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import numpy as np
from typing import List
import os
import logging
LOGGER = logging.getLogger("BallonTranslator")
try:
from paddleocr import PaddleOCR
PADDLE_OCR_AVAILABLE = True
except ImportError:
PADDLE_OCR_AVAILABLE = False
LOGGER.debug(
"PaddleOCR is not installed, so the module will not be initialized. \nCheck this issue https://github.com/dmMaze/BallonsTranslator/issues/835#issuecomment-2772940806"
)
import cv2
import re
from .base import OCRBase, register_OCR, DEFAULT_DEVICE, DEVICE_SELECTOR, TextBlock
# Specify the path for storing PaddleOCR models
PADDLE_OCR_PATH = os.path.join("data", "models", "paddle-ocr")
# Set an environment variable to store PaddleOCR models
os.environ["PPOCR_HOME"] = PADDLE_OCR_PATH
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
if PADDLE_OCR_AVAILABLE:
@register_OCR("paddle_ocr")
class PaddleOCRModule(OCRBase):
# Mapping language names to PaddleOCR codes
lang_map = {
"Chinese & English": "ch",
"English": "en",
"French": "fr",
"German": "german",
"Japanese": "japan",
"Korean": "korean",
"Chinese Traditional": "chinese_cht",
"Italian": "it",
"Spanish": "es",
"Portuguese": "pt",
"Russian": "ru",
"Ukrainian": "uk",
"Belarusian": "be",
"Telugu": "te",
"Saudi Arabia": "sa",
"Tamil": "ta",
"Afrikaans": "af",
"Azerbaijani": "az",
"Bosnian": "bs",
"Czech": "cs",
"Welsh": "cy",
"Danish": "da",
"Dutch": "nl",
"Norwegian": "no",
"Polish": "pl",
"Romanian": "ro",
"Slovak": "sk",
"Slovenian": "sl",
"Albanian": "sq",
"Swedish": "sv",
"Swahili": "sw",
"Tagalog": "tl",
"Turkish": "tr",
"Uzbek": "uz",
"Vietnamese": "vi",
"Mongolian": "mn",
"Arabic": "ar",
"Hindi": "hi",
"Uyghur": "ug",
"Persian": "fa",
"Urdu": "ur",
"Serbian (Latin)": "rs_latin",
"Occitan": "oc",
"Marathi": "mr",
"Nepali": "ne",
"Serbian (Cyrillic)": "rs_cyrillic",
"Bulgarian": "bg",
"Estonian": "et",
"Irish": "ga",
"Croatian": "hr",
"Hungarian": "hu",
"Indonesian": "id",
"Icelandic": "is",
"Kurdish": "ku",
"Lithuanian": "lt",
"Latvian": "lv",
"Maori": "mi",
"Malay": "ms",
"Maltese": "mt",
"Adyghe": "ady",
"Kabardian": "kbd",
"Avar": "ava",
"Dargwa": "dar",
"Ingush": "inh",
"Lak": "lbe",
"Lezghian": "lez",
"Tabassaran": "tab",
"Bihari": "bh",
"Maithili": "mai",
"Angika": "ang",
"Bhojpuri": "bho",
"Magahi": "mah",
"Nagpur": "sck",
"Newari": "new",
"Goan Konkani": "gom",
}
params = {
"language": {
"type": "selector",
"options": list(lang_map.keys()),
"value": "English", # Default language
"description": "Select the language for OCR",
},
"device": DEVICE_SELECTOR(),
"use_angle_cls": {
"type": "checkbox",
"value": False,
"description": "Enable angle classification for rotated text",
},
"ocr_version": {
"type": "selector",
"options": ["PP-OCRv4", "PP-OCRv3", "PP-OCRv2", "PP-OCR"],
"value": "PP-OCRv4",
"description": "Select the OCR model version",
},
"enable_mkldnn": {
"type": "checkbox",
"value": False,
"description": "Enable MKL-DNN for CPU acceleration",
},
"det_limit_side_len": {
"value": 960,
"description": "Maximum side length for text detection",
},
"rec_batch_num": {
"value": 6,
"description": "Batch size for text recognition",
},
"drop_score": {
"value": 0.5,
"description": "Confidence threshold for text recognition",
},
"text_case": {
"type": "selector",
"options": ["Uppercase", "Capitalize Sentences", "Lowercase"],
"value": "Capitalize Sentences",
"description": "Text case transformation",
},
"output_format": {
"type": "selector",
"options": ["Single Line", "As Recognized"],
