1313 lines
47 KiB
Python
1313 lines
47 KiB
Python
import re
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import os
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import io
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from pathlib import Path
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import sys
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import platform
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import logging
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from math import sqrt, sin, cos, atan2
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import json
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import base64
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from urllib.parse import urlparse, parse_qs
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from dataclasses import dataclass, field, asdict
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from typing import List, Optional
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import jaconv
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import numpy as np
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from PIL import Image
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from loguru import logger
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import requests
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try:
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from manga_ocr import MangaOcr as MOCR
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except ImportError:
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pass
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try:
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import Vision
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import objc
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from AppKit import NSData, NSImage, NSBundle
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from CoreFoundation import CFRunLoopRunInMode, kCFRunLoopDefaultMode, CFRunLoopStop, CFRunLoopGetCurrent
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except ImportError:
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pass
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try:
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from google.cloud import vision
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from google.oauth2 import service_account
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from google.api_core.exceptions import ServiceUnavailable
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except ImportError:
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pass
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try:
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from azure.ai.vision.imageanalysis import ImageAnalysisClient
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from azure.ai.vision.imageanalysis.models import VisualFeatures
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from azure.core.credentials import AzureKeyCredential
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from azure.core.exceptions import ServiceRequestError
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except ImportError:
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pass
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try:
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import easyocr
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except ImportError:
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pass
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try:
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from rapidocr import RapidOCR as ROCR
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from rapidocr import EngineType, LangDet, LangRec, ModelType, OCRVersion
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except ImportError:
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pass
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try:
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import winocr
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except ImportError:
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pass
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try:
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import oneocr
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except ImportError:
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pass
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try:
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import pyjson5
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except ImportError:
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pass
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try:
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import betterproto
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from .lens_betterproto import *
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import random
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except ImportError:
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pass
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try:
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import fpng_py
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optimized_png_encode = True
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except:
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optimized_png_encode = False
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@dataclass
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class BoundingBox:
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"""
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Represents the normalized coordinates of a detected element.
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All values are floats between 0.0 and 1.0.
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"""
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center_x: float
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center_y: float
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width: float
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height: float
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rotation_z: Optional[float] = None # Optional rotation in radians
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@dataclass
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class Word:
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"""Represents a single recognized word and its properties."""
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text: str
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bounding_box: BoundingBox
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separator: Optional[str] = None # The character(s) that follow the word, e.g., a space
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@dataclass
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class Line:
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"""Represents a single line of text, composed of words."""
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bounding_box: BoundingBox
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words: List[Word] = field(default_factory=list)
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text: Optional[str] = None
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@dataclass
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class Paragraph:
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"""Represents a block of text, composed of lines."""
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bounding_box: BoundingBox
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lines: List[Line] = field(default_factory=list)
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writing_direction: Optional[str] = None # Optional: e.g., "LEFT_TO_RIGHT"
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@dataclass
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class ImageProperties:
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"""Stores the original dimensions of the processed image."""
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width: int
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height: int
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@dataclass
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class OcrResult:
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"""The root object for a complete OCR analysis of an image."""
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image_properties: ImageProperties
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paragraphs: List[Paragraph] = field(default_factory=list)
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def empty_post_process(text):
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return text
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def input_to_pil_image(img):
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is_path = False
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if isinstance(img, Image.Image):
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pil_image = img
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elif isinstance(img, (bytes, bytearray)):
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pil_image = Image.open(io.BytesIO(img))
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elif isinstance(img, Path):
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is_path = True
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try:
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pil_image = Image.open(img)
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pil_image.load()
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except (UnidentifiedImageError, OSError) as e:
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return None
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else:
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raise ValueError(f'img must be a path, PIL.Image or bytes object, instead got: {img}')
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return pil_image, is_path
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def pil_image_to_bytes(img, img_format='png', png_compression=6, jpeg_quality=80, optimize=False):
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if img_format == 'png' and optimized_png_encode and not optimize:
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raw_data = img.convert('RGBA').tobytes()
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image_bytes = fpng_py.fpng_encode_image_to_memory(raw_data, img.width, img.height)
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else:
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image_bytes = io.BytesIO()
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if img_format == 'jpeg':
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img = img.convert('RGB')
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img.save(image_bytes, format=img_format, compress_level=png_compression, quality=jpeg_quality, optimize=optimize, subsampling=0)
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image_bytes = image_bytes.getvalue()
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return image_bytes
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def pil_image_to_numpy_array(img):
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return np.array(img.convert('RGBA'))
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def limit_image_size(img, max_size):
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img_bytes = pil_image_to_bytes(img)
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if len(img_bytes) <= max_size:
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return img_bytes, 'png'
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scaling_factor = 0.60 if any(x > 2000 for x in img.size) else 0.75
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new_w = int(img.width * scaling_factor)
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new_h = int(img.height * scaling_factor)
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resized_img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
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resized_img_bytes = pil_image_to_bytes(resized_img)
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if len(resized_img_bytes) <= max_size:
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return resized_img_bytes, 'png'
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for _ in range(2):
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jpeg_quality = 80
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while jpeg_quality >= 60:
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img_bytes = pil_image_to_bytes(img, 'jpeg', jpeg_quality=jpeg_quality, optimize=True)
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if len(img_bytes) <= max_size:
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return img_bytes, 'jpeg'
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jpeg_quality -= 5
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img = resized_img
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return False, ''
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class MangaOcr:
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name = 'mangaocr'
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readable_name = 'Manga OCR'
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key = 'm'
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available = False
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local = True
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manual_language = False
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coordinate_support = False
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def __init__(self, config={'pretrained_model_name_or_path':'kha-white/manga-ocr-base','force_cpu': False}):
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if 'manga_ocr' not in sys.modules:
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logger.warning('manga-ocr not available, Manga OCR will not work!')
