972 lines
36 KiB
Python
972 lines
36 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
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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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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_onnxruntime import RapidOCR as ROCR
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import urllib.request
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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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def empty_post_process(text):
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return text
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def post_process(text):
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text = ' '.join([''.join(i.split()) for i in text.splitlines()])
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text = text.replace('…', '...')
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text = re.sub('[・.]{2,}', lambda x: (x.end() - x.start()) * '.', text)
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text = jaconv.h2z(text, ascii=True, digit=True)
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return text
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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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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_or_path):
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if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
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img = Image.open(img_or_path)
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elif isinstance(img_or_path, Image.Image):
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img = img_or_path
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else:
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raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
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x = (True, self.model(img))
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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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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_or_path):
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if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
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img = Image.open(img_or_path)
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elif isinstance(img_or_path, Image.Image):
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img = img_or_path
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else:
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raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
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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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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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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 __call__(self, img_or_path):
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if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
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img = Image.open(img_or_path)
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elif isinstance(img_or_path, Image.Image):
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img = img_or_path
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else:
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raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
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request = LensOverlayServerRequest()
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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
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request.objects_request.request_context.request_id.analytics_id = random.randbytes(16)
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request.objects_request.request_context.request_id.routing_info = LensOverlayRoutingInfo()
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request.objects_request.request_context.client_context.platform = Platform.WEB
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request.objects_request.request_context.client_context.surface = Surface.CHROMIUM
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request.objects_request.request_context.client_context.locale_context.language = 'ja'
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request.objects_request.request_context.client_context.locale_context.region = 'Asia/Tokyo'
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request.objects_request.request_context.client_context.locale_context.time_zone = '' # not set by chromium
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request.objects_request.request_context.client_context.app_id = '' # not set by chromium
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filter = AppliedFilter()
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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]
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request.objects_request.image_data.image_metadata.height = image_data[2]
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payload = request.SerializeToString()
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headers = {
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'Host': 'lensfrontend-pa.googleapis.com',
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'Connection': 'keep-alive',
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'Content-Type': 'application/x-protobuf',
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'X-Goog-Api-Key': 'AIzaSyDr2UxVnv_U85AbhhY8XSHSIavUW0DC-sY',
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'Sec-Fetch-Site': 'none',
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'Sec-Fetch-Mode': 'no-cors',
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'Sec-Fetch-Dest': 'empty',
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'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',
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'Accept-Encoding': 'gzip, deflate, br, zstd',
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'Accept-Language': 'ja-JP;q=0.6,ja;q=0.5'
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}
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try:
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res = requests.post('https://lensfrontend-pa.googleapis.com/v1/crupload', data=payload, headers=headers, timeout=20)
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except requests.exceptions.Timeout:
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return (False, 'Request timeout!')
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except requests.exceptions.ConnectionError:
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return (False, 'Connection error!')
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if res.status_code != 200:
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return (False, 'Unknown error!')
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response_proto = LensOverlayServerResponse().FromString(res.content)
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response_dict = response_proto.to_dict(betterproto.Casing.SNAKE)
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res = ''
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text = response_dict['objects_response']['text']
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if 'text_layout' in text:
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paragraphs = text['text_layout']['paragraphs']
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for paragraph in paragraphs:
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for line in paragraph['lines']:
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for word in line['words']:
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res += word['plain_text'] + word['text_separator']
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res += '\n'
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x = (True, res)
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return x
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def _preprocess(self, img):
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if img.width * img.height > 3000000:
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aspect_ratio = img.width / img.height
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new_w = int(sqrt(3000000 * aspect_ratio))
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new_h = int(new_w / aspect_ratio)
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img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
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return (pil_image_to_bytes(img), img.width, img.height)
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|
|
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class GoogleLensWeb:
|
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name = 'glensweb'
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readable_name = 'Google Lens (web)'
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key = 'k'
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available = False
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|
|
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def __init__(self):
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if 'pyjson5' not in sys.modules:
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logger.warning('pyjson5 not available, Google Lens (web) will not work!')
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else:
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self.regex = re.compile(r'(\w+)=([^&]+)')
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self.requests_session = requests.Session()
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self.available = True
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logger.info('Google Lens (web) ready')
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|
|
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def __call__(self, img_or_path):
|
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if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
|
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img = Image.open(img_or_path)
|
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elif isinstance(img_or_path, Image.Image):
|
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img = img_or_path
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else:
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raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
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|
|
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url = 'https://lens.google.com/v3/upload'
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files = {'encoded_image': ('image.png', self._preprocess(img), 'image/png')}
|
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headers = {
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'Host': 'lens.google.com',
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'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:136.0) Gecko/20100101 Firefox/136.0',
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'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
|
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'Accept-Language': 'ja-JP;q=0.6,ja;q=0.5',
|
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'Accept-Encoding': 'gzip, deflate, br, zstd',
|
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'Referer': 'https://www.google.com/',
|
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'Origin': 'https://www.google.com',
|
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'Alt-Used': 'lens.google.com',
|
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'Connection': 'keep-alive',
|
|
'Upgrade-Insecure-Requests': '1',
|
|
'Sec-Fetch-Dest': 'document',
|
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'Sec-Fetch-Mode': 'navigate',
|
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'Sec-Fetch-Site': 'same-site',
|
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'Priority': 'u=0, i',
|
|
'TE': 'trailers'
|
|
}
|
|
cookies = {'SOCS': 'CAESEwgDEgk0ODE3Nzk3MjQaAmVuIAEaBgiA_LyaBg'}
|
|
|
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try:
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res = self.requests_session.post(url, files=files, headers=headers, cookies=cookies, timeout=20, allow_redirects=False)
|
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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!')
