import cv2
import numpy as np
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
from torch.utils.data import Dataset
from random import random, choice, shuffle
from io import BytesIO
from PIL import Image
from PIL import ImageFile
from scipy.ndimage.filters import gaussian_filter
import pickle
import os

ImageFile.LOAD_TRUNCATED_IMAGES = True


def recursively_read(rootdir, must_contain, exts=["png", "jpg", "JPEG", "jpeg"]):
    out = [] 
    for r, d, f in os.walk(rootdir):
        for file in f:
            if (file.split('.')[1] in exts)  and  (must_contain in os.path.join(r, file)):
                out.append(os.path.join(r, file))
    return out


def get_list(path, must_contain=''):
    if ".pickle" in path:
        with open(path, 'rb') as f:
            image_list = pickle.load(f)
        image_list = [ item for item in image_list if must_contain in item   ]
    else:
        image_list = recursively_read(path, must_contain)
    return image_list


def dataset_folder(opt, root):
    if opt.mode == 'binary':
        return binary_dataset(opt, root)
    if opt.mode == 'filename':
        return FileNameDataset(opt, root)
    raise ValueError('opt.mode needs to be binary or filename.')


def binary_dataset(opt, root):
    dset = datasets.ImageFolder(
            root,
            transforms.Compose([
                transforms.RandomCrop(opt.cropSize) if opt.isTrain else transforms.CenterCrop(opt.cropSize),
                transforms.RandomHorizontalFlip() if opt.isTrain and not opt.no_flip else transforms.Lambda(lambda img: img),
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
            ]))
    return dset


class FileNameDataset(datasets.ImageFolder):
    def name(self):
        return 'FileNameDataset'

    def __init__(self, opt, root):
        self.opt = opt
        super().__init__(root)

    def __getitem__(self, index):
        # Loading sample
        path, target = self.samples[index]
        return path


def data_augment(img, opt):
    img = np.array(img)
    if img.ndim == 2:
        img = np.expand_dims(img, axis=2)
        img = np.repeat(img, 3, axis=2)

    if random() < opt.blur_prob:
        sig = sample_continuous(opt.blur_sig)
        gaussian_blur(img, sig)

    if random() < opt.jpg_prob:
        method = sample_discrete(opt.jpg_method)
        qual = sample_discrete(opt.jpg_qual)
        img = jpeg_from_key(img, qual, method)

    return Image.fromarray(img)


def sample_continuous(s):
    if len(s) == 1:
        return s[0]
    if len(s) == 2:
        rg = s[1] - s[0]
        return random() * rg + s[0]
    raise ValueError("Length of iterable s should be 1 or 2.")


def sample_discrete(s):
    if len(s) == 1:
        return s[0]
    return choice(s)


def gaussian_blur(img, sigma):
    gaussian_filter(img[:,:,0], output=img[:,:,0], sigma=sigma)
    gaussian_filter(img[:,:,1], output=img[:,:,1], sigma=sigma)
    gaussian_filter(img[:,:,2], output=img[:,:,2], sigma=sigma)


def cv2_jpg(img, compress_val):
    img_cv2 = img[:,:,::-1]
    encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), compress_val]
    result, encimg = cv2.imencode('.jpg', img_cv2, encode_param)
    decimg = cv2.imdecode(encimg, 1)
    return decimg[:,:,::-1]


def pil_jpg(img, compress_val):
    out = BytesIO()
    img = Image.fromarray(img)
    img.save(out, format='jpeg', quality=compress_val)
    img = Image.open(out)
    # load from memory before ByteIO closes
    img = np.array(img)
    out.close()
    return img


jpeg_dict = {'cv2': cv2_jpg, 'pil': pil_jpg}
def jpeg_from_key(img, compress_val, key):
    method = jpeg_dict[key]
    return method(img, compress_val)


rz_dict = {'bilinear': Image.BILINEAR,
           'bicubic': Image.BICUBIC,
           'lanczos': Image.LANCZOS,
           'nearest': Image.NEAREST}
def custom_resize(img, opt):
    interp = sample_discrete(opt.rz_interp)
    return TF.resize(img, opt.loadSize, interpolation=rz_dict[interp])