Source code for datumaro.plugins.data_formats.camvid

# Copyright (C) 2020-2023 Intel Corporation
#
# SPDX-License-Identifier: MIT

import glob
import logging as log
import os
import os.path as osp
from collections import OrderedDict
from enum import Enum, auto
from typing import List, Optional, Tuple

import numpy as np

from datumaro.components.annotation import (
    AnnotationType,
    CompiledMask,
    LabelCategories,
    Mask,
    MaskCategories,
)
from datumaro.components.dataset_base import DatasetItem, SubsetBase
from datumaro.components.dataset_item_storage import ItemStatus
from datumaro.components.errors import AnnotationExportError, InvalidAnnotationError, MediaTypeError
from datumaro.components.exporter import Exporter
from datumaro.components.format_detection import FormatDetectionContext
from datumaro.components.importer import ImportContext, Importer
from datumaro.components.media import Image
from datumaro.util import find, str_to_bool
from datumaro.util.annotation_util import make_label_id_mapping
from datumaro.util.image import save_image
from datumaro.util.mask_tools import generate_colormap, lazy_mask, paint_mask
from datumaro.util.meta_file_util import has_meta_file, is_meta_file, parse_meta_file

CamvidLabelMap = OrderedDict(
    [
        ("Void", (0, 0, 0)),
        ("Animal", (64, 128, 64)),
        ("Archway", (192, 0, 128)),
        ("Bicyclist", (0, 128, 192)),
        ("Bridge", (0, 128, 64)),
        ("Building", (128, 0, 0)),
        ("Car", (64, 0, 128)),
        ("CartLuggagePram", (64, 0, 192)),
        ("Child", (192, 128, 64)),
        ("Column_Pole", (192, 192, 128)),
        ("Fence", (64, 64, 128)),
        ("LaneMkgsDriv", (128, 0, 192)),
        ("LaneMkgsNonDriv", (192, 0, 64)),
        ("Misc_Text", (128, 128, 64)),
        ("MotorcycycleScooter", (192, 0, 192)),
        ("OtherMoving", (128, 64, 64)),
        ("ParkingBlock", (64, 192, 128)),
        ("Pedestrian", (64, 64, 0)),
        ("Road", (128, 64, 128)),
        ("RoadShoulder", (128, 128, 192)),
        ("Sidewalk", (0, 0, 192)),
        ("SignSymbol", (192, 128, 128)),
        ("Sky", (128, 128, 128)),
        ("SUVPickupTruck", (64, 128, 192)),
        ("TrafficCone", (0, 0, 64)),
        ("TrafficLight", (0, 64, 64)),
        ("Train", (192, 64, 128)),
        ("Tree", (128, 128, 0)),
        ("Truck_Bus", (192, 128, 192)),
        ("Tunnel", (64, 0, 64)),
        ("VegetationMisc", (192, 192, 0)),
        ("Wall", (64, 192, 0)),
    ]
)


