def test_run_parallel_ssim_fextractor(self):
print 'test on running SSIM feature extractor in parallel...'
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.fextractors, results = run_executors_in_parallel(
SsimFeatureExtractor,
[asset, asset_original],
fifo_mode=True,
delete_workdir=True,
parallelize=True,
result_store=None,
)
self.assertAlmostEqual(results[0]['SSIM_feature_ssim_score'], 0.857692208333, places=4)
self.assertAlmostEqual(results[0]['SSIM_feature_ssim_l_score'], 0.9923925625, places=4)
self.assertAlmostEqual(results[0]['SSIM_feature_ssim_c_score'], 0.9611274375, places=4)
self.assertAlmostEqual(results[0]['SSIM_feature_ssim_s_score'], 0.897844791667, places=4)
self.assertAlmostEqual(results[1]['SSIM_feature_ssim_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['SSIM_feature_ssim_l_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['SSIM_feature_ssim_c_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['SSIM_feature_ssim_s_score'], 1.0, places=4)
def test_run_parallel_moment_fextractor(self):
print 'test on running Moment feature extractor in parallel...'
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.fextractors, results = run_executors_in_parallel(
MomentFeatureExtractor,
[asset, asset_original],
fifo_mode=True,
delete_workdir=True,
parallelize=True,
result_store=None,
)
self.assertAlmostEqual(results[0]['Moment_feature_ref1st_score'],
59.788567297525134)
self.assertAlmostEqual(results[0]['Moment_feature_ref2nd_score'],
4696.668388042269)
self.assertAlmostEqual(results[0]['Moment_feature_refvar_score'],
1121.519917231203)
self.assertAlmostEqual(results[0]['Moment_feature_dis1st_score'],
61.332006624999984)
self.assertAlmostEqual(results[0]['Moment_feature_dis2nd_score'],
4798.659574041666)
self.assertAlmostEqual(results[0]['Moment_feature_disvar_score'],
1036.837184348847)
self.assertAlmostEqual(results[1]['Moment_feature_ref1st_score'],
59.788567297525134)
self.assertAlmostEqual(results[1]['Moment_feature_ref2nd_score'],
4696.668388042269)
self.assertAlmostEqual(results[1]['Moment_feature_refvar_score'],
1121.519917231203)
self.assertAlmostEqual(results[1]['Moment_feature_dis1st_score'],
59.788567297525134)
self.assertAlmostEqual(results[1]['Moment_feature_dis2nd_score'],
4696.668388042269)
self.assertAlmostEqual(results[1]['Moment_feature_disvar_score'],
1121.519917231203)
def test_run_vmaf_legacy_runner_not_unique(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=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=0,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=ref_path,
asset_dict={
'width': 576,
'height': 324
})
with self.assertRaises(AssertionError):
self.runner = VmafLegacyQualityRunner([asset, asset_original],
None,
fifo_mode=True)
def test_run_parallel_psnr_runner(self):
print 'test on running PSNR quality runner in parallel...'
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.runners, results = run_executors_in_parallel(
PsnrQualityRunner, [asset, asset_original],
fifo_mode=True,
delete_workdir=True,
parallelize=True,
result_store=None)
self.assertAlmostEqual(results[0]['PSNR_score'], 30.755063979166664)
self.assertAlmostEqual(results[1]['PSNR_score'], 60.0)
def test_explain_vmaf_results(self):
print 'test on running VMAF runner with local explainer...'
