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e16aadc 1c927c2 e16aadc 1c927c2 e16aadc 1c927c2 e16aadc 1c927c2 e16aadc 1c927c2 e16aadc 1c927c2 e16aadc | 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 | """场景匹配器 — DINOv2-only 模式（无 MASt3R）
复杂场景判定改为纯 DINOv2 余弦相似度，不调用 MASt3R 3D 几何匹配。
阈值由 config.DINOV2_ONLY_SAME_THRESHOLD 控制。
支持 DINOv2 patch 级别匹配可视化。
"""
import torch
from module.config import DEVICE, DINOV2_ONLY_SAME_THRESHOLD
class SceneMatcher:
"""DINOv2-only 场景匹配器"""
def __init__(self, model=None, dinov2_extractor=None, device=None):
self.model = model # None（MASt3R 已禁用）
self.dinov2_extractor = dinov2_extractor
self.device = device or DEVICE
def compare(self, img_path1, img_path2, dinov2_sim_override=None):
"""比对两张图片是否为同一场景（DINOv2-only）
Returns:
dict: 与原始 SceneMatcher.compare() 兼容的结果格式
"""
# 获取 DINOv2 相似度
if dinov2_sim_override is not None:
dinov2_sim = float(dinov2_sim_override)
elif self.dinov2_extractor is not None:
try:
dinov2_sim = self.dinov2_extractor.compute_similarity(img_path1, img_path2)
if dinov2_sim is None:
dinov2_sim = 0.0
except Exception:
dinov2_sim = 0.0
else:
dinov2_sim = 0.0
# DINOv2-only 判定
is_same = dinov2_sim >= DINOV2_ONLY_SAME_THRESHOLD
# 如果判定为同一场景，计算 patch 匹配信息（用于可视化）
patch_match_info = None
if is_same and self.dinov2_extractor is not None:
try:
patch_match_info = self.dinov2_extractor.compute_patch_matches(img_path1, img_path2, top_k=50)
except Exception as e:
print(f" [可视化] patch 匹配计算失败: {e}")
return {
'is_same_scene': bool(is_same),
'mast3r_is_same': None,
'dinov2_is_same': bool(is_same),
'similarity_score': round(float(dinov2_sim), 4),
'match_count': len(patch_match_info['matches']) if patch_match_info else 0,
'raw_match_count': len(patch_match_info['matches']) if patch_match_info else 0,
'inlier_ratio': 0.0,
'avg_confidence': round(float(dinov2_sim), 4),
'dinov2_similarity': round(float(dinov2_sim), 4),
'gamma_info': [],
'patch_match_info': patch_match_info,
}
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