[docs] class CamvidPath: LABELMAP_FILE = "label_colors.txt" SEGM_DIR = "annot" IMAGE_EXT = ".jpg" MASK_EXT = ".png"
[docs] def parse_label_map(path): if not path: return None label_map = OrderedDict() with open(path, "r", encoding="utf-8") as f: for line in f: # skip empty and commented lines line = line.strip() if not line or line and line[0] == "#": continue # color, name label_desc = line.strip().split() if 2 < len(label_desc): name = label_desc[3] color = tuple([int(c) for c in label_desc[:-1]]) else: name = label_desc[0] color = None if name in label_map: raise InvalidAnnotationError("Label '%s' is already defined" % name) label_map[name] = color return label_map
[docs] def write_label_map(path, label_map): with open(path, "w", encoding="utf-8") as f: for label_name, label_desc in label_map.items(): if label_desc: color_rgb = " ".join(str(c) for c in label_desc) else: color_rgb = "" f.write("%s %s\n" % (color_rgb, label_name))
[docs] def make_camvid_categories(label_map=None): if label_map is None: label_map = CamvidLabelMap # There must always be a label with color (0, 0, 0) at index 0 bg_label = find(label_map.items(), lambda x: x[1] == (0, 0, 0)) if bg_label is not None: bg_label = bg_label[0] else: bg_label = "background" if bg_label not in label_map: has_colors = any(v is not None for v in label_map.values()) color = (0, 0, 0) if has_colors else None label_map[bg_label] = color label_map.move_to_end(bg_label, last=False) categories = {} label_categories = LabelCategories() for label in label_map: label_categories.add(label) categories[AnnotationType.label] = label_categories has_colors = any(v is not None for v in label_map.values()) if not has_colors: # generate new colors colormap = generate_colormap(len(label_map)) else: # only copy defined colors label_id = lambda label: label_categories.find(label)[0] colormap = { label_id(name): (desc[0], desc[1], desc[2]) for name, desc in label_map.items() if desc } mask_categories = MaskCategories(colormap) mask_categories.inverse_colormap # pylint: disable=pointless-statement categories[AnnotationType.mask] = mask_categories return categories
def _parse_annotation_line(line: str) -> Tuple[str, Optional[str]]: line = line.strip() objects = line.split('"') if 1 < len(objects): if len(objects) == 5: objects[0] = objects[1] objects[1] = objects[3] else: raise InvalidAnnotationError( "Line %s: unexpected number " "of quotes in filename" % line ) else: objects = line.split() if len(objects) > 1: return objects[0], objects[1].lstrip("/") else: return objects[0], None
[docs] class CamvidBase(SubsetBase): def __init__( self, path: str, *, subset: Optional[str] = None, ctx: Optional[ImportContext] = None, ): assert osp.isfile(path), path self._path = path self._dataset_dir = osp.dirname(path) if not subset: subset = osp.splitext(osp.basename(path))[0] super().__init__(subset=subset, ctx=ctx) self._categories = self._load_categories(self._dataset_dir) self._items = list(self._load_items(path).values()) def _load_categories(self, path): label_map = None if has_meta_file(path): label_map = parse_meta_file(path) else: label_map_path = osp.join(path, CamvidPath.LABELMAP_FILE) if osp.isfile(label_map_path): label_map = parse_label_map(label_map_path) else: label_map = CamvidLabelMap self._labels = [label for label in label_map] return make_camvid_categories(label_map) def _load_items(self, path): items = {} labels = self._categories[AnnotationType.label]._indices labels = {labels[label_name]: label_name for label_name in labels} with open(path, encoding="utf-8") as f: for line in f: image, gt = _parse_annotation_line(line) item_id = osp.splitext(osp.join(*image.split("/")[2:]))[0] image_path = osp.join(self._dataset_dir, image.lstrip("/")) item_annotations = [] if gt is not None: gt_path = osp.join(self._dataset_dir, gt) mask = lazy_mask( gt_path, self._categories[AnnotationType.mask].inverse_colormap ) mask = mask() # loading mask through cache classes = np.unique(mask) for label_id in classes: if labels[label_id] in self._labels: image = self._lazy_extract_mask(mask, label_id) item_annotations.append(Mask(image=image, label=label_id)) items[item_id] = DatasetItem( id=item_id, subset=self._subset, media=Image.from_file(path=image_path), annotations=item_annotations, ) return items @staticmethod def _lazy_extract_mask(mask, c): return lambda: mask == c
[docs] class CamvidImporter(Importer): _ANNO_EXT = ".txt"
[docs] @classmethod def detect(cls, context: FormatDetectionContext) -> None: annot_path = context.require_file( f"*{cls._ANNO_EXT}", exclude_fnames=CamvidPath.LABELMAP_FILE ) with context.probe_text_file( annot_path, "must be a CamVid-like annotation file", ) as f: for line in f: _, mask_path = _parse_annotation_line(line) if mask_path is not None: break else: # If there are no masks in the entire file, it's probably # not actually a CamVid file. raise Exception context.require_file(glob.escape(mask_path))
[docs] @classmethod def find_sources(cls, path): return cls._find_sources_recursive( path, ".txt", "camvid", file_filter=lambda p: osp.basename(p) != CamvidPath.LABELMAP_FILE, )
[docs] @classmethod def get_file_extensions(cls) -> List[str]: return [cls._ANNO_EXT]
[docs] class LabelmapType(Enum): camvid = auto() source = auto()
[docs] class CamvidExporter(Exporter): DEFAULT_IMAGE_EXT = CamvidPath.IMAGE_EXT @staticmethod def _get_labelmap(s): if osp.isfile(s): return s try: return LabelmapType[s].name except KeyError: import argparse raise argparse.ArgumentTypeError()
[docs] @classmethod def build_cmdline_parser(cls, **kwargs): parser = super().build_cmdline_parser(**kwargs) parser.add_argument( "--apply-colormap", type=str_to_bool, default=True, help="Use colormap for class masks (default: %(default)s)", ) parser.add_argument( "--label-map", type=cls._get_labelmap, default=None, help="Labelmap file path or one of %s" % ", ".join(t.name for t in LabelmapType), ) return parser
