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Checks that every sample's metadata is internally consistent and safe for
downstream ML consumption: the signature was actually detected, its polygon
is a valid non-degenerate geometry, every declared bounding box is
well-formed and lies on the page, no NaN/Inf values leaked into the JSON,
metadata is schema-complete, and the difficulty score is exactly
reproducible from its own recorded components and weights.
"""
from __future__ import annotations
import math
from dataclasses import dataclass, field
from typing import Any
from shapely.geometry import Polygon
from src.config.schema import DifficultyWeights, ValidationConfig
from src.utils.logging_utils import get_logger
from src.verification.case_types import CASE_TABLE
logger = get_logger(__name__)
REQUIRED_TOP_LEVEL_KEYS = (
"document_id", "template", "page", "page_size", "signer", "signature_source",
"placement", "rendering", "difficulty", "quality", "verification", "augmentation", "geometry", "metrics",
)
# Case types whose ground truth is an intentionally blank target box -- a
# real signature is not expected to be detected there.
_BLANK_CASE_TYPES = frozenset({"2a", "5a"})
@dataclass
class ValidationIssue:
field: str
message: str
severity: str # "error" | "warning"
@dataclass
class ValidationReport:
sample_id: str
passed: bool
issues: list[ValidationIssue] = field(default_factory=list)
@property
def errors(self) -> list[ValidationIssue]:
return [i for i in self.issues if i.severity == "error"]
@property
def warnings(self) -> list[ValidationIssue]:
return [i for i in self.issues if i.severity == "warning"]
class ValidationEngine:
"""Validates one sample's exported metadata for correctness and completeness.
Example:
>>> engine = ValidationEngine(cfg.validation, cfg.difficulty)
>>> report = engine.validate(metadata)
>>> assert report.passed
"""
def __init__(self, config: ValidationConfig, difficulty_weights: DifficultyWeights) -> None:
self.config = config
self.weights = difficulty_weights
def validate(self, metadata: dict[str, Any]) -> ValidationReport:
"""Run every validation check against one sample's metadata dict."""
sample_id = str(metadata.get("document_id", "unknown"))
issues: list[ValidationIssue] = []
issues += self._check_completeness(metadata)
issues += self._check_no_nan(metadata)
issues += self._check_signature_exists(metadata)
issues += self._check_polygon_valid(metadata)
issues += self._check_boxes(metadata)
issues += self._check_difficulty_reproducible(metadata)
issues += self._check_verification_consistency(metadata)
has_errors = any(i.severity == "error" for i in issues)
has_warnings = any(i.severity == "warning" for i in issues)
passed = not has_errors and not (self.config.strict and has_warnings)
return ValidationReport(sample_id=sample_id, passed=passed, issues=issues)
def _check_completeness(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
issues = []
for key in REQUIRED_TOP_LEVEL_KEYS:
if key not in metadata:
issues.append(ValidationIssue(key, f"Missing required top-level key '{key}'", "error"))
signer = metadata.get("signer", {})
for key in ("name", "role"):
if not signer.get(key):
issues.append(ValidationIssue(f"signer.{key}", f"Missing signer.{key}", "error"))
return issues
def _check_no_nan(self, metadata: dict[str, Any], path: str = "") -> list[ValidationIssue]:
issues: list[ValidationIssue] = []