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.runner = VmafQualityRunnerWithLocalExplainer(
[asset, asset_original],
None, fifo_mode=True,
delete_workdir=True,
result_store=None,
optional_dict2={'explainer': LocalExplainer(neighbor_samples=100)}
)
np.random.seed(0)
self.runner.run()
results = self.runner.results
self.assertAlmostEqual(results[0]['VMAF_score'], 65.4488588759, places=4)
self.assertAlmostEqual(results[1]['VMAF_score'], 99.2259317881, places=4)
expected_feature_names = ['VMAF_feature_adm2_score',
'VMAF_feature_motion_score',
'VMAF_feature_vif_scale0_score',
'VMAF_feature_vif_scale1_score',
'VMAF_feature_vif_scale2_score',
'VMAF_feature_vif_scale3_score']
weights = np.mean(results[0]['VMAF_scores_exps']['feature_weights'], axis=0)
self.assertAlmostEqual(weights[0], 0.75441663, places=4)
self.assertAlmostEqual(weights[1], 0.06816105, places=4)
self.assertAlmostEqual(weights[2], -0.10934421, places=4)
self.assertAlmostEqual(weights[3], 0.22051127, places=4)
self.assertAlmostEqual(weights[4], 0.12517884, places=4)
self.assertAlmostEqual(weights[5], 0.04639162, places=4)
self.assertEqual(results[0]['VMAF_scores_exps']['feature_names'],
expected_feature_names)
weights = np.mean(results[1]['VMAF_scores_exps']['feature_weights'], axis=0)
self.assertAlmostEqual(weights[0], 0.77096087, places=4)
self.assertAlmostEqual(weights[1], 0.01491754, places=4)
self.assertAlmostEqual(weights[2], -0.08025557, places=4)
self.assertAlmostEqual(weights[3], 0.2511188, places=4)
self.assertAlmostEqual(weights[4], 0.14953561, places=4)
self.assertAlmostEqual(weights[5], 0.07960753, places=4)
self.assertEqual(results[1]['VMAF_scores_exps']['feature_names'],
expected_feature_names)
def test_duration_sec(self):
asset = Asset(
dataset="test",
content_id=0,
asset_id=0,
ref_path="",
dis_path="",
asset_dict={
'ref_start_frame': 2,
'ref_end_frame': 2,
'dis_start_frame': 3,
'dis_end_frame': 3
},
)
self.assertEquals(asset.ref_duration_sec, None)
self.assertEquals(asset.dis_duration_sec, None)
asset = Asset(
dataset="test",
content_id=0,
asset_id=0,
ref_path="",
dis_path="",
asset_dict={
'ref_start_frame': 0,
'ref_end_frame': 23,
'dis_start_frame': 3,
'dis_end_frame': 26,
'fps': 24
},
)
self.assertEquals(asset.ref_duration_sec, 1.0)
self.assertEquals(asset.dis_duration_sec, 1.0)
def test_run_vmaf_runner_flat(self):
print 'test on running VMAF runner on flat pattern...'
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': 1920,
'height': 1080
})
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': 1920,
'height': 1080
})
self.runner = VmafQualityRunner(
[asset, asset_original],
None,
fifo_mode=True,
delete_workdir=True,
result_store=self.result_store,
)
self.runner.run()
results = self.runner.results
self.assertAlmostEqual(results[0]['VMAF_score'], 99.419836087060176)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_scale0_score'],
1.0)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_scale1_score'],
1.0)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_scale2_score'],
1.0)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_scale3_score'],
1.0)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'], 0.0)
self.assertAlmostEqual(results[0]['VMAF_feature_adm2_score'], 1.0)
self.assertAlmostEqual(results[1]['VMAF_score'], 99.419836087060176)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_scale0_score'],
1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_scale1_score'],
1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_scale2_score'],
1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_scale3_score'],
1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'], 0.0)
self.assertAlmostEqual(results[1]['VMAF_feature_adm2_score'], 1.0)
def test_to_normalized_dict_10le(self):
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
ref_path="dir/refvideo.yuv420p10le.yuv",
dis_path="dir/disvideo.yuv420p10le.yuv",
asset_dict={
'width': 720,
'height': 480,
'start_frame': 2,
'end_frame': 2
})
self.assertEquals(
asset.to_normalized_dict(), {
'asset_dict': {
'end_frame': 2,
'height': 480,
'start_frame': 2,
'width': 720
},
'asset_id': 0,
'content_id': 0,
'dataset': 'test',
'dis_path': 'disvideo.yuv420p10le.yuv',
