mirror of
https://github.com/lllin000/PaperForge.git
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685 lines
21 KiB
Python
685 lines
21 KiB
Python
from __future__ import annotations
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from pathlib import Path
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from typing import Any
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def test_figure_object_markdown_links_image_and_legend() -> None:
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from paperforge.worker.ocr_objects import render_figure_object_markdown
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md = render_figure_object_markdown({
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"figure_id": "figure_001",
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"page": 4,
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"caption": "Figure 1. Example.",
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"image_relpath": "assets/figures/figure_001.jpg",
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"confidence": 0.91,
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})
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assert "# Figure 1" in md
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assert "" in md
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assert "## Legend" in md
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assert "Figure 1. Example." in md
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def test_table_object_markdown_includes_image_and_caption() -> None:
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from paperforge.worker.ocr_objects import render_table_object_markdown
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md = render_table_object_markdown({
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"table_id": "table_001",
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"page": 5,
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"caption": "Table 1. Results.",
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"image_relpath": "assets/tables/table_001.jpg",
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"confidence": 0.88,
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})
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assert "# Table 1" in md
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assert "" in md
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assert "## Caption" in md
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assert "Table 1. Results." in md
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def test_orphan_object_markdown() -> None:
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from paperforge.worker.ocr_objects import render_figure_object_markdown
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md = render_figure_object_markdown({
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"figure_id": "orphan_001",
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"page": 6,
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"caption": "",
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"image_relpath": "assets/orphans/orphan_001.jpg",
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"confidence": 0.3,
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})
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assert "# Orphan Media" in md
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assert "" in md
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def test_render_figure_markdown_with_image_when_cropped() -> None:
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from paperforge.worker.ocr_objects import render_figure_object_markdown
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md = render_figure_object_markdown({
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"figure_id": "figure_001",
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"page": 4,
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"caption": "Figure 1. Example.",
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"image_relpath": "assets/figures/figure_001.jpg",
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"confidence": 0.91,
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"was_cropped": True,
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})
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assert "" in md
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def test_render_figure_markdown_without_image_when_not_cropped() -> None:
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from paperforge.worker.ocr_objects import render_figure_object_markdown
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md = render_figure_object_markdown({
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"figure_id": "figure_001",
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"page": 4,
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"caption": "Figure 1. Example.",
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"image_relpath": "assets/figures/figure_001.jpg",
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"confidence": 0.91,
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"was_cropped": False,
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})
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assert "![](" not in md
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assert "" not in md
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def test_legend_only_matched_figure_does_not_use_unmatched_assets(tmp_path: Path) -> None:
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from paperforge.worker.ocr_objects import extract_and_write_objects
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render_root = tmp_path / "render"
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asset_root = tmp_path / "assets"
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figure_inventory: dict[str, Any] = {
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"matched_figures": [
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{
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"text": "Figure 1. A legend-only figure.",
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"page": 3,
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"confidence": 0.85,
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"cluster_bbox": None,
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"matched_assets": [],
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}
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],
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"unmatched_assets": [
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{"bbox": [100, 100, 200, 200], "page": 1},
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{"bbox": [300, 300, 400, 400], "page": 1},
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],
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"rejected_legends": [],
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"figure_legends": [],
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"figure_assets": [],
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"official_figure_count": 1,
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"unresolved_clusters": [],
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}
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extract_and_write_objects(
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pdf_path=None,
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figure_inventory=figure_inventory,
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table_inventory={"tables": [], "unmatched_assets": []},
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asset_root=asset_root,
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render_root=render_root,
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)
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render_files = sorted((render_root / "figures").glob("*.md"))
