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
from typing import Callable
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, *args, **kwargs):
        super().__init__(*args, n_channels=3, **kwargs)

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, *args, **kwargs):
        super().__init__(*args, n_channels=1, **kwargs)

class DepthRepresentation(NpzRepresentation):
    """DepthRepresentation. Implements depth task-specific stuff, like spectral map for plots."""
    def __init__(self, name: str, min_depth: float, max_depth: float, *args, **kwargs):
        super().__init__(name, n_channels=1, *args, **kwargs)
        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, *args, **kwargs):
        super().__init__(*args, n_channels=3, **kwargs)

class OpticalFlowRepresentation(NpzRepresentation):
    """OpticalFlowRepresentation. Implements flow task-specific stuff, like using flow_vis."""
    def __init__(self, *args, **kwargs):
        super().__init__(*args, n_channels=2, **kwargs)

    @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 semantic task-specific stuff, like argmaxing if needed"""
    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

# class SemanticMapper(SemanticRepresentation):
#     """Maps one or more semantic segmentations to a final one + a merge fn. Copy-pasta from VRE"""
#     def __init__(*args, original_classes: list[list[str]], mapping: list[dict[str, list[str]]],
#                  color_map: list[tuple[int, int, int]],
#                  merge_fn: Callable[[list[np.ndarray]], np.ndarray] | None = None, **kwargs):
#         super().__init__(*args, classes=list(mapping[0].keys()), color_map=color_map, **kwargs)
#         assert len(self.dependencies) >= 1, "No dependencies provided. Need at least one semantic segmentation to map."
#         assert isinstance(mapping, list), type(mapping)
#         assert len(mapping) == (B := len(self.dependencies)), (len(mapping), B)
#         assert (A := len(original_classes)) == len(self.dependencies), (A, B)
#         assert all(m.keys() == mapping[0].keys() for m in mapping), [list(m.keys()) for m in mapping]
#         assert len(color_map) == len(mapping[0].keys()), (len(color_map), len(mapping[0].keys()))
#         self.original_classes = original_classes
#         self.mapping = mapping

#     def _make_one(self, semantic_dep_data: np.ndarray, mapping: dict[str, list[str]],
#                   original_classes: list[str]) -> np.ndarray:
#         assert semantic_dep_data.dtype in (np.uint8, np.uint16), semantic_dep_data.dtype
#         mapping_ix = {list(mapping.keys()).index(k): [original_classes.index(_v)
#                                                       for _v in v] for k, v in mapping.items()}
#         flat_mapping = {}
#         for k, v in mapping_ix.items():
#             for _v in v:
#                 flat_mapping[_v] = k
#         mapped_data = np.vectorize(flat_mapping.get)(semantic_dep_data).astype(np.uint8)
#         return mapped_data

    

_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", dependencies=["rgb"]),
    "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),
}