# Copyright 2020 - 2022 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#     http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from sklearn.model_selection import KFold  ## K折交叉验证
import pickle
import os
import json
import math
import numpy as np
import torch
from monai import transforms
import SimpleITK as sitk
from tqdm import tqdm 
from torch.utils.data import Dataset 
import glob 
from light_training.dataloading.utils import unpack_dataset
import random 
import torch
import numpy as np 
from scipy.ndimage import distance_transform_edt as distance
from skimage import segmentation as skimage_seg
from skimage.morphology import dilation, disk
import scipy.ndimage as ndimage

def get_edge_points(img):
    """
    get edge points of a binary segmentation result
    """
    dim = len(img.shape)
    if (dim == 2):
        strt = ndimage.generate_binary_structure(2, 1)
    else:
        strt = ndimage.generate_binary_structure(3, 1) 
    ero = ndimage.binary_erosion(img, strt)
    edge = np.asarray(img, np.uint8) - np.asarray(ero, np.uint8)
    return edge

def edge_3d(image_3d):
    # image_3d = torch.from_numpy(image_3d)
    return_edge = np.zeros_like(image_3d)

    for i in range(image_3d.shape[0]):
        for j in range(image_3d.shape[1]):
            return_edge[i, j] = get_edge_points(image_3d[i, j])
    
    return return_edge

def compute_sdf(img_gt, out_shape):
    """
    compute the signed distance map of binary mask
    input: segmentation, shape = (batch_size,c, x, y, z)
    output: the Signed Distance Map (SDM) 
    sdf(x) = 0; x in segmentation boundary
             -inf|x-y|; x in segmentation
             +inf|x-y|; x out of segmentation
    normalize sdf to [-1,1]

    """

    img_gt = img_gt.astype(np.uint8)
    normalized_sdf = np.zeros(out_shape)

    for b in range(out_shape[0]): # batch size
        for c in range(out_shape[1]):
            posmask = img_gt[b, c].astype(np.bool_)
            if posmask.any():
                negmask = ~posmask
                posdis = distance(posmask)
                negdis = distance(negmask)
                boundary = skimage_seg.find_boundaries(posmask, mode='inner').astype(np.uint8)
                sdf = (negdis-np.min(negdis))/(np.max(negdis)-np.min(negdis)) - (posdis-np.min(posdis))/(np.max(posdis)-np.min(posdis))
                sdf[boundary==1] = 0
                normalized_sdf[b][c] = sdf
                assert np.min(sdf) == -1.0, print(np.min(posdis), np.max(posdis), np.min(negdis), np.max(negdis))
                assert np.max(sdf) ==  1.0, print(np.min(posdis), np.min(negdis), np.max(posdis), np.max(negdis))

    return normalized_sdf

def convert_labels(labels):
    ## TC, WT and ET
    labels = labels[None, None]
    result = [(labels == 1) | (labels == 3), (labels == 1) | (labels == 3) | (labels == 2), labels == 3]
    
    return torch.cat(result, dim=1).float()

class MedicalDataset(Dataset):
    def __init__(self, datalist, test=False) -> None:
        super().__init__()
        
        self.datalist = datalist
        self.test = test 

        self.data_cached = []
        for p in tqdm(self.datalist, total=len(self.datalist)):
            info = self.load_pkl(p)

            self.data_cached.append(info)

        ## unpacking
        print(f"unpacking data ....")
        # for 
        folder = []
        for p in self.datalist:
            f = os.path.dirname(p)
            if f not in folder:
                folder.append(f)
        for f in folder:
            unpack_dataset(f, 
                        unpack_segmentation=True,
                        overwrite_existing=False,
                        num_processes=8)


        print(f"data length is {len(self.datalist)}")
        
    def load_pkl(self, data_path):
        pass 
        properties_path = f"{data_path[:-4]}.pkl"
        df = open(properties_path, "rb")
        info = pickle.load(df)

        return info 
        
    def read_data(self, data_path):
        
        image_path = data_path.replace(".npz", ".npy")
        seg_path = data_path.replace(".npz", "_seg.npy")
        image_data = np.load(image_path, "r")
      
        seg_data = None 
        if not self.test:
            seg_data = np.load(seg_path, "r")
        return image_data, seg_data

    # def post(self, batch_data):
    #     seg = convert_labels(batch_data["seg"]).numpy()
    #     seg_shape = seg.shape
    #     seg_edge = edge_3d(seg)
    #     seg_sdm = 1 - compute_sdf(seg, out_shape=seg_shape)
    #     seg_sdm = seg_sdm + seg_edge

    #     seg_edge = torch.from_numpy(seg_edge)
    #     seg_sdm = torch.from_numpy(seg_sdm)

    #     batch_data["seg_edge"] = seg_edge
    #     batch_data["seg_sdm"] = seg_sdm

    #     print(f"post!!!!!!!!!")
    #     return batch_data

    def __getitem__(self, i):
        
        image, seg = self.read_data(self.datalist[i])

        properties = self.data_cached[i]
        case_name = properties["name"]

        if seg is not None:
            sdm = np.load(os.path.join("./data/fullres/train_sdm/", f"{case_name}_seg_sdm.npy"), "r")