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assert len(render_files) == 3, (
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"One matched figure note + two orphan notes from unmatched_assets"
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)
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figure_note = render_root / "figures" / "figure_001.md"
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assert figure_note.exists()
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content = figure_note.read_text()
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assert "![](" not in content, (
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"Legend-only figure must not contain any image reference"
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)
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# Verify orphan notes still get image paths
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orphan_contents = (render_root / "figures" / "orphan_001.md").read_text()
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assert "" in orphan_contents
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def test_stabilize_object_wikilink_uses_correct_relative_path() -> None:
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from paperforge.worker.ocr_objects import render_figure_object_markdown
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md = render_figure_object_markdown({
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"figure_id": "figure_001",
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"page": 5,
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"caption": "Figure 1. Results.",
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"image_relpath": "assets/figures/figure_001.jpg",
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"confidence": 0.9,
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})
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assert "" in md
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def test_unresolved_cluster_object_emission(tmp_path: Path) -> None:
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from paperforge.worker.ocr_objects import extract_and_write_objects
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render_root = tmp_path / "render"
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asset_root = tmp_path / "assets"
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figure_inventory: dict[str, Any] = {
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"matched_figures": [],
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"unmatched_assets": [],
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"rejected_legends": [],
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"figure_legends": [],
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"figure_assets": [],
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"official_figure_count": 0,
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"unresolved_clusters": [
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{
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"cluster_id": "unresolved_cluster_001",
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"page": 9,
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"cluster_bbox": [363, 237, 1075, 1016],
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"media_block_ids": ["p9_b2", "p9_b3", "p9_b4", "p9_b5", "p9_b6", "p9_b7"],
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}
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],
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}
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extract_and_write_objects(
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pdf_path=None,
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figure_inventory=figure_inventory,
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table_inventory={"tables": [], "unmatched_assets": []},
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asset_root=asset_root,
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render_root=render_root,
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)
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render_files = sorted((render_root / "figures").glob("*.md"))
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assert len(render_files) == 1, (
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"Unresolved cluster should produce exactly one object note"
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)
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content = render_files[0].read_text()
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assert "# Figure 4" not in content, (
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"Cluster without a valid legend must not be titled with a figure number"
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)
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assert "unresolved_cluster_001" in render_files[0].stem, (
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"Object note should be identified by its cluster ID"
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)
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assert "unresolved_cluster_001.jpg" in content, (
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"Markdown must reference the whole-cluster crop image, not individual panel crops"
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)
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assert (render_root / "figures" / "unresolved_cluster_001.md").exists()
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assert not (render_root / "figures" / "cluster_001.md").exists()
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def test_held_figures_do_not_emit_object_notes(tmp_path: Path) -> None:
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from paperforge.worker.ocr_objects import extract_and_write_objects
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render_root = tmp_path / "render"
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asset_root = tmp_path / "assets"
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figure_inventory: dict[str, Any] = {
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"matched_figures": [],
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"held_figures": [
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{
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"figure_id": "held_figure_001",
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"legend_block_id": "p10_b1",
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"page": 10,
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"text": "Figure 1",
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"figure_number": 1,
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"hold_reason": "insufficient_legend_evidence",
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}
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],
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"unmatched_assets": [],
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"rejected_legends": [],
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"figure_legends": [],
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"figure_assets": [],
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"official_figure_count": 0,
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"unresolved_clusters": [],
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}
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extract_and_write_objects(
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pdf_path=None,
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figure_inventory=figure_inventory,
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table_inventory={"tables": [], "unmatched_assets": []},
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asset_root=asset_root,
