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304d9e02bb
### What problem does this PR solve? Http API based on onnx model. pdf_parser.py to golang ### Type of change - [x] Refactoring
76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
"""TSR adapter — wraps TableStructureRecognizer and converts output to wire format."""
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import io
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import logging
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from typing import List
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from PIL import Image
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from deepdoc.vision.table_structure_recognizer import TableStructureRecognizer
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logger = logging.getLogger(__name__)
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# OSS model label → Go tsrLabels index (labels are identical)
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# Go-side (internal/parser/deepdoc.go):
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# var tsrLabels = []string{
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# "table", "table column", "table row",
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# "table column header", "table projected row header",
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# "table spanning cell",
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# }
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TSR_CLASS_MAP = {
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"table": 0,
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"table column": 1,
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"table row": 2,
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"table column header": 3,
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"table projected row header": 4,
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"table spanning cell": 5,
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}
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class TSRAdapter:
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"""Calls TableStructureRecognizer and converts elements to wire format."""
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def __init__(self, model_dir: str, thr: float = 0.2):
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self.model_dir = model_dir
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self.thr = thr
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self._tsr: TableStructureRecognizer | None = None
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def load(self):
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"""Initialize the TSR model. Called once per worker."""
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self._tsr = TableStructureRecognizer()
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def __call__(self, image_data: bytes) -> List[List[float]]:
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"""
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Args:
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image_data: JPEG image bytes (cropped table region).
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Returns:
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List of [x0, y0, x1, y1, score, class_id] for each structural element.
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"""
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if self._tsr is None:
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raise RuntimeError("TSRAdapter.load() must be called before inference")
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img = Image.open(io.BytesIO(image_data)).convert("RGB")
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width, height = img.size
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tables = self._tsr([img], thr=self.thr)
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result = []
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for tbl_elements in tables:
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for elem in tbl_elements:
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label = elem["label"]
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class_id = TSR_CLASS_MAP.get(label)
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if class_id is None:
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logger.warning("TSR: unknown label '%s', skipping", label)
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continue
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x0 = max(0.0, min(float(elem["x0"]), width))
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y0 = max(0.0, min(float(elem["top"]), height))
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x1 = max(0.0, min(float(elem["x1"]), width))
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y1 = max(0.0, min(float(elem["bottom"]), height))
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score = float(elem["score"])
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result.append([x0, y0, x1, y1, score, float(class_id)])
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return result
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