚排文字的左边画一条红线
# app_font = QFont('Microsoft YaHei UI')
# fontMetrics = QFontMetrics(app_font)
# rect = fontMetrics.boundingRect(text[0])
# textWidth = rect.width()
pass
# blk.text = self.ocrSubWidget.sub_text(text)
cv2.imwrite("E:/xxxxxxxxxxxxxxxx.jpg", img_draw)
pass
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