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fb9c7be 4a5ed93 fb9c7be 4a5ed93 fb9c7be 4a5ed93 fb9c7be 4a5ed93 fb9c7be | 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 | """
Pure feature display logic — no Bokeh dependencies.
Builds the data needed to render a feature's detail view: stats HTML,
top MEI image list, and NSD sample basename. Called by panels/feature.py.
"""
import os
from PIL import Image as _PILImage
from .rendering import (
THUMB, load_image, render_zoomed_overlay, _img_pool,
)
def build_stats_html(feat: int, freq_val, feat_name: str,
auto_name: str) -> str:
"""Return the feature header HTML (name + label)."""
if freq_val == 0:
label = ('<span style="color:#dc2626;font-size:13px;margin-left:8px;'
'font-weight:500">dead feature</span>')
elif feat_name:
label = (f'<div style="color:#2563eb;font-style:italic;'
f'font-size:14px;margin-top:4px">{feat_name}</div>')
elif auto_name:
label = (f'<div style="color:#059669;font-style:italic;'
f'font-size:14px;margin-top:4px">{auto_name}</div>')
else:
label = ''
return (f'<h2 style="margin:4px 0;font-size:22px;font-weight:700;'
f'color:#1a1d23">Feature {feat}</h2>' + label)
def build_top_mei_items(feat: int, ds: dict, n_display: int = 9,
zoom_patches: int = 16,
heatmap_alpha: float = 1.0):
"""Load and render top MEI images for a feature.
Returns (top_infos, top_img_is, subset_label) where:
top_infos — list of (PIL.Image, caption_str) tuples
top_img_is — parallel list of dataset image indices
subset_label — " [NSD sub01]" or ""
"""
use_nsd = ds.get('nsd_top_img_idx') is not None
top_idx = ds['nsd_top_img_idx'] if use_nsd else ds['top_img_idx']
top_hm = ds.get('nsd_top_heatmaps') if use_nsd else ds.get('top_heatmaps')
subset_label = " [NSD sub01]" if use_nsd else ""
def _render_one(ranking_idx):
img_i = top_idx[feat, ranking_idx].item()
try:
hm = None
if top_hm is not None and ds['heatmap_patch_grid'] > 1:
hm = top_hm[feat, ranking_idx].float().numpy()
hm = hm.reshape(ds['heatmap_patch_grid'],
ds['heatmap_patch_grid'])
if hm is None:
plain = load_image(img_i).resize((THUMB, THUMB))
return (plain, "")
img_out = render_zoomed_overlay(
img_i, hm, size=THUMB,
zoom_patches=zoom_patches,
alpha=heatmap_alpha,
center='peak',
)
return (img_out, "")
except (FileNotFoundError, OSError):
return None
except Exception as e:
return (_PILImage.new("RGB", (THUMB, THUMB), "gray"),
f"Error: {e}")
# Build work list
work = []
for j in range(min(n_display, top_idx.shape[1])):
img_i = top_idx[feat, j].item()
if img_i < 0:
break
work.append((j, img_i))
# Load MEIs in parallel
top_infos = []
top_img_is = []
for (j, img_i), item in zip(
work, _img_pool.map(lambda w: _render_one(w[0]), work)):
if item is not None:
top_infos.append(item)
top_img_is.append(img_i)
return top_infos, top_img_is, subset_label
def get_nsd_sample_basename(feat: int, ds: dict) -> str | None:
"""Return the NSD basename for the top image of a feature, or None."""
if ds.get('nsd_top_img_idx') is None:
return None
top_i = ds['nsd_top_img_idx'][feat, 0].item()
if top_i < 0:
return None
return os.path.splitext(os.path.basename(ds['image_paths'][top_i]))[0]
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