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render_root=render_root,
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)
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render_files = sorted((render_root / "figures").glob("*.md"))
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assert render_files == []
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def test_held_tables_do_not_emit_object_notes(tmp_path: Path) -> None:
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from paperforge.worker.ocr_objects import extract_and_write_objects
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render_root = tmp_path / "render"
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asset_root = tmp_path / "assets"
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table_inventory: dict[str, Any] = {
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"tables": [],
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"held_tables": [
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{
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"table_id": "held_table_001",
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"caption_block_id": "p12_b1",
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"page": 12,
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"caption_text": "Table 2.",
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"table_number": 2,
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"hold_reason": "insufficient_caption_evidence",
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}
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],
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"unmatched_captions": [],
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"unmatched_assets": [],
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"official_table_count": 0,
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}
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extract_and_write_objects(
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pdf_path=None,
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figure_inventory={"matched_figures": [], "unmatched_assets": [], "unresolved_clusters": []},
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table_inventory=table_inventory,
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asset_root=asset_root,
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render_root=render_root,
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)
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render_files = sorted((render_root / "tables").glob("*.md"))
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assert render_files == []
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def test_crop_asset_uses_ocr_page_coordinates_when_dimensions_provided(tmp_path: Path) -> None:
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import fitz
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from PIL import Image
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from paperforge.worker.ocr_objects import _crop_asset_from_pdf
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pdf_path = tmp_path / "sample.pdf"
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doc = fitz.open()
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page = doc.new_page(width=300, height=400)
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page.draw_rect(fitz.Rect(25, 25, 50, 50), color=(1, 0, 0), fill=(1, 0, 0))
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doc.save(pdf_path)
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doc.close()
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dst = tmp_path / "crop.jpg"
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ok = _crop_asset_from_pdf(
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pdf_path,
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1,
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[50, 50, 100, 100],
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dst,
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page_width=600,
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page_height=800,
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page_cache_dir=tmp_path / "pages",
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)
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assert ok is True
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with Image.open(dst) as img:
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assert img.size == (50, 50)
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def test_crop_asset_prefers_cached_page_image_when_available(tmp_path: Path) -> None:
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from PIL import Image
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from paperforge.worker.ocr_objects import _crop_asset_from_pdf
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page_cache_dir = tmp_path / "pages"
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page_cache_dir.mkdir()
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page_image = page_cache_dir / "page_001.jpg"
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Image.new("RGB", (600, 800), "white").save(page_image)
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dst = tmp_path / "crop.jpg"
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ok = _crop_asset_from_pdf(
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tmp_path / "missing.pdf",
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1,
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[50, 50, 100, 100],
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dst,
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page_cache_dir=page_cache_dir,
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)
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assert ok is True
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with Image.open(dst) as img:
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assert img.size == (50, 50)
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def test_figure_legend_math_normalized() -> None:
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from paperforge.worker.ocr_objects import render_figure_object_markdown
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md = render_figure_object_markdown({
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"figure_id": "figure_001",
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"caption": "Expression of $ ^{7} $ mRNA",
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"page": 1,
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"was_cropped": True,
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"image_relpath": "figures/figure_001.jpg",
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})
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assert "$^{7}$" in md
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assert "Expression of" in md
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def test_table_object_markdown_renders_owned_notes() -> None:
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from paperforge.worker.ocr_objects import render_table_object_markdown
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md = render_table_object_markdown({
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"table_id": "table_001",
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"page": 5,
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"caption": "Table 1. Results.",
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"image_relpath": "assets/tables/table_001.jpg",
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"note_texts": ["* p < 0.05", "Data are mean \u00b1 SD."],
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})
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assert "## Notes" in md
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assert "* p < 0.05" in md