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else:
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logger.disable('manga_ocr')
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logging.getLogger('transformers').setLevel(logging.ERROR) # silence transformers >=4.46 warnings
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from manga_ocr import ocr
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ocr.post_process = empty_post_process
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logger.info(f'Loading Manga OCR model')
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self.model = MOCR(config['pretrained_model_name_or_path'], config['force_cpu'])
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self.available = True
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logger.info('Manga OCR ready')
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def __call__(self, img):
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img, is_path = input_to_pil_image(img)
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if not img:
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return (False, 'Invalid image provided')
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x = (True, self.model(img))
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if is_path:
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img.close()
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return x
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class GoogleVision:
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name = 'gvision'
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readable_name = 'Google Vision'
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key = 'g'
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available = False
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local = False
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manual_language = False
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coordinate_support = False
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def __init__(self):
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if 'google.cloud' not in sys.modules:
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logger.warning('google-cloud-vision not available, Google Vision will not work!')
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else:
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logger.info(f'Parsing Google credentials')
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google_credentials_file = os.path.join(os.path.expanduser('~'),'.config','google_vision.json')
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try:
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google_credentials = service_account.Credentials.from_service_account_file(google_credentials_file)
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self.client = vision.ImageAnnotatorClient(credentials=google_credentials)
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self.available = True
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logger.info('Google Vision ready')
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except:
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logger.warning('Error parsing Google credentials, Google Vision will not work!')
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def __call__(self, img):
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img, is_path = input_to_pil_image(img)
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if not img:
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return (False, 'Invalid image provided')
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image_bytes = self._preprocess(img)
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image = vision.Image(content=image_bytes)
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try:
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response = self.client.text_detection(image=image)
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except ServiceUnavailable:
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return (False, 'Connection error!')
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except:
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return (False, 'Unknown error!')
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texts = response.text_annotations
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res = texts[0].description if len(texts) > 0 else ''
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x = (True, res)
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if is_path:
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img.close()
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return x
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def _preprocess(self, img):
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return pil_image_to_bytes(img)
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class GoogleLens:
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name = 'glens'
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readable_name = 'Google Lens'
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key = 'l'
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available = False
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local = False
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manual_language = False
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coordinate_support = True
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def __init__(self):
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if 'betterproto' not in sys.modules:
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logger.warning('betterproto not available, Google Lens will not work!')
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else:
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self.available = True
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logger.info('Google Lens ready')
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def _to_generic_result(self, response, img_width, img_height):
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paragraphs = []
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if 'objects_response' in response and 'text' in response['objects_response']:
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text_data = response['objects_response']['text']
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if 'text_layout' in text_data:
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for p in text_data['text_layout'].get('paragraphs', []):
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lines = []
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for l in p.get('lines', []):
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words = []
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for w in l.get('words', []):
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w_bbox = w.get('geometry', {}).get('bounding_box', {})
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word = Word(
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text=w.get('plain_text', ''),
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separator=w.get('text_separator'),
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bounding_box=BoundingBox(
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center_x=w_bbox.get('center_x'),
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center_y=w_bbox.get('center_y'),
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width=w_bbox.get('width'),
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height=w_bbox.get('height'),
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rotation_z=w_bbox.get('rotation_z')
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)
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)
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words.append(word)
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l_bbox = l.get('geometry', {}).get('bounding_box', {})
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line = Line(
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bounding_box=BoundingBox(
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center_x=l_bbox.get('center_x'),
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center_y=l_bbox.get('center_y'),
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width=l_bbox.get('width'),
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height=l_bbox.get('height'),
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rotation_z=l_bbox.get('rotation_z')
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),
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words=words
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)
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lines.append(line)
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p_bbox = p.get('geometry', {}).get('bounding_box', {})
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paragraph = Paragraph(
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bounding_box=BoundingBox(
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center_x=p_bbox.get('center_x'),
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center_y=p_bbox.get('center_y'),
|
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width=p_bbox.get('width'),
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height=p_bbox.get('height'),
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|
rotation_z=p_bbox.get('rotation_z')
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),
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lines=lines,
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writing_direction=p.get('writing_direction')
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)
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paragraphs.append(paragraph)
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|
|
|
return OcrResult(
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image_properties=ImageProperties(width=img_width, height=img_height),
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paragraphs=paragraphs
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)
|
|
|
|
def __call__(self, img):
|
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img, is_path = input_to_pil_image(img)
|
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if not img:
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return (False, 'Invalid image provided')
|
|
|
|
request = LensOverlayServerRequest()
|
|
|
|
request.objects_request.request_context.request_id.uuid = random.randint(0, 2**64 - 1)
|
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request.objects_request.request_context.request_id.sequence_id = 0
|
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request.objects_request.request_context.request_id.image_sequence_id = 0
|
|
request.objects_request.request_context.request_id.analytics_id = random.randbytes(16)
|
|
request.objects_request.request_context.request_id.routing_info = LensOverlayRoutingInfo()
|
|
|
|
request.objects_request.request_context.client_context.platform = Platform.WEB
|
|
request.objects_request.request_context.client_context.surface = Surface.CHROMIUM
|
|
|
|
request.objects_request.request_context.client_context.locale_context.language = 'ja'
|
|
request.objects_request.request_context.client_context.locale_context.region = 'Asia/Tokyo'
|
|
request.objects_request.request_context.client_context.locale_context.time_zone = '' # not set by chromium
|
|
|
|
request.objects_request.request_context.client_context.app_id = '' # not set by chromium
|
|
|
|
filter = AppliedFilter()
|
|
filter.filter_type = LensOverlayFilterType.AUTO_FILTER
|
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request.objects_request.request_context.client_context.client_filters.filter.append(filter)
|
|
|
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image_data = self._preprocess(img)
|
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request.objects_request.image_data.payload.image_bytes = image_data[0]
|
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request.objects_request.image_data.image_metadata.width = image_data[1]
|
|
request.objects_request.image_data.image_metadata.height = image_data[2]
|
|
|
|
payload = request.SerializeToString()
|
|
|
|
headers = {
|
|
'Host': 'lensfrontend-pa.googleapis.com',
|
|
'Connection': 'keep-alive',
|
|
'Content-Type': 'application/x-protobuf',
|
|
'X-Goog-Api-Key': 'AIzaSyDr2UxVnv_U85AbhhY8XSHSIavUW0DC-sY',
|
|
'Sec-Fetch-Site': 'none',
|
|
'Sec-Fetch-Mode': 'no-cors',
|
|
'Sec-Fetch-Dest': 'empty',
|
|
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36',
|
|
'Accept-Encoding': 'gzip, deflate, br, zstd',
|
|
'Accept-Language': 'ja-JP;q=0.6,ja;q=0.5'
|
|
}
|
|
|
|
try:
|
|
res = requests.post('https://lensfrontend-pa.googleapis.com/v1/crupload', data=payload, headers=headers, timeout=20)
|
|
except requests.exceptions.Timeout:
|
|
return (False, 'Request timeout!')
