205 lines
8.4 KiB
Python
205 lines
8.4 KiB
Python
import os
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# 🔥 强制设置 HF 镜像
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os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
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import sys
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import cv2
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import numpy as np
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import argparse
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from PIL import Image
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from torchvision import transforms
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from diffusers import StableDiffusionPipeline
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from tqdm import tqdm
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# === 配置 ===
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MODEL_ID = "Manojb/stable-diffusion-2-1-base"
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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THRESHOLD = 0.40 # ⬆️ 稍微调高阈值，进一步过滤误报
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IMG_RESIZE = 224
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class DiffSimSemantic(nn.Module):
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def __init__(self, device):
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super().__init__()
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print(f"🚀 [系统] 初始化 DiffSim (语义增强版)...")
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self.pipe = StableDiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.float16).to(device)
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self.pipe.set_progress_bar_config(disable=True)
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# 冻结参数
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self.pipe.vae.requires_grad_(False)
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self.pipe.unet.requires_grad_(False)
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self.pipe.text_encoder.requires_grad_(False)
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# 预计算空文本 Embedding
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with torch.no_grad():
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prompt = ""
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text_inputs = self.pipe.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.pipe.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids.to(device)
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self.empty_text_embeds = self.pipe.text_encoder(text_input_ids)[0]
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self.features = {}
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# 🔥 修改 Hooks：只抓取深层特征，忽略浅层纹理
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# up_blocks.1 (纹理层) -> ❌ 移除，太敏感，容易误报
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# up_blocks.2 (结构层) -> ✅ 保留，判断形状
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# up_blocks.3 (语义层) -> ✅ 核心，判断物体类别
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for name, layer in self.pipe.unet.named_modules():
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# 我们不再 Hook up_blocks.1，因为它对光照和纹理太敏感
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if "up_blocks.2" in name and name.endswith("resnets.2"):
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layer.register_forward_hook(self.get_hook("feat_structure"))
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elif "up_blocks.3" in name and name.endswith("resnets.2"):
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layer.register_forward_hook(self.get_hook("feat_semantic"))
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def get_hook(self, name):
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def hook(model, input, output):
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self.features[name] = output
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return hook
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def extract_features(self, images):
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latents = self.pipe.vae.encode(images).latent_dist.sample() * self.pipe.vae.config.scaling_factor
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batch_size = latents.shape[0]
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t = torch.zeros(batch_size, device=DEVICE, dtype=torch.long)
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encoder_hidden_states = self.empty_text_embeds.expand(batch_size, -1, -1)
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self.pipe.unet(latents, t, encoder_hidden_states=encoder_hidden_states)
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return {k: v.clone() for k, v in self.features.items()}
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def robust_similarity(self, f1, f2, kernel_size=5):
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"""
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抗视差匹配：
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🔥 将 kernel_size 默认值提升到 5
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允许更大的几何错位（应对30m高度的视差）
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"""
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f1 = F.normalize(f1, dim=1)
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f2 = F.normalize(f2, dim=1)
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padding = kernel_size // 2
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b, c, h, w = f2.shape
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f2_unfolded = F.unfold(f2, kernel_size=kernel_size, padding=padding)
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f2_unfolded = f2_unfolded.view(b, c, kernel_size*kernel_size, h, w)
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sim_map = (f1.unsqueeze(2) * f2_unfolded).sum(dim=1)
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max_sim, _ = sim_map.max(dim=1)
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return max_sim
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def compute_batch_distance(self, batch_p1, batch_p2):
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feat_a = self.extract_features(batch_p1)
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feat_b = self.extract_features(batch_p2)
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total_score = 0
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# 🔥 调整后的权重策略：纯粹关注结构和语义
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# 0.0 -> 纹理 (彻底忽略颜色深浅、阴影)
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# 0.4 -> 结构 (feat_structure): 关注形状变化
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# 0.6 -> 语义 (feat_semantic): 关注物体存在性 (最像 DreamSim 的部分)
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weights = {"feat_structure": 0.4, "feat_semantic": 0.6}
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for name, w in weights.items():
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fa, fb = feat_a[name].float(), feat_b[name].float()
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# 对所有层都启用抗视差匹配，增加鲁棒性
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# kernel_size=5 能容忍更大的像素位移
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sim_map = self.robust_similarity(fa, fb, kernel_size=5)