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assert "Data are mean \u00b1 SD." in md
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def test_table_object_markdown_renders_note_band_texts_in_notes_section() -> None:
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from paperforge.worker.ocr_objects import render_table_object_markdown
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md = render_table_object_markdown(
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{
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"table_id": "table_001",
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"page": 5,
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"caption": "Table 1. Results.",
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"image_relpath": "assets/tables/table_001.jpg",
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"note_texts": ["* p < 0.05", "Data are mean \u00b1 SD."],
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"note_match_reason": "note_band_geometry_match",
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}
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)
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assert "## Notes" in md
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assert "* p < 0.05" in md
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assert "Data are mean \u00b1 SD." in md
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def test_table_caption_math_normalized() -> None:
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from paperforge.worker.ocr_objects import render_table_object_markdown
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md = render_table_object_markdown({
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"table_id": "table_001",
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"formal_table_number": 1,
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"caption": "IC$ _{50} $ values ($ \\\\mu $M)",
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"image_relpath": "tables/table_001.jpg",
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"page": 1,
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})
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assert "$_{50}$" in md
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assert "$\\\\mu$M" in md
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# === Phase 1: cache-first and shared-PDF guardrails ===
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def test_crop_asset_uses_cached_page_without_opening_pdf(tmp_path: Path, monkeypatch) -> None:
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from PIL import Image
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from paperforge.worker.ocr_objects import _crop_asset_from_pdf
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page_cache_dir = tmp_path / "pages"
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page_cache_dir.mkdir()
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Image.new("RGB", (600, 800), "white").save(page_cache_dir / "page_001.jpg")
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called = {"count": 0}
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def _boom(*args, **kwargs):
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called["count"] += 1
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raise AssertionError("fitz.open should not be called on cache hit")
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monkeypatch.setattr("fitz.open", _boom)
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dst = tmp_path / "crop.jpg"
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ok = _crop_asset_from_pdf(
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None,
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1,
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[50, 50, 100, 100],
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dst,
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page_cache_dir=page_cache_dir,
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)
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assert ok is True
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assert called["count"] == 0
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def test_extract_objects_opens_pdf_once_across_multiple_cache_miss_pages(tmp_path: Path, monkeypatch) -> None:
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import fitz
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from paperforge.worker.ocr_objects import extract_and_write_objects
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pdf_path = tmp_path / "sample.pdf"
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doc = fitz.open()
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for _ in range(3):
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page = doc.new_page(width=300, height=400)
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page.draw_rect(fitz.Rect(25, 25, 75, 75), color=(1, 0, 0), fill=(1, 0, 0))
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doc.save(pdf_path)
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doc.close()
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figure_inventory = {
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"matched_figures": [
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{
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"figure_id": "figure_001",
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"text": "Figure 1.",
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"page": 1,
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"cluster_bbox": [50, 50, 150, 150],
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"matched_assets": [],
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},
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{
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"figure_id": "figure_002",
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"text": "Figure 2.",
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"page": 2,
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"cluster_bbox": [160, 50, 260, 150],
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"matched_assets": [],
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},
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{
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"figure_id": "figure_003",
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"text": "Figure 3.",
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"page": 3,
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"cluster_bbox": [80, 80, 200, 200],
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"matched_assets": [],
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},
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],
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"unmatched_assets": [],
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"rejected_legends": [],
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"figure_legends": [],
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"figure_assets": [],
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"official_figure_count": 3,
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"unresolved_clusters": [],
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}
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open_count = {"count": 0}
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real_open = fitz.open
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def _counting_open(*args, **kwargs):
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open_count["count"] += 1
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return real_open(*args, **kwargs)
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monkeypatch.setattr("fitz.open", _counting_open)
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extract_and_write_objects(
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pdf_path=pdf_path,