|
|
except requests.exceptions.ConnectionError:
|
|
return (False, 'Connection error!')
|
|
|
|
if res.status_code != 200:
|
|
return (False, 'Unknown error!')
|
|
|
|
response_proto = LensOverlayServerResponse().FromString(res.content)
|
|
response_dict = response_proto.to_dict(betterproto.Casing.SNAKE)
|
|
|
|
ocr_result = self._to_generic_result(response_dict, img.width, img.height)
|
|
x = (True, ocr_result)
|
|
|
|
if is_path:
|
|
img.close()
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
if img.width * img.height > 3000000:
|
|
aspect_ratio = img.width / img.height
|
|
new_w = int(sqrt(3000000 * aspect_ratio))
|
|
new_h = int(new_w / aspect_ratio)
|
|
img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
|
|
|
|
return (pil_image_to_bytes(img), img.width, img.height)
|
|
|
|
class GoogleLensWeb:
|
|
name = 'glensweb'
|
|
readable_name = 'Google Lens (web)'
|
|
key = 'k'
|
|
available = False
|
|
local = False
|
|
manual_language = False
|
|
coordinate_support = False
|
|
|
|
def __init__(self):
|
|
if 'pyjson5' not in sys.modules:
|
|
logger.warning('pyjson5 not available, Google Lens (web) will not work!')
|
|
else:
|
|
self.requests_session = requests.Session()
|
|
self.available = True
|
|
logger.info('Google Lens (web) ready')
|
|
|
|
def __call__(self, img):
|
|
img, is_path = input_to_pil_image(img)
|
|
if not img:
|
|
return (False, 'Invalid image provided')
|
|
|
|
url = 'https://lens.google.com/v3/upload'
|
|
files = {'encoded_image': ('image.png', self._preprocess(img), 'image/png')}
|
|
headers = {
|
|
'Host': 'lens.google.com',
|
|
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:136.0) Gecko/20100101 Firefox/136.0',
|
|
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
|
|
'Accept-Language': 'ja-JP;q=0.6,ja;q=0.5',
|
|
'Accept-Encoding': 'gzip, deflate, br, zstd',
|
|
'Referer': 'https://www.google.com/',
|
|
'Origin': 'https://www.google.com',
|
|
'Alt-Used': 'lens.google.com',
|
|
'Connection': 'keep-alive',
|
|
'Upgrade-Insecure-Requests': '1',
|
|
'Sec-Fetch-Dest': 'document',
|
|
'Sec-Fetch-Mode': 'navigate',
|
|
'Sec-Fetch-Site': 'same-site',
|
|
'Priority': 'u=0, i',
|
|
'TE': 'trailers'
|
|
}
|
|
cookies = {'SOCS': 'CAESEwgDEgk0ODE3Nzk3MjQaAmVuIAEaBgiA_LyaBg'}
|
|
|
|
try:
|
|
res = self.requests_session.post(url, files=files, headers=headers, cookies=cookies, timeout=20, allow_redirects=False)
|
|
except requests.exceptions.Timeout:
|
|
return (False, 'Request timeout!')
|
|
except requests.exceptions.ConnectionError:
|
|
return (False, 'Connection error!')
|
|
|
|
if res.status_code != 303:
|
|
return (False, 'Unknown error!')
|
|
|
|
redirect_url = res.headers.get('Location')
|
|
if not redirect_url:
|
|
return (False, 'Error getting redirect URL!')
|
|
|
|
parsed_url = urlparse(redirect_url)
|
|
query_params = parse_qs(parsed_url.query)
|
|
|
|
if ('vsrid' not in query_params) or ('gsessionid' not in query_params):
|
|
return (False, 'Unknown error!')
|
|
|
|
try:
|
|
res = self.requests_session.get(f"https://lens.google.com/qfmetadata?vsrid={query_params['vsrid'][0]}&gsessionid={query_params['gsessionid'][0]}", timeout=20)
|
|
except requests.exceptions.Timeout:
|
|
return (False, 'Request timeout!')
|
|
except requests.exceptions.ConnectionError:
|
|
return (False, 'Connection error!')
|
|
|
|
if (len(res.text.splitlines()) != 3):
|
|
return (False, 'Unknown error!')
