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Source code for mmdet.datasets.cityscapes

# Copyright (c) OpenMMLab. All rights reserved.
# Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/datasets/cityscapes.py # noqa
# and https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa

import glob
import os
import os.path as osp
import tempfile
from collections import OrderedDict

import mmcv
import numpy as np
import pycocotools.mask as maskUtils
from mmcv.utils import print_log

from .builder import DATASETS
from .coco import CocoDataset


[docs]@DATASETS.register_module() class CityscapesDataset(CocoDataset): CLASSES = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle') def _filter_imgs(self, min_size=32): """Filter images too small or without ground truths.""" valid_inds = [] # obtain images that contain annotation ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values()) # obtain images that contain annotations of the required categories ids_in_cat = set() for i, class_id in enumerate(self.cat_ids): ids_in_cat |= set(self.coco.cat_img_map[class_id]) # merge the image id sets of the two conditions and use the merged set # to filter out images if self.filter_empty_gt=True ids_in_cat &= ids_with_ann valid_img_ids = [] for i, img_info in enumerate(self.data_infos): img_id = img_info['id'] ann_ids = self.coco.getAnnIds(imgIds=[img_id]) ann_info = self.coco.loadAnns(ann_ids) all_iscrowd = all([_['iscrowd'] for _ in ann_info]) if self.filter_empty_gt and (self.img_ids[i] not in ids_in_cat or all_iscrowd): continue if min(img_info['width'], img_info['height']) >= min_size: valid_inds.append(i) valid_img_ids.append(img_id) self.img_ids = valid_img_ids return valid_inds def _parse_ann_info(self, img_info, ann_info): """Parse bbox and mask annotation. Args: img_info (dict): Image info of an image. ann_info (list[dict]): Annotation info of an image. Returns: dict: A dict containing the following keys: bboxes, \ bboxes_ignore, labels, masks, seg_map. \ "masks" are already decoded into binary masks. """ gt_bboxes = [] gt_labels = [] gt_bboxes_ignore = [] gt_masks_ann = [] for i, ann in enumerate(ann_info): if ann.get('ignore', False): continue x1, y1, w, h = ann['bbox'] if ann['area'] <= 0 or w < 1 or h < 1: continue if ann['category_id'] not in self.cat_ids: continue bbox = [x1, y1, x1 + w, y1 + h] if ann.get('iscrowd', False): gt_bboxes_ignore.append(bbox) else: gt_bboxes.append(bbox) gt_labels.append(self.cat2label[ann['category_id']]) gt_masks_ann.append(ann['segmentation']) if gt_bboxes: gt_bboxes = np.array(gt_bboxes, dtype=np.float32) gt_labels = np.array(gt_labels, dtype=np.int64) else: gt_bboxes = np.zeros((0, 4), dtype=np.float32) gt_labels = np.array([], dtype=np.int64) if gt_bboxes_ignore: gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32) else: gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32) ann = dict( bboxes=gt_bboxes, labels=gt_labels, bboxes_ignore=gt_bboxes_ignore, masks=gt_masks_ann, seg_map=img_info['segm_file']) return ann
[docs] def results2txt(self, results, outfile_prefix): """Dump the detection results to a txt file. Args: results (list[list | tuple]): Testing results of the dataset. outfile_prefix (str): The filename prefix of the json files. If the prefix is "somepath/xxx", the txt files will be named "somepath/xxx.txt". Returns: list[str]: Result txt files which contains corresponding \ instance segmentation images. """ try: import cityscapesscripts.helpers.labels as CSLabels except ImportError: raise ImportError('Please run "pip install citscapesscripts" to ' 'install cityscapesscripts first.') result_files = [] os.makedirs(outfile_prefix, exist_ok=True) prog_bar = mmcv.ProgressBar(len(self)) for idx in range(len(self)): result = results[idx] filename = self.data_infos[idx]['filename'] basename = osp.splitext(osp.basename(filename))[0] pred_txt = osp.join(outfile_prefix, basename + '_pred.txt') bbox_result, segm_result = result bboxes = np.vstack(bbox_result) # segm results if isinstance(segm_result, tuple): # Some detectors use different scores for bbox and mask, # like Mask Scoring R-CNN. Score of segm will be used instead # of bbox score. segms = mmcv.concat_list(segm_result[0]) mask_score = segm_result[1] else: # use bbox score for mask score segms = mmcv.concat_list(segm_result) mask_score = [bbox[-1] for bbox in bboxes] labels = [ np.full(bbox.shape[0], i, dtype=np.int32) for i, bbox in enumerate(bbox_result) ] labels = np.concatenate(labels) assert len(bboxes) == len(segms) == len(labels) num_instances = len(bboxes) prog_bar.update() with open(pred_txt, 'w') as fout: for i in range(num_instances): pred_class = labels[i] classes = self.CLASSES[pred_class] class_id = CSLabels.name2label[classes].id score = mask_score[i] mask = maskUtils.decode(segms[i]).astype(np.uint8) png_filename = osp.join(outfile_prefix, basename + f'_{i}_{classes}.png') mmcv.imwrite(mask, png_filename) fout.write(f'{osp.basename(png_filename)} {class_id} ' f'{score}\n') result_files.append(pred_txt) return result_files
