from torch.utils.data import DataLoader, Dataset
import cv2
import os
import rasterio
import torch
import numpy as np
from pyproj import Transformer
from datetime import date

S3_OLCI_SCALE = [0.0139465,0.0133873,0.0121481,0.0115198,0.0100953,0.0123538,0.00879161,0.00876539,
                    0.0095103,0.00773378,0.00675523,0.0071996,0.00749684,0.0086512,0.00526779,0.00530267,
                    0.00493004,0.00549962,0.00502847,0.00326378,0.00324118]

LC100_CLSID = {
    0: 0, # unknown
    20: 1,
    30: 2,
    40: 3,
    50: 4,
    60: 5,
    70: 6,
    80: 7,
    90: 8,
    100: 9,
    111: 10,
    112: 11,
    113: 12,
    114: 13,
    115: 14,
    116: 15,
    121: 16,
    122: 17,
    123: 18,
    124: 19,
    125: 20,
    126: 21,
    200: 22, # ocean
}



class S3OLCI_LC100ClsDataset(Dataset):
    '''
    6908/1727 train/test images 96x96x21
    23 classes multilabel LULC
    nodata: -inf
    time series: 1-4 time stamps / location
    
    '''
    def __init__(self, root_dir, mode='static', split='train', meta=False):
        self.root_dir = root_dir
        self.mode = mode
        self.meta = meta
        self.img_dir = os.path.join(root_dir, split, 's3_olci')
        self.lc100_cls = os.path.join(root_dir, split, 'lc100_multilabel.csv')
        self.fnames = []
        self.labels = []
        with open(self.lc100_cls, 'r') as f:
            lines = f.readlines()
            for line in lines:
                self.fnames.append(line.strip().split(',')[0])
                self.labels.append(list(map(int, line.strip().split(',')[1:])))

        if self.mode == 'static':
            self.static_csv = os.path.join(root_dir, split, 'static_fnames.csv')
            with open(self.static_csv, 'r') as f:
                lines = f.readlines()
                self.static_img = {}
                for line in lines:
                    dirname = line.strip().split(',')[0]
                    img_fname = line.strip().split(',')[1]
                    self.static_img[dirname] = img_fname
                
        if self.meta:
            self.reference_date = date(1970, 1, 1)


    def __len__(self):
        return len(self.fnames)
    
    def __getitem__(self, idx):
        fname = self.fnames[idx]

        s3_path = os.path.join(self.img_dir, fname)
        if self.mode == 'static':
            img_fname = self.static_img[fname]
            s3_paths = [os.path.join(s3_path, img_fname)]
        else:
            img_fnames = os.listdir(s3_path)
            s3_paths = []
            for img_fname in img_fnames:
                s3_paths.append(os.path.join(s3_path, img_fname))
        
        imgs = []
        img_paths = []
        meta_infos = []
        for img_path in s3_paths:
            with rasterio.open(img_path) as src:
                img = src.read()
                chs = []
                for b in range(21):
                    ch = cv2.resize(img[b], (96,96), interpolation=cv2.INTER_CUBIC)
                    chs.append(ch)
                img = np.stack(chs)
                img[np.isnan(img)] = 0
                for b in range(21):
                    img[b] = img[b]*S3_OLCI_SCALE[b]
                img = torch.from_numpy(img).float()


                if self.meta:
                    # get lon, lat
                    cx,cy = src.xy(src.height // 2, src.width // 2)
                    # convert to lon, lat
                    #crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326')
                    #lon, lat = crs_transformer.transform(cx,cy)
                    lon, lat = cx, cy
                    # get time
                    img_fname = os.path.basename(img_path)
                    date_str = img_fname.split('_')[1][:8]
                    date_obj = date(int(date_str[:4]), int(date_str[4:6]), int(date_str[6:8]))
                    delta = (date_obj - self.reference_date).days
                    meta_info = np.array([lon, lat, delta, 0]).astype(np.float32)
                else:
                    meta_info = np.array([np.nan,np.nan,np.nan,np.nan]).astype(np.float32)

            imgs.append(img)
            img_paths.append(img_path)
            meta_infos.append(meta_info)

        if self.mode == 'series':
            # pad to 4 images if less than 4
            while len(imgs) < 4:
                imgs.append(img)
                img_paths.append(img_path)
                meta_infos.append(meta_info)

        label = self.labels[idx]
        labels = torch.zeros(23)
        # turn into one-hot
        for l in label:
            cls_id = LC100_CLSID[l]
            labels[cls_id] = 1

        if self.mode == 'static':
            return imgs[0], meta_infos[0], labels
        elif self.mode == 'series':
            return imgs[0], imgs[1], imgs[2], imgs[3], meta_infos[0], meta_infos[1], meta_infos[2], meta_infos[3], labels

if __name__ == '__main__':
    dataset = S3OLCI_LC100ClsDataset(root_dir='../data/downstream/cgls_lc100', mode='static', split=None, meta=True)
    dataloader = DataLoader(dataset, batch_size=64, shuffle=False, num_workers=4)
    for i, data in enumerate(dataloader):
        #print(data[0].shape)
        #print(data[1].shape)
        #print(data[1])
        #print(data[2])
        #print(data[0].max())
        #break
        pass