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79574cc dbab656 79574cc dbab656 79574cc dbab656 79574cc | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 | """Rendu HTML « Profil d'image du corpus » — Sprint 93 (A.II.7).
Suite directe ``picarones/core/image_predictive.py``. Pattern
identique aux autres rendus : server-side, pas de JS, anti-
injection systématique.
Vue
---
Deux blocs dans une section unique :
1. **Complexité paléographique** : moyenne, médiane, min, max,
écart-type sur l'ensemble du corpus.
2. **Homogénéité du corpus** : score combiné + détail par
feature (mean, stdev, contribution normalisée).
Adaptive : ``""`` si pas de données.
Note d'intégration
------------------
Module pur — l'utilisateur compose :
.. code-block:: python
from picarones.measurements.image_predictive import aggregate_corpus_predictive
from picarones.report.image_predictive_render import (
build_image_predictive_html,
)
qualities = [doc.image_quality.as_dict() for doc in benchmark.docs]
agg = aggregate_corpus_predictive(qualities)
html = build_image_predictive_html(agg, labels)
"""
from __future__ import annotations
from html import escape as _e
from typing import Optional
def _color_for_score(score: float) -> str:
"""Vert (faible) → orange → rouge (élevé)."""
f = max(0.0, min(1.0, score))
if f < 0.5:
t = f / 0.5
r = int(167 + (235 - 167) * t)
g = int(240 + (180 - 240) * t)
b = int(167 + (60 - 167) * t)
else:
t = (f - 0.5) / 0.5
r = int(235 + (220 - 235) * t)
g = int(180 + (50 - 180) * t)
b = int(60 + (50 - 60) * t)
return f"#{r:02x}{g:02x}{b:02x}"
_FEATURE_LABEL_KEYS = {
"noise_level": "imgpred_feat_noise",
"sharpness_score": "imgpred_feat_sharpness",
"contrast_score": "imgpred_feat_contrast",
"rotation_degrees": "imgpred_feat_rotation",
}
def _render_complexity_block(
aggregated: dict, labels: dict[str, str],
) -> str:
h_complex = labels.get(
"imgpred_complexity", "Complexité paléographique",
)
h_mean = labels.get("imgpred_mean", "Moyenne")
h_median = labels.get("imgpred_median", "Médiane")
h_min = labels.get("imgpred_min", "Min")
h_max = labels.get("imgpred_max", "Max")
h_stdev = labels.get("imgpred_stdev", "Écart-type")
h_docs = labels.get("imgpred_docs", "Docs")
mean = float(aggregated.get("complexity_mean") or 0.0)
median = float(aggregated.get("complexity_median") or 0.0)
mn = float(aggregated.get("complexity_min") or 0.0)
mx = float(aggregated.get("complexity_max") or 0.0)
sd = float(aggregated.get("complexity_stdev") or 0.0)
n_docs = int(aggregated.get("n_docs") or 0)
color_mean = _color_for_score(mean)
return (
f'<div style="font-weight:600;margin:.4rem 0 .3rem 0">'
f'{_e(h_complex)}</div>'
'<table style="border-collapse:collapse;width:100%;'
'font-size:.9rem;margin-bottom:.8rem">'
f'<thead><tr>'
f'<th style="padding:.4rem .6rem;text-align:right;'
f'border-bottom:1px solid #ccc;font-weight:600">{_e(h_mean)}</th>'
f'<th style="padding:.4rem .6rem;text-align:right;'
f'border-bottom:1px solid #ccc;font-weight:600">{_e(h_median)}</th>'
f'<th style="padding:.4rem .6rem;text-align:right;'
f'border-bottom:1px solid #ccc;font-weight:600">{_e(h_min)}</th>'
f'<th style="padding:.4rem .6rem;text-align:right;'
f'border-bottom:1px solid #ccc;font-weight:600">{_e(h_max)}</th>'
f'<th style="padding:.4rem .6rem;text-align:right;'
f'border-bottom:1px solid #ccc;font-weight:600">{_e(h_stdev)}</th>'
f'<th style="padding:.4rem .6rem;text-align:right;'
f'border-bottom:1px solid #ccc;font-weight:600">{_e(h_docs)}</th>'
f'</tr></thead>'
f'<tbody><tr>'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'background:{color_mean};font-family:monospace;font-weight:600">'
