def evaluate_semanticsegmentation(prediction_directory, groundtruth_directory):
prediction_files = [os.path.join(prediction_directory, f)\
for f in os.listdir(prediction_directory)]
groundtruth_files = [os.path.join(groundtruth_directory, f)\
for f in os.listdir(groundtruth_directory)\
if "_labelIds.png" in f]
print(prediction_files)
print(groundtruth_files)
evalPixelLevelSemanticLabeling.evaluateImgLists(prediction_files,
groundtruth_files, evalPixelLevelSemanticLabeling.args)
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)
print("result_files", result_files)
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,
'gtCoarse_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
def run(self, prediction_filenames, ground_truth_filenames):
# finally run evaluation
from cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling import evaluateImgLists
args = {'quiet': True}
confusion_matrix = evaluateImgLists(prediction_filenames,
ground_truth_filenames, args)
import numpy as np
np.savetxt("{}/confusion_matrix.csv".format(self._log_dir),
confusion_matrix)
# scipy.misc.imsave(path, dst)
groundTruthImgList.append(src)
predictionImgList = []
for i in range(4):
src = 'logs/MODEL2001tmp_gta25k20_segment_LinkNet_csEval_256x512_bs4_flip_crop_bright_lr2e-4/sample/predictionImg%02d.png'%i
# dst = trainId2Id(scipy.misc.imread(src))
# path = 'tmp/2.png'
# scipy.misc.imsave(path, dst)
predictionImgList.append(src)
# for i in range(1):
# # pred
# path = '{}/predictionImg{:02d}.jpg'.format(sample_dir,i)
# predictionImgList.append(path)
# # pdb.set_trace()
# # imsave(trainId2Id(np.expand_dims(pred_mask_B_ts[i,:,:],0)), [1,1], path)
# pred = trainId2Id(pred_mask_B_ts[i,:,:])
# scipy.misc.imsave(path, pred)
# # gt
# path = '{}/groundTruthImg{:02d}.jpg'.format(sample_dir,i)
# groundTruthImgList.append(path)
# # imsave(trainId2Id(np.expand_dims(mask_B_ts_ori[i,:,:],0)), [1,1], path)
# # pdb.set_trace()
# gt = trainId2Id(mask_B_ts_ori[i,:,:])
# scipy.misc.imsave(path, gt)
# CSUPPORT = False
cs_seg_eval.evaluateImgLists(predictionImgList, groundTruthImgList, cs_seg_eval.args)
# # cs_seg_eval.evaluatePair(predictionImgFileName, groundTruthImgFileName, confMatrix, instanceStats, perImageStats, args)