# geometry.centroid_px / distance_from_box_center_px are deliberate
# NaN sentinels (see GeometryAnalyzer._empty_result) when nothing was
# detected -- not a data-quality bug, so they're excluded from the walk.
skip_paths: set = set()
if not metadata.get("geometry", {}).get("detected", True):
skip_paths = {"geometry.centroid_px[0]", "geometry.centroid_px[1]", "geometry.distance_from_box_center_px"}
def walk(value: Any, cur_path: str) -> None:
if cur_path in skip_paths:
return
if isinstance(value, dict):
for k, v in value.items():
walk(v, f"{cur_path}.{k}" if cur_path else str(k))
elif isinstance(value, (list, tuple)):
for i, v in enumerate(value):
walk(v, f"{cur_path}[{i}]")
elif isinstance(value, float):
if math.isnan(value) or math.isinf(value):
issues.append(ValidationIssue(cur_path, f"Non-finite value ({value}) at {cur_path}", "error"))
walk(metadata, path)
return issues
@staticmethod
def _expects_blank_target(metadata: dict[str, Any]) -> bool:
verification = metadata.get("verification") or {}
return verification.get("case_type") in _BLANK_CASE_TYPES
def _check_signature_exists(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
geometry = metadata.get("geometry", {})
if self._expects_blank_target(metadata):
return []
if not geometry.get("detected", False):
return [ValidationIssue("geometry.detected", "Signature was not detected in the rendered document", "error")]
if geometry.get("ink_pixels", 0) <= 0:
return [ValidationIssue("geometry.ink_pixels", "Signature detected but has zero ink pixels", "error")]
return []
def _check_polygon_valid(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
issues: list[ValidationIssue] = []
if not metadata.get("geometry", {}).get("detected", False) and self._expects_blank_target(metadata):
return []
poly_pts = metadata.get("geometry", {}).get("signature_polygon_px", [])
if len(poly_pts) < 3:
issues.append(ValidationIssue("geometry.signature_polygon_px", "Polygon has fewer than 3 points", "error"))
return issues
try:
poly = Polygon(poly_pts)
if not poly.is_valid:
issues.append(ValidationIssue("geometry.signature_polygon_px", "Polygon geometry is invalid", "error"))
elif poly.area <= 0:
issues.append(ValidationIssue("geometry.signature_polygon_px", "Polygon has zero area", "error"))
except Exception as exc: # pragma: no cover - defensive
issues.append(ValidationIssue("geometry.signature_polygon_px", f"Polygon construction failed: {exc}", "error"))
return issues
def _check_boxes(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
issues: list[ValidationIssue] = []
geometry = metadata.get("geometry", {})
page_w, page_h = metadata.get("page_size", [None, None])
eps = self.config.max_coordinate_epsilon
for field_name in ("target_box", "actual_box", "signature_bbox_px", "box_px"):
box = geometry.get(field_name)
if not box or len(box) != 4:
issues.append(ValidationIssue(f"geometry.{field_name}", "Box missing or malformed", "error"))
continue
x1, y1, x2, y2 = box
if x2 <= x1 or y2 <= y1:
issues.append(ValidationIssue(f"geometry.{field_name}", f"Box has non-positive extent: {box}", "error"))
if page_w is not None and page_h is not None:
if x1 < -eps or y1 < -eps or x2 > page_w + eps or y2 > page_h + eps:
issues.append(ValidationIssue(
f"geometry.{field_name}",
f"Box {box} lies outside page bounds (0,0,{page_w},{page_h})",
"warning",
))
return issues
def _check_difficulty_reproducible(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
difficulty = metadata.get("difficulty", {})
required = ("misplacement", "whitespace", "faintness", "smallness", "low_texture", "score", "tier")
if not all(k in difficulty for k in required):
return [ValidationIssue("difficulty", "Difficulty block incomplete", "error")]
w = self.weights
recomputed = (
w.misplacement * difficulty["misplacement"]
+ w.whitespace * difficulty["whitespace"]
+ w.faintness * difficulty["faintness"]
+ w.smallness * difficulty["smallness"]
+ w.low_texture * difficulty["low_texture"]
)
if abs(recomputed - difficulty["score"]) > 1e-3:
return [ValidationIssue(
"difficulty.score",
f"Score {difficulty['score']} does not match recomputed weighted sum {recomputed:.4f}",
"error",
)]
if recomputed < w.easy_threshold:
expected_tier = "easy"
elif recomputed < w.hard_threshold:
expected_tier = "medium"
else:
expected_tier = "hard"
if expected_tier != difficulty["tier"]:
return [ValidationIssue(
"difficulty.tier", f"Tier '{difficulty['tier']}' does not match expected '{expected_tier}'", "error",
)]
return []
def _check_verification_consistency(self, metadata: dict[str, Any]) -> list[ValidationIssue]:
verification = metadata.get("verification") or {}
case_type = verification.get("case_type")
if case_type is None:
return []
spec = CASE_TABLE.get(case_type)
if spec is None:
return [ValidationIssue("verification.case_type", f"Unknown case_type '{case_type}'", "error")]
issues: list[ValidationIssue] = []
if verification.get("verdict") != spec.verdict:
issues.append(ValidationIssue(
"verification.verdict",
f"verdict '{verification.get('verdict')}' does not match case_type '{case_type}' expected '{spec.verdict}'",
"error",
))
if verification.get("reject_code") != spec.reject_code:
issues.append(ValidationIssue(
"verification.reject_code",
f"reject_code '{verification.get('reject_code')}' does not match case_type '{case_type}' expected '{spec.reject_code}'",
"error",
))
return issues
@dataclass
class ValidationSummary:
"""Aggregate statistics over a batch of per-sample validation reports."""
total: int
passed: int
failed: int
total_errors: int
total_warnings: int
failed_sample_ids: list[str]
def as_dict(self) -> dict[str, Any]:
return {
"total": self.total,
"passed": self.passed,
"failed": self.failed,
"pass_rate": round(self.passed / self.total, 4) if self.total else 0.0,
"total_errors": self.total_errors,
"total_warnings": self.total_warnings,
"failed_sample_ids": self.failed_sample_ids,
}
def summarize(reports: list[ValidationReport]) -> ValidationSummary:
"""Aggregate a list of per-sample validation reports into a batch summary."""
passed = sum(1 for r in reports if r.passed)
return ValidationSummary(
total=len(reports),
passed=passed,
failed=len(reports) - passed,
total_errors=sum(len(r.errors) for r in reports),
total_warnings=sum(len(r.warnings) for r in reports),
failed_sample_ids=[r.sample_id for r in reports if not r.passed],
)
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