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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 skimage.color import rgb2hsv
from overrides import overrides
from matplotlib.cm import Spectral # 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, min_depth: float, max_depth: float):
super().__init__(name, n_channels=1)
self.min_depth = min_depth
self.max_depth = max_depth
@overrides
def load_from_disk(self, path: Path) -> tr.Tensor:
"""Reads the npz data from the disk and transforms it properly"""
res = super().load_from_disk(path)
res_clip = res.clip(self.min_depth, self.max_depth)
return res_clip
@overrides
def plot_fn(self, x: tr.Tensor) -> np.ndarray:
x = x.detach().clip(0, 1).squeeze().cpu().numpy()
_min, _max = np.percentile(x, [1, 95])
x = np.nan_to_num((x - _min) / (_max - _min), False, 0, 0, 0).clip(0, 1)
y: np.ndarray = Spectral(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]]
coco_classes = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard",
"cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
"scissors", "teddy bear", "hair drier", "toothbrush", "banner", "blanket", "bridge", "cardboard",
"counter", "curtain", "door-stuff", "floor-wood", "flower", "fruit", "gravel", "house", "light",
"mirror-stuff", "net", "pillow", "platform", "playingfield", "railroad", "river", "road", "roof",
"sand", "sea", "shelf", "snow", "stairs", "tent", "towel", "wall-brick", "wall-stone", "wall-tile",
"wall-wood", "water-other", "window-blind", "window-other", "tree-merged", "fence-merged",
"ceiling-merged", "sky-other-merged", "cabinet-merged", "table-merged", "floor-other-merged",
"pavement-merged", "mountain-merged", "grass-merged", "dirt-merged", "paper-merged",
"food-other-merged", "building-other-merged", "rock-merged", "wall-other-merged", "rug-merged"]
coco_color_map = [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100],
[0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30],
[165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157],
[110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92],
[209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255],
[78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174],
[255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95],
[9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65],
[0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208],
[153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120],
[183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185],
[79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45],
[196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208],
[255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198],
[210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181],
[70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195],
[106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124],
[73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255],
[137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142],
[255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156],
[209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228],
[206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]]
mapillary_classes = ["Bird", "Ground Animal", "Curb", "Fence", "Guard Rail", "Barrier", "Wall", "Bike Lane",
"Crosswalk - Plain", "Curb Cut", "Parking", "Pedestrian Area", "Rail Track", "Road",
"Service Lane", "Sidewalk", "Bridge", "Building", "Tunnel", "Person", "Bicyclist",
"Motorcyclist", "Other Rider", "Lane Marking - Crosswalk", "Lane Marking - General",
"Mountain", "Sand", "Sky", "Snow", "Terrain", "Vegetation", "Water", "Banner", "Bench",
"Bike Rack", "Billboard", "Catch Basin", "CCTV Camera", "Fire Hydrant", "Junction Box",
"Mailbox", "Manhole", "Phone Booth", "Pothole", "Street Light", "Pole", "Traffic Sign Frame",
"Utility Pole", "Traffic Light", "Traffic Sign (Back)", "Traffic Sign (Front)", "Trash Can",
"Bicycle", "Boat", "Bus", "Car", "Caravan", "Motorcycle", "On Rails", "Other Vehicle", "Trailer",
"Truck", "Wheeled Slow", "Car Mount", "Ego Vehicle"]
mapillary_color_map = [[165, 42, 42], [0, 192, 0], [196, 196, 196], [190, 153, 153], [180, 165, 180], [90, 120, 150],
[102, 102, 156], [128, 64, 255], [140, 140, 200], [170, 170, 170], [250, 170, 160], [96, 96, 96],
[230, 150, 140], [128, 64, 128], [110, 110, 110], [244, 35, 232], [150, 100, 100], [70, 70, 70],
[150, 120, 90], [220, 20, 60], [255, 0, 0], [255, 0, 100], [255, 0, 200], [200, 128, 128],
[255, 255, 255], [64, 170, 64], [230, 160, 50], [70, 130, 180], [190, 255, 255], [152, 251, 152],
[107, 142, 35], [0, 170, 30], [255, 255, 128], [250, 0, 30], [100, 140, 180], [220, 220, 220],
[220, 128, 128], [222, 40, 40], [100, 170, 30], [40, 40, 40], [33, 33, 33], [100, 128, 160],
[142, 0, 0], [70, 100, 150], [210, 170, 100], [153, 153, 153], [128, 128, 128], [0, 0, 80],
[250, 170, 30], [192, 192, 192], [220, 220, 0], [140, 140, 20], [119, 11, 32], [150, 0, 255],
[0, 60, 100], [0, 0, 142], [0, 0, 90], [0, 0, 230], [0, 80, 100], [128, 64, 64], [0, 0, 110],
[0, 0, 70], [0, 0, 192], [32, 32, 32], [120, 10, 10]]
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", min_depth=0, max_depth=0.999),
"depth_sfm_manual202204": DepthRepresentation("depth_sfm_manual202204", min_depth=0, max_depth=300),
"depth_ufo": DepthRepresentation("depth_ufo", min_depth=0, max_depth=1),
"depth_marigold": DepthRepresentation("depth_marigold", min_depth=0, max_depth=1),
"normals_sfm_manual202204": NormalsRepresentation("normals_sfm_manual202204"),
"opticalflow_rife": OpticalFlowRepresentation("opticalflow_rife"),
"semantic_segprop8": SemanticRepresentation("semantic_segprop8", classes=8, color_map=_color_map),
"semantic_mask2former_swin_mapillary_converted":
SemanticRepresentation("semantic_mask2former_swin_mapillary_converted", classes=8, color_map=_color_map),
"semantic_mask2former_coco_47429163_0":
SemanticRepresentation("semantic_mask2former_coco_47429163_0", classes=coco_classes, color_map=coco_color_map),
"semantic_mask2former_mapillary_49189528_0":
SemanticRepresentation("semantic_mask2former_mapillary_49189528_0", classes=mapillary_classes,
color_map=mapillary_color_map),
"softseg_gb": NpzRepresentation("softseg_gb", 3),
}
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