feat: Add pipeline timings and toggle visualization, establish debug settings (#183)
* Add settings to turn visualization on or off Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Add profiling code to all models Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Refactor and fix profiling codes Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Visualization codes output PNG to debug dir Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Fixes for time logging Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Optimize imports Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Update lockfile Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Add start_timestamps to ProfilingItem Signed-off-by: Christoph Auer <cau@zurich.ibm.com> --------- Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
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@@ -8,8 +8,11 @@ import pandas as pd
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from docling_core.types.doc import BoundingBox, CoordOrigin
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from docling.datamodel.base_models import OcrCell, Page
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from docling.datamodel.document import ConversionResult
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from docling.datamodel.pipeline_options import TesseractCliOcrOptions
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from docling.datamodel.settings import settings
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from docling.models.base_ocr_model import BaseOcrModel
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from docling.utils.profiling import TimeRecorder
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_log = logging.getLogger(__name__)
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@@ -102,7 +105,9 @@ class TesseractOcrCliModel(BaseOcrModel):
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return df_filtered
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def __call__(self, page_batch: Iterable[Page]) -> Iterable[Page]:
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def __call__(
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self, conv_res: ConversionResult, page_batch: Iterable[Page]
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) -> Iterable[Page]:
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if not self.enabled:
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yield from page_batch
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@@ -113,62 +118,67 @@ class TesseractOcrCliModel(BaseOcrModel):
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if not page._backend.is_valid():
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yield page
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else:
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ocr_rects = self.get_ocr_rects(page)
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with TimeRecorder(conv_res, "ocr"):
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all_ocr_cells = []
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for ocr_rect in ocr_rects:
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# Skip zero area boxes
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if ocr_rect.area() == 0:
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continue
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high_res_image = page._backend.get_page_image(
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scale=self.scale, cropbox=ocr_rect
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ocr_rects = self.get_ocr_rects(page)
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all_ocr_cells = []
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for ocr_rect in ocr_rects:
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# Skip zero area boxes
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if ocr_rect.area() == 0:
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continue
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high_res_image = page._backend.get_page_image(
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scale=self.scale, cropbox=ocr_rect
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)
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with tempfile.NamedTemporaryFile(
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suffix=".png", mode="w"
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) as image_file:
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fname = image_file.name
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high_res_image.save(fname)
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df = self._run_tesseract(fname)
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# _log.info(df)
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# Print relevant columns (bounding box and text)
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for ix, row in df.iterrows():
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text = row["text"]
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conf = row["conf"]
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l = float(row["left"])
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b = float(row["top"])
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w = float(row["width"])
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h = float(row["height"])
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t = b + h
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r = l + w
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cell = OcrCell(
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id=ix,
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text=text,
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confidence=conf / 100.0,
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bbox=BoundingBox.from_tuple(
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coord=(
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(l / self.scale) + ocr_rect.l,
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(b / self.scale) + ocr_rect.t,
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(r / self.scale) + ocr_rect.l,
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(t / self.scale) + ocr_rect.t,
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),
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origin=CoordOrigin.TOPLEFT,
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),
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)
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all_ocr_cells.append(cell)
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## Remove OCR cells which overlap with programmatic cells.
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filtered_ocr_cells = self.filter_ocr_cells(
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all_ocr_cells, page.cells
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)
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with tempfile.NamedTemporaryFile(
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suffix=".png", mode="w"
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) as image_file:
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fname = image_file.name
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high_res_image.save(fname)
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df = self._run_tesseract(fname)
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# _log.info(df)
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# Print relevant columns (bounding box and text)
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for ix, row in df.iterrows():
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text = row["text"]
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conf = row["conf"]
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l = float(row["left"])
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b = float(row["top"])
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w = float(row["width"])
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h = float(row["height"])
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t = b + h
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r = l + w
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cell = OcrCell(
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id=ix,
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text=text,
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confidence=conf / 100.0,
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bbox=BoundingBox.from_tuple(
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coord=(
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(l / self.scale) + ocr_rect.l,
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(b / self.scale) + ocr_rect.t,
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(r / self.scale) + ocr_rect.l,
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(t / self.scale) + ocr_rect.t,
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),
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origin=CoordOrigin.TOPLEFT,
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),
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)
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all_ocr_cells.append(cell)
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## Remove OCR cells which overlap with programmatic cells.
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filtered_ocr_cells = self.filter_ocr_cells(all_ocr_cells, page.cells)
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page.cells.extend(filtered_ocr_cells)
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page.cells.extend(filtered_ocr_cells)
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# DEBUG code:
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# self.draw_ocr_rects_and_cells(page, ocr_rects)
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if settings.debug.visualize_ocr:
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self.draw_ocr_rects_and_cells(conv_res, page, ocr_rects)
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yield page
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