import os
# 🚀 强制使用国内镜像
os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"

import torch
import torch.nn as nn
import torch.nn.functional as F
import cv2
import numpy as np
import argparse
from PIL import Image
from torchvision import transforms
from diffusers import StableDiffusionPipeline
from tqdm import tqdm

# =========================================================================
# PART 1: DiffSim 官方核心逻辑还原
# 基于: https://github.com/showlab/DiffSim/blob/main/diffsim/models/diffsim.py
# =========================================================================

class DiffSim(nn.Module):
    def __init__(self, model_id="Manojb/stable-diffusion-2-1-base", device="cuda"):
        super().__init__()
        self.device = device
        print(f"🚀 [Core] Loading Official DiffSim Logic (Backbone: {model_id})...")
        
        # 1. 加载 SD 模型
        try:
            self.pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(device)
        except Exception as e:
            print(f"❌ 模型加载失败，尝试加载默认 ID... Error: {e}")
            self.pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", torch_dtype=torch.float16).to(device)
            
        self.pipe.set_progress_bar_config(disable=True)
        
        # 2. 冻结参数 (Freeze)
        self.pipe.vae.requires_grad_(False)
        self.pipe.unet.requires_grad_(False)
        self.pipe.text_encoder.requires_grad_(False)
        
        # 3. 预计算空文本 Embedding (Unconditional Guidance)
        with torch.no_grad():
            prompt = ""
            text_input = self.pipe.tokenizer(prompt, padding="max_length", max_length=77, truncation=True, return_tensors="pt")
            self.empty_embeds = self.pipe.text_encoder(text_input.input_ids.to(device))[0]

        self.features = {}
        self._register_official_hooks()

    def _register_official_hooks(self):
        """
        DiffSim 官方策略: 提取 up_blocks.1 (Semantic) 和 up_blocks.2 (Structure)
        """
        self.target_layers = {
            "up_blocks.1.resnets.1": "feat_semantic",  # 语义层
            "up_blocks.2.resnets.1": "feat_structure"  # 结构层
        }
        
        print(f"🔧 [Hook] Registered Layers: {list(self.target_layers.values())}")
        
        for name, layer in self.pipe.unet.named_modules():
            if name in self.target_layers:
                alias = self.target_layers[name]
                layer.register_forward_hook(self._get_hook(alias))

    def _get_hook(self, name):
        def hook(model, input, output):
            self.features[name] = output
        return hook

    def extract_features(self, images):
        # VAE Encoding
        latents = self.pipe.vae.encode(images).latent_dist.sample() * self.pipe.vae.config.scaling_factor
        
        # UNet Inference
        t = torch.zeros(latents.shape[0], device=self.device, dtype=torch.long)
        encoder_hidden_states = self.empty_embeds.expand(latents.shape[0], -1, -1)
        
        self.features = {} # Reset buffer
        self.pipe.unet(latents, t, encoder_hidden_states=encoder_hidden_states)
        
        return {k: v.clone() for k, v in self.features.items()}

    def calculate_robust_similarity(self, feat_a, feat_b, kernel_size=3):
        """
        官方核心算法: Spatially Robust Similarity
        公式: S(p) = max_{q in Neighbor(p)} cos(F1(p), F2(q))
        """
        # Normalize vectors
        feat_a = F.normalize(feat_a, dim=1)
        feat_b = F.normalize(feat_b, dim=1)
        
        if kernel_size <= 1:
            # 严格对齐 (Pixel-wise Cosine Similarity)
            return (feat_a * feat_b).sum(dim=1)
        
        # 邻域搜索 (Sliding Window Matching)
        b, c, h, w = feat_b.shape
        padding = kernel_size // 2
        
        # Unfold feature B to find neighbors
        feat_b_unfolded = F.unfold(feat_b, kernel_size=kernel_size, padding=padding)
        feat_b_unfolded = feat_b_unfolded.view(b, c, kernel_size*kernel_size, h, w)
        