def __init__(self, extractor, save_dir, apply_colormap=True, label_map=None, **kwargs): super().__init__(extractor, save_dir, **kwargs) self._apply_colormap = apply_colormap if label_map is None: label_map = LabelmapType.source.name self._load_categories(label_map) def _apply_impl(self): if self._extractor.media_type() and not issubclass(self._extractor.media_type(), Image): raise MediaTypeError("Media type is not an image") os.makedirs(self._save_dir, exist_ok=True) for subset_name, subset in self._extractor.subsets().items(): segm_list = {} for item in subset: image_path = self._make_image_filename(item, subdir=subset_name) if self._save_media and item.media: self._save_image(item, osp.join(self._save_dir, image_path)) masks = [a for a in item.annotations if a.type == AnnotationType.mask] if masks: compiled_mask = CompiledMask.from_instance_masks( masks, instance_labels=[self._label_id_mapping(m.label) for m in masks] ) mask_path = osp.join( subset_name + CamvidPath.SEGM_DIR, item.id + CamvidPath.MASK_EXT ) self.save_segm(osp.join(self._save_dir, mask_path), compiled_mask.class_mask) segm_list[item.id] = (image_path, mask_path) else: segm_list[item.id] = (image_path, "") self.save_segm_lists(subset_name, segm_list) self.save_label_map()
[docs] def save_segm(self, path, mask, colormap=None): if self._apply_colormap: if colormap is None: colormap = self._categories[AnnotationType.mask].colormap mask = paint_mask(mask, colormap) save_image(path, mask, create_dir=True)
[docs] def save_segm_lists(self, subset_name, segm_list): ann_file = osp.join(self._save_dir, subset_name + ".txt") if not segm_list: if self._patch and subset_name in self._patch.updated_subsets: if osp.isfile(ann_file): os.remove(ann_file) return with open(ann_file, "w", encoding="utf-8") as f: for image_path, mask_path in segm_list.values(): image_path = "/" + image_path.replace("\\", "/") mask_path = mask_path.replace("\\", "/") if 1 < len(image_path.split()) or 1 < len(mask_path.split()): image_path = '"' + image_path + '"' mask_path = '"' + mask_path + '"' f.write("%s %s\n" % (image_path, mask_path))
[docs] def save_label_map(self): if self._save_dataset_meta: self._save_meta_file(self._save_dir) else: path = osp.join(self._save_dir, CamvidPath.LABELMAP_FILE) labels = self._extractor.categories()[AnnotationType.label] if len(self._label_map) > len(labels): self._label_map.pop("background") write_label_map(path, self._label_map)
def _load_categories(self, label_map_source): if label_map_source == LabelmapType.camvid.name: # use the default Camvid colormap label_map = CamvidLabelMap elif ( label_map_source == LabelmapType.source.name and AnnotationType.mask not in self._extractor.categories() ): # generate colormap for input labels labels = self._extractor.categories().get(AnnotationType.label, LabelCategories()) label_map = OrderedDict((item.name, None) for item in labels.items) elif ( label_map_source == LabelmapType.source.name and AnnotationType.mask in self._extractor.categories() ): # use source colormap labels = self._extractor.categories()[AnnotationType.label] colors = self._extractor.categories()[AnnotationType.mask] label_map = OrderedDict() for idx, item in enumerate(labels.items): color = colors.colormap.get(idx) if color is not None: label_map[item.name] = color elif isinstance(label_map_source, dict): label_map = OrderedDict(sorted(label_map_source.items(), key=lambda e: e[0])) elif isinstance(label_map_source, str) and osp.isfile(label_map_source): if is_meta_file(label_map_source): label_map = parse_meta_file(label_map_source) else: label_map = parse_label_map(label_map_source) else: raise AnnotationExportError( "Wrong labelmap specified, " "expected one of %s or a file path" % ", ".join(t.name for t in LabelmapType) ) self._categories = make_camvid_categories(label_map) self._label_map = label_map self._label_id_mapping = self._make_label_id_map() def _make_label_id_map(self): map_id, id_mapping, src_labels, dst_labels = make_label_id_mapping( self._extractor.categories().get(AnnotationType.label), self._categories[AnnotationType.label], ) void_labels = [ src_label for src_id, src_label in src_labels.items() if src_label not in dst_labels ] if void_labels: log.warning( "The following labels are remapped to background: %s" % ", ".join(void_labels) ) log.debug( "Saving segmentations with the following label mapping: \n%s" % "\n".join( [ "#%s '%s' -> #%s '%s'" % ( src_id, src_label, id_mapping[src_id], self._categories[AnnotationType.label].items[id_mapping[src_id]].name, ) for src_id, src_label in src_labels.items() ] ) ) return map_id
[docs] @classmethod def patch(cls, dataset, patch, save_dir, **kwargs): for subset in patch.updated_subsets: conv = cls(dataset.get_subset(subset), save_dir=save_dir, **kwargs) conv._patch = patch conv.apply() conv = cls(dataset, save_dir=save_dir, **kwargs) for (item_id, subset), status in patch.updated_items.items(): if status != ItemStatus.removed: item = patch.data.get(item_id, subset) else: item = DatasetItem(item_id, subset=subset) if not (status == ItemStatus.removed or not item.media): continue image_path = osp.join(save_dir, conv._make_image_filename(item, subdir=subset)) if osp.isfile(image_path): os.unlink(image_path) mask_path = osp.join( save_dir, subset + CamvidPath.SEGM_DIR, item.id + CamvidPath.MASK_EXT ) if osp.isfile(mask_path): os.remove(mask_path) for subset in patch.updated_subsets: ann_dir = osp.join(save_dir, subset + CamvidPath.SEGM_DIR) if osp.isdir(ann_dir) and not os.listdir(ann_dir): os.rmdir(ann_dir) img_dir = osp.join(save_dir, subset) if osp.isdir(img_dir) and not os.listdir(img_dir): os.rmdir(img_dir)