#!/usr/bin/env python3
import sys
from pathlib import Path
sys.path.append(Path(__file__).parents[1].__str__())
from dronescapes_reader import MultiTaskDataset
from dronescapes_reader.dronescapes_representations import dronescapes_task_types
from pprint import pprint
from torch.utils.data import DataLoader
import random
import numpy as np

def main():
    assert len(sys.argv) == 2, f"Usage ./dronescapes_viewer.py /path/to/dataset"
    reader = MultiTaskDataset(sys.argv[1], task_names=list(dronescapes_task_types.keys()),
                              task_types=dronescapes_task_types, handle_missing_data="fill_nan",
                              normalization="min_max", cache_task_stats=True)
    print(reader)

    print("== Shapes ==")
    pprint(reader.data_shape)

    print("== Random loaded item ==")
    rand_ix = random.randint(0, len(reader))
    data, name, repr_names = reader[rand_ix] # get a random item
    pprint({k: v for k, v in data.items()})

    img_data = {}
    for k, v in data.items():
        img_data[k] = reader.name_to_task[k].plot_fn(v) if v is not None else np.zeros((*reader.data_shape[k][0:2], 3))

    print("== Random loaded batch ==")
    batch_data, name, repr_names = reader[rand_ix: min(len(reader), rand_ix + 5)] # get a random batch
    pprint({k: v for k, v in batch_data.items()}) # Nones are converted to 0s automagically

    print("== Random loaded batch using torch DataLoader ==")
    loader = DataLoader(reader, collate_fn=reader.collate_fn, batch_size=5, shuffle=True)
    batch_data, name, repr_names = next(iter(loader))
    pprint({k: v for k, v in batch_data.items()}) # Nones are converted to 0s automagically

if __name__ == "__main__":
    main()