'ref_path': 'refvideo.yuv420p10le.yuv',
'workdir': ''
})
def test_use_path_as_workpath(self):
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
ref_path="dir/refvideo.yuv",
dis_path="dir/disvideo.yuv",
asset_dict={
'width': 720,
'height': 480,
'start_frame': 2,
'end_frame': 2,
'quality_width': 1920,
'quality_height': 1080
},
workdir_root="workdir")
expected_ref_workfile_path_re = \
r"^workdir/[a-zA-Z0-9-]+/" \
r"ref_test_0_0_refvideo_720x480_2to2_vs_disvideo_720x480_2to2_q_1920x1080"
expected_dis_workfile_path_re = \
r"^workdir/[a-zA-Z0-9-]+/" \
r"dis_test_0_0_refvideo_720x480_2to2_vs_disvideo_720x480_2to2_q_1920x1080"
self.assertTrue(
re.match(expected_ref_workfile_path_re, asset.ref_workfile_path))
self.assertTrue(
re.match(expected_dis_workfile_path_re, asset.dis_workfile_path))
self.assertFalse('use_path_as_workpath' in asset.asset_dict)
asset.use_path_as_workpath = True
self.assertTrue('use_path_as_workpath' in asset.asset_dict)
self.assertTrue(asset.asset_dict['use_path_as_workpath'])
self.assertEquals(asset.ref_workfile_path, 'dir/refvideo.yuv')
self.assertEquals(asset.dis_workfile_path, 'dir/disvideo.yuv')
def test_run_psnr_fextractor(self):
print 'test on running PSNR 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 = PsnrFeatureExtractor(
[asset, asset_original],
None, fifo_mode=True,
result_store=None
)
self.fextractor.run()
results = self.fextractor.results
self.assertAlmostEqual(results[0]['PSNR_feature_psnr_score'], 30.755063979166664, places=4)
self.assertAlmostEqual(results[1]['PSNR_feature_psnr_score'], 60.0, places=4)
def test_dis_width_height(self):
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
ref_path="",
dis_path="",
asset_dict={
'dis_width': 1920,
'dis_height': 1080,
})
self.assertEquals(asset.dis_width_height, (1920, 1080))
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
ref_path="",
dis_path="",
asset_dict={
'dis_width': 1920,
'dis_height': 1080,
'width': 720,
'height': 480
})
self.assertEquals(asset.dis_width_height, (1920, 1080))
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
ref_path="",
dis_path="",
asset_dict={
'width': 720,
'height': 480
})
self.assertEquals(asset.dis_width_height, (720, 480))
def test_run_asset_extractor(self):
print 'test on running asset 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=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 = AssetExtractor([asset, asset_original],
None,
fifo_mode=True)
self.fextractor.run()
results = self.fextractor.results
self.assertEqual(
str(results[0]['asset']),
'test_0_1_src01_hrc00_576x324_576x324_vs_src01_hrc01_576x324_576x324_q_160x90'
)
self.assertEqual(
str(results[1]['asset']),
'test_0_2_src01_hrc00_576x324_576x324_vs_src01_hrc00_576x324_576x324_q_160x90'
)
self.fextractor.run(parallelize=True)
results = self.fextractor.results
self.assertEqual(
str(results[0]['asset']),
'test_0_1_src01_hrc00_576x324_576x324_vs_src01_hrc01_576x324_576x324_q_160x90'
)
self.assertEqual(
str(results[1]['asset']),
'test_0_2_src01_hrc00_576x324_576x324_vs_src01_hrc00_576x324_576x324_q_160x90'
)
def test_feature_assembler_selected_atom_feature(self):
print 'test on feature assembler with selected atom features...'
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.fassembler = FeatureAssembler(
feature_dict={'VMAF_feature': ['vif', 'motion']},
feature_option_dict=None,
assets=[asset, asset_original],
logger=None,
fifo_mode=True,
delete_workdir=True,
result_store=None,
parallelize=True,
)
self.fassembler.run()
results = self.fassembler.results
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'],
0.44455808333333313)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'],
3.5916076041666667)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'], 1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'],
3.5916076041666667)
with self.assertRaises(KeyError):
results[0]['VMAF_feature_ansnr_scores']
with self.assertRaises(KeyError):
results[0]['VMAF_feature_ansnr_score']
with self.assertRaises(KeyError):
results[0]['VMAF_feature_adm_scores']
with self.assertRaises(KeyError):
results[0]['VMAF_feature_adm_score']
def test_run_parallel_ms_ssim_fextractor(self):
print 'test on running MS-SSIM feature extractor in parallel...'
