File size: 5,147 Bytes
97b64b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | """Renders a multi-panel debug figure for one generated sample.
Shows the full rendered document with the target box, detected bounding
box, and detected polygon overlaid; a crop of the signature field; a mask
reconstructed from the detected ink polygon; the resolved coverage metrics;
and a difficulty-component breakdown bar chart.
"""
from __future__ import annotations
from pathlib import Path
from typing import Any
import cv2
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from src.utils.logging_utils import get_logger
logger = get_logger(__name__)
def _draw_overlays(image: np.ndarray, metadata: dict[str, Any]) -> np.ndarray:
out = image.copy()
geometry = metadata["geometry"]
def as_int_box(box):
return tuple(int(round(v)) for v in box)
tb = as_int_box(geometry["target_box"])
ab = as_int_box(geometry["actual_box"])
cv2.rectangle(out, (tb[0], tb[1]), (tb[2], tb[3]), (0, 200, 0), 2)
cv2.rectangle(out, (ab[0], ab[1]), (ab[2], ab[3]), (30, 80, 230), 2)
polygon = geometry.get("signature_polygon_px") or []
if len(polygon) >= 3:
pts = np.array(polygon, dtype=np.int32).reshape(-1, 1, 2)
cv2.polylines(out, [pts], isClosed=True, color=(220, 30, 30), thickness=2)
return out
def _crop_region(image: np.ndarray, box: list, pad: int = 25) -> np.ndarray:
h, w = image.shape[:2]
x1, y1, x2, y2 = (int(round(v)) for v in box)
x1, y1 = max(x1 - pad, 0), max(y1 - pad, 0)
x2, y2 = min(x2 + pad, w), min(y2 + pad, h)
if x2 <= x1 or y2 <= y1:
return image
return image[y1:y2, x1:x2]
def _reconstruct_mask(shape: tuple[int, int], box: list, polygon: list) -> np.ndarray:
mask = np.zeros(shape, dtype=np.uint8)
if len(polygon) >= 3:
pts = np.array(polygon, dtype=np.int32).reshape(-1, 1, 2)
cv2.fillPoly(mask, [pts], 255)
x1, y1, x2, y2 = (int(round(v)) for v in box)
pad = 25
x1c, y1c = max(x1 - pad, 0), max(y1 - pad, 0)
x2c, y2c = min(x2 + pad, shape[1]), min(y2 + pad, shape[0])
if x2c <= x1c or y2c <= y1c:
return mask
return mask[y1c:y2c, x1c:x2c]
def save_visualization(path: "str | Path", final_image: np.ndarray, metadata: dict[str, Any]) -> None:
"""Render and save a multi-panel debug figure for one generated sample.
Args:
path: Output PNG path.
final_image: The fully rendered (and augmented) document.
metadata: The sample's full metadata dict, as built by
:class:`~src.metadata.metadata_exporter.MetadataExporter`.
"""
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
geometry = metadata["geometry"]
difficulty = metadata["difficulty"]
metrics = metadata["metrics"]
overlaid = _draw_overlays(final_image, metadata)
crop = _crop_region(final_image, geometry["actual_box"])
mask = _reconstruct_mask(final_image.shape[:2], geometry["actual_box"], geometry["signature_polygon_px"])
fig = plt.figure(figsize=(15, 9.5), dpi=110)
fig.suptitle(
f"{metadata['document_id']} | template={metadata['template']} | box={metadata['geometry']['target_box']}",
x=0.02, ha="left", fontsize=11, family="monospace",
)
grid = fig.add_gridspec(2, 3, width_ratios=[2.0, 1.0, 1.0], height_ratios=[1.0, 1.0])
ax_full = fig.add_subplot(grid[:, 0])
ax_full.imshow(overlaid)
ax_full.axis("off")
ax_crop = fig.add_subplot(grid[0, 1])
ax_crop.imshow(crop)
ax_crop.set_title("Signature field (cropped)")
ax_crop.axis("off")
ax_mask = fig.add_subplot(grid[0, 2])
ax_mask.imshow(mask, cmap="gray")
ax_mask.set_title("Detected ink mask")
ax_mask.axis("off")
ax_metrics = fig.add_subplot(grid[1, 1])
ax_metrics.axis("off")
metric_lines = [
f"box_coverage_pct: {metrics['box_coverage_pct']:.2f}",
f"containment_pct: {metrics['containment_pct']:.2f}",
f"ink_fill_ratio: {metrics['ink_fill_ratio']:.3f}",
f"polygon_overlap: {metrics['polygon_overlap_proxy']:.3f}",
f"dist_from_center: {metrics['distance_from_box_center_px']:.1f}px",
f"margin_util: {metrics['margin_utilization']:.3f}",
f"mean_dark_int: {metrics['mean_dark_intensity']:.1f}",
f"gray_stddev: {metrics['gray_stddev']:.1f}",
f"signer: {metadata['signer']['name']} ({metadata['signer']['role']})",
]
ax_metrics.text(0.0, 1.0, "\n".join(metric_lines), va="top", ha="left", family="monospace", fontsize=9)
ax_metrics.set_title("Coverage metrics")
ax_diff = fig.add_subplot(grid[1, 2])
components = ["misplacement", "whitespace", "faintness", "smallness", "low_texture"]
values = [difficulty[c] for c in components]
colors = ["#d62728" if v >= 0.66 else "#ff7f0e" if v >= 0.33 else "#2ca02c" for v in values]
ax_diff.barh(components, values, color=colors)
ax_diff.set_xlim(0, 1)
ax_diff.set_title(f"Difficulty: {difficulty['score']:.3f} ({difficulty['tier']})")
fig.tight_layout(rect=(0.0, 0.0, 1.0, 0.94))
fig.savefig(path)
plt.close(fig)
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