|
|
|
|
lens_object = pyjson5.loads(res.text.splitlines()[2])
|
|
|
|
res = []
|
|
text = lens_object[0][2][0][0]
|
|
for paragraph in text:
|
|
for line in paragraph[1]:
|
|
for word in line[0]:
|
|
res.append(word[1] + word[2])
|
|
|
|
x = (True, res)
|
|
|
|
if is_path:
|
|
img.close()
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
if img.width * img.height > 3000000:
|
|
aspect_ratio = img.width / img.height
|
|
new_w = int(sqrt(3000000 * aspect_ratio))
|
|
new_h = int(new_w / aspect_ratio)
|
|
img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
|
|
|
|
return pil_image_to_bytes(img)
|
|
|
|
class Bing:
|
|
name = 'bing'
|
|
readable_name = 'Bing'
|
|
key = 'b'
|
|
available = False
|
|
local = False
|
|
manual_language = False
|
|
coordinate_support = True
|
|
|
|
def __init__(self):
|
|
self.requests_session = requests.Session()
|
|
self.available = True
|
|
logger.info('Bing ready')
|
|
|
|
def _quad_to_center_bbox(self, quad):
|
|
center_x = (quad['topLeft']['x'] + quad['topRight']['x'] + quad['bottomRight']['x'] + quad['bottomLeft']['x']) / 4
|
|
center_y = (quad['topLeft']['y'] + quad['topRight']['y'] + quad['bottomRight']['y'] + quad['bottomLeft']['y']) / 4
|
|
|
|
width1 = sqrt((quad['topRight']['x'] - quad['topLeft']['x'])**2 + (quad['topRight']['y'] - quad['topLeft']['y'])**2)
|
|
width2 = sqrt((quad['bottomRight']['x'] - quad['bottomLeft']['x'])**2 + (quad['bottomRight']['y'] - quad['bottomLeft']['y'])**2)
|
|
avg_width = (width1 + width2) / 2
|
|
|
|
height1 = sqrt((quad['bottomLeft']['x'] - quad['topLeft']['x'])**2 + (quad['bottomLeft']['y'] - quad['topLeft']['y'])**2)
|
|
height2 = sqrt((quad['bottomRight']['x'] - quad['topRight']['x'])**2 + (quad['bottomRight']['y'] - quad['topRight']['y'])**2)
|
|
avg_height = (height1 + height2) / 2
|
|
|
|
return BoundingBox(center_x=center_x, center_y=center_y, width=avg_width, height=avg_height)
|
|
|
|
def _to_generic_result(self, response, img_width, img_height):
|
|
paragraphs = []
|
|
text_tag = None
|
|
for tag in response.get('tags', []):
|
|
if tag.get('displayName') == '##TextRecognition':
|
|
text_tag = tag
|
|
break
|
|
|
|
if text_tag:
|
|
text_action = None
|
|
for action in text_tag.get('actions', []):
|
|
if action.get('_type') == 'ImageKnowledge/TextRecognitionAction':
|
|
text_action = action
|
|
break
|
|
|
|
if text_action:
|
|
for p in text_action.get('data', {}).get('regions', []):
|
|
lines = []
|
|
for l in p.get('lines', []):
|
|
words = []
|
|
for w in l.get('words', []):
|
|
word = Word(
|
|
text=w.get('text', ''),
|
|
bounding_box=self._quad_to_center_bbox(w['boundingBox'])
|
|
)
|
|
words.append(word)
|
|
|
|
line = Line(
|
|
text=l.get('text', ''),
|
|
bounding_box=self._quad_to_center_bbox(l['boundingBox']),
|
|
words=words
|
|
)
|
|
lines.append(line)
|
|
|
|
# Bing doesn't provide paragraph-level separators, so we add a newline
|
|
if lines and lines[-1].words:
|
|
lines[-1].words[-1].separator = '\n'
|
|
|
|
paragraph = Paragraph(
|
|
bounding_box=self._quad_to_center_bbox(p['boundingBox']),
|
|
lines=lines
|
|
)
|
|
paragraphs.append(paragraph)
|
|
|
|
return OcrResult(
|
|
image_properties=ImageProperties(width=img_width, height=img_height),
|
|
paragraphs=paragraphs
|
|
)
|
|
|
|
def __call__(self, img):
|
|
img, is_path = input_to_pil_image(img)
|
|
if not img:
|
|
return (False, 'Invalid image provided')
|
|
|
|
img_bytes = self._preprocess(img)
|
|
if not img_bytes:
|
|
return (False, 'Image is too big!')
|
|
|
|
upload_url = 'https://www.bing.com/images/search?view=detailv2&iss=sbiupload'
|
|
upload_headers = {
|
|
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
|
|
'accept-language': 'ja-JP;q=0.6,ja;q=0.5',
|
|
'cache-control': 'max-age=0',
|
|
'origin': 'https://www.bing.com',
|
|
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:136.0) Gecko/20100101 Firefox/136.0',
|
|
}
|
|
files = {
|
|
'imgurl': (None, ''),
|
|
'cbir': (None, 'sbi'),
|
|
'imageBin': (None, img_bytes)
|
|
}
|
|
|
|
for _ in range(2):
|
|
api_host = urlparse(upload_url).netloc
|
|
try:
|
|
res = self.requests_session.post(upload_url, headers=upload_headers, files=files, timeout=20, allow_redirects=False)
|
|
except requests.exceptions.Timeout:
|
|
return (False, 'Request timeout!')
|
|
except requests.exceptions.ConnectionError:
|
|
return (False, 'Connection error!')
|
|
|
|
if res.status_code != 302:
|
|
return (False, 'Unknown error!')
|
|
|
|
redirect_url = res.headers.get('Location')
|
|
if not redirect_url:
|
|
return (False, 'Error getting redirect URL!')
|
|
if not redirect_url.startswith('https://'):
|
|
break
|
|
upload_url = redirect_url
|
|
|
|
parsed_url = urlparse(redirect_url)
|
|
query_params = parse_qs(parsed_url.query)
|
|
|
|
image_insights_token = query_params.get('insightsToken')
|
|
if not image_insights_token:
|
|
return (False, 'Error getting token!')
|
|
image_insights_token = image_insights_token[0]
|
|
|
|
api_url = f'https://{api_host}/images/api/custom/knowledge'
|
|
api_headers = {
|
|
'accept': '*/*',
|
|
'accept-language': 'ja-JP;q=0.6,ja;q=0.5',
|
|
'origin': 'https://www.bing.com',
|
|
'referer': f'https://www.bing.com/images/search?view=detailV2&insightstoken={image_insights_token}',
|
|
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:136.0) Gecko/20100101 Firefox/136.0',
|
|
}
|
|
api_data_json = {
|
|
'imageInfo': {'imageInsightsToken': image_insights_token, 'source': 'Url'},
|
|
'knowledgeRequest': {'invokedSkills': ['OCR'], 'index': 1}
|
|
}
|
|
files = {
|
|
'knowledgeRequest': (None, json.dumps(api_data_json), 'application/json')
|
|
}
|
|
|
|
try:
|
|
res = self.requests_session.post(api_url, headers=api_headers, files=files, timeout=20)
|
|
except requests.exceptions.Timeout:
|
|
return (False, 'Request timeout!')