[docs] def format_results(self, results, txtfile_prefix=None): """Format the results to txt (standard format for Cityscapes evaluation). Args: results (list): Testing results of the dataset. txtfile_prefix (str | None): The prefix of txt 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. Returns: tuple: (result_files, tmp_dir), result_files is a dict containing \ the json filepaths, tmp_dir is the temporal directory created \ for saving txt/png files when txtfile_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: {} != {}'. format(len(results), len(self))) 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: {} != {}'. format(len(results), len(self))) if txtfile_prefix is None: tmp_dir = tempfile.TemporaryDirectory() txtfile_prefix = osp.join(tmp_dir.name, 'results') else: tmp_dir = None result_files = self.results2txt(results, txtfile_prefix) return result_files, tmp_dir
[docs] def evaluate(self, results, metric='bbox', logger=None, outfile_prefix=None, classwise=False, proposal_nums=(100, 300, 1000), iou_thrs=np.arange(0.5, 0.96, 0.05)): """Evaluation in Cityscapes/COCO protocol. Args: results (list[list | tuple]): Testing results of the dataset. metric (str | list[str]): Metrics to be evaluated. Options are 'bbox', 'segm', 'proposal', 'proposal_fast'. logger (logging.Logger | str | None): Logger used for printing related information during evaluation. Default: None. outfile_prefix (str | None): The prefix of output file. It includes the file path and the prefix of filename, e.g., "a/b/prefix". If results are evaluated with COCO protocol, it would be the prefix of output json file. For example, the metric is 'bbox' and 'segm', then json files would be "a/b/prefix.bbox.json" and "a/b/prefix.segm.json". If results are evaluated with cityscapes protocol, it would be the prefix of output txt/png files. The output files would be png images under folder "a/b/prefix/xxx/" and the file name of images would be written into a txt file "a/b/prefix/xxx_pred.txt", where "xxx" is the video name of cityscapes. If not specified, a temp file will be created. Default: None. classwise (bool): Whether to evaluating the AP for each class. proposal_nums (Sequence[int]): Proposal number used for evaluating recalls, such as recall@100, recall@1000. Default: (100, 300, 1000). iou_thrs (Sequence[float]): IoU threshold used for evaluating recalls. If set to a list, the average recall of all IoUs will also be computed. Default: 0.5. Returns: dict[str, float]: COCO style evaluation metric or cityscapes mAP \ and AP@50. """ eval_results = dict() metrics = metric.copy() if isinstance(metric, list) else [metric] if 'cityscapes' in metrics: eval_results.update( self._evaluate_cityscapes(results, outfile_prefix, logger)) metrics.remove('cityscapes') # left metrics are all coco metric if len(metrics) > 0: # create CocoDataset with CityscapesDataset annotation self_coco = CocoDataset(self.ann_file, self.pipeline.transforms, None, self.data_root, self.img_prefix, self.seg_prefix, self.proposal_file, self.test_mode, self.filter_empty_gt) # TODO: remove this in the future # reload annotations of correct class self_coco.CLASSES = self.CLASSES self_coco.data_infos = self_coco.load_annotations(self.ann_file) eval_results.update( self_coco.evaluate(results, metrics, logger, outfile_prefix, classwise, proposal_nums, iou_thrs)) return eval_results
def _evaluate_cityscapes(self, results, txtfile_prefix, logger): """Evaluation in Cityscapes protocol. Args: results (list): Testing results of the dataset. txtfile_prefix (str | None): The prefix of output txt file logger (logging.Logger | str | None): Logger used for printing related information during evaluation. Default: None. Returns: dict[str: float]: Cityscapes evaluation results, contains 'mAP' \ and 'AP@50'. """ try: import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as CSEval # noqa except ImportError: raise ImportError('Please run "pip install citscapesscripts" 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, txtfile_prefix) if tmp_dir is None: result_dir = osp.join(txtfile_prefix, 'results') else: result_dir = osp.join(tmp_dir.name, 'results') eval_results = OrderedDict() print_log(f'Evaluating results under {result_dir} ...', logger=logger) # set global states in cityscapes evaluation API CSEval.args.cityscapesPath = os.path.join(self.img_prefix, '../..') CSEval.args.predictionPath = os.path.abspath(result_dir) CSEval.args.predictionWalk = None CSEval.args.JSONOutput = False CSEval.args.colorized = False CSEval.args.gtInstancesFile = os.path.join(result_dir, 'gtInstances.json') CSEval.args.groundTruthSearch = os.path.join( self.img_prefix.replace('leftImg8bit', 'gtFine'), '*/*_gtFine_instanceIds.png') groundTruthImgList = glob.glob(CSEval.args.groundTruthSearch) assert len(groundTruthImgList), 'Cannot find ground truth images' \ f' in {CSEval.args.groundTruthSearch}.' predictionImgList = [] for gt in groundTruthImgList: predictionImgList.append(CSEval.getPrediction(gt, CSEval.args)) CSEval_results = CSEval.evaluateImgLists(predictionImgList, groundTruthImgList, CSEval.args)['averages'] eval_results['mAP'] = CSEval_results['allAp'] eval_results['AP@50'] = CSEval_results['allAp50%'] if tmp_dir is not None: tmp_dir.cleanup() return eval_results
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