def test_run_strred_fextractor(self):
ref_path, dis_path, asset, asset_original = set_default_576_324_videos_for_testing(
)
self.fextractor = StrredFeatureExtractor([asset, asset_original],
None,
fifo_mode=True,
result_store=None)
self.fextractor.run(parallelize=True)
results = self.fextractor.results
self.assertAlmostEqual(results[0]['STRRED_feature_srred_score'],
3.0166328541666663,
places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_trred_score'],
7.338665770833333,
places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_strred_score'],
22.336452104611016,
places=4)
self.assertAlmostEqual(
results[0]['STRRED_feature_strred_all_same_score'],
22.138060270044175,
places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_srred_score'],
0.0,
places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_trred_score'],
0.0,
places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_strred_score'],
0.0,
places=4)
def _run_on_asset(self, asset):
vmaf_fassembler = self._get_feature_assembler_instance(asset)
vmaf_fassembler.run()
feature_result = vmaf_fassembler.results[0]
result_dict = {}
result_dict.update(feature_result.result_dict.copy()) # add feature result
result_dict[self.get_scores_key()] = feature_result.result_dict[
StrredFeatureExtractor.get_scores_key('strred')] # add strred score
del result_dict[StrredFeatureExtractor.get_scores_key('strred')] # delete redundant
return Result(asset, self.executor_id, result_dict)
def test_run_strred_fextractor_blackframes(self):
print 'test on running STRRED feature extractor on flat frames...'
ref_path = VmafConfig.test_resource_path("yuv", "flat_1920_1080_0.yuv")
dis_path = VmafConfig.test_resource_path("yuv", "flat_1920_1080_10.yuv")
asset = Asset(dataset="test", content_id=0, asset_id=0,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={'width':576, 'height':324})
asset_original = Asset(dataset="test", content_id=0, asset_id=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={'width':576, 'height':324})
from vmaf.core.result_store import FileSystemResultStore
result_store = FileSystemResultStore(logger=None)
self.fextractor = StrredFeatureExtractor(
[asset, asset_original],
None, fifo_mode=True,
result_store=result_store
)
print ' running for the first time with fresh calculation...'
self.fextractor.run(parallelize=True)
result0, result1 = self.fextractor.results
import os
self.assertTrue(os.path.exists(result_store._get_result_file_path(result0)))
self.assertTrue(os.path.exists(result_store._get_result_file_path(result1)))
print ' running for the second time with stored results...'
self.fextractor.run(parallelize=True)
results = self.fextractor.results
# ignore NaN
for result in results:
result.set_score_aggregate_method(ListStats.nonemean)
self.assertAlmostEqual(results[0]['STRRED_feature_srred_score'], 1220.5679849999999, places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_trred_score'], 50983.3097155, places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_strred_score'], 62228595.6081, places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_srred_score'], 0.0, places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_trred_score'], 0.0, places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_strred_score'], 0.0, places=4)
def test_run_strred_fextractor(self):
print 'test on running STRRED feature extractor...'
ref_path = VmafConfig.test_resource_path("yuv",
"src01_hrc00_576x324.yuv")
dis_path = VmafConfig.test_resource_path("yuv",
"src01_hrc01_576x324.yuv")
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={
'width': 576,
'height': 324
})
asset_original = Asset(dataset="test",
content_id=0,
asset_id=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={
'width': 576,
'height': 324
})
self.fextractor = StrredFeatureExtractor([asset, asset_original],
None,
fifo_mode=True,
result_store=None)
self.fextractor.run(parallelize=True)
results = self.fextractor.results
self.assertAlmostEqual(results[0]['STRRED_feature_srred_score'],
3.0114681041666671,
places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_trred_score'],
7.3039486249999994,
places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_strred_score'],
21.995608318659482,
places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_srred_score'],
0.0,
places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_trred_score'],
0.0,
places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_strred_score'],
0.0,
places=4)
class ParallelMatlabFeatureExtractorTestNew(unittest.TestCase):
def tearDown(self):
if hasattr(self, 'fextractor'):
self.fextractor.remove_results()
pass
def test_run_strred_fextractor(self):
print 'test on running STRRED feature extractor, with parallelization...'
ref_path, dis_path, asset, asset_original = set_default_576_324_videos_for_testing()
self.fextractor = StrredFeatureExtractor(
[asset, asset_original],
None, fifo_mode=True,
result_store=None
)
self.fextractor.run(parallelize=True)
results = self.fextractor.results
self.assertAlmostEqual(results[0]['STRRED_feature_srred_score'], 3.0166328541666663, places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_trred_score'], 7.338665770833333, places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_strred_score'], 22.336452104611016, places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_strred_all_same_score'], 22.138060270044175, places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_srred_score'], 0.0, places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_trred_score'], 0.0, places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_strred_score'], 0.0, places=4)
def test_run_strred_fextractor_blackframes(self):
print 'test on running STRRED feature extractor on flat frames...'
ref_path = VmafConfig.test_resource_path("yuv", "flat_1920_1080_0.yuv")
dis_path = VmafConfig.test_resource_path("yuv", "flat_1920_1080_10.yuv")
asset = Asset(dataset="test", content_id=0, asset_id=0,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={'width':576, 'height':324})
asset_original = Asset(dataset="test", content_id=0, asset_id=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={'width':576, 'height':324})
from vmaf.core.result_store import FileSystemResultStore
result_store = FileSystemResultStore(logger=None)
self.fextractor = StrredFeatureExtractor(
[asset, asset_original],
None, fifo_mode=True,
result_store=result_store
)
print ' running for the first time with fresh calculation...'
self.fextractor.run(parallelize=True)
result0, result1 = self.fextractor.results
import os
self.assertTrue(os.path.exists(result_store._get_result_file_path(result0)))
self.assertTrue(os.path.exists(result_store._get_result_file_path(result1)))
print ' running for the second time with stored results...'
self.fextractor.run(parallelize=True)
results = self.fextractor.results
# ignore NaN
for result in results:
result.set_score_aggregate_method(ListStats.nonemean)
self.assertAlmostEqual(results[0]['STRRED_feature_srred_score'], 1220.5679849999999, places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_trred_score'], 50983.3097155, places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_strred_score'], 62228595.6081, places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_srred_score'], 0.0, places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_trred_score'], 0.0, places=4)
self.assertAlmostEqual(results[1]['STRRED_feature_strred_score'], 0.0, places=4)