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.fextractors, results = run_executors_in_parallel(
MsSsimFeatureExtractor,
[asset, asset_original],
fifo_mode=True,
delete_workdir=True,
parallelize=True,
result_store=None,
)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_score'], 0.9632498125, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_l_scale0_score'], 0.9923925625, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_c_scale0_score'], 0.9611274375, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_s_scale0_score'], 0.897844791667, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_l_scale1_score'], 0.9954706875, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_c_scale1_score'], 0.9857694375, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_s_scale1_score'], 0.941185875, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_l_scale2_score'], 0.997437458333, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_c_scale2_score'], 0.997034020833, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_s_scale2_score'], 0.977992145833, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_l_scale3_score'], 0.998035583333, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_c_scale3_score'], 0.999588104167, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_s_scale3_score'], 0.99387125, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_l_scale4_score'], 0.9995495, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_c_scale4_score'], 0.999907625, places=4)
self.assertAlmostEqual(results[0]['MS_SSIM_feature_ms_ssim_s_scale4_score'], 0.998222583333, places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_l_scale0_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_c_scale0_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_s_scale0_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_l_scale1_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_c_scale1_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_s_scale1_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_l_scale2_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_c_scale2_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_s_scale2_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_l_scale3_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_c_scale3_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_s_scale3_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_l_scale4_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_c_scale4_score'], 1., places=4)
self.assertAlmostEqual(results[1]['MS_SSIM_feature_ms_ssim_s_scale4_score'], 1., places=4)
def test_run_vmaf_runner_with_rf_model(self):
print 'test on running VMAF runner with custom input model...'
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.runner = VmafQualityRunner(
[asset, asset_original],
None,
fifo_mode=True,
delete_workdir=True,
result_store=self.result_store,
optional_dict={
'model_filepath':
config.ROOT + "/resource/model/nflx_vmaff_rf_v1.pkl",
})
self.runner.run()
results = self.runner.results
self.assertAlmostEqual(results[0]['VMAF_score'], 73.79861111111113)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'],
0.44455808333333313)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'],
3.5916076041666667)
self.assertAlmostEqual(results[0]['VMAF_feature_adm_score'],
0.9155242291666666)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'],
22.533456770833329)
self.assertAlmostEqual(results[1]['VMAF_score'], 98.22048611111109)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'], 1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'],
3.5916076041666667)
self.assertAlmostEqual(results[1]['VMAF_feature_adm_score'], 1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'],
30.030914145833322)
def test_run_parallel_dis_y_fextractor(self):
print 'test on running dis YUV raw video extractor in parallel (disabled)...'
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
})
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
})
h5py_file = DisYUVRawVideoExtractor.open_h5py_file(self.h5py_filepath)
optional_dict2 = {'h5py_file': h5py_file}
self.fextractors, results = run_executors_in_parallel(
DisYUVRawVideoExtractor,
[asset, asset_original],
fifo_mode=True,
delete_workdir=True,
parallelize=False, # Can't run parallel: can't pickle FileID objects
result_store=None,
optional_dict={'channels': 'yu'},
optional_dict2=optional_dict2)
self.assertAlmostEqual(np.mean(results[0]['dis_y']),
61.332006579182384,
places=4)
self.assertAlmostEquals(np.mean(results[1]['dis_y']),
59.788567297525148,
places=4)
self.assertAlmostEqual(np.mean(results[0]['dis_u']),
115.23227407335962,
places=4)
self.assertAlmostEquals(np.mean(results[1]['dis_u']),
114.49701717535437,
places=4)
with self.assertRaises(KeyError):
np.mean(results[0]['dis_v'])
DisYUVRawVideoExtractor.close_h5py_file(h5py_file)
def test_feature_assembler_whole_feature(self):
print 'test on feature assembler with whole feature...'
