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"""Dronescapes representations -- adds various loading/writing/image showing capabilities to dronescapes tasks"""
from __future__ import annotations
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
from torch.nn import functional as F
class ColorRepresentation(NpzRepresentation):
def load_from_disk(self, path: Path) -> tr.Tensor:
res = super().load_from_disk(path)
return res.float() / 255
def save_to_disk(self, data: tr.Tensor, path: Path):
return super().save_to_disk((data * 255).byte(), path)
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)
assert 0 <= min_depth < max_depth, (min_depth, max_depth)
self.min_depth = min_depth
self.max_depth = max_depth
@overrides
def plot_fn(self, x: tr.Tensor) -> np.ndarray:
x = x.detach().cpu().numpy()
x = np.clip(x, 0, 1)
y = hot(x.squeeze())[..., 0:3]
y = np.uint8(y * 255)
return y
def load_from_disk(self, path: Path) -> tr.Tensor:
res = super().load_from_disk(path)
res = res.float().clip(self.min_depth, self.max_depth)
res = (res - self.min_depth) / (self.max_depth - self.min_depth)
return res
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.squeeze().nan_to_num(0).detach().cpu().numpy())
def load_from_disk(self, path: Path) -> tr.Tensor:
res = super().load_from_disk(path).float()
return res
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 and self.n_classes > 1, (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}"
res = F.one_hot(res.long(), num_classes=self.n_classes).float()
return res
@overrides
def plot_fn(self, x: tr.Tensor) -> np.ndarray:
x = x.squeeze().nan_to_num(0).detach().argmax(-1).cpu().numpy()
new_images = np.zeros((*x.shape, 3), dtype=np.uint8)
for i in range(self.n_classes):
new_images[x == i] = self.color_map[i]
return new_images
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