"value": "As Recognized",
"description": "Text output format",
},
}
device = DEFAULT_DEVICE
def __init__(self, **params) -> None:
super().__init__(**params)
self.language = self.params["language"]["value"]
self.device = self.params["device"]["value"]
self.use_angle_cls = self.params["use_angle_cls"]["value"]
self.ocr_version = self.params["ocr_version"]["value"]
self.enable_mkldnn = self.params["enable_mkldnn"]["value"]
self.det_limit_side_len = self.params["det_limit_side_len"]["value"]
self.rec_batch_num = self.params["rec_batch_num"]["value"]
self.drop_score = self.params["drop_score"]["value"]
self.text_case = self.params["text_case"]["value"]
self.output_format = self.params["output_format"]["value"]
self.model = None
self._setup_logging()
self._load_model()
def _setup_logging(self):
if self.debug_mode:
logging.getLogger("ppocr").setLevel(logging.DEBUG)
logging.getLogger("paddleocr").setLevel(logging.DEBUG)
logging.getLogger("predict_system").setLevel(logging.DEBUG)
else:
logging.getLogger("ppocr").setLevel(logging.WARNING)
logging.getLogger("paddleocr").setLevel(logging.WARNING)
logging.getLogger("predict_system").setLevel(logging.WARNING)
def _load_model(self):
lang_code = self.lang_map[self.language]
use_gpu = True if self.device == "cuda" else False
if self.debug_mode:
self.logger.info(
f"Loading PaddleOCR model for language: {self.language} ({lang_code}), GPU: {use_gpu}"
)
self.model = PaddleOCR(
use_angle_cls=self.use_angle_cls,
lang=lang_code,
use_gpu=use_gpu,
ocr_version=self.ocr_version,
enable_mkldnn=self.enable_mkldnn,
det_limit_side_len=self.det_limit_side_len,
rec_batch_num=self.rec_batch_num,
drop_score=self.drop_score,
det_model_dir=os.path.join(
PADDLE_OCR_PATH, lang_code, self.ocr_version, "det"
),
rec_model_dir=os.path.join(
PADDLE_OCR_PATH, lang_code, self.ocr_version, "rec"
),
cls_model_dir=(
os.path.join(PADDLE_OCR_PATH, lang_code, self.ocr_version, "cls")
if self.use_angle_cls
else None
),
)
def ocr_img(self, img: np.ndarray) -> str:
if self.debug_mode:
self.logger.debug(f"Starting OCR for image size: {img.shape}")
result = self.model.ocr(img, det=True, rec=True, cls=self.use_angle_cls)
if self.debug_mode:
self.logger.debug(f"OCR recognition result: {result}")
text = self._process_result(result)
return text
def _ocr_blk_list(
self, img: np.ndarray, blk_list: List[TextBlock], *args, **kwargs
):
im_h, im_w = img.shape[:2]
for blk in blk_list:
x1, y1, x2, y2 = blk.xyxy
if 0 <= x1 < x2 <= im_w and 0 <= y1 < y2 <= im_h:
cropped_img = img[y1:y2, x1:x2]
try:
result = self.model.ocr(
cropped_img, det=True, rec=True, cls=self.use_angle_cls
)
# Extract raw text from OCR result
raw_texts = []
if (
isinstance(result, list)
and len(result) > 0
and isinstance(result[0], list)
):
for line in result[0]:
if (
isinstance(line, list)
and len(line) > 1
and isinstance(line[1], (list, tuple))
and len(line[1]) > 0
):
raw_texts.append(line[1][0])
raw_text = " ".join(raw_texts)
if self.debug_mode:
self.logger.debug(
f"Raw OCR text from block ({x1}, {y1}, {x2}, {y2}): {raw_text}"
)
# Process the OCR result
text = self._process_result(result)
if self.debug_mode:
self.logger.debug(
f"Processed text from block ({x1}, {y1}, {x2}, {y2}): {text}"
)
blk.text = text if text else ""
except Exception as e:
if self.debug_mode:
self.logger.error(f"Error recognizing block: {str(e)}")
blk.text = ""
else:
if self.debug_mode:
self.logger.warning(
"Invalid text block coordinates for target image"
)
blk.text = ""