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.fassembler = FeatureAssembler(
feature_dict={'VMAF_feature': 'all'},
feature_option_dict=None,
assets=[asset, asset_original],
logger=None,
fifo_mode=True,
delete_workdir=True,
result_store=None,
parallelize=True,
)
self.fassembler.run()
results = self.fassembler.results
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'],
0.44455808333333313)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'],
3.5916076041666667)
self.assertAlmostEqual(results[0]['VMAF_feature_adm2_score'],
0.9254334398006141)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'],
22.533456770833329)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'], 1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'],
3.5916076041666667)
self.assertAlmostEqual(results[1]['VMAF_feature_adm2_score'], 1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'],
30.030914145833322)
def test_run_dis_yuv_raw_video_extractor(self):
print 'test on running dis YUV raw video 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=1,
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=2,
workdir_root=config.ROOT + "/workspace/workdir",
ref_path=ref_path,
dis_path=ref_path,
asset_dict={
'width': 576,
'height': 324
})
h5py_file = DisYUVRawVideoExtractor.open_h5py_file(self.h5py_filepath)
self.fextractor = DisYUVRawVideoExtractor(
[asset, asset_original],
None,
fifo_mode=False,
optional_dict={'channels': 'yu'},
optional_dict2={'h5py_file': h5py_file})
self.fextractor.run()
results = self.fextractor.results
self.assertAlmostEqual(np.mean(results[0]['dis_y']),
61.332006579182384,
places=4)
self.assertAlmostEquals(np.mean(results[1]['dis_y']),
59.788567297525148,
places=4)
self.assertAlmostEqual(np.mean(results[0]['dis_u']),
115.23227407335962,
places=4)
self.assertAlmostEquals(np.mean(results[1]['dis_u']),
114.49701717535437,
places=4)
with self.assertRaises(KeyError):
np.mean(results[0]['dis_v'])
DisYUVRawVideoExtractor.close_h5py_file(h5py_file)
def test_run_vamf_fextractor(self):
print 'test on running VMAF 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 = VmafFeatureExtractor(
[asset, asset_original],
None, fifo_mode=True,
result_store=None
)
self.fextractor.run()
results = self.fextractor.results
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'], 0.44455808333333313, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'], 3.5916076041666667, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_adm2_score'], 0.9254334398006141, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'], 22.533456770833329, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_num_score'], 644527.3311971038, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_den_score'], 1449635.3812459996, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_adm_num_score'], 6899.815530270836, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_adm_den_score'], 7535.801140312499, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_anpsnr_score'], 34.15266368750002, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_scale0_score'], 0.3655846219305399, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_scale1_score'], 0.7722301581694561, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_scale2_score'], 0.8681486658208089, places=4)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_scale3_score'], 0.9207121810522212, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'], 3.5916076041666667, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_adm2_score'], 1.0, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'], 30.030914145833322, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_num_score'], 1449635.3522745417, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_den_score'], 1449635.3812459996, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_adm_num_score'], 7535.801140312499, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_adm_den_score'], 7535.801140312499, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_anpsnr_score'], 41.65012097916668, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_scale0_score'], 1.0000000132944864, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_scale1_score'], 0.9999998271651448, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_scale2_score'], 0.9999998649680067, places=4)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_scale3_score'], 0.9999998102499, places=4)
def test_run_vamf_legacy_runner_10le(self):
print 'test on running VMAF (legacy) runner on 10 bit le...'
ref_path = config.ROOT + "/resource/yuv/src01_hrc00_576x324.yuv422p10le.yuv"
dis_path = config.ROOT + "/resource/yuv/src01_hrc01_576x324.yuv422p10le.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,
'yuv_type': 'yuv422p10le'
})
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,
'yuv_type': 'yuv422p10le'
})
self.runner = VmafLegacyQualityRunner([asset, asset_original],
None,
fifo_mode=False,
delete_workdir=True,
result_store=None)
self.runner.run()
results = self.runner.results
self.assertAlmostEqual(results[0]['VMAF_legacy_score'],
60.27316952679754)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'],
0.44455808333333313)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'],
3.5916076041666667)
self.assertAlmostEqual(results[0]['VMAF_feature_adm_score'],
0.9155242291666666)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'],
22.533456770833329)
self.assertAlmostEqual(results[1]['VMAF_legacy_score'],
95.65756240092573)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'], 1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'],
3.5916076041666667)
self.assertAlmostEqual(results[1]['VMAF_feature_adm_score'], 1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'],
30.030914145833322)
def test_start_end_frame(self):
asset = Asset(
dataset="test",
content_id=0,
asset_id=0,
ref_path="",
dis_path="",
asset_dict={
'ref_start_frame': 2,
'ref_end_frame': 2,
'dis_start_frame': 3,
'dis_end_frame': 3
},
)
self.assertEquals(asset.ref_start_end_frame, (2, 2))
self.assertEquals(asset.dis_start_end_frame, (3, 3))
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
ref_path="",
dis_path="",
asset_dict={
'start_frame': 2,
'end_frame': 2
})
self.assertEquals(asset.ref_start_end_frame, (2, 2))
self.assertEquals(asset.dis_start_end_frame, (2, 2))
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
ref_path="",
dis_path="",
asset_dict={
'fps': 24,
'duration_sec': 2
})
self.assertEquals(asset.ref_start_end_frame, (0, 47))
self.assertEquals(asset.dis_start_end_frame, (0, 47))
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
ref_path="",
dis_path="",
asset_dict={
'fps': 24,
'start_sec': 2,
'end_sec': 3
})
self.assertEquals(asset.ref_start_end_frame, (48, 71))
self.assertEquals(asset.dis_start_end_frame, (48, 71))
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_parallel_vmaf_legacy_runner(self):
print 'test on running VMAF (legacy) quality runner in parallel...'