        # Calculate cosine sim between A and all neighbors of B
        # Shape: [B, K*K, H, W]
        sim_map = (feat_a.unsqueeze(2) * feat_b_unfolded).sum(dim=1)
        
        # Take the best match (Max Pooling logic)
        best_sim, _ = sim_map.max(dim=1)
        
        return best_sim

    def forward(self, batch_t1, batch_t2, w_struct, w_sem, kernel_size):
        f1 = self.extract_features(batch_t1)
        f2 = self.extract_features(batch_t2)
        
        total_dist = 0
        
        # Semantic Distance
        if w_sem > 0 and "feat_semantic" in f1:
            sim = self.calculate_robust_similarity(f1["feat_semantic"], f2["feat_semantic"], kernel_size)
            dist = 1.0 - sim
            total_dist += dist.mean(dim=[1, 2]) * w_sem
            
        # Structure Distance
        if w_struct > 0 and "feat_structure" in f1:
            sim = self.calculate_robust_similarity(f1["feat_structure"], f2["feat_structure"], kernel_size)
            dist = 1.0 - sim
            total_dist += dist.mean(dim=[1, 2]) * w_struct
            
        return total_dist

# =========================================================================
# PART 2: 增强后处理逻辑 (Post-Processing)
# 这一部分不在 DiffSim 官方库中，是为了实际工程落地增加的去噪模块
# =========================================================================

def engineering_post_process(heatmap_full, img_bg, args, patch_size):
    h, w = heatmap_full.shape
    
    # 1. 动态范围归一化
    # 避免最大值过小(纯净背景)时，强制放大噪点
    local_max = heatmap_full.max()
    safe_max = max(local_max, 0.25) # 设定一个基准置信度，低于此值不拉伸
    
    heatmap_norm = (heatmap_full / safe_max * 255).clip(0, 255).astype(np.uint8)
    
    # 保存原始数据供调试
    cv2.imwrite("debug_raw_heatmap.png", heatmap_norm)
    
    # 2. 高斯滤波 (去散斑)
    heatmap_blur = cv2.GaussianBlur(heatmap_norm, (5, 5), 0)
    
    # 3. 阈值截断 (Hard Thresholding)
    _, binary = cv2.threshold(heatmap_blur, int(255 * args.thresh), 255, cv2.THRESH_BINARY)
    
    # 4. 形态学闭运算 (Merging)
    # 将破碎的邻近区域融合为一个整体
    kernel_morph = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
    binary_closed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel_morph)
    
    # 5. 可视化绘制
    heatmap_color = cv2.applyColorMap(heatmap_norm, cv2.COLORMAP_JET)
    result_img = cv2.addWeighted(img_bg, 0.4, heatmap_color, 0.6, 0)
    
    contours, _ = cv2.findContours(binary_closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    box_count = 0
    # 面积过滤: 忽略小于切片面积 3% 的噪点
    min_area = (patch_size ** 2) * 0.03
    
    for cnt in contours:
        area = cv2.contourArea(cnt)
        if area > min_area:
            box_count += 1
            x, y, bw, bh = cv2.boundingRect(cnt)
            
            # 绘制：白边 + 红框
            cv2.rectangle(result_img, (x, y), (x+bw, y+bh), (255, 255, 255), 4)
            cv2.rectangle(result_img, (x, y), (x+bw, y+bh), (0, 0, 255), 2)
            
            # 分数标签
            score_val = heatmap_full[y:y+bh, x:x+bw].mean()
            label = f"{score_val:.2f}"
            
            # 标签背景
            cv2.rectangle(result_img, (x, y-22), (x+55, y), (0,0,255), -1)
            cv2.putText(result_img, label, (x+5, y-6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255,255,255), 2)
            
    return result_img, box_count

# =========================================================================
# PART 3: 执行脚本
# =========================================================================

def main():
    parser = argparse.ArgumentParser(description="DiffSim Local Implementation")
    parser.add_argument("t1", help="Reference Image")
    parser.add_argument("t2", help="Query Image")
    parser.add_argument("out", default="result.jpg")
    