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figure_inventory=figure_inventory,
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table_inventory={"tables": [], "unmatched_assets": []},
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asset_root=tmp_path / "assets",
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render_root=tmp_path / "render",
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page_dimensions_by_page={1: (600, 800), 2: (600, 800), 3: (600, 800)},
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)
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assert open_count["count"] == 1
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def test_extract_objects_renders_same_page_once_for_multiple_crops(tmp_path: Path, monkeypatch) -> None:
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import fitz
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from paperforge.worker.ocr_objects import extract_and_write_objects
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pdf_path = tmp_path / "sample.pdf"
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doc = fitz.open()
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page = doc.new_page(width=300, height=400)
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page.draw_rect(fitz.Rect(25, 25, 75, 75), color=(1, 0, 0), fill=(1, 0, 0))
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doc.save(pdf_path)
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doc.close()
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figure_inventory = {
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"matched_figures": [
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{"figure_id": "figure_001", "text": "Figure 1.", "page": 1, "cluster_bbox": [50, 50, 150, 150], "matched_assets": []},
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{"figure_id": "figure_002", "text": "Figure 2.", "page": 1, "cluster_bbox": [160, 50, 260, 150], "matched_assets": []},
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],
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"unmatched_assets": [],
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"rejected_legends": [],
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"figure_legends": [],
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"figure_assets": [],
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"official_figure_count": 2,
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"unresolved_clusters": [],
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}
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render_calls = {"count": 0}
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from paperforge.worker import ocr as ocr_module
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real_render = ocr_module.render_pdf_page_cached
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def _counting_render(*args, **kwargs):
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render_calls["count"] += 1
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return real_render(*args, **kwargs)
|
|
|
|
monkeypatch.setattr(ocr_module, "render_pdf_page_cached", _counting_render)
|
|
|
|
extract_and_write_objects(
|
|
pdf_path=pdf_path,
|
|
figure_inventory=figure_inventory,
|
|
table_inventory={"tables": [], "unmatched_assets": []},
|
|
asset_root=tmp_path / "assets",
|
|
render_root=tmp_path / "render",
|
|
page_dimensions_by_page={1: (600, 800)},
|
|
)
|
|
|
|
assert render_calls["count"] == 1
|
|
|
|
|
|
def test_extract_objects_cache_hit_does_not_eager_open_shared_pdf(tmp_path: Path, monkeypatch) -> None:
|
|
import fitz
|
|
from PIL import Image
|
|
|
|
from paperforge.worker.ocr_objects import extract_and_write_objects
|
|
|
|
pdf_path = tmp_path / "sample.pdf"
|
|
doc = fitz.open()
|
|
page = doc.new_page(width=300, height=400)
|
|
page.draw_rect(fitz.Rect(25, 25, 75, 75), color=(1, 0, 0), fill=(1, 0, 0))
|
|
doc.save(pdf_path)
|
|
doc.close()
|
|
|
|
pages_dir = tmp_path / "pages"
|
|
pages_dir.mkdir()
|
|
Image.new("RGB", (600, 800), "white").save(pages_dir / "page_001.jpg")
|
|
|
|
def _boom(*args, **kwargs):
|
|
raise AssertionError("fitz.open should not be called when page cache already exists")
|
|
|
|
monkeypatch.setattr("fitz.open", _boom)
|
|
|
|
figure_inventory = {
|
|
"matched_figures": [
|
|
{
|
|
"figure_id": "figure_001",
|
|
"text": "Figure 1.",
|
|
"page": 1,
|
|
"cluster_bbox": [50, 50, 150, 150],
|
|
"matched_assets": [],
|
|
}
|
|
],
|
|
"unmatched_assets": [],
|
|
"rejected_legends": [],
|
|
"figure_legends": [],
|
|
"figure_assets": [],
|
|
"official_figure_count": 1,
|
|
"unresolved_clusters": [],
|
|
}
|
|
|
|
extract_and_write_objects(
|
|
pdf_path=pdf_path,
|
|
figure_inventory=figure_inventory,
|
|
table_inventory={"tables": [], "unmatched_assets": []},
|
|
asset_root=tmp_path / "assets",
|
|
render_root=tmp_path / "render",
|
|
page_dimensions_by_page={1: (600, 800)},
|
|
)
|
|
|
|
assert (tmp_path / "assets" / "figures" / "figure_001.jpg").exists()
|
|
|
|
|
|
def test_extract_objects_pdf_open_failure_still_writes_markdown(tmp_path: Path, monkeypatch) -> None:
|
|
from paperforge.worker.ocr_objects import extract_and_write_objects
|
|
|
|
pdf_path = tmp_path / "broken.pdf"
|
|
pdf_path.touch()
|
|
|
|
figure_inventory = {
|
|
"matched_figures": [
|
|
{
|
|
"figure_id": "figure_001",
|
|
"text": "Figure 1. Example.",
|
|
"page": 1,
|
|
"cluster_bbox": [50, 50, 150, 150],
|
|
"matched_assets": [],
|
|
}
|
|
],
|
|
"unmatched_assets": [],
|
|
"rejected_legends": [],
|
|
"figure_legends": [],
|
|
"figure_assets": [],
|
|
"official_figure_count": 1,
|
|
"unresolved_clusters": [],
|
|
}
|
|
|
|
def _boom(*args, **kwargs):
|
|
raise RuntimeError("cannot open pdf")
|
|
|
|
monkeypatch.setattr("fitz.open", _boom)
|
|
|
|
extract_and_write_objects(
|
|
pdf_path=pdf_path,
|
|
figure_inventory=figure_inventory,
|
|
table_inventory={"tables": [], "unmatched_assets": []},
|
|
asset_root=tmp_path / "assets",
|
|
render_root=tmp_path / "render",
|
|
page_dimensions_by_page={1: (600, 800)},
|
|
)
|
|
|
|
note = tmp_path / "render" / "figures" / "figure_001.md"
|
|
assert note.exists()
|
|
|
|
|
|
# === figure legend completeness integration (Task 8) ===
|
|
|
|
|
|
def test_figure_inventory_completeness_fields_present() -> None:
|
|
"""Completeness metadata is present even for empty inventory."""
|
|
from paperforge.worker.ocr_figures import build_figure_inventory
|
|
|
|
inventory = build_figure_inventory([])
|
|
c = inventory["figure_legend_completeness"]
|
|
assert "total" in c
|
|
assert "accounted_for" in c
|
|
assert "gap_count" in c
|
|
assert "details" in c
|
|
|
|
|
|
def test_extract_and_write_objects_with_held_figures_and_completeness(tmp_path: Path) -> None:
|
|
"""Completeness data coexists with held figures in object extraction."""
|
|
from paperforge.worker.ocr_objects import extract_and_write_objects
|
|
|
|
render_root = tmp_path / "render"
|
|
asset_root = tmp_path / "assets"
|
|
|
|
figure_inventory: dict[str, Any] = {
|
|
"matched_figures": [],
|
|
"held_figures": [
|
|
{
|
|
"figure_id": "held_figure_001",
|
|
"legend_block_id": "p10_b1",
|
|
"page": 10,
|
|
"text": "Figure 1",
|
|
"figure_number": 1,
|
|
"hold_reason": "insufficient_legend_evidence",
|
|
}
|
|
],
|
|
"unmatched_assets": [],
|
|
"rejected_legends": [],
|
|
"figure_legends": [],
|
|
"figure_assets": [],
|
|
"official_figure_count": 0,
|
|
"unresolved_clusters": [],
|
|
"figure_legend_completeness": {
|
|
"total": 1,
|
|
"accounted_for": 1,
|
|
"gap_count": 0,
|
|
"details": [{"block_id": "p10_b1", "figure_number": 1, "status": "held", "page": 10}],
|
|
},
|
|
}
|
|
|
|
extract_and_write_objects(
|
|
pdf_path=None,
|
|
figure_inventory=figure_inventory,
|
|
table_inventory={"tables": [], "unmatched_assets": []},
|
|
asset_root=asset_root,
|
|
render_root=render_root,
|
|
)
|
|
|
|
render_files = sorted((render_root / "figures").glob("*.md"))
|
|
assert render_files == []
|