class ParallelFeatureExtractorTestNew(unittest.TestCase):
def tearDown(self):
if hasattr(self, 'fextractor'):
self.fextractor.remove_results()
pass
def test_run_vmaf_fextractor_with_resampling(self):
ref_path = config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv"
dis_path = config.ROOT + "/resource/yuv/src01_hrc01_576x324.yuv"
asset = Asset(dataset="test",
content_id=0,
asset_id=1,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=dis_path,
asset_dict={
'width': 576,
'height': 324,
'quality_width': 160,
'quality_height': 90
})
asset_original = Asset(dataset="test",
content_id=0,
asset_id=2,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=ref_path,
asset_dict={
'width': 576,
'height': 324,
'quality_width': 160,
'quality_height': 90
})
self.fextractor = VmafFeatureExtractor([asset, asset_original],
None,
fifo_mode=False)
self.fextractor.run(parallelize=False)
results = self.fextractor.results
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'],
0.782546520833,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'],
1.3216766875,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_adm_score'],
0.98229347916666665,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'],
28.0085990417,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'],
1.0,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'],
1.3216766875,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_adm_score'],
1.0,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'],
31.811350125,
places=4)
def test_run_vmaf_fextractor_with_cropping(self):
# crop_cmd: 288:162:144:81 - crop to 288x162 with upper-left pixel
# starting at coordinate (144, 81)
ref_path = config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv"
dis_path = config.ROOT + "/resource/yuv/src01_hrc01_576x324.yuv"
asset = Asset(dataset="test",
content_id=0,
asset_id=1,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=dis_path,
asset_dict={
'width': 576,
'height': 324,
'crop_cmd': '288:162:144:81',
'quality_width': 288,
'quality_height': 162,
})
asset_original = Asset(dataset="test",
content_id=0,
asset_id=2,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=ref_path,
asset_dict={
'width': 576,
'height': 324,
'crop_cmd': '288:162:144:81',
'quality_width': 288,
'quality_height': 162,
})
self.fextractor = VmafFeatureExtractor([asset, asset_original],
None,
fifo_mode=False)
self.fextractor.run(parallelize=False)
results = self.fextractor.results
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'],
0.45365762500000012,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'],
2.8779373333333331,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_adm2_score'],
0.93894987889874548,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'],
23.942050354166668,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'],
1.0,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'],
2.8779373333333331,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_adm2_score'],
1.0,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'],
31.71648420833333,
places=4)
def test_run_vmaf_fextractor_with_padding(self):
# pad_cmd: iw+100:ih+100:50:50 - pad to (iw+100)x(ih+100), where iw is
# input width, ih is input height, and starting point is (-50, -50)
ref_path = config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv"
dis_path = config.ROOT + "/resource/yuv/src01_hrc01_576x324.yuv"
asset = Asset(dataset="test",
content_id=0,
asset_id=1,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=dis_path,
asset_dict={
'width': 576,
'height': 324,
'pad_cmd': 'iw+100:ih+100:50:50',
'quality_width': 676,
'quality_height': 424,
})
asset_original = Asset(dataset="test",
content_id=0,
asset_id=2,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=ref_path,
asset_dict={
'width': 576,
'height': 324,
'pad_cmd': 'iw+100:ih+100:50:50',
'quality_width': 676,
'quality_height': 424,
})
self.fextractor = VmafFeatureExtractor([asset, asset_original],
None,
fifo_mode=True)
self.fextractor.run(parallelize=False)
results = self.fextractor.results
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'],
0.51023564583333325,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'],
2.6397702083333332,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_adm2_score'],
0.94112949409549829,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'],
26.893242291666667,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'],
1.0,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'],
2.6397702083333332,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_adm2_score'],
1.0,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'],
34.306043416666668,
places=4)
def test_run_vmaf_fextractor_with_cropping_and_padding_to_original_wh(
self):
# crop_cmd: 288:162:144:81 - crop to the center 288x162 image
# pad_cmd: iw+288:ih+162:144:81 - pad back to the original size
ref_path = config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv"
dis_path = config.ROOT + "/resource/yuv/src01_hrc01_576x324.yuv"
asset = Asset(dataset="test",
content_id=0,
asset_id=1,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=dis_path,
asset_dict={
'width': 576,
'height': 324,
'crop_cmd': '288:162:144:81',
'pad_cmd': 'iw+288:ih+162:144:81',
'quality_width': 576,
'quality_height': 324,
})
asset_original = Asset(dataset="test",
content_id=0,
asset_id=2,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=ref_path,
asset_dict={
'width': 576,
'height': 324,
'crop_cmd': '288:162:144:81',
'pad_cmd': 'iw+288:ih+162:144:81',
'quality_width': 576,
'quality_height': 324,
})
self.fextractor = VmafFeatureExtractor([asset, asset_original],
None,
fifo_mode=True)
self.fextractor.run(parallelize=True)
results = self.fextractor.results
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'],
0.64106379166666672,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'],
0.7203213958333331,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_adm2_score'],
0.94700196017089999,
places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'],
32.78451041666667,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'],
1.0,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'],
0.7203213958333331,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_adm2_score'],
1.0,
places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'],
40.280504208333333,
places=4)
def test_run_strred_fextractor(self):
print 'test on running STRRED feature extractor...'
