Lemontropia-Suite/modules/ocr_backends/easyocr_backend.py

185 lines
5.8 KiB
Python

"""
Lemontropia Suite - EasyOCR Backend
Text recognition using EasyOCR - lighter than PaddleOCR.
"""
import numpy as np
import logging
from typing import List, Optional
from . import BaseOCRBackend, OCRTextRegion
logger = logging.getLogger(__name__)
class EasyOCRBackend(BaseOCRBackend):
"""
OCR backend using EasyOCR.
Pros:
- Lighter than PaddleOCR
- Good accuracy
- Supports many languages
- Can run on CPU reasonably well
Cons:
- First run downloads models (~100MB)
- Slower than OpenCV EAST
Installation: pip install easyocr
"""
NAME = "easyocr"
SUPPORTS_GPU = True
def __init__(self, use_gpu: bool = True, lang: str = 'en', **kwargs):
super().__init__(use_gpu=use_gpu, lang=lang, **kwargs)
self.reader = None
self._gpu_available = False
# Language mapping
self.lang_map = {
'en': 'en',
'sv': 'sv', # Swedish
'de': 'de',
'fr': 'fr',
'es': 'es',
'latin': 'latin',
}
def _initialize(self) -> bool:
"""Initialize EasyOCR reader."""
try:
import easyocr
# Map language code
easyocr_lang = self.lang_map.get(self.lang, 'en')
# Check GPU availability
self._gpu_available = self._check_gpu()
use_gpu_flag = self.use_gpu and self._gpu_available
logger.info(f"Initializing EasyOCR (lang={easyocr_lang}, gpu={use_gpu_flag})")
# Create reader
# EasyOCR downloads models automatically on first run
self.reader = easyocr.Reader(
[easyocr_lang],
gpu=use_gpu_flag,
verbose=False
)
self._available = True
self._version = easyocr.__version__ if hasattr(easyocr, '__version__') else 'unknown'
logger.info(f"EasyOCR initialized successfully (GPU: {use_gpu_flag})")
return True
except ImportError:
self._error_msg = "EasyOCR not installed. Run: pip install easyocr"
logger.warning(self._error_msg)
return False
except Exception as e:
# Handle specific PyTorch/CUDA errors
error_str = str(e).lower()
if 'cuda' in error_str or 'c10' in error_str or 'gpu' in error_str:
self._error_msg = f"EasyOCR GPU initialization failed: {e}"
logger.warning(f"{self._error_msg}. Try with use_gpu=False")
# Try CPU fallback
if self.use_gpu:
logger.info("Attempting EasyOCR CPU fallback...")
self.use_gpu = False
return self._initialize()
else:
self._error_msg = f"EasyOCR initialization failed: {e}"
logger.error(self._error_msg)
return False
def _check_gpu(self) -> bool:
"""Check if GPU is available for EasyOCR."""
try:
import torch
if torch.cuda.is_available():
logger.info(f"CUDA available: {torch.cuda.get_device_name(0)}")
return True
# Check MPS (Apple Silicon)
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
logger.info("Apple MPS available")
return True
return False
except ImportError:
return False
except Exception as e:
logger.debug(f"GPU check failed: {e}")
return False
def extract_text(self, image: np.ndarray) -> List[OCRTextRegion]:
"""
Extract text from image using EasyOCR.
Args:
image: Input image (BGR format from OpenCV)
Returns:
List of detected text regions with recognized text
"""
if not self._available or self.reader is None:
logger.error("EasyOCR backend not initialized")
return []
try:
# EasyOCR expects RGB format
if len(image.shape) == 3:
import cv2
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
else:
image_rgb = image
# Run OCR
results = self.reader.readtext(image_rgb)
regions = []
for detection in results:
# EasyOCR returns: (bbox, text, confidence)
bbox, text, conf = detection
# Calculate bounding box from polygon
# bbox is list of 4 points: [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
x_coords = [p[0] for p in bbox]
y_coords = [p[1] for p in bbox]
x = int(min(x_coords))
y = int(min(y_coords))
w = int(max(x_coords) - x)
h = int(max(y_coords) - y)
regions.append(OCRTextRegion(
text=text.strip(),
confidence=float(conf),
bbox=(x, y, w, h),
language=self.lang
))
logger.debug(f"EasyOCR detected {len(regions)} text regions")
return regions
except Exception as e:
logger.error(f"EasyOCR extraction failed: {e}")
return []
def get_info(self):
"""Get backend information."""
info = super().get_info()
info.gpu_accelerated = self._gpu_available and self.use_gpu
return info