f'{mean:.3f}</td>'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'font-family:monospace">{median:.3f}</td>'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'font-family:monospace">{mn:.3f}</td>'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'font-family:monospace">{mx:.3f}</td>'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'font-family:monospace">{sd:.3f}</td>'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'font-family:monospace">{n_docs}</td>'
f'</tr></tbody></table>'
)
def _render_homogeneity_block(
homogeneity: dict, labels: dict[str, str],
) -> str:
h_homo = labels.get(
"imgpred_homogeneity", "Homogénéité du corpus",
)
h_feat = labels.get("imgpred_feature", "Feature")
h_mean = labels.get("imgpred_feat_mean", "Moyenne")
h_stdev = labels.get("imgpred_feat_stdev", "Écart-type")
h_norm = labels.get(
"imgpred_feat_norm", "Contribution normalisée",
)
score = float(homogeneity.get("score") or 0.0)
color = _color_for_score(score)
parts = [
f'<div style="font-weight:600;margin:.4rem 0 .3rem 0">'
f'{_e(h_homo)} : '
f'<span style="background:{color};padding:.1rem .4rem;'
f'border-radius:.3rem;font-family:monospace">{score:.3f}</span>'
f'</div>',
'<table style="border-collapse:collapse;width:100%;'
'font-size:.9rem">',
'<thead><tr>',
]
for col in (h_feat, h_mean, h_stdev, h_norm):
parts.append(
f'<th style="padding:.4rem .6rem;text-align:left;'
f'border-bottom:1px solid #ccc;font-weight:600">'
f'{_e(col)}</th>'
)
parts.append("</tr></thead><tbody>")
per_feat = homogeneity.get("per_feature") or {}
for key, label_key in _FEATURE_LABEL_KEYS.items():
if key not in per_feat:
continue
slot = per_feat[key]
feat_label = labels.get(label_key, key)
feat_mean = float(slot.get("mean") or 0.0)
feat_stdev = float(slot.get("stdev") or 0.0)
feat_norm = float(slot.get("normalised") or 0.0)
norm_color = _color_for_score(feat_norm)
parts.append(
f'<tr>'
f'<td style="padding:.4rem .6rem">{_e(feat_label)}</td>'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'font-family:monospace">{feat_mean:.3f}</td>'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'font-family:monospace">{feat_stdev:.3f}</td>'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'background:{norm_color};font-family:monospace">'
f'{feat_norm:.3f}</td>'
f'</tr>'
)
parts.append("</tbody></table>")
return "".join(parts)
def build_image_predictive_html(
aggregated: Optional[dict],
labels: Optional[dict[str, str]] = None,
) -> str:
"""Construit la vue HTML « Profil d'image du corpus ».
Parameters
----------
aggregated:
Sortie de ``aggregate_corpus_predictive``. Si ``None``
ou ``n_docs == 0``, retourne ``""``.
labels:
Dict i18n. Clés sous le préfixe ``imgpred_*``.
"""
if not aggregated:
return ""
if not aggregated.get("n_docs"):
return ""
labels = labels or {}
title = labels.get(
"imgpred_title", "Profil d'image du corpus",
)
note = labels.get(
"imgpred_note",
"Score de complexité paléographique combinant bruit, "
"flou, faible contraste et rotation. Le score "
"d'homogénéité signale si la moyenne globale est fiable "
"(corpus uniforme) ou trompeuse (corpus hétérogène — "
"voir alors la vue stratifiée).",
)
parts = [
'<section class="imgpred-section" style="margin:1rem 0">',
f'<h3 style="margin:0 0 .3rem 0">{_e(title)}</h3>',
f'<div style="font-size:.85rem;opacity:.75;margin-bottom:.6rem">'
f'{_e(note)}</div>',
]
parts.append(_render_complexity_block(aggregated, labels))
homo = aggregated.get("homogeneity")
if isinstance(homo, dict):
parts.append(_render_homogeneity_block(homo, labels))
parts.append("</section>")
return "".join(parts)
__all__ = ["build_image_predictive_html"]
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