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import os.path as osp
import tempfile
import annotator.uniformer.mmcv as mmcv
import numpy as np
from annotator.uniformer.mmcv.utils import print_log
from PIL import Image
from .builder import DATASETS
from .custom import CustomDataset
@DATASETS.register_module()
class CityscapesDataset(CustomDataset):
"""Cityscapes dataset.
The ``img_suffix`` is fixed to '_leftImg8bit.png' and ``seg_map_suffix`` is
fixed to '_gtFine_labelTrainIds.png' for Cityscapes dataset.
"""
CLASSES = ('road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
'bicycle')
PALETTE = [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
[190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0],
[107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60],
[255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100],
[0, 80, 100], [0, 0, 230], [119, 11, 32]]
def __init__(self, **kwargs):
super(CityscapesDataset, self).__init__(
img_suffix='_leftImg8bit.png',
seg_map_suffix='_gtFine_labelTrainIds.png',
**kwargs)
@staticmethod
def _convert_to_label_id(result):
"""Convert trainId to id for cityscapes."""
if isinstance(result, str):
result = np.load(result)
import cityscapesscripts.helpers.labels as CSLabels
result_copy = result.copy()
for trainId, label in CSLabels.trainId2label.items():
result_copy[result == trainId] = label.id
return result_copy
def results2img(self, results, imgfile_prefix, to_label_id):
"""Write the segmentation results to images.
Args:
results (list[list | tuple | ndarray]): Testing results of the
dataset.
imgfile_prefix (str): The filename prefix of the png files.
If the prefix is "somepath/xxx",
the png files will be named "somepath/xxx.png".
to_label_id (bool): whether convert output to label_id for
submission
Returns:
list[str: str]: result txt files which contains corresponding
semantic segmentation images.
"""
mmcv.mkdir_or_exist(imgfile_prefix)
result_files = []
prog_bar = mmcv.ProgressBar(len(self))
for idx in range(len(self)):
result = results[idx]
if to_label_id:
result = self._convert_to_label_id(result)
filename = self.img_infos[idx]['filename']
basename = osp.splitext(osp.basename(filename))[0]
png_filename = osp.join(imgfile_prefix, f'{basename}.png')
output = Image.fromarray(result.astype(np.uint8)).convert('P')
import cityscapesscripts.helpers.labels as CSLabels
palette = np.zeros((len(CSLabels.id2label), 3), dtype=np.uint8)
for label_id, label in CSLabels.id2label.items():
palette[label_id] = label.color
output.putpalette(palette)
output.save(png_filename)
result_files.append(png_filename)
prog_bar.update()
return result_files
def format_results(self, results, imgfile_prefix=None, to_label_id=True):
"""Format the results into dir (standard format for Cityscapes
evaluation).
Args:
results (list): Testing results of the dataset.
imgfile_prefix (str | None): The prefix of images files. It
includes the file path and the prefix of filename, e.g.,
"a/b/prefix". If not specified, a temp file will be created.
Default: None.
to_label_id (bool): whether convert output to label_id for
submission. Default: False
Returns:
tuple: (result_files, tmp_dir), result_files is a list containing
the image paths, tmp_dir is the temporal directory created
for saving json/png files when img_prefix is not specified.
"""
assert isinstance(results, list), 'results must be a list'
assert len(results) == len(self), (
'The length of results is not equal to the dataset len: '
f'{len(results)} != {len(self)}')
if imgfile_prefix is None:
tmp_dir = tempfile.TemporaryDirectory()
imgfile_prefix = tmp_dir.name
else:
tmp_dir = None
result_files = self.results2img(results, imgfile_prefix, to_label_id)
return result_files, tmp_dir
def evaluate(self,
results,
metric='mIoU',
logger=None,
imgfile_prefix=None,
efficient_test=False):
"""Evaluation in Cityscapes/default protocol.
Args:
results (list): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
logger (logging.Logger | None | str): Logger used for printing
related information during evaluation. Default: None.
imgfile_prefix (str | None): The prefix of output image file,
for cityscapes evaluation only. It includes the file path and
the prefix of filename, e.g., "a/b/prefix".
If results are evaluated with cityscapes protocol, it would be
the prefix of output png files. The output files would be
png images under folder "a/b/prefix/xxx.png", where "xxx" is
the image name of cityscapes. If not specified, a temp file
will be created for evaluation.
Default: None.
Returns:
dict[str, float]: Cityscapes/default metrics.
"""
eval_results = dict()
metrics = metric.copy() if isinstance(metric, list) else [metric]
if 'cityscapes' in metrics:
eval_results.update(
self._evaluate_cityscapes(results, logger, imgfile_prefix))
metrics.remove('cityscapes')
if len(metrics) > 0:
eval_results.update(
super(CityscapesDataset,
self).evaluate(results, metrics, logger, efficient_test))
return eval_results
def _evaluate_cityscapes(self, results, logger, imgfile_prefix):
"""Evaluation in Cityscapes protocol.
Args:
results (list): Testing results of the dataset.
logger (logging.Logger | str | None): Logger used for printing
related information during evaluation. Default: None.
imgfile_prefix (str | None): The prefix of output image file
Returns:
dict[str: float]: Cityscapes evaluation results.
"""
try:
import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as CSEval # noqa
except ImportError:
raise ImportError('Please run "pip install cityscapesscripts" to '
'install cityscapesscripts first.')
msg = 'Evaluating in Cityscapes style'
if logger is None:
msg = '\n' + msg
print_log(msg, logger=logger)
result_files, tmp_dir = self.format_results(results, imgfile_prefix)
if tmp_dir is None:
result_dir = imgfile_prefix
else:
result_dir = tmp_dir.name
eval_results = dict()
print_log(f'Evaluating results under {result_dir} ...', logger=logger)
CSEval.args.evalInstLevelScore = True
CSEval.args.predictionPath = osp.abspath(result_dir)
CSEval.args.evalPixelAccuracy = True
CSEval.args.JSONOutput = False
seg_map_list = []
pred_list = []
# when evaluating with official cityscapesscripts,
# **_gtFine_labelIds.png is used
for seg_map in mmcv.scandir(
self.ann_dir, 'gtFine_labelIds.png', recursive=True):
seg_map_list.append(osp.join(self.ann_dir, seg_map))
pred_list.append(CSEval.getPrediction(CSEval.args, seg_map))
eval_results.update(
CSEval.evaluateImgLists(pred_list, seg_map_list, CSEval.args))
if tmp_dir is not None:
tmp_dir.cleanup()
return eval_results
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