|
|
|
|
location_params = dict(self.regex.findall(res.headers['Location']))
|
|
|
|
if ('vsrid' not in location_params) or ('gsessionid' not in location_params):
|
|
return (False, 'Unknown error!')
|
|
|
|
try:
|
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res = self.requests_session.get(f"https://lens.google.com/qfmetadata?vsrid={location_params['vsrid']}&gsessionid={location_params['gsessionid']}", 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 += word[1] + word[2]
|
|
res += '\n'
|
|
|
|
x = (True, res)
|
|
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
|
|
|
|
def __init__(self):
|
|
self.requests_session = requests.Session()
|
|
self.available = True
|
|
logger.info('Bing ready')
|
|
|
|
def __call__(self, img_or_path):
|
|
if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
|
|
img = Image.open(img_or_path)
|
|
elif isinstance(img_or_path, Image.Image):
|
|
img = img_or_path
|
|
else:
|
|
raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
|
|
|
|
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()
|
|
|
|
res = ''
|
|
text_tag = None
|
|
for tag in data['tags']:
|
|
if tag.get('displayName') == '##TextRecognition':
|
|
text_tag = tag
|
|
break
|
|
if text_tag:
|
|
text_action = None
|
|
for action in text_tag['actions']:
|
|
if action.get('_type') == 'ImageKnowledge/TextRecognitionAction':
|
|
text_action = action
|
|
break
|
|
if text_action:
|
|
regions = text_action['data'].get('regions', [])
|
|
for region in regions:
|
|
for line in region.get('lines', []):
|
|
res += line['text'] + '\n'
|
|
|
|
x = (True, res)
|
|
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 = max(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
|
|
|
|
def __init__(self):
|
|
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
|
|
logger.info('Apple Vision ready')
|
|
|
|
def __call__(self, img_or_path):
|
|
if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
|
|
img = Image.open(img_or_path)
|
|
elif isinstance(img_or_path, Image.Image):
|
|
img = img_or_path
|
|
else:
|
|
raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
|
|
|
|
with objc.autorelease_pool():
|
|
req = Vision.VNRecognizeTextRequest.alloc().init()
|
|
|
|
req.setRevision_(Vision.VNRecognizeTextRequestRevision3)
|
|
req.setRecognitionLevel_(Vision.VNRequestTextRecognitionLevelAccurate)
|
|
req.setUsesLanguageCorrection_(True)
|
|
req.setRecognitionLanguages_(['ja','en'])
|
|
|
|
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 += result.text() + '\n'
|
|
x = (True, res)
|
|
else:
|
|
x = (False, 'Unknown error!')
|
|
|
|
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
|
|
|
|
def __init__(self):
|
|
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.available = True
|
|
logger.info('Apple Live Text ready')
|
|
|
|
def __call__(self, img_or_path):
|
|
if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
|
|
img = Image.open(img_or_path)
|
|
elif isinstance(img_or_path, Image.Image):
|
|
img = img_or_path
|
|
else:
|
|
raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
|
|
|
|
with objc.autorelease_pool():
|
|
analyzer = self.VKCImageAnalyzer.alloc().init()
|
|
req = self.VKCImageAnalyzerRequest.alloc().initWithImage_requestType_(self._preprocess(img), 1) #VKAnalysisTypeText
|
|
req.setLocales_(['ja','en'])
|
|
self.result = None
|
|
analyzer.processRequest_progressHandler_completionHandler_(req, lambda progress: None, self._process)
|
|
|
|
CFRunLoopRunInMode(kCFRunLoopDefaultMode, 10.0, False)
|
|
|
|
if self.result == None:
|
|
return (False, 'Unknown error!')
|
|
return (True, self.result)
|
|
|
|
def _process(self, analysis, error):
|
|
res = ''
|
|
lines = analysis.allLines()
|
|
if lines:
|
|
for line in lines:
|
|
res += line.string() + '\n'
|
|
self.result = res
|
|
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
|
|
|
|
def __init__(self, config={}):
|
|
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.available = True
|
|
logger.info('WinRT OCR ready')
|
|
else:
|
|
try:
|
|
self.url = config['url']
|
|
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_or_path):
|
|
if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
|
|
img = Image.open(img_or_path)
|
|
elif isinstance(img_or_path, Image.Image):
|
|
img = img_or_path
|
|
else:
|
|
raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
|
|
|
|
if sys.platform == 'win32':
|
|
res = winocr.recognize_pil_sync(img, lang='ja')['text']
|
|
else:
|
|
params = {'lang': 'ja'}
|
|
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)
|
|
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
|
|
|
|
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 __call__(self, img_or_path):
|
|
if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
|
|
img = Image.open(img_or_path)
|
|
elif isinstance(img_or_path, Image.Image):
|
|
img = img_or_path
|
|
else:
|
|
raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
|
|
|
|
if sys.platform == 'win32':
|
|
try:
|
|
res = self.model.recognize_pil(img)['text']
|
|
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!')
