import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import torch
import torchvision.transforms as transforms
from mpl_toolkits.mplot3d import Axes3D
from tqdm import tqdm
import clip  # For CLIP model

def load_models(device):
    """Load both DINOv2 and CLIP models"""
    # DINOv2
    dinov2 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14').to(device).eval()
    
    # CLIP
    clip_model, clip_preprocess = clip.load("ViT-B/32", device=device)
    
    return {
        'dinov2': dinov2,
        'clip': clip_model,
        'clip_preprocess': clip_preprocess
    }

def load_frames(folder_path, frame_ext='.png'):
    frame_files = sorted(
        [f for f in os.listdir(folder_path) if f.endswith(frame_ext)],
        key=lambda x: int(x.split('.')[0]))
    return [Image.open(os.path.join(folder_path, f)).convert('RGB') for f in tqdm(frame_files, desc="Loading frames")]

def extract_embeddings(frames, model_dict, model_type, batch_size=8, device='cuda'):
    embeddings = []
    
    with torch.no_grad():
        if model_type == 'dinov2':
            transform = transforms.Compose([
                transforms.Resize((224, 224)),
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
            ])
            
            for i in tqdm(range(0, len(frames), batch_size), desc="Extracting DINOv2 embeddings"):
                batch = torch.stack([transform(frames[j]) for j in range(i, min(i+batch_size, len(frames)))]).to(device)
                outputs = model_dict['dinov2'](batch)
                embeddings.append(outputs.cpu().numpy())
                
        elif model_type == 'clip':
            for i in tqdm(range(0, len(frames), batch_size), desc="Extracting CLIP embeddings"):
                batch = [model_dict['clip_preprocess'](frames[j]) for j in range(i, min(i+batch_size, len(frames)))]
                batch = torch.stack(batch).to(device)
                outputs = model_dict['clip'].encode_image(batch)
                embeddings.append(outputs.cpu().numpy())
    
    return np.concatenate(embeddings)

def visualize_tsne(embeddings, title_suffix="", perplexity=15):
    plt.figure(figsize=(20, 8))
    
    # 2D t-SNE
    plt.subplot(1, 2, 1)
    tsne_2d = TSNE(n_components=2, perplexity=perplexity, random_state=42)
    emb_2d = tsne_2d.fit_transform(embeddings)
    plt.scatter(emb_2d[:, 0], emb_2d[:, 1], c=range(len(embeddings)), cmap='viridis', alpha=0.7)
    plt.colorbar(label='Frame Number')
    plt.title(f'2D t-SNE {title_suffix}')
    
    # 3D t-SNE
    ax = plt.subplot(1, 2, 2, projection='3d')
    tsne_3d = TSNE(n_components=3, perplexity=perplexity, random_state=42)
    emb_3d = tsne_3d.fit_transform(embeddings)
    sc = ax.scatter(emb_3d[:, 0], emb_3d[:, 1], emb_3d[:, 2], 
                   c=range(len(embeddings)), cmap='viridis', alpha=0.7)
    plt.colorbar(sc, label='Frame Number')
    ax.set_title(f'3D t-SNE {title_suffix}')
    
    plt.tight_layout()
    plt.show()

def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('folder_path', type=str)
    parser.add_argument('--ext', type=str, default='.png')
    parser.add_argument('--batch_size', type=int, default=8)
    parser.add_argument('--perplexity', type=int, default=15)
    args = parser.parse_args()
    
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    print(f"Using device: {device}")
    
    # Load models
    print("Loading models...")
    models = load_models(device)
    frames = load_frames(args.folder_path, args.ext)
    
    # DINOv2 Visualization
    print("\nProcessing DINOv2 embeddings...")
    dinov2_emb = extract_embeddings(frames, models, 'dinov2', args.batch_size, device)
    visualize_tsne(dinov2_emb, "(DINOv2)", args.perplexity)
    
    # CLIP Visualization
    print("\nProcessing CLIP embeddings...")
    clip_emb = extract_embeddings(frames, models, 'clip', args.batch_size, device)
    visualize_tsne(clip_emb, "(CLIP)", args.perplexity)

if __name__ == '__main__':
    main()