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dist = 1 - sim_map.mean(dim=[1, 2])
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total_score += dist * w
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return total_score
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# ==========================================
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# 辅助函数 (保持不变)
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# ==========================================
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def get_transforms():
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return transforms.Compose([
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transforms.Resize((IMG_RESIZE, IMG_RESIZE)),
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5])
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])
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def scan_and_draw(model, t1_path, t2_path, output_path, patch_size, stride, batch_size):
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img1_cv = cv2.imread(t1_path)
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img2_cv = cv2.imread(t2_path)
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if img1_cv is None or img2_cv is None: return
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h, w = img2_cv.shape[:2]
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img1_cv = cv2.resize(img1_cv, (w, h))
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preprocess = get_transforms()
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print(f"🔪 [切片] 开始扫描... 尺寸: {w}x{h}, 忽略纹理细节，专注语义差异")
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patches1, patches2, coords = [], [], []
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for y in range(0, h - patch_size + 1, stride):
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for x in range(0, w - patch_size + 1, stride):
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crop1 = img1_cv[y:y+patch_size, x:x+patch_size]
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crop2 = img2_cv[y:y+patch_size, x:x+patch_size]
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p1 = preprocess(Image.fromarray(cv2.cvtColor(crop1, cv2.COLOR_BGR2RGB)))
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p2 = preprocess(Image.fromarray(cv2.cvtColor(crop2, cv2.COLOR_BGR2RGB)))
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patches1.append(p1); patches2.append(p2); coords.append((x, y))
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if not patches1: return
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all_distances = []
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for i in tqdm(range(0, len(patches1), batch_size), unit="batch"):
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b1 = torch.stack(patches1[i:i+batch_size]).to(DEVICE, dtype=torch.float16)
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b2 = torch.stack(patches2[i:i+batch_size]).to(DEVICE, dtype=torch.float16)
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with torch.no_grad():
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all_distances.append(model.compute_batch_distance(b1, b2).cpu())
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distances = torch.cat(all_distances)
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heatmap = np.zeros((h, w), dtype=np.float32)
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count_map = np.zeros((h, w), dtype=np.float32)
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max_score = 0
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for idx, score in enumerate(distances):
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val = score.item()
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x, y = coords[idx]
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if val > max_score: max_score = val
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heatmap[y:y+patch_size, x:x+patch_size] += val
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count_map[y:y+patch_size, x:x+patch_size] += 1
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count_map[count_map == 0] = 1
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heatmap_avg = heatmap / count_map
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# 可视化
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norm_factor = max(max_score, 0.1)
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heatmap_vis = (heatmap_avg / norm_factor * 255).clip(0, 255).astype(np.uint8)
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heatmap_color = cv2.applyColorMap(heatmap_vis, cv2.COLORMAP_JET)
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blended_img = cv2.addWeighted(img2_cv, 0.4, heatmap_color, 0.6, 0)
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_, thresh = cv2.threshold(heatmap_vis, int(255 * THRESHOLD), 255, cv2.THRESH_BINARY)
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contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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result_img = blended_img.copy()
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for cnt in contours:
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if cv2.contourArea(cnt) > (patch_size**2)*0.05:
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x, y, bw, bh = cv2.boundingRect(cnt)
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cv2.rectangle(result_img, (x, y), (x+bw, y+bh), (255, 255, 255), 4)
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cv2.rectangle(result_img, (x, y), (x+bw, y+bh), (0, 0, 255), 2)
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label = f"Diff: {heatmap_avg[y:y+bh, x:x+bw].mean():.2f}"
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cv2.rectangle(result_img, (x, y-25), (x+130, y), (0,0,255), -1)
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cv2.putText(result_img, label, (x+5, y-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)
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output_full_path = output_path if os.path.isabs(output_path) else os.path.join("/app/data", output_path)
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os.makedirs(os.path.dirname(output_full_path), exist_ok=True)
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cv2.imwrite(output_full_path, result_img)
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print(f"🎯 完成! 最大差异分: {max_score:.4f}, 结果已保存: {output_full_path}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("t1"); parser.add_argument("t2"); parser.add_argument("out", nargs="?", default="result.jpg")
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parser.add_argument("-c", "--crop", type=int, default=224)
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parser.add_argument("-s", "--step", type=int, default=0)
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parser.add_argument("-b", "--batch", type=int, default=16)
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args = parser.parse_args()
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scan_and_draw(DiffSimSemantic(DEVICE), args.t1, args.t2, args.out, args.crop, args.step if args.step>0 else args.crop//2, args.batch)
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