def _run_on_asset(self, asset):
speed_fassembler = self._get_feature_assembler_instance(asset)
speed_fassembler.run()
feature_result = speed_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[
SpEEDMatlabFeatureExtractor.get_scores_key('speed_4')] # add SpEED score at scale 4
return Result(asset, self.executor_id, result_dict)
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)
def test_run_SpEED_matlab_fextractor(self):
print 'test on running SpEED (Matlab) feature extractor...'
ref_path, dis_path, asset, asset_original = set_default_576_324_videos_for_testing(
)
self.fextractor = SpEEDMatlabFeatureExtractor([asset, asset_original],
None,
fifo_mode=True,
result_store=None)
self.fextractor.run(parallelize=True)
results = self.fextractor.results
# S-SpEED assertions on first frame
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_sspeed_2_scores'][0],
13.510418,
places=4)
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_sspeed_3_scores'][0],
7.211881,
places=4)
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_sspeed_4_scores'][0],
4.921501,
places=4)
# T-SpEED assertions on third frame
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_tspeed_2_scores'][2],
32.994605,
places=4)
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_tspeed_3_scores'][2],
22.404285,
places=4)
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_tspeed_4_scores'][2],
15.233468,
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)
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...'
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)
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_SpEED_matlab_fextractor(self):
print 'test on running SpEED (Matlab) feature extractor...'
ref_path, dis_path, asset, asset_original = set_default_576_324_videos_for_testing(
)
self.fextractor = SpEEDMatlabFeatureExtractor([asset, asset_original],
None,
fifo_mode=True,
result_store=None)
self.fextractor.run(parallelize=True)
results = self.fextractor.results
# S-SpEED assertions on first frame
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_sspeed_2_scores'][0],
13.510418,
places=4)
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_sspeed_3_scores'][0],
7.211881,
places=4)
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_sspeed_4_scores'][0],
4.921501,
places=4)
# T-SpEED assertions on third frame
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_tspeed_2_scores'][2],
32.994605,
places=4)
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_tspeed_3_scores'][2],
22.404285,
places=4)
self.assertAlmostEqual(results[0].result_dict[self.fextractor.TYPE +
'_tspeed_4_scores'][2],
15.233468,
places=4)