#!/usr/bin/env python3
import sys
from pathlib import Path
sys.path.append(Path(__file__).parents[1].__str__())
from functools import partial
from dronescapes_reader import MultiTaskDataset, DepthRepresentation, OpticalFlowRepresentation, SemanticRepresentation
from pprint import pprint
from torch.utils.data import DataLoader
import random

def main():
    sema_repr = partial(SemanticRepresentation, classes=8, color_map=[[0, 255, 0], [0, 127, 0], [255, 255, 0],
                                                                      [255, 255, 255], [255, 0, 0], [0, 0, 255],
                                                                      [0, 255, 255], [127, 127, 63]])
    reader = MultiTaskDataset(sys.argv[1], handle_missing_data="fill_none",
                              task_types={"depth_dpt": DepthRepresentation("depth_dpt", min_depth=0, max_depth=0.999),
                                          "depth_sfm_manual202204": DepthRepresentation("depth_sfm_manual202204",
                                                                                        min_depth=0, max_depth=300),
                                          "opticalflow_rife": OpticalFlowRepresentation,
                                          "semantic_segprop8": sema_repr,
                                          "semantic_mask2former_swin_mapillary_converted": sema_repr})
    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()})

    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()