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

def semantic_mapper(semantic_original: np.ndarray, mapping: dict[str, list[str]],
                    original_classes: list[str]) -> np.ndarray:
    """maps a bigger semantic segmentation to a smaller one"""
    assert len(semantic_original.shape) == 2, f"Only argmaxed data supported, got: {semantic_original.shape}"
    assert np.issubdtype(semantic_original.dtype, np.integer), semantic_original.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_original).astype(np.uint8)
    return mapped_data

class TaskMapper(NpzRepresentation):
    def __init__(self, *args, merge_fn: Callable[[list[np.ndarray]], tr.Tensor], **kwargs):
        super().__init__(*args, **kwargs)
        assert len(self.dependencies) > 0 and self.dep_names[0] != self.name, "Need at least one dependency"
        self.merge_fn = merge_fn

    def load_from_disk(self, path: Path | list[Path]) -> tr.Tensor:
        paths = [path] if isinstance(path, Path) else path
        dep_data = [dep.load_from_disk(path) for dep, path in zip(self.dependencies, paths)]
        return self.merge_fn(dep_data)

    def plot_fn(self, x):
        raise NotImplementedError("Must be overriden by the user")

color_map_8classes = [[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]]

class SemanticMask2FormerMapillaryConvertedPaper(TaskMapper):
    def __init__(self, dep: NpzRepresentation):
        super().__init__("semantic_mask2former_swin_mapillary_converted",
                         dependencies=[dep], merge_fn=self._merge_fn, n_channels=8)
        self.mapping = {
            "land": ["Terrain", "Sand", "Snow"],
            "forest": ["Vegetation"],
            "residential": ["Building", "Utility Pole", "Pole", "Fence", "Wall", "Manhole", "Street Light", "Curb",
                            "Guard Rail", "Caravan", "Junction Box", "Traffic Sign (Front)", "Billboard", "Banner",
                            "Mailbox", "Traffic Sign (Back)", "Bench", "Fire Hydrant", "Trash Can", "CCTV Camera",
                            "Traffic Light", "Barrier", "Rail Track", "Phone Booth", "Curb Cut", "Traffic Sign Frame",
                            "Bike Rack"],
            "road": ["Road", "Lane Marking - General", "Sidewalk", "Bridge", "Other Vehicle", "Motorcyclist", "Pothole",
                    "Catch Basin", "Car Mount", "Tunnel", "Parking", "Service Lane", "Lane Marking - Crosswalk",
                    "Pedestrian Area", "On Rails", "Bike Lane", "Crosswalk - Plain"],
            "little-objects": ["Car", "Person", "Truck", "Boat", "Wheeled Slow", "Trailer", "Ground Animal", "Bicycle",
                            "Motorcycle", "Bird", "Bus", "Ego Vehicle", "Bicyclist", "Other Rider"],
            "water": ["Water"],
            "sky": ["Sky"],
            "hill": ["Mountain"]
        }
        self.color_map = color_map_8classes
        self.original_classes = mapillary_classes
        self.classes = list(self.mapping.keys())
        self.n_classes = len(self.classes)

    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

    def _merge_fn(self, dep_data: list[np.ndarray]) -> tr.Tensor:
        m2f_mapillary = dep_data[0].argmax(-1).numpy()
        m2f_mapillary_converted = semantic_mapper(m2f_mapillary, self.mapping, self.original_classes)
        converted_oh = F.one_hot(tr.from_numpy(m2f_mapillary_converted).long(), num_classes=self.n_classes).float()
        return converted_oh

_tasks: list[NpzRepresentation] = [ # some pre-baked representations
    rgb := RGBRepresentation("rgb"),
    HSVRepresentation("hsv", dependencies=[rgb]),
    EdgesRepresentation("edges_dexined"),
    EdgesRepresentation("edges_gb"),
    DepthRepresentation("depth_dpt", min_depth=0, max_depth=0.999),
    DepthRepresentation("depth_sfm_manual202204", min_depth=0, max_depth=300),
    DepthRepresentation("depth_ufo", min_depth=0, max_depth=1),
    DepthRepresentation("depth_marigold", min_depth=0, max_depth=1),
    NormalsRepresentation("normals_sfm_manual202204"),
    OpticalFlowRepresentation("opticalflow_rife"),
    SemanticRepresentation("semantic_segprop8", classes=8, color_map=color_map_8classes),
    SemanticRepresentation("semantic_mask2former_coco_47429163_0", classes=coco_classes, color_map=coco_color_map),
    m2f_mapillary := SemanticRepresentation("semantic_mask2former_mapillary_49189528_0", classes=mapillary_classes,
                                            color_map=mapillary_color_map),
    SemanticMask2FormerMapillaryConvertedPaper(m2f_mapillary),
    NpzRepresentation("softseg_gb", 3),
]
dronescapes_task_types: dict[str, NpzRepresentation] = {task.name: task for task in _tasks}