|
|
except requests.exceptions.ConnectionError:
|
|
return (False, 'Connection error!')
|
|
|
|
if res.status_code != 200:
|
|
return (False, 'Unknown error!')
|
|
|
|
data = res.json()
|
|
|
|
ocr_result = self._to_generic_result(data, img.width, img.height)
|
|
x = (True, ocr_result)
|
|
|
|
if is_path:
|
|
img.close()
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
max_pixel_size = 4000
|
|
max_byte_size = 767772
|
|
res = None
|
|
|
|
if any(x > max_pixel_size for x in img.size):
|
|
resize_factor = min(max_pixel_size / img.width, max_pixel_size / img.height)
|
|
new_w = int(img.width * resize_factor)
|
|
new_h = int(img.height * resize_factor)
|
|
img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
|
|
|
|
img_bytes, _ = limit_image_size(img, max_byte_size)
|
|
|
|
if img_bytes:
|
|
res = base64.b64encode(img_bytes).decode('utf-8')
|
|
|
|
return res
|
|
|
|
class AppleVision:
|
|
name = 'avision'
|
|
readable_name = 'Apple Vision'
|
|
key = 'a'
|
|
available = False
|
|
local = True
|
|
manual_language = True
|
|
coordinate_support = False
|
|
|
|
def __init__(self, language='ja'):
|
|
if sys.platform != 'darwin':
|
|
logger.warning('Apple Vision is not supported on non-macOS platforms!')
|
|
elif int(platform.mac_ver()[0].split('.')[0]) < 13:
|
|
logger.warning('Apple Vision is not supported on macOS older than Ventura/13.0!')
|
|
else:
|
|
self.available = True
|
|
self.language = [language, 'en']
|
|
logger.info('Apple Vision ready')
|
|
|
|
def __call__(self, img):
|
|
img, is_path = input_to_pil_image(img)
|
|
if not img:
|
|
return (False, 'Invalid image provided')
|
|
|
|
with objc.autorelease_pool():
|
|
req = Vision.VNRecognizeTextRequest.alloc().init()
|
|
|
|
req.setRevision_(Vision.VNRecognizeTextRequestRevision3)
|
|
req.setRecognitionLevel_(Vision.VNRequestTextRecognitionLevelAccurate)
|
|
req.setUsesLanguageCorrection_(True)
|
|
req.setRecognitionLanguages_(self.language)
|
|
|
|
handler = Vision.VNImageRequestHandler.alloc().initWithData_options_(
|
|
self._preprocess(img), None
|
|
)
|
|
|
|
success = handler.performRequests_error_([req], None)
|
|
res = []
|
|
if success[0]:
|
|
for result in req.results():
|
|
res.append(result.text())
|
|
x = (True, res)
|
|
else:
|
|
x = (False, 'Unknown error!')
|
|
|
|
if is_path:
|
|
img.close()
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
return pil_image_to_bytes(img, 'tiff')
|
|
|
|
class AppleLiveText:
|
|
name = 'alivetext'
|
|
readable_name = 'Apple Live Text'
|
|
key = 'd'
|
|
available = False
|
|
local = True
|
|
manual_language = True
|
|
coordinate_support = True
|
|
|
|
def __init__(self, language='ja'):
|
|
if sys.platform != 'darwin':
|
|
logger.warning('Apple Live Text is not supported on non-macOS platforms!')
|
|
elif int(platform.mac_ver()[0].split('.')[0]) < 13:
|
|
logger.warning('Apple Live Text is not supported on macOS older than Ventura/13.0!')
|
|
else:
|
|
app_info = NSBundle.mainBundle().infoDictionary()
|
|
app_info['LSBackgroundOnly'] = '1'
|
|
self.VKCImageAnalyzer = objc.lookUpClass('VKCImageAnalyzer')
|
|
self.VKCImageAnalyzerRequest = objc.lookUpClass('VKCImageAnalyzerRequest')
|
|
objc.registerMetaDataForSelector(
|
|
b'VKCImageAnalyzer',
|
|
b'processRequest:progressHandler:completionHandler:',
|
|
{
|
|
'arguments': {
|
|
3: {
|
|
'callable': {
|
|
'retval': {'type': b'v'},
|
|
'arguments': {
|
|
0: {'type': b'^v'},
|
|
1: {'type': b'd'},
|
|
}
|
|
}
|
|
},
|
|
4: {
|
|
'callable': {
|
|
'retval': {'type': b'v'},
|
|
'arguments': {
|
|
0: {'type': b'^v'},
|
|
1: {'type': b'@'},
|
|
2: {'type': b'@'},
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
)
|
|
self.language = [language, 'en']
|
|
self.available = True
|
|
logger.info('Apple Live Text ready')
|
|
|
|
def __call__(self, img):
|
|
img, is_path = input_to_pil_image(img)
|
|
if not img:
|
|
return (False, 'Invalid image provided')
|
|
|
|
self.result = None
|
|
|
|
with objc.autorelease_pool():
|
|
analyzer = self.VKCImageAnalyzer.alloc().init()
|
|
req = self.VKCImageAnalyzerRequest.alloc().initWithImage_requestType_(self._preprocess(img), 1) #VKAnalysisTypeText
|
|
req.setLocales_(self.language)
|
|
analyzer.processRequest_progressHandler_completionHandler_(req, lambda progress: None, self._process)
|
|
|
|
CFRunLoopRunInMode(kCFRunLoopDefaultMode, 10.0, False)
|
|
|
|
if self.result == None:
|
|
return (False, 'Unknown error!')