def _process_result(self, result):
try:
if not result or result[0] is None:
return ""
if (
isinstance(result, list)
and len(result) > 0
and isinstance(result[0], list)
):
result = result[0]
raw_texts = []
for line in result:
if (
isinstance(line, list)
and len(line) > 1
and isinstance(line[1], (list, tuple))
and len(line[1]) > 0
):
text = line[1][0]
raw_texts.append(text)
# Depending on the output_format, we concatenate the lines
if self.output_format == "Single Line":
joined_text = " ".join(raw_texts)
# Text cleaning
joined_text = re.sub(r"-(?!\w)", "", joined_text)
joined_text = re.sub(r"\s+", " ", joined_text)
elif self.output_format == "As Recognized":
joined_text = " ".join(
raw_texts
) # Combine with spaces to create a single text
# Clean up text, preserve line breaks
joined_text = re.sub(r"-(?!\w)", "", joined_text)
joined_text = re.sub(r"\s+", " ", joined_text)
else:
joined_text = " ".join(raw_texts)
joined_text = re.sub(r"-(?!\w)", "", joined_text)
joined_text = re.sub(r"\s+", " ", joined_text)
# Apply case conversion to all text
processed_text = self._apply_text_case(joined_text)
processed_text = self._apply_punctuation_and_spacing(processed_text)
if self.debug_mode:
self.logger.debug(f"Final processed text: {processed_text}")
return processed_text
except Exception as e:
if self.debug_mode:
self.logger.error(f"Error processing OCR result: {str(e)}")
return ""
def _apply_text_case(self, text: str) -> str:
if self.text_case == "Uppercase":
return text.upper()
elif self.text_case == "Capitalize Sentences":
return self._capitalize_sentences(text)
elif self.text_case == "Lowercase":
return text.lower()
else:
return text # No change if the mode is not recognized
def _capitalize_sentences(self, text: str) -> str:
def process_sentence(sentence):
words = sentence.split()
if not words:
return ""
if len(words) == 1:
return words[0].capitalize()
else:
return " ".join(
[words[0].capitalize()] + [word.lower() for word in words[1:]]
)
# We divide into sentences only by punctuation marks
sentences = re.split(r"(?<=[.!?…])\s+", text)
return " ".join(process_sentence(sentence) for sentence in sentences)
def _apply_punctuation_and_spacing(self, text: str) -> str:
text = re.sub(r"\s+([,.!?…])", r"\1", text)
text = re.sub(r"([,.!?…])(?!\s)(?![,.!?…])", r"\1 ", text)
text = re.sub(r"([,.!?…])\s+([,.!?…])", r"\1\2", text)
return text.strip()
def updateParam(self, param_key: str, param_content):
super().updateParam(param_key, param_content)
if param_key in [
"language",
"device",
"use_angle_cls",
"ocr_version",
"enable_mkldnn",
"det_limit_side_len",
"rec_batch_num",
"drop_score",
]:
self.language = self.params["language"]["value"]
self.device = self.params["device"]["value"]
self.use_angle_cls = self.params["use_angle_cls"]["value"]
self.ocr_version = self.params["ocr_version"]["value"]
self.enable_mkldnn = self.params["enable_mkldnn"]["value"]
self.det_limit_side_len = self.params["det_limit_side_len"]["value"]
self.rec_batch_num = self.params["rec_batch_num"]["value"]
self.drop_score = self.params["drop_score"]["value"]
self._load_model()
elif param_key == "text_case":
self.text_case = self.params["text_case"]["value"]
elif param_key == "output_format":
self.output_format = self.params["output_format"]["value"]
else:
# If PaddleOCR is not installed, you can define a stub or alternative module
logging.info("PaddleOCR module will not be loaded as the library is not installed.")
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