"""Dronescapes representations -- adds various loading/writing/image showing capabilities to dronescapes tasks"""
from pathlib import Path
import numpy as np
import torch as tr
import flow_vis
from overrides import overrides
from matplotlib.cm import hot # pylint: disable=no-name-in-module
from .multitask_dataset import NpzRepresentation

class DepthRepresentation(NpzRepresentation):
    """DepthRepresentation. Implements depth task-specific stuff, like hotmap."""
    def __init__(self, *args, min_depth: float, max_depth: float, **kwargs):
        super().__init__(*args, **kwargs)
        self.min_depth = min_depth
        self.max_depth = max_depth

    @overrides
    def plot_fn(self, x: tr.Tensor) -> np.ndarray:
        x = x.numpy()
        x = np.clip(x, self.min_depth, self.max_depth)
        x = np.nan_to_num((x - x.min()) / (x.max() - x.min()), 0)
        y = hot(x)[..., 0:3]
        y = np.uint8(y * 255)
        return y

class OpticalFlowRepresentation(NpzRepresentation):
    """OpticalFlowRepresentation. Implements depth task-specific stuff, like using flow_vis."""
    @overrides
    def plot_fn(self, x: tr.Tensor) -> np.ndarray:
        return flow_vis.flow_to_color(x.numpy())

class SemanticRepresentation(NpzRepresentation):
    """SemanticRepresentation. Implements depth task-specific stuff, like using flow_vis."""
    def __init__(self, *args, classes: int | list[str], color_map: list[tuple[int, int, int]], **kwargs):
        super().__init__(*args, **kwargs)
        self.classes = list(range(classes)) if isinstance(classes, int) else classes
        self.n_classes = len(self.classes)
        self.color_map = color_map
        assert len(color_map) == self.n_classes, (color_map, self.n_classes)

    @overrides
    def load_from_disk(self, path: Path) -> tr.Tensor:
        res = super().load_from_disk(path)
        assert len(res.shape) == 2, f"Only argmaxed data supported, got: {res.shape}"
        return res

    @overrides
    def plot_fn(self, x: tr.Tensor) -> np.ndarray:
        new_images = np.zeros((*x.shape, 3), dtype=np.uint8)
        x = x.numpy()
        for i in range(self.n_classes):
            new_images[x == i] = self.color_map[i]
        return new_images