|
|
|
|
ocr_response = OcrResult(
|
|
image_properties=ImageProperties(width=img.width, height=img.height),
|
|
paragraphs=self.result
|
|
)
|
|
x = (True, ocr_response)
|
|
|
|
if is_path:
|
|
img.close()
|
|
return x
|
|
|
|
def _process(self, analysis, error):
|
|
lines = []
|
|
response_lines = analysis.allLines()
|
|
if response_lines:
|
|
for l in response_lines:
|
|
words = []
|
|
for i, w in enumerate(l.children()):
|
|
w_bbox = w.quad().boundingBox()
|
|
word = Word(
|
|
text=w.string(),
|
|
bounding_box=BoundingBox(
|
|
width=w_bbox.size.width,
|
|
height=w_bbox.size.height,
|
|
center_x=w_bbox.origin.x + (w_bbox.size.width / 2),
|
|
center_y=w_bbox.origin.y + (w_bbox.size.height / 2),
|
|
rotation_z=0.0
|
|
)
|
|
)
|
|
words.append(word)
|
|
|
|
l_bbox = l.quad().boundingBox()
|
|
line = Line(
|
|
text=l.string(),
|
|
bounding_box=BoundingBox(
|
|
width=l_bbox.size.width,
|
|
height=l_bbox.size.height,
|
|
center_x=l_bbox.origin.x + (l_bbox.size.width / 2),
|
|
center_y=l_bbox.origin.y + (l_bbox.size.height / 2),
|
|
rotation_z=0.0
|
|
),
|
|
words=words
|
|
)
|
|
lines.append(line)
|
|
|
|
# Create a single paragraph to hold all lines
|
|
if lines:
|
|
# Approximate paragraph bbox by combining all line bboxes
|
|
all_line_bboxes = [l.bounding_box for l in lines]
|
|
min_x = min(b.center_x - b.width / 2 for b in all_line_bboxes)
|
|
max_x = max(b.center_x + b.width / 2 for b in all_line_bboxes)
|
|
min_y = min(b.center_y - b.height / 2 for b in all_line_bboxes)
|
|
max_y = max(b.center_y + b.height / 2 for b in all_line_bboxes)
|
|
|
|
p_bbox = BoundingBox(
|
|
center_x=(min_x + max_x) / 2,
|
|
center_y=(min_y + max_y) / 2,
|
|
width=max_x - min_x,
|
|
height=max_y - min_y
|
|
)
|
|
paragraph = Paragraph(bounding_box=p_bbox, lines=lines)
|
|
paragraphs = [paragraph]
|
|
else:
|
|
paragraphs = []
|
|
|
|
self.result = paragraphs
|
|
CFRunLoopStop(CFRunLoopGetCurrent())
|
|
|
|
def _preprocess(self, img):
|
|
image_bytes = pil_image_to_bytes(img, 'tiff')
|
|
ns_data = NSData.dataWithBytes_length_(image_bytes, len(image_bytes))
|
|
ns_image = NSImage.alloc().initWithData_(ns_data)
|
|
return ns_image
|
|
|
|
class WinRTOCR:
|
|
name = 'winrtocr'
|
|
readable_name = 'WinRT OCR'
|
|
key = 'w'
|
|
available = False
|
|
local = True
|
|
manual_language = True
|
|
coordinate_support = False
|
|
|
|
def __init__(self, config={}, language='ja'):
|
|
if sys.platform == 'win32':
|
|
if int(platform.release()) < 10:
|
|
logger.warning('WinRT OCR is not supported on Windows older than 10!')
|
|
elif 'winocr' not in sys.modules:
|
|
logger.warning('winocr not available, WinRT OCR will not work!')
|
|
else:
|
|
self.language = language
|
|
self.available = True
|
|
logger.info('WinRT OCR ready')
|
|
else:
|
|
try:
|
|
self.url = config['url']
|
|
self.language = language
|
|
self.available = True
|
|
logger.info('WinRT OCR ready')
|
|
except:
|
|
logger.warning('Error reading URL from config, WinRT OCR will not work!')
|
|
|
|
def __call__(self, img):
|
|
img, is_path = input_to_pil_image(img)
|
|
if not img:
|
|
return (False, 'Invalid image provided')
|
|
|
|
if sys.platform == 'win32':
|
|
res = winocr.recognize_pil_sync(img, lang=self.language)['text']
|
|
else:
|
|
params = {'lang': self.language}
|
|
try:
|
|
res = requests.post(self.url, params=params, data=self._preprocess(img), timeout=3)
|
|
except requests.exceptions.Timeout:
|
|
return (False, 'Request timeout!')
|
|
except requests.exceptions.ConnectionError:
|
|
return (False, 'Connection error!')
|
|
|
|
if res.status_code != 200:
|
|
return (False, 'Unknown error!')
|
|
|
|
res = res.json()['text']
|
|
|
|
x = (True, res)
|
|
|
|
if is_path:
|
|
img.close()
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
return pil_image_to_bytes(img, png_compression=1)
|
|
|
|
class OneOCR:
|
|
name = 'oneocr'
|
|
readable_name = 'OneOCR'
|
|
key = 'z'
|
|
available = False
|
|
local = True
|
|
manual_language = False
|
|
coordinate_support = True
|
|
|
|
def __init__(self, config={}):
|
|
if sys.platform == 'win32':
|
|
if int(platform.release()) < 10:
|
|
logger.warning('OneOCR is not supported on Windows older than 10!')
|
|
elif 'oneocr' not in sys.modules:
|
|
logger.warning('oneocr not available, OneOCR will not work!')
|
|
else:
|
|
try:
|
|
self.model = oneocr.OcrEngine()
|
|
except RuntimeError as e:
|
|
logger.warning(e + ', OneOCR will not work!')
|
|
else:
|
|
self.available = True
|
|
logger.info('OneOCR ready')
|
|
else:
|
|
try:
|
|
self.url = config['url']
|
|
self.available = True
|
|
logger.info('OneOCR ready')
|
|
except:
|
|
logger.warning('Error reading URL from config, OneOCR will not work!')
