dronescapes_reader/dronescapes_representations.py

"""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 skimage.color import rgb2hsv
from overrides import overrides
from matplotlib.cm import hot # pylint: disable=no-name-in-module
from torch.nn import functional as F

try:
    from npz_representation import NpzRepresentation
except ImportError:
    from .npz_representation import NpzRepresentation

class RGBRepresentation(NpzRepresentation):
    def __init__(self, name: str):
        super().__init__(name, n_channels=3)

class HSVRepresentation(RGBRepresentation):
    @overrides
    def load_from_disk(self, path: Path) -> tr.Tensor:
        rgb = super().load_from_disk(path)
        return tr.from_numpy(rgb2hsv(rgb)).float()

class EdgesRepresentation(NpzRepresentation):
    def __init__(self, name: str):
        super().__init__(name, n_channels=1)

class DepthRepresentation(NpzRepresentation):
    """DepthRepresentation. Implements depth task-specific stuff, like hotmap."""
    def __init__(self, name: str):
        super().__init__(name, n_channels=1)

    @overrides
    def plot_fn(self, x: tr.Tensor) -> np.ndarray:
        x = x.detach().clip(0, 1).squeeze().cpu().numpy()
        _min, _max = np.percentile(x, [5, 95])
        x = np.nan_to_num((x - _min) / (_max - _min), False, 0, 0, 0).clip(0, 1)
        y: np.ndarray = hot(x)[..., 0:3] * 255
        return y.astype(np.uint8)

class NormalsRepresentation(NpzRepresentation):
    def __init__(self, name: str):
        super().__init__(name, n_channels=3)

class OpticalFlowRepresentation(NpzRepresentation):
    """OpticalFlowRepresentation. Implements depth task-specific stuff, like using flow_vis."""
    def __init__(self, name: str):
        super().__init__(name, n_channels=2)

    @overrides
    def plot_fn(self, x: tr.Tensor) -> np.ndarray:
        _min, _max = x.min(0)[0].min(0)[0], x.max(0)[0].max(0)[0]
        x = ((x - _min) / (_max - _min)).nan_to_num(0, 0, 0).detach().cpu().numpy()
        return flow_vis.flow_to_color(x)

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):
        self.n_classes = len(list(range(classes)) if isinstance(classes, int) else classes)
        super().__init__(*args, **kwargs, n_channels=self.n_classes)
        self.classes = list(range(classes)) if isinstance(classes, int) else 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)
        if len(res.shape) == 3:
            assert res.shape[-1] == self.n_classes, f"Expected {self.n_classes} (HxWxC), got {res.shape[-1]}"
            res = res.argmax(-1)
        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_argmax = x.squeeze().nan_to_num(0).detach().argmax(-1).cpu().numpy()
        new_images = np.zeros((*x_argmax.shape, 3), dtype=np.uint8)
        for i in range(self.n_classes):
            new_images[x_argmax == i] = self.color_map[i]
        return new_images

_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]]
_m2f_name = "semantic_mask2former_swin_mapillary_converted"
dronescapes_task_types = { # some pre-baked representations
    "rgb": RGBRepresentation("rgb"),
    "hsv": HSVRepresentation("hsv"),
    "edges_dexined": EdgesRepresentation("edges_dexined"),
    "edges_gb": EdgesRepresentation("edges_gb"),
    "depth_dpt": DepthRepresentation("depth_dpt"),
    "depth_sfm_manual202204": DepthRepresentation("depth_sfm_manual202204"),
    "depth_ufo": DepthRepresentation("depth_ufo"),
    "normals_sfm_manual202204": NormalsRepresentation("normals_sfm_manual202204"),
    "opticalflow_rife": OpticalFlowRepresentation("opticalflow_rife"),
    "semantic_segprop8": SemanticRepresentation("semantic_segprop8", classes=8, color_map=_color_map),
    _m2f_name: SemanticRepresentation(_m2f_name, classes=8, color_map=_color_map),
    "softseg_gb": NpzRepresentation("softseg_gb", 3),
}