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"""Dronescapes representations -- adds various loading/writing/image showing capabilities to dronescapes tasks""" |
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from __future__ import annotations |
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from pathlib import Path |
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import numpy as np |
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import torch as tr |
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import flow_vis |
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from overrides import overrides |
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from matplotlib.cm import hot |
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from .multitask_dataset import NpzRepresentation |
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from torch.nn import functional as F |
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class ColorRepresentation(NpzRepresentation): |
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def load_from_disk(self, path: Path) -> tr.Tensor: |
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res = super().load_from_disk(path) |
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return res.float() / 255 |
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def save_to_disk(self, data: tr.Tensor, path: Path): |
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return super().save_to_disk((data * 255).byte(), path) |
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class EdgesRepresentation(NpzRepresentation): |
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def load_from_disk(self, path: Path) -> tr.Tensor: |
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res = super().load_from_disk(path).float() |
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assert len(res.shape) == 3 and res.shape[-1] == 1 |
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return res |
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def plot_fn(self, x: tr.Tensor) -> np.ndarray: |
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return (x.repeat(1, 1, 3) * 255).cpu().numpy().astype(np.uint8) |
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class DepthRepresentation(NpzRepresentation): |
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"""DepthRepresentation. Implements depth task-specific stuff, like hotmap.""" |
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def __init__(self, *args, min_depth: float, max_depth: float, **kwargs): |
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super().__init__(*args, **kwargs) |
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assert 0 <= min_depth < max_depth, (min_depth, max_depth) |
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self.min_depth = min_depth |
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self.max_depth = max_depth |
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@overrides |
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def plot_fn(self, x: tr.Tensor) -> np.ndarray: |
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x = x.detach().clip(0, 1).squeeze().cpu().numpy() |
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y: np.ndarray = hot(x)[..., 0:3] * 255 |
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return y.astype(np.uint8) |
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def load_from_disk(self, path: Path) -> tr.Tensor: |
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res = super().load_from_disk(path) |
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res = res.float().clip(self.min_depth, self.max_depth) |
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res = (res - self.min_depth) / (self.max_depth - self.min_depth) |
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return res |
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class OpticalFlowRepresentation(NpzRepresentation): |
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"""OpticalFlowRepresentation. Implements depth task-specific stuff, like using flow_vis.""" |
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@overrides |
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def plot_fn(self, x: tr.Tensor) -> np.ndarray: |
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return flow_vis.flow_to_color(x.squeeze().nan_to_num(0).detach().cpu().numpy()) |
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def load_from_disk(self, path: Path) -> tr.Tensor: |
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res = super().load_from_disk(path).float() |
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return res |
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class SemanticRepresentation(NpzRepresentation): |
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"""SemanticRepresentation. Implements depth task-specific stuff, like using flow_vis.""" |
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def __init__(self, *args, classes: int | list[str], color_map: list[tuple[int, int, int]], **kwargs): |
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super().__init__(*args, **kwargs) |
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self.classes = list(range(classes)) if isinstance(classes, int) else classes |
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self.n_classes = len(self.classes) |
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self.color_map = color_map |
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assert len(color_map) == self.n_classes and self.n_classes > 1, (color_map, self.n_classes) |
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@overrides |
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def load_from_disk(self, path: Path) -> tr.Tensor: |
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res = super().load_from_disk(path) |
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assert len(res.shape) == 2, f"Only argmaxed data supported, got: {res.shape}" |
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res = F.one_hot(res.long(), num_classes=self.n_classes).float() |
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return res |
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@overrides |
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def plot_fn(self, x: tr.Tensor) -> np.ndarray: |
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x = x.squeeze().nan_to_num(0).detach().argmax(-1).cpu().numpy() |
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new_images = np.zeros((*x.shape, 3), dtype=np.uint8) |
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for i in range(self.n_classes): |
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new_images[x == i] = self.color_map[i] |
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return new_images |
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