|
|
|
|
def _pixel_quad_to_center_bbox(self, rect, img_width, img_height):
|
|
x_coords = [rect['x1'], rect['x2'], rect['x3'], rect['x4']]
|
|
y_coords = [rect['y1'], rect['y2'], rect['y3'], rect['y4']]
|
|
|
|
center_x_px = sum(x_coords) / 4
|
|
center_y_px = sum(y_coords) / 4
|
|
|
|
width_px = (abs(rect['x2'] - rect['x1']) + abs(rect['x3'] - rect['x4'])) / 2
|
|
height_px = (abs(rect['y4'] - rect['y1']) + abs(rect['y3'] - rect['y2'])) / 2
|
|
|
|
return BoundingBox(
|
|
center_x=center_x_px / img_width,
|
|
center_y=center_y_px / img_height,
|
|
width=width_px / img_width,
|
|
height=height_px / img_height
|
|
)
|
|
|
|
def _to_generic_result(self, response, img_width, img_height):
|
|
lines = []
|
|
for l in response.get('lines', []):
|
|
words = []
|
|
for i, w in enumerate(l.get('words', [])):
|
|
word = Word(
|
|
text=w.get('text', ''),
|
|
bounding_box=self._pixel_quad_to_center_bbox(w['bounding_rect'], img_width, img_height)
|
|
)
|
|
words.append(word)
|
|
|
|
line = Line(
|
|
text=l.get('text', ''),
|
|
bounding_box=self._pixel_quad_to_center_bbox(l['bounding_rect'], img_width, img_height),
|
|
words=words
|
|
)
|
|
lines.append(line)
|
|
|
|
# Create a single paragraph to hold all lines
|
|
if lines:
|
|
# Approximate paragraph bbox by combining all line bboxes
|
|
all_line_bboxes = [l.bounding_box for l in lines]
|
|
min_x = min(b.center_x - b.width / 2 for b in all_line_bboxes)
|
|
max_x = max(b.center_x + b.width / 2 for b in all_line_bboxes)
|
|
min_y = min(b.center_y - b.height / 2 for b in all_line_bboxes)
|
|
max_y = max(b.center_y + b.height / 2 for b in all_line_bboxes)
|
|
|
|
p_bbox = BoundingBox(
|
|
center_x=(min_x + max_x) / 2,
|
|
center_y=(min_y + max_y) / 2,
|
|
width=max_x - min_x,
|
|
height=max_y - min_y
|
|
)
|
|
paragraph = Paragraph(bounding_box=p_bbox, lines=lines)
|
|
paragraphs = [paragraph]
|
|
else:
|
|
paragraphs = []
|
|
|
|
return OcrResult(
|
|
image_properties=ImageProperties(width=img_width, height=img_height),
|
|
paragraphs=paragraphs
|
|
)
|
|
|
|
def __call__(self, img):
|
|
img, is_path = input_to_pil_image(img)
|
|
if not img:
|
|
return (False, 'Invalid image provided')
|
|
|
|
if sys.platform == 'win32':
|
|
try:
|
|
raw_res = self.model.recognize_pil(img)
|
|
except RuntimeError as e:
|
|
return (False, e)
|
|
else:
|
|
try:
|
|
res = requests.post(self.url, data=self._preprocess(img), timeout=3)
|
|
except requests.exceptions.Timeout:
|
|
return (False, 'Request timeout!')
|
|
except requests.exceptions.ConnectionError:
|
|
return (False, 'Connection error!')
|
|
|
|
if res.status_code != 200:
|
|
return (False, 'Unknown error!')
|
|
|
|
raw_res = res.json()
|
|
|
|
ocr_response = self._to_generic_result(raw_res, img.width, img.height)
|
|
x = (True, ocr_response)
|
|
|
|
if is_path:
|
|
img.close()
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
return pil_image_to_bytes(img, png_compression=1)
|
|
|
|
class AzureImageAnalysis:
|
|
name = 'azure'
|
|
readable_name = 'Azure Image Analysis'
|
|
key = 'v'
|
|
available = False
|
|
local = False
|
|
manual_language = False
|
|
coordinate_support = False
|
|
|
|
def __init__(self, config={}):
|
|
if 'azure.ai.vision.imageanalysis' not in sys.modules:
|
|
logger.warning('azure-ai-vision-imageanalysis not available, Azure Image Analysis will not work!')
|
|
else:
|
|
logger.info(f'Parsing Azure credentials')
|
|
try:
|
|
self.client = ImageAnalysisClient(config['endpoint'], AzureKeyCredential(config['api_key']))
|
|
self.available = True
|
|
logger.info('Azure Image Analysis ready')
|
|
except:
|
|
logger.warning('Error parsing Azure credentials, Azure Image Analysis will not work!')
|
|
|
|
def __call__(self, img):
|
|
img, is_path = input_to_pil_image(img)
|
|
if not img:
|
|
return (False, 'Invalid image provided')
|
|
|
|
try:
|
|
read_result = self.client.analyze(image_data=self._preprocess(img), visual_features=[VisualFeatures.READ])
|
|
except ServiceRequestError:
|
|
return (False, 'Connection error!')
|
|
except:
|
|
return (False, 'Unknown error!')
|
|
|
|
res = []
|
|
if read_result.read:
|
|
for block in read_result.read.blocks:
|
|
for line in block.lines:
|
|
res.append(line.text)
|
|
else:
|
|
return (False, 'Unknown error!')
|
|
|
|
x = (True, res)
|
|
|
|
if is_path:
|
|
img.close()
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
if any(x < 50 for x in img.size):
|
|
resize_factor = max(50 / img.width, 50 / img.height)
|
|
new_w = int(img.width * resize_factor)
|
|
new_h = int(img.height * resize_factor)
|
|
img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
|
|
|
|
return pil_image_to_bytes(img)
|
|
|
|
class EasyOCR:
|
|
name = 'easyocr'
|
|
readable_name = 'EasyOCR'
|
|
key = 'e'
|
|
available = False
|
|
local = True
|
|
manual_language = True
|
|
coordinate_support = False
|
|
|
|
def __init__(self, config={'gpu': True}, language='ja'):
|
|
if 'easyocr' not in sys.modules:
|
|
logger.warning('easyocr not available, EasyOCR will not work!')
