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# Copyright (C) 2020 Intel Corporation
#
# SPDX-License-Identifier: MIT
import os
import os.path as osp
from collections import OrderedDict
from enum import Enum
from glob import glob
import numpy as np
from datumaro.components.converter import Converter
from datumaro.components.extractor import (AnnotationType, CompiledMask,
DatasetItem, Importer, LabelCategories, Mask,
MaskCategories, SourceExtractor)
from datumaro.util import find, str_to_bool
from datumaro.util.image import save_image
from datumaro.util.mask_tools import lazy_mask, paint_mask, generate_colormap
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))
])
class CamvidPath:
LABELMAP_FILE = 'label_colors.txt'
SEGM_DIR = "annot"
IMAGE_EXT = '.png'
def parse_label_map(path):
if not path:
return None
label_map = OrderedDict()
with open(path, 'r') 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 ValueError("Label '%s' is already defined" % name)
label_map[name] = color
return label_map
def write_label_map(path, label_map):
with open(path, 'w') 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))
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, desc in label_map.items():
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() }
mask_categories = MaskCategories(colormap)
mask_categories.inverse_colormap # pylint: disable=pointless-statement
categories[AnnotationType.mask] = mask_categories
return categories
class CamvidExtractor(SourceExtractor):
def __init__(self, path):
assert osp.isfile(path), path
self._path = path
self._dataset_dir = osp.dirname(path)
super().__init__(subset=osp.splitext(osp.basename(path))[0])
self._categories = self._load_categories(self._dataset_dir)
self._items = list(self._load_items(path).values())
def _load_categories(self, path):
label_map = None
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 = {}
with open(path, encoding='utf-8') as f:
for line in f:
objects = line.split()
image = objects[0]
item_id = ('/'.join(image.split('/')[2:]))[:-len(CamvidPath.IMAGE_EXT)]
image_path = osp.join(self._dataset_dir,
(image, image[1:])[image[0] == '/'])
item_annotations = []
if 1 < len(objects):
gt = objects[1]
gt_path = osp.join(self._dataset_dir,
(gt, gt[1:]) [gt[0] == '/'])
inverse_cls_colormap = \
self._categories[AnnotationType.mask].inverse_colormap
mask = lazy_mask(gt_path, inverse_cls_colormap)
# loading mask through cache
mask = mask()
classes = np.unique(mask)
labels = self._categories[AnnotationType.label]._indices
labels = { labels[label_name]: label_name
for label_name in labels }
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,
image=image_path, annotations=item_annotations)
return items
@staticmethod
def _lazy_extract_mask(mask, c):
return lambda: mask == c
class CamvidImporter(Importer):
@classmethod
def find_sources(cls, path):
subset_paths = [p for p in glob(osp.join(path, '**.txt'), recursive=True)
if osp.basename(p) != CamvidPath.LABELMAP_FILE]
sources = []
for subset_path in subset_paths:
sources += cls._find_sources_recursive(
subset_path, '.txt', 'camvid')
return sources
LabelmapType = Enum('LabelmapType', ['camvid', 'source'])
class CamvidConverter(Converter):
DEFAULT_IMAGE_EXT = '.png'
@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))
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(self):
subset_dir = self._save_dir
os.makedirs(subset_dir, exist_ok=True)
for subset_name, subset in self._extractor.subsets().items():
segm_list = {}
for item in subset:
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])
self.save_segm(osp.join(subset_dir,
subset_name + CamvidPath.SEGM_DIR,
item.id + CamvidPath.IMAGE_EXT),
compiled_mask.class_mask)
segm_list[item.id] = True
else:
segm_list[item.id] = False
if self._save_images:
self._save_image(item, osp.join(subset_dir, subset_name,
item.id + CamvidPath.IMAGE_EXT))
self.save_segm_lists(subset_name, segm_list)
self.save_label_map()
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)
def save_segm_lists(self, subset_name, segm_list):
if not segm_list:
return
ann_file = osp.join(self._save_dir, subset_name + '.txt')
with open(ann_file, 'w') as f:
for item in segm_list:
if segm_list[item]:
path_mask = '/%s/%s' % (subset_name + CamvidPath.SEGM_DIR,
item + CamvidPath.IMAGE_EXT)
else:
path_mask = ''
f.write('/%s/%s %s\n' % (subset_name,
item + CamvidPath.IMAGE_EXT, path_mask))
def save_label_map(self):
path = osp.join(self._save_dir, CamvidPath.LABELMAP_FILE)
labels = self._extractor.categories()[AnnotationType.label]._indices
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):
label_map = parse_label_map(label_map_source)
else:
raise Exception("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):
source_labels = {
id: label.name for id, label in
enumerate(self._extractor.categories().get(
AnnotationType.label, LabelCategories()).items)
}
target_labels = {
label.name: id for id, label in
enumerate(self._categories[AnnotationType.label].items)
}
id_mapping = {
src_id: target_labels.get(src_label, 0)
for src_id, src_label in source_labels.items()
}
def map_id(src_id):
return id_mapping.get(src_id, 0)
return map_id
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