|
|
|
|
res = res.json()['text']
|
|
|
|
x = (True, res)
|
|
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
|
|
|
|
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_or_path):
|
|
if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
|
|
img = Image.open(img_or_path)
|
|
elif isinstance(img_or_path, Image.Image):
|
|
img = img_or_path
|
|
else:
|
|
raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
|
|
|
|
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 += line.text + '\n'
|
|
else:
|
|
return (False, 'Unknown error!')
|
|
|
|
x = (True, res)
|
|
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
|
|
|
|
def __init__(self, config={'gpu': True}):
|
|
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(['ja','en'], gpu=config['gpu'])
|
|
self.available = True
|
|
logger.info('EasyOCR ready')
|
|
|
|
def __call__(self, img_or_path):
|
|
if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
|
|
img = Image.open(img_or_path)
|
|
elif isinstance(img_or_path, Image.Image):
|
|
img = img_or_path
|
|
else:
|
|
raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
|
|
|
|
res = ''
|
|
read_result = self.model.readtext(self._preprocess(img), detail=0)
|
|
for text in read_result:
|
|
res += text + '\n'
|
|
|
|
x = (True, res)
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
return pil_image_to_numpy_array(img)
|
|
|
|
class RapidOCR:
|
|
name = 'rapidocr'
|
|
readable_name = 'RapidOCR'
|
|
key = 'r'
|
|
available = False
|
|
|
|
def __init__(self):
|
|
if 'rapidocr_onnxruntime' not in sys.modules:
|
|
logger.warning('rapidocr_onnxruntime not available, RapidOCR will not work!')
|
|
else:
|
|
rapidocr_model_file = os.path.join(os.path.expanduser('~'),'.cache','rapidocr_japan_PP-OCRv4_rec_infer.onnx')
|
|
if not os.path.isfile(rapidocr_model_file):
|
|
logger.info('Downloading RapidOCR model ' + rapidocr_model_file)
|
|
try:
|
|
cache_folder = os.path.join(os.path.expanduser('~'),'.cache')
|
|
if not os.path.isdir(cache_folder):
|
|
os.makedirs(cache_folder)
|
|
urllib.request.urlretrieve('https://github.com/AuroraWright/owocr/raw/master/rapidocr_japan_PP-OCRv4_rec_infer.onnx', rapidocr_model_file)
|
|
except:
|
|
logger.warning('Download failed. RapidOCR will not work!')
|
|
return
|
|
|
|
logger.info('Loading RapidOCR model')
|
|
self.model = ROCR(rec_model_path=rapidocr_model_file)
|
|
logging.getLogger().setLevel(logging.ERROR)
|
|
self.available = True
|
|
logger.info('RapidOCR ready')
|
|
|
|
def __call__(self, img_or_path):
|
|
if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
|
|
img = Image.open(img_or_path)
|
|
elif isinstance(img_or_path, Image.Image):
|
|
img = img_or_path
|
|
else:
|
|
raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
|
|
|
|
res = ''
|
|
read_results, elapsed = self.model(self._preprocess(img))
|
|
if read_results:
|
|
for read_result in read_results:
|
|
res += read_result[1] + '\n'
|
|
|
|
x = (True, res)
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
return pil_image_to_numpy_array(img)
|
|
|
|
class OCRSpace:
|
|
name = 'ocrspace'
|
|
readable_name = 'OCRSpace'
|
|
key = 'o'
|
|
available = False
|
|
|
|
def __init__(self, config={}):
|
|
try:
|
|
self.api_key = config['api_key']
|
|
self.max_byte_size = config.get('file_size_limit', 1000000)
|
|
self.available = True
|
|
logger.info('OCRSpace ready')
|
|
except:
|
|
logger.warning('Error reading API key from config, OCRSpace will not work!')
|
|
|
|
def __call__(self, img_or_path):
|
|
if isinstance(img_or_path, str) or isinstance(img_or_path, Path):
|
|
img = Image.open(img_or_path)
|
|
elif isinstance(img_or_path, Image.Image):
|
|
img = img_or_path
|
|
else:
|
|
raise ValueError(f'img_or_path must be a path or PIL.Image, instead got: {img_or_path}')
|
|
|
|
img_bytes, img_extension = self._preprocess(img)
|
|
if not img_bytes:
|
|
return (False, 'Image is too big!')
|
|
|
|
data = {
|
|
'apikey': self.api_key,
|
|
'language': 'jpn'
|
|
}
|
|
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)
|
|
return x
|
|
|
|
def _preprocess(self, img):
|
|
return limit_image_size(img, self.max_byte_size)
|