|
|
else:
|
|
logger.info('Loading EasyOCR model')
|
|
logging.getLogger('easyocr.easyocr').setLevel(logging.ERROR)
|
|
self.model = easyocr.Reader([language,'en'], gpu=config['gpu'])
|
|
self.available = True
|
|
logger.info('EasyOCR ready')
|
|
|
|
def __call__(self, img):
|
|
img, is_path = input_to_pil_image(img)
|
|
if not img:
|
|
return (False, 'Invalid image provided')
|
|
|
|
res = []
|
|
read_result = self.model.readtext(self._preprocess(img), detail=0)
|
|
for text in read_result:
|
|
res.append(text)
|
|
|
|
x = (True, res)
|
|
|
|
if is_path:
|
|
img.close()
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
return pil_image_to_numpy_array(img)
|
|
|
|
class RapidOCR:
|
|
name = 'rapidocr'
|
|
readable_name = 'RapidOCR'
|
|
key = 'r'
|
|
available = False
|
|
local = True
|
|
manual_language = True
|
|
coordinate_support = False
|
|
|
|
def __init__(self, config={'high_accuracy_detection': False, 'high_accuracy_recognition': True}, language='ja'):
|
|
if 'rapidocr' not in sys.modules:
|
|
logger.warning('rapidocr not available, RapidOCR will not work!')
|
|
else:
|
|
logger.info('Loading RapidOCR model')
|
|
lang_rec = self.language_to_model_language(language)
|
|
self.model = ROCR(params={
|
|
'Det.engine_type': EngineType.ONNXRUNTIME,
|
|
'Det.lang_type': LangDet.CH,
|
|
'Det.model_type': ModelType.SERVER if config['high_accuracy_detection'] else ModelType.MOBILE,
|
|
'Det.ocr_version': OCRVersion.PPOCRV5,
|
|
'Rec.engine_type': EngineType.ONNXRUNTIME,
|
|
'Rec.lang_type': lang_rec,
|
|
'Rec.model_type': ModelType.SERVER if config['high_accuracy_recognition'] else ModelType.MOBILE,
|
|
'Rec.ocr_version': OCRVersion.PPOCRV5,
|
|
'Global.log_level': 'error'
|
|
})
|
|
self.available = True
|
|
logger.info('RapidOCR ready')
|
|
|
|
def language_to_model_language(self, language):
|
|
if language == 'ja':
|
|
return LangRec.CH
|
|
if language == 'zh':
|
|
return LangRec.CH
|
|
elif language == 'ko':
|
|
return LangRec.KOREAN
|
|
elif language == 'ru':
|
|
return LangRec.ESLAV
|
|
elif language == 'el':
|
|
return LangRec.EL
|
|
elif language == 'th':
|
|
return LangRec.TH
|
|
else:
|
|
return LangRec.LATIN
|
|
|
|
def __call__(self, img):
|
|
img, is_path = input_to_pil_image(img)
|
|
if not img:
|
|
return (False, 'Invalid image provided')
|
|
|
|
res = []
|
|
read_results = self.model(self._preprocess(img))
|
|
if read_results:
|
|
for read_result in read_results.txts:
|
|
res.append(read_result)
|
|
|
|
x = (True, res)
|
|
|
|
if is_path:
|
|
img.close()
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
return pil_image_to_numpy_array(img)
|
|
|
|
class OCRSpace:
|
|
name = 'ocrspace'
|
|
readable_name = 'OCRSpace'
|
|
key = 'o'
|
|
available = False
|
|
local = False
|
|
manual_language = True
|
|
coordinate_support = False
|
|
|
|
def __init__(self, config={}, language='ja'):
|
|
try:
|
|
self.api_key = config['api_key']
|
|
self.max_byte_size = config.get('file_size_limit', 1000000)
|
|
self.engine_version = config.get('engine_version', 2)
|
|
self.language = self.language_to_model_language(language)
|
|
self.available = True
|
|
logger.info('OCRSpace ready')
|
|
except:
|
|
logger.warning('Error reading API key from config, OCRSpace will not work!')
|
|
|
|
def language_to_model_language(self, language):
|
|
if language == 'ja':
|
|
return 'jpn'
|
|
if language == 'zh':
|
|
return 'chs'
|
|
elif language == 'ko':
|
|
return 'kor'
|
|
elif language == 'ar':
|
|
return 'ara'
|
|
elif language == 'ru':
|
|
return 'rus'
|
|
elif language == 'el':
|
|
return 'gre'
|
|
elif language == 'th':
|
|
return 'tha'
|
|
else:
|
|
return 'auto'
|
|
|
|
def __call__(self, img):
|
|
img, is_path = input_to_pil_image(img)
|
|
if not img:
|
|
return (False, 'Invalid image provided')
|
|
|
|
img_bytes, img_extension = self._preprocess(img)
|
|
if not img_bytes:
|
|
return (False, 'Image is too big!')
|
|
|
|
data = {
|
|
'apikey': self.api_key,
|
|
'language': self.language,
|
|
'OCREngine': str(self.engine_version)
|
|
}
|
|
files = {'file': ('image.' + img_extension, img_bytes, 'image/' + img_extension)}
|
|
|
|
try:
|
|
res = requests.post('https://api.ocr.space/parse/image', data=data, files=files, timeout=20)
|
|
except requests.exceptions.Timeout:
|
|
return (False, 'Request timeout!')
|
|
except requests.exceptions.ConnectionError:
|
|
return (False, 'Connection error!')
|
|
|
|
if res.status_code != 200:
|
|
return (False, 'Unknown error!')
|
|
|
|
res = res.json()
|
|
|
|
if isinstance(res, str):
|
|
return (False, 'Unknown error!')
|
|
if res['IsErroredOnProcessing']:
|
|
return (False, res['ErrorMessage'])
|
|
|
|
res = res['ParsedResults'][0]['ParsedText']
|
|
x = (True, res)
|
|
|
|
if is_path:
|
|
img.close()
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
return limit_image_size(img, self.max_byte_size)
|