import glob
import os
import numpy as np
import cv2
import multiprocessing.pool as mpp
import multiprocessing as mp
import time
import argparse
import torch
import random

"""
python loveda_mask_convert.py \
    --mask-dir /home/zrh/datasets/loveda-Conv-small/train/Urban/masks_png \
    --output-mask-dir /home/zrh/datasets/loveda-Conv-small/train/Urban/masks_png_convert
python loveda_mask_convert.py --mask-dir data/LoveDA/Train/Urban/masks_png --output-mask-dir data/LoveDA/Train/Urban/masks_png_convert
python loveda_mask_convert.py --mask-dir data/LoveDA/Val/Rural/masks_png --output-mask-dir data/LoveDA/Val/Rural/masks_png_convert
python loveda_mask_convert.py --mask-dir data/LoveDA/Val/Urban/masks_png --output-mask-dir data/LoveDA/Val/Urban/masks_png_convert
"""

SEED = 42

CLASSES = ('background', 'building', 'road', 'water', 'barren', 'forest',
           'agricultural')

PALETTE = [[255, 255, 255], [255, 0, 0], [255, 255, 0], [0, 0, 255],
           [159, 129, 183], [0, 255, 0], [255, 195, 128]]


def seed_everything(seed):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = True


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--mask-dir", default="data/LoveDA/Train/Rural/masks_png")
    parser.add_argument("--output-mask-dir", default="data/LoveDA/Train/Rural/masks_png_convert")
    return parser.parse_args()


def convert_label(mask):
    mask[mask == 0] = 8
    mask -= 1

    return mask


def label2rgb(mask):
    h, w = mask.shape[0], mask.shape[1]
    mask_rgb = np.zeros(shape=(h, w, 3), dtype=np.uint8)
    mask_convert = mask[np.newaxis, :, :]
    mask_rgb[np.all(mask_convert == 0, axis=0)] = [255, 255, 255]
    mask_rgb[np.all(mask_convert == 1, axis=0)] = [255, 0, 0]
    mask_rgb[np.all(mask_convert == 2, axis=0)] = [255, 255, 0]
    mask_rgb[np.all(mask_convert == 3, axis=0)] = [0, 0, 255]
    mask_rgb[np.all(mask_convert == 4, axis=0)] = [159, 129, 183]
    mask_rgb[np.all(mask_convert == 5, axis=0)] = [0, 255, 0]
    mask_rgb[np.all(mask_convert == 6, axis=0)] = [255, 195, 128]
    return mask_rgb


def patch_format(inp):
    (mask_path, masks_output_dir) = inp
    # print(mask_path, masks_output_dir)
    mask_filename = os.path.splitext(os.path.basename(mask_path))[0]
    mask = cv2.imread(mask_path, cv2.IMREAD_UNCHANGED)
    label = convert_label(mask)
    rgb_label = label2rgb(label.copy())
    rgb_label = cv2.cvtColor(rgb_label, cv2.COLOR_RGB2BGR)
    out_mask_path_rgb = os.path.join(masks_output_dir + '_rgb', "{}.png".format(mask_filename))
    cv2.imwrite(out_mask_path_rgb, rgb_label)

    out_mask_path = os.path.join(masks_output_dir, "{}.png".format(mask_filename))
    cv2.imwrite(out_mask_path, label)


if __name__ == "__main__":
    seed_everything(SEED)
    args = parse_args()
    masks_dir = args.mask_dir
    masks_output_dir = args.output_mask_dir
    mask_paths = glob.glob(os.path.join(masks_dir, "*.png"))

    print("1111")
    print(masks_output_dir)
    if not os.path.exists(masks_output_dir):
        print("1111")
        print(masks_output_dir)
        os.makedirs(masks_output_dir)
        os.makedirs(masks_output_dir + '_rgb')

    inp = [(mask_path, masks_output_dir) for mask_path in mask_paths]

    t0 = time.time()
    mpp.Pool(processes=mp.cpu_count()).map(patch_format, inp)
    t1 = time.time()
    split_time = t1 - t0
    print('images spliting spends: {} s'.format(split_time))


