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  1. def incremental_filter(image_pool, space, style, mood, 
  2.                         color, materials, layout, lighting):
  3.  
  4.     # Stage 1 — Tier 1: hard filter, keep top 50%
  5.     candidates = filter_by(image_pool, [
  6.         CLIP_MAPPINGS["space"][space],
  7.         CLIP_MAPPINGS["color"][color],
  8.         ", ".join([CLIP_MAPPINGS["materials"][m] for m in materials]),
  9.     ], keep_top=0.5)
  10.  
  11.     # Stage 2 — Tier 2: re-rank, keep top 50% of remaining
  12.     candidates = filter_by(candidates, [
  13.         CLIP_MAPPINGS["style"][style],
  14.         CLIP_MAPPINGS["lighting"][lighting],
  15.     ], keep_top=0.5)
  16.  
  17.     # Stage 3 — Tier 3: final re-rank
  18.     candidates = filter_by(candidates, [
  19.         CLIP_MAPPINGS["layout"][layout],
  20.         CLIP_MAPPINGS["mood"][mood],
  21.     ], keep_top=1.0)   # keep all, just re-rank
  22.  
  23.     return candidates
  24.  
  25.  
  26. def filter_by(images, descriptions, keep_top=0.5):
  27.     scored = []
  28.     for image in images:
  29.         score = sum(clip_similarity(image, d) for d in descriptions)
  30.         scored.append((image, score))
  31.  
  32.     scored.sort(key=lambda x: x[1], reverse=True)
  33.     cutoff = max(1, int(len(scored) * keep_top))
  34.     return [img for img, _ in scored[:cutoff]]# your code goes here
Success #stdin #stdout 0.01s 7100KB
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https://ideone.com/Zh2rw9
language:
Python (cpython 2.7.16)
created:
4 hours ago
visibility:
public

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