    # DiffSim 官方推荐参数
    parser.add_argument("--w_struct", type=float, default=0.4)
    parser.add_argument("--w_sem", type=float, default=0.6)
    parser.add_argument("--kernel", type=int, default=3, help="Robust Kernel Size (1, 3, 5)")
    
    # 工程化参数
    parser.add_argument("--gamma", type=float, default=1.0)
    parser.add_argument("--thresh", type=float, default=0.3)
    parser.add_argument("-c", "--crop", type=int, default=224)
    parser.add_argument("-b", "--batch", type=int, default=16)
    parser.add_argument("--model", default="Manojb/stable-diffusion-2-1-base")
    
    # 兼容性冗余参数
    parser.add_argument("--step", type=int, default=0)
    parser.add_argument("--w_tex", type=float, default=0.0)

    args = parser.parse_args()
    
    # 1. Image IO
    t1 = cv2.imread(args.t1)
    t2 = cv2.imread(args.t2)
    if t1 is None or t2 is None:
        print("❌ Error reading images.")
        return

    # Resize to match T2
    h, w = t2.shape[:2]
    t1 = cv2.resize(t1, (w, h))
    
    # 2. Preprocessing
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5])
    ])
    
    patches1, patches2, coords = [], [], []
    stride = args.crop // 2  # 50% Overlap
    
    print(f"🔪 Slicing images ({w}x{h}) with stride {stride}...")
    for y in range(0, h - args.crop + 1, stride):
        for x in range(0, w - args.crop + 1, stride):
            c1 = t1[y:y+args.crop, x:x+args.crop]
            c2 = t2[y:y+args.crop, x:x+args.crop]
            
            p1 = transform(Image.fromarray(cv2.cvtColor(c1, cv2.COLOR_BGR2RGB)))
            p2 = transform(Image.fromarray(cv2.cvtColor(c2, cv2.COLOR_BGR2RGB)))
            
            patches1.append(p1); patches2.append(p2); coords.append((x, y))
            
    if not patches1: return

    # 3. Model Inference
    model = DiffSim(args.model)
    scores = []
    
    print(f"🧠 Running DiffSim Inference on {len(patches1)} patches...")
    with torch.no_grad():
        for i in tqdm(range(0, len(patches1), args.batch)):
            b1 = torch.stack(patches1[i:i+args.batch]).to("cuda", dtype=torch.float16)
            b2 = torch.stack(patches2[i:i+args.batch]).to("cuda", dtype=torch.float16)
            
            batch_dist = model(b1, b2, args.w_struct, args.w_sem, args.kernel)
            scores.append(batch_dist.cpu())
            
    all_scores = torch.cat(scores).float().numpy()
    
    # 4. Reconstruct Heatmap
    heatmap_full = np.zeros((h, w), dtype=np.float32)
    count_map = np.zeros((h, w), dtype=np.float32) + 1e-6
    
    # Apply Gamma *before* merging
    if args.gamma != 1.0:
        all_scores = np.power(all_scores, args.gamma)
        
    for idx, score in enumerate(all_scores):
        x, y = coords[idx]
        heatmap_full[y:y+args.crop, x:x+args.crop] += score
        count_map[y:y+args.crop, x:x+args.crop] += 1
        
    heatmap_avg = heatmap_full / count_map
    
    # 5. Post-Processing & Draw
    print("🎨 Post-processing results...")
    final_img, count = engineering_post_process(heatmap_avg, t2, args, args.crop)
    
    cv2.imwrite(args.out, final_img)
    print(f"✅ Done! Found {count} regions. Saved to {args.out}")

if __name__ == "__main__":
    main()