279 lines
10 KiB
Python
279 lines
10 KiB
Python
import os
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# 🔥 强制设置 HF 镜像 (必须放在最前面)
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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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# 使用 SD 1.5，无需鉴权，且对小切片纹理更敏感
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MODEL_ID = "runwayml/stable-diffusion-v1-5"
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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THRESHOLD = 0.35
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IMG_RESIZE = 224
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# ==========================================
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# 🔥 核心：DiffSim Pro 模型定义 (修复版)
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# ==========================================
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class DiffSimPro(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 Pro (基于 {MODEL_ID})...")
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if device == "cuda":
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print(f"✅ [硬件确认] 正在使用显卡: {torch.cuda.get_device_name(0)}")
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else:
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print("❌ [警告] 未检测到显卡，正在使用 CPU 慢速运行！")
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# 1. 加载 SD 模型
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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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# 🔥【修复逻辑】：预先计算“空文本”的 Embedding
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# UNet 必须要有这个 encoder_hidden_states 参数才能运行
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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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# 获取空文本特征 [1, 77, 768]
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self.empty_text_embeds = self.pipe.text_encoder(text_input_ids)[0]
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# 2. 定义特征容器和 Hooks
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self.features = {}
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# 注册 Hooks：抓取 纹理(1)、结构(2)、语义(3)
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for name, layer in self.pipe.unet.named_modules():
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if "up_blocks.1" in name and name.endswith("resnets.2"):
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layer.register_forward_hook(self.get_hook("feat_high"))
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elif "up_blocks.2" in name and name.endswith("resnets.2"):
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layer.register_forward_hook(self.get_hook("feat_mid"))
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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_low"))
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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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""" VAE Encode -> UNet Forward -> Hook Features """
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# 1. VAE 编码
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latents = self.pipe.vae.encode(images).latent_dist.sample() * self.pipe.vae.config.scaling_factor
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# 2. 准备参数
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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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# 🔥【修复逻辑】：将空文本 Embedding 扩展到当前 Batch 大小
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# 形状变为 [batch_size, 77, 768]
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encoder_hidden_states = self.empty_text_embeds.expand(batch_size, -1, -1)
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# 3. UNet 前向传播 (带上 encoder_hidden_states)
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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=3):
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""" 抗视差匹配算法 """
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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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# 权重：结构层(mid)最重要
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weights = {"feat_high": 0.2, "feat_mid": 0.5, "feat_low": 0.3}
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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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if name == "feat_high":
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sim_map = self.robust_similarity(fa, fb, kernel_size=3)
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dist = 1 - sim_map.mean(dim=[1, 2])
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else:
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dist = 1 - F.cosine_similarity(fa.flatten(1), fb.flatten(1))
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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 & 扫描逻辑 (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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# 1. OpenCV 读取
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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:
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print("❌ 错误: 无法读取图片")
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return
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# 强制 Resize 对齐
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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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# 2. 准备滑动窗口
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print(f"🔪 [切片] 开始扫描... 尺寸: {w}x{h}")
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print(f" - 切片大小: {patch_size}, 步长: {stride}, 批次: {batch_size}")
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patches1 = []
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patches2 = []
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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)
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patches2.append(p2)
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coords.append((x, y))
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if not patches1:
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print("⚠️ 图片太小，无法切片")
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return
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total_patches = len(patches1)
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print(f"🧠 [推理] 共 {total_patches} 个切片，开始 DiffSim Pro 计算...")
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all_distances = []
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# 3. 批量推理
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for i in tqdm(range(0, total_patches, batch_size), unit="batch"):
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batch_p1 = torch.stack(patches1[i : i + batch_size]).to(DEVICE, dtype=torch.float16)
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batch_p2 = torch.stack(patches2[i : i + batch_size]).to(DEVICE, dtype=torch.float16)
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with torch.no_grad():
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dist_batch = model.compute_batch_distance(batch_p1, batch_p2)
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all_distances.append(dist_batch.cpu())
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distances = torch.cat(all_distances)
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# 4. 生成原始热力数据
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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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# 5. 后处理
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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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alpha = 0.4
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beta = 1.0 - alpha
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blended_img = cv2.addWeighted(img2_cv, alpha, heatmap_color, beta, 0)
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# 画框
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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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found_issue = False
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for cnt in contours:
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area = cv2.contourArea(cnt)
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min_area = (patch_size * patch_size) * 0.05
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if area > min_area:
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found_issue = True
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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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roi_score = heatmap_avg[y:y+bh, x:x+bw].mean()
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label = f"Diff: {roi_score:.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
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if not os.path.isabs(output_path) and not output_path.startswith("."):
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output_full_path = os.path.join("/app/data", output_path)
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os.makedirs(os.path.dirname(output_full_path) if os.path.dirname(output_full_path) else ".", exist_ok=True)
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cv2.imwrite(output_full_path, result_img)
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print("="*40)
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print(f"🎯 扫描完成! 最大差异分: {max_score:.4f}")
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if found_issue:
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print(f"⚠️ 警告: 检测到潜在违建区域!")
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print(f"🖼️ 热力图结果已保存至: {output_full_path}")
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print("="*40)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="DiffSim Pro 违建检测 (抗视差版)")
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parser.add_argument("t1", help="基准图路径")
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parser.add_argument("t2", help="现状图路径")
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parser.add_argument("out", nargs="?", default="heatmap_diffsim.jpg", help="输出文件名")
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parser.add_argument("-c", "--crop", type=int, default=224, help="切片大小")
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parser.add_argument("-s", "--step", type=int, default=0, help="滑动步长")
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parser.add_argument("-b", "--batch", type=int, default=16, help="批次大小")
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args = parser.parse_args()
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stride = args.step if args.step > 0 else args.crop // 2
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# 初始化模型
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diffsim_model = DiffSimPro(DEVICE)
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print(f"📂 启动热力图扫描: {args.t1} vs {args.t2}")
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scan_and_draw(diffsim_model, args.t1, args.t2, args.out, args.crop, stride, args.batch)
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