            # print(seg.shape, sdm.shape)
            sdm = sdm[0]
            seg = np.concatenate([seg, sdm], axis=0)

            # print(f"sdm sum is {sdm.sum()}")
        if seg is None:
            return {
                "data": image,
                "properties": properties
            }
        else :
            return {
                "data": image,
                "seg": seg,
                "properties": properties
            }

    def __len__(self):
        return len(self.datalist)

def get_kfold_data(data_paths, n_splits, shuffle=False):
    X = np.arange(len(data_paths))
    kfold = KFold(n_splits=n_splits, shuffle=shuffle)  ## kfold为KFolf类的一个对象
    return_res = []
    for a, b in kfold.split(X):
        fold_train = []
        fold_val = []
        for i in a:
            fold_train.append(data_paths[i])
        for j in b:
            fold_val.append(data_paths[j])
        return_res.append({"train_data": fold_train, "val_data": fold_val})

    return return_res

def get_kfold_loader(data_dir, fold=0, test_dir=None):

    all_paths = glob.glob(f"{data_dir}/*.npz")
    fold_data = get_kfold_data(all_paths, 5)[fold]

    train_datalist = fold_data["train_data"]
    val_datalist = fold_data["val_data"]  

    print(f"training data is {len(train_datalist)}")
    print(f"validation data is {len(val_datalist)}")
    train_ds = MedicalDataset(train_datalist)
    
    val_ds = MedicalDataset(val_datalist)

    if test_dir is not None:
        test_paths = glob.glob(f"{test_dir}/*.npz")
        test_ds = MedicalDataset(test_paths, test=True)
    else:
        test_ds = None 

    loader = [train_ds, val_ds, test_ds]

    return loader

def get_all_training_loader(data_dir, fold=0, test_dir=None):
    ## train all labeled data 
    ## fold denote the validation data in training data
    all_paths = glob.glob(f"{data_dir}/*.npz")
    fold_data = get_kfold_data(all_paths, 5)[fold]

    train_datalist = all_paths
    val_datalist = fold_data["val_data"]  

    print(f"training data is {len(train_datalist)}")
    print(f"validation data is {len(val_datalist)}")
    train_ds = MedicalDataset(train_datalist)
    
    val_ds = MedicalDataset(val_datalist)

    if test_dir is not None:
        test_paths = glob.glob(f"{test_dir}/*.npz")
        test_ds = MedicalDataset(test_paths, test=True)
    else:
        test_ds = None 

    loader = [train_ds, val_ds, test_ds]

    return loader

def get_train_val_test_loader_seperate(train_dir, val_dir, test_dir=None):
    train_datalist = glob.glob(f"{train_dir}/*.npz")
    val_datalist = glob.glob(f"{val_dir}/*.npz")

    print(f"training data is {len(train_datalist)}")
    print(f"validation data is {len(val_datalist)}")

    if test_dir is not None:
        test_datalist = glob.glob(f"{test_dir}/*.npz")
        print(f"test data is {len(test_datalist)}")
        test_ds = MedicalDataset(test_datalist, test=True)
    else :
        test_ds = None

    train_ds = MedicalDataset(train_datalist)
    val_ds = MedicalDataset(val_datalist)
    
    loader = [train_ds, val_ds, test_ds]

    return loader

def get_train_val_test_loader_from_train(data_dir, train_rate=0.7, val_rate=0.1, test_rate=0.2):
    ## train all labeled data 
    ## fold denote the validation data in training data
    all_paths = glob.glob(f"{data_dir}/*.npz")
    # fold_data = get_kfold_data(all_paths, 5)[fold]

    train_number = int(len(all_paths) * train_rate)
    val_number = int(len(all_paths) * val_rate)
    test_number = int(len(all_paths) * test_rate)

    random.shuffle(all_paths)

    train_datalist = all_paths[:train_number]
    val_datalist = all_paths[train_number: train_number + val_number]
    test_datalist = all_paths[-test_number:] 

    print(f"training data is {len(train_datalist)}")
    print(f"validation data is {len(val_datalist)}")
    print(f"test data is {len(test_datalist)}")

    train_ds = MedicalDataset(train_datalist)
    val_ds = MedicalDataset(val_datalist)
    test_ds = MedicalDataset(test_datalist)

    loader = [train_ds, val_ds, test_ds]

    return loader

def get_multi_dir_training_loader(data_dir, fold=0, test_dir=None):
    ## train all labeled data 
    ## fold denote the validation data in training data
    all_paths = []
    for p in data_dir:
        paths = glob.glob(f"{p}/*.npz")
        for pp in paths:
            all_paths.append(pp)

    # print(all_paths)
    fold_data = get_kfold_data(all_paths, 5)[fold]

    train_datalist = all_paths
    val_datalist = fold_data["val_data"]  

    print(f"training data is {len(train_datalist)}")
    print(f"validation data is {len(val_datalist)}")
    train_ds = MedicalDataset(train_datalist)
    
    val_ds = MedicalDataset(val_datalist)

    if test_dir is not None:
        test_paths = glob.glob(f"{test_dir}/*.npz")
        test_ds = MedicalDataset(test_paths, test=True)
    else:
        test_ds = None 

    loader = [train_ds, val_ds, test_ds]

    return loader