ref_path = config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv"
dis_path = config.ROOT + "/resource/yuv/src01_hrc01_576x324.yuv"
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
workdir_root=config.ROOT + "/workspace/workdir",
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=config.ROOT + "/workspace/workdir",
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'],
4.8845008541666664,
places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_trred_score'],
8.9429378333333336,
places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_strred_score'],
44.002554138184131,
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 = config.ROOT + "/resource/yuv/flat_1920_1080_0.yuv"
dis_path = config.ROOT + "/resource/yuv/flat_1920_1080_10.yuv"
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
workdir_root=config.ROOT + "/workspace/workdir",
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=config.ROOT + "/workspace/workdir",
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'],
5829.2644469999996,
places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_trred_score'],
13086.862734,
places=4)
self.assertAlmostEqual(results[0]['STRRED_feature_strred_score'],
62207779.127545856,
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)
class ParallelFeatureExtractorTestNew(unittest.TestCase):
def tearDown(self):
if hasattr(self, 'fextractor'):
self.fextractor.remove_results()
pass
def test_run_vmaf_fextractor_with_resampling(self):
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=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={'width':576, 'height':324,
'quality_width':160, 'quality_height':90})
asset_original = Asset(dataset="test", content_id=0, asset_id=2,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={'width':576, 'height':324,
'quality_width':160, 'quality_height':90})
self.fextractor = VmafFeatureExtractor(
[asset, asset_original], None, fifo_mode=False)
self.fextractor.run(parallelize=False)
results = self.fextractor.results
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'], 0.782546520833, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'],1.3216766875, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_adm_score'], 0.98229347916666665, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'], 28.0085990417, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'], 1.3216766875, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_adm_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'], 31.811350125, places=4)
def test_run_vmaf_fextractor_with_cropping(self):
# crop_cmd: 288:162:144:81 - crop to 288x162 with upper-left pixel
# starting at coordinate (144, 81)
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=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={'width':576, 'height':324,
'crop_cmd':'288:162:144:81',
'quality_width':288, 'quality_height':162,
})
asset_original = Asset(dataset="test", content_id=0, asset_id=2,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={'width':576, 'height':324,
'crop_cmd':'288:162:144:81',
'quality_width':288, 'quality_height':162,
})
self.fextractor = VmafFeatureExtractor(
[asset, asset_original], None, fifo_mode=False)
self.fextractor.run(parallelize=False)
results = self.fextractor.results
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'], 0.45365762500000012, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'], 2.8779373333333331, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_adm2_score'],0.93894987889874548, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'], 23.942050354166668, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'], 2.8779373333333331, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_adm2_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'], 31.71648420833333, places=4)
def test_run_vmaf_fextractor_with_padding(self):
# pad_cmd: iw+100:ih+100:50:50 - pad to (iw+100)x(ih+100), where iw is
# input width, ih is input height, and starting point is (-50, -50)
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=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={'width':576, 'height':324,
'pad_cmd': 'iw+100:ih+100:50:50',
'quality_width':676, 'quality_height':424,
})
asset_original = Asset(dataset="test", content_id=0, asset_id=2,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={'width':576, 'height':324,
'pad_cmd': 'iw+100:ih+100:50:50',
'quality_width':676, 'quality_height':424,
})
self.fextractor = VmafFeatureExtractor(
[asset, asset_original], None, fifo_mode=True)
self.fextractor.run(parallelize=False)
results = self.fextractor.results
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'], 0.51023564583333325, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'], 2.6397702083333332, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_adm2_score'],0.94112949409549829, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'], 26.893242291666667, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'], 2.6397702083333332, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_adm2_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'], 34.306043416666668, places=4)
def test_run_vmaf_fextractor_with_cropping_and_padding_to_original_wh(self):
# crop_cmd: 288:162:144:81 - crop to the center 288x162 image
# pad_cmd: iw+288:ih+162:144:81 - pad back to the original size
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=1,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=dis_path,
asset_dict={'width':576, 'height':324,
'crop_cmd':'288:162:144:81',
'pad_cmd': 'iw+288:ih+162:144:81',
'quality_width':576, 'quality_height':324,
})
asset_original = Asset(dataset="test", content_id=0, asset_id=2,
workdir_root=VmafConfig.workdir_path(),
ref_path=ref_path,
dis_path=ref_path,
asset_dict={'width':576, 'height':324,
'crop_cmd':'288:162:144:81',
'pad_cmd': 'iw+288:ih+162:144:81',
'quality_width':576, 'quality_height':324,
})
self.fextractor = VmafFeatureExtractor(
[asset, asset_original], None, fifo_mode=True)
self.fextractor.run(parallelize=True)
results = self.fextractor.results
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'], 0.64106379166666672, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'], 0.7203213958333331, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_adm2_score'],0.94700196017089999, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'], 32.78451041666667, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'], 0.7203213958333331, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_adm2_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'], 40.280504208333333, 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)
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)