import nibabel as nib
import matplotlib.pyplot as plt
import numpy as np
import os

def load_nifti(file_path):
    """載入NIfTI檔案"""
    return nib.load(file_path).get_fdata()

def create_colored_mask(data):
    """創建彩色遮罩"""
    color_dict = {
        1: [144/255, 238/255, 144/255],  # Brainstem - 綠色
        2: [255/255, 218/255, 150/255],  # Right_Eye - 淺黃色
        3: [205/255, 170/255, 125/255],  # Left_Eye - 棕色
        4: [135/255, 206/255, 235/255],  # Optic_Chiasm - 藍色
        5: [255/255, 99/255, 71/255],    # Right_Optic_Nerve - 亮珊瑚紅
        6: [255/255, 160/255, 122/255],  # Left_Optic_Nerve - 淺珊瑚紅
        7: [255/255, 0/255, 0/255]       # TV - 亮紅色
    }
    
    mask = np.zeros((*data.shape, 4))
    for label, color in color_dict.items():
        organ_mask = (data == label)
        if np.any(organ_mask):
            mask[organ_mask] = [*color, 1.0]
    return mask

def get_display_bounds(image):
    """直接返回較大的顯示區域"""
    h, w = image.shape
    # 使用圖像中心點
    center_h = h // 2
    center_w = w // 2
    # 取圖像大小的 85% 作為顯示範圍
    radius_h = int(h * 0.425)
    radius_w = int(w * 0.425)
    
    return (
        max(center_h - radius_h, 0),
        min(center_h + radius_h, h),
        max(center_w - radius_w, 0),
        min(center_w + radius_w, w)
    )

def check_tv_in_slice(slice_data):
    """檢查切片中是否包含TV（label 7）"""
    return 7 in np.unique(slice_data)

def find_tv_slices(ground_truth):
    """找出所有包含TV的切片"""
    tv_slices = []
    tv_pixels = []
    total_slices = ground_truth.shape[2]
    
    for slice_num in range(total_slices):
        slice_data = ground_truth[:, :, slice_num]
        if check_tv_in_slice(slice_data):
            tv_pixel_count = np.sum(slice_data == 7)
            tv_slices.append(slice_num)
            tv_pixels.append(tv_pixel_count)
    
    sorted_indices = np.argsort(tv_pixels)[::-1]
    sorted_slices = [tv_slices[i] for i in sorted_indices]
    sorted_pixels = [tv_pixels[i] for i in sorted_indices]
    
    return sorted_slices, sorted_pixels

def save_slice(image, ground_truth, prediction, slice_num, save_path):
    """保存指定切片的三列比較圖（原始影像、Ground Truth和預測結果）"""
    plt.clf()
    fig, axes = plt.subplots(1, 3, figsize=(36, 11))
    plt.subplots_adjust(wspace=0.01)
    
    img_slice = np.rot90(image[:, :, slice_num])
    rmin, rmax, cmin, cmax = get_display_bounds(img_slice)
    
    # 調整圖像對比度
    p2, p98 = np.percentile(img_slice, (2, 98))
    img_slice = np.clip(img_slice, p2, p98)
    img_slice = (img_slice - p2) / (p98 - p2)
    
    # 1. Original Image
    axes[0].imshow(img_slice[rmin:rmax, cmin:cmax], cmap='gray', zorder=1)
    axes[0].set_title('Original Image', fontsize=32, pad=20, weight='bold')
    axes[0].axis('off')
    
    # 2. Ground Truth
    gt_slice = np.rot90(ground_truth[:, :, slice_num])
    gt_mask = create_colored_mask(gt_slice)
    
    axes[1].imshow(img_slice[rmin:rmax, cmin:cmax], cmap='gray', zorder=1)
    axes[1].imshow(gt_mask[rmin:rmax, cmin:cmax], zorder=2)
    axes[1].set_title('Ground Truth', fontsize=32, pad=20, weight='bold')
    axes[1].axis('off')
    
    # 3. Prediction
    pred_slice = np.rot90(prediction[:, :, slice_num])
    pred_mask = create_colored_mask(pred_slice)
    
    axes[2].imshow(img_slice[rmin:rmax, cmin:cmax], cmap='gray', zorder=1)
    axes[2].imshow(pred_mask[rmin:rmax, cmin:cmax], zorder=2)
    axes[2].set_title('Prediction', fontsize=32, pad=20, weight='bold')
    axes[2].axis('off')
    
    plt.suptitle('')
    plt.savefig(save_path, bbox_inches='tight', dpi=300, pad_inches=0.05)
    plt.close()
    print(f"已保存切片 {slice_num} 至: {save_path}")

if __name__ == "__main__":
    # 設定路徑
    image_path = '/mnt/1248/onlylian/nnUNet/nnUNet_raw/Dataset222_OAR_TV/imagesTs/OAR_TV_098_0001.nii.gz'
    gt_path = '/mnt/1248/onlylian/nnUNet/nnUNet_raw/Dataset222_OAR_TV/labelsTs/OAR_TV_098.nii.gz'
    pred_path = '/mnt/1248/onlylian/CT_CTC_T1_OAR_TV/output_predictions/3d_fullres/fold_4/OAR_TV_098.nii.gz'
    save_dir = '/mnt/1248/onlylian/CT_CTC_T1_OAR_TV'
    
    os.makedirs(save_dir, exist_ok=True)
    
    print("正在載入影像...")
    image = load_nifti(image_path)
    ground_truth = load_nifti(gt_path)
    prediction = load_nifti(pred_path)
    print("影像載入完成")
    
    print("\n尋找包含TV的切片...")
    tv_slices, tv_pixels = find_tv_slices(ground_truth)
    
    if tv_slices:
        print(f"\n找到 {len(tv_slices)} 個包含TV的切片:")
        for slice_num, pixel_count in zip(tv_slices, tv_pixels):
            print(f"切片 {slice_num}: {pixel_count} 像素")
    else:
        print("未找到包含TV的切片")
    
    print("\n開始切片選擇和保存過程...")
    while True:
        try:
            slice_num = int(input("\n請輸入要保存的切片編號 (-1 退出): "))
            if slice_num == -1:
                print("程序結束")
                break
            if 0 <= slice_num < ground_truth.shape[2]:
                save_path = os.path.join(save_dir, f'comparison_OAR_TV_098_4_{slice_num:03d}.png')
                save_slice(image, ground_truth, prediction, slice_num, save_path)
            else:
                print(f"切片編號必須在 0 到 {ground_truth.shape[2]-1} 之間")
        except ValueError:
            print("請輸入有效的數字")
        except Exception as e:
            print(f"發生錯誤: {str(e)}")