#!/usr/bin/env python3
"""
快速测试脚本 - 验证MedSAM3流程是否正常工作
处理单个病例并显示结果
"""

import os
import sys
import numpy as np
import torch
from pathlib import Path

# 添加SAM3路径
sys.path.insert(0, '/root/githubs/sam3')


def test_preprocessing():
    """测试数据预处理"""
    print("\n" + "="*50)
    print("Testing Data Preprocessing...")
    print("="*50)
    
    from preprocess_brats import load_brats_case, convert_to_frames, \
        save_segmentation_masks, get_tumor_bbox_and_center, save_prompt_info
    
    # 配置路径
    case_dir = "/data/yty/brats2023/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData/BraTS-GLI-00000-000"
    output_dir = "/data/yty/brats23_sam3_test"
    
    if not Path(case_dir).exists():
        print(f"Test case not found: {case_dir}")
        return None
    
    print(f"Loading case: {case_dir}")
    
    # 加载数据
    data, seg, affine = load_brats_case(case_dir)
    print(f"  Data shape: {data.shape}")
    print(f"  Seg shape: {seg.shape if seg is not None else 'None'}")
    
    # 转换为帧
    case_name = Path(case_dir).name
    frames_dir, num_slices = convert_to_frames(
        data, output_dir, case_name,
        modality_idx=0,  # T1ce
        target_size=(512, 512)
    )
    print(f"  Converted to {num_slices} frames: {frames_dir}")
    
    # 保存mask
    masks_dir = save_segmentation_masks(
        seg, output_dir, case_name,
        target_size=(512, 512)
    )
    print(f"  Saved masks: {masks_dir}")
    
    # 获取提示信息
    original_size = data.shape[2:4]
    slice_idx, bbox, center = get_tumor_bbox_and_center(seg)
    print(f"  Tumor center slice: {slice_idx}")
    print(f"  Original bbox: {bbox}")
    print(f"  Original center: {center}")
    
    # 保存提示信息
    prompt_info = save_prompt_info(
        output_dir, case_name, slice_idx, bbox, center,
        original_size, target_size=(512, 512)
    )
    print(f"  Scaled bbox: {prompt_info['bbox']}")
    print(f"  Scaled center: {prompt_info['center']}")
    
    print("\n✅ Preprocessing test passed!")
    return output_dir


def test_sam3_loading():
    """测试SAM3模型加载"""
    print("\n" + "="*50)
    print("Testing SAM3 Model Loading...")
    print("="*50)
    
    checkpoint_path = "/data/yty/sam3/sam3.pt"
    
    if not Path(checkpoint_path).exists():
        print(f"Checkpoint not found: {checkpoint_path}")
        return False
    
    print(f"Loading checkpoint: {checkpoint_path}")
    
    try:
        from sam3.model_builder import build_sam3_video_model
        
        model = build_sam3_video_model(
            checkpoint_path=checkpoint_path,
            load_from_HF=False,
            device='cuda' if torch.cuda.is_available() else 'cpu'
        )
        
        print(f"  Model loaded successfully!")
        print(f"  Device: {next(model.parameters()).device}")
        
        print("\n✅ Model loading test passed!")
        return True
        
    except Exception as e:
        print(f"Error loading model: {e}")
        import traceback
        traceback.print_exc()
        return False


def test_inference(processed_dir):
    """测试推理"""
    print("\n" + "="*50)
    print("Testing SAM3 Inference...")
    print("="*50)
    
    if processed_dir is None:
        print("Skipping inference test (no processed data)")
        return
    
    checkpoint_path = "/data/yty/sam3/sam3.pt"
    
    try:
        from infer_brats_sam3 import MedSAM3VideoInference, load_prompt_info
        
        # 初始化模型
        print("Initializing MedSAM3VideoInference...")
        model = MedSAM3VideoInference(
            checkpoint_path=checkpoint_path,
            device='cuda' if torch.cuda.is_available() else 'cpu'
        )
        
        # 获取测试病例
        case_dirs = sorted([d for d in Path(processed_dir).iterdir() if d.is_dir()])
        if not case_dirs:
            print("No processed cases found")
            return
        
        case_dir = case_dirs[0]
        case_name = case_dir.name
        frames_dir = case_dir / "frames"
        
        print(f"Testing on case: {case_name}")
        
        # 加载提示信息
        prompt_info = load_prompt_info(case_dir)
        if prompt_info is None:
            print("No prompt info found")
            return
        
        print(f"  Prompt slice: {prompt_info['slice_idx']}")
        print(f"  Bbox: {prompt_info['bbox']}")
        
        # 运行推理
        print("Running inference...")
        pred_masks = model.segment_3d_volume(
            frames_dir=str(frames_dir),
            prompt_slice_idx=prompt_info['slice_idx'],
            prompt_type='box',
            bbox=prompt_info['bbox']
        )
        
        print(f"  Output shape: {pred_masks.shape}")
        print(f"  Non-zero slices: {np.sum(pred_masks.sum(axis=(1,2)) > 0)}")
        
        print("\n✅ Inference test passed!")
        
    except Exception as e:
        print(f"Error in inference: {e}")
        import traceback
        traceback.print_exc()


def main():
    print("="*60)
    print(" MedSAM3 BraTS Quick Test")
    print("="*60)
    
    # 检查CUDA
    print(f"\nCUDA available: {torch.cuda.is_available()}")
    if torch.cuda.is_available():
        print(f"CUDA device: {torch.cuda.get_device_name(0)}")
    
    # 测试预处理
    processed_dir = test_preprocessing()
    
    # 测试模型加载
    model_ok = test_sam3_loading()
    
    # 测试推理（如果模型加载成功）
    if model_ok:
        test_inference(processed_dir)
    
    print("\n" + "="*60)
    print(" Quick Test Complete!")
    print("="*60)


if __name__ == "__main__":
    main()