feat add glm-ocr core
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liuyuanchuang
@@ -2,26 +2,17 @@
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import time
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import uuid
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import cv2
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from io import BytesIO
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from fastapi import APIRouter, Depends, HTTPException, Request, Response
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from app.core.dependencies import (
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get_image_processor,
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get_layout_detector,
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get_ocr_service,
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get_mineru_ocr_service,
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get_glmocr_service,
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get_glmocr_endtoend_service,
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)
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from app.core.config import get_settings
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from app.core.logging_config import get_logger, RequestIDAdapter
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from app.schemas.image import ImageOCRRequest, ImageOCRResponse
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from app.services.image_processor import ImageProcessor
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from app.services.layout_detector import LayoutDetector
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from app.services.ocr_service import OCRService, MineruOCRService, GLMOCRService
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settings = get_settings()
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from app.services.ocr_service import GLMOCREndToEndService
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router = APIRouter()
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logger = get_logger()
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@@ -33,100 +24,38 @@ async def process_image_ocr(
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http_request: Request,
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response: Response,
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image_processor: ImageProcessor = Depends(get_image_processor),
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layout_detector: LayoutDetector = Depends(get_layout_detector),
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mineru_service: MineruOCRService = Depends(get_mineru_ocr_service),
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paddle_service: OCRService = Depends(get_ocr_service),
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glmocr_service: GLMOCRService = Depends(get_glmocr_service),
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glmocr_service: GLMOCREndToEndService = Depends(get_glmocr_endtoend_service),
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) -> ImageOCRResponse:
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"""Process an image and extract content as LaTeX, Markdown, and MathML.
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The processing pipeline:
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1. Load and preprocess image (add 30% whitespace padding)
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2. Detect layout using DocLayout-YOLO
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3. Based on layout:
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- If plain text exists: use PP-DocLayoutV2 for mixed recognition
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- Otherwise: use PaddleOCR-VL with formula prompt
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4. Convert output to LaTeX, Markdown, and MathML formats
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1. Load and preprocess image
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2. Detect layout regions using PP-DocLayoutV3
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3. Crop each region and recognize with GLM-OCR via vLLM (task-specific prompts)
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4. Aggregate region results into Markdown
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5. Convert to LaTeX, Markdown, and MathML formats
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Note: OMML conversion is not included due to performance overhead.
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Use the /convert/latex-to-omml endpoint to convert LaTeX to OMML separately.
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"""
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# Get or generate request ID
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request_id = http_request.headers.get("x-request-id", str(uuid.uuid4()))
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response.headers["x-request-id"] = request_id
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# Create logger adapter with request_id
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log = RequestIDAdapter(logger, {"request_id": request_id})
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log.request_id = request_id
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try:
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log.info("Starting image OCR processing")
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start = time.time()
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# Preprocess image (load only, no padding yet)
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preprocess_start = time.time()
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image = image_processor.preprocess(
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image_url=request.image_url,
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image_base64=request.image_base64,
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)
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# Apply padding only for layout detection
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processed_image = image
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if image_processor and settings.is_padding:
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processed_image = image_processor.add_padding(image)
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ocr_result = glmocr_service.recognize(image)
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preprocess_time = time.time() - preprocess_start
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log.debug(f"Image loading completed in {preprocess_time:.3f}s")
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# Layout detection (using padded image if padding is enabled)
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layout_start = time.time()
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layout_info = layout_detector.detect(processed_image)
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layout_time = time.time() - layout_start
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log.info(f"Layout detection completed in {layout_time:.3f}s")
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# OCR recognition (use original image without padding)
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ocr_start = time.time()
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if layout_info.MixedRecognition:
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recognition_method = "MixedRecognition (MinerU)"
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log.info(f"Using {recognition_method}")
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# Convert original image (without padding) to bytes
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success, encoded_image = cv2.imencode(".png", image)
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if not success:
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raise RuntimeError("Failed to encode image")
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image_bytes = BytesIO(encoded_image.tobytes())
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image_bytes.seek(0) # Ensure position is at the beginning
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ocr_result = mineru_service.recognize(image_bytes)
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else:
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recognition_method = "FormulaOnly (GLMOCR)"
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log.info(f"Using {recognition_method}")
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# Try GLM-OCR first, fallback to MinerU if token limit exceeded
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try:
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ocr_result = glmocr_service.recognize(image)
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except Exception as e:
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error_msg = str(e)
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# Check if error is due to token limit (max_model_len exceeded)
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if "max_model_len" in error_msg or "decoder prompt" in error_msg or "BadRequestError" in error_msg:
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log.warning(f"GLM-OCR failed due to token limit: {error_msg}")
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log.info("Falling back to MinerU for recognition")
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recognition_method = "FormulaOnly (MinerU fallback)"
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# Convert original image to bytes for MinerU
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success, encoded_image = cv2.imencode(".png", image)
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if not success:
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raise RuntimeError("Failed to encode image")
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image_bytes = BytesIO(encoded_image.tobytes())
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image_bytes.seek(0)
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ocr_result = mineru_service.recognize(image_bytes)
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else:
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# Re-raise other errors
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raise
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ocr_time = time.time() - ocr_start
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total_time = time.time() - preprocess_start
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log.info(f"OCR processing completed - Method: {recognition_method}, " f"Layout time: {layout_time:.3f}s, OCR time: {ocr_time:.3f}s, " f"Total time: {total_time:.3f}s")
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log.info(f"OCR completed in {time.time() - start:.3f}s")
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except RuntimeError as e:
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log.error(f"OCR processing failed: {str(e)}", exc_info=True)
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