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.runners, results = run_executors_in_parallel(
VmafLegacyQualityRunner, [asset, asset_original],
fifo_mode=True,
delete_workdir=True,
parallelize=True,
result_store=None)
self.assertAlmostEqual(results[0]['VMAF_legacy_score'],
60.27316952679754)
self.assertAlmostEqual(results[0]['VMAF_feature_vif_score'],
0.44455808333333313)
self.assertAlmostEqual(results[0]['VMAF_feature_motion_score'],
3.5916076041666667)
self.assertAlmostEqual(results[0]['VMAF_feature_adm_score'],
0.9155242291666666)
self.assertAlmostEqual(results[0]['VMAF_feature_ansnr_score'],
22.533456770833329)
self.assertAlmostEqual(results[1]['VMAF_legacy_score'],
95.65756240092573)
self.assertAlmostEqual(results[1]['VMAF_feature_vif_score'], 1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_motion_score'],
3.5916076041666667)
self.assertAlmostEqual(results[1]['VMAF_feature_adm_score'], 1.0)
self.assertAlmostEqual(results[1]['VMAF_feature_ansnr_score'],
30.030914145833322)
def test_hash_equal(self):
asset1 = Asset(dataset="test",
content_id=0,
asset_id=2,
ref_path="dir/refvideo.yuv",
dis_path="dir/disvideo.yuv",
asset_dict={
'width': 720,
'height': 480,
'quality_width': 1920,
'quality_height': 1080
})
asset2 = Asset(dataset="test",
content_id=0,
asset_id=2,
ref_path="dir/refvideo.yuv",
dis_path="dir/disvideo.yuv",
asset_dict={
'width': 720,
'height': 480,
'quality_width': 1920,
'quality_height': 1080
})
asset3 = Asset(dataset="test",
content_id=0,
asset_id=2,
ref_path="my/dir/refvideo.yuv",
dis_path="my/dir/disvideo.yuv",
asset_dict={
'width': 720,
'height': 480,
'quality_width': 1920,
'quality_height': 1080
})
asset4 = Asset(dataset="test",
content_id=0,
asset_id=2,
ref_path="my/dir/refvideo.yuv",
dis_path="my/dir/disvideo.avi",
asset_dict={
'width': 720,
'height': 480,
'quality_width': 1920,
'quality_height': 1080
})
self.assertTrue(asset1 == asset2)
self.assertTrue(asset2 == asset3)
self.assertFalse(asset3 == asset4)
self.assertTrue(hash(asset2) == hash(asset3))
self.assertFalse(hash(asset1) == hash(asset4))
def test_run_vmafossexec_runner_with_notyuv(self):
print 'test on running VMAF runner...'
ref_path = config.ROOT + "/python/test/resource/icpf/frame%08d.icpf"
dis_path = config.ROOT + "/python/test/resource/icpf/frame%08d.icpf"
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={
'yuv_type': 'notyuv',
'quality_width': 720,
'quality_height': 480,
})
self.runner = VmafossExecQualityRunner([asset],
None,
fifo_mode=True,
delete_workdir=True,
result_store=None)
self.runner.run()
results = self.runner.results
self.assertAlmostEqual(results[0]['VMAFOSSEXEC_score'],
98.860796771266365,
places=4)
def test_noref_moment_fextractor(self):
print 'test on running Moment noref feature extractor on Assets...'
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 = MomentNorefFeatureExtractor([asset, asset_original],
None,
fifo_mode=True,
result_store=None)
self.fextractor.run()
results = self.fextractor.results
self.assertAlmostEqual(results[0]['Moment_noref_feature_1st_score'],
61.332006624999984)
self.assertAlmostEqual(results[0]['Moment_noref_feature_2nd_score'],
4798.659574041666)
self.assertAlmostEqual(results[0]['Moment_noref_feature_var_score'],
1036.8371843488285)
self.assertAlmostEqual(results[1]['Moment_noref_feature_1st_score'],
59.788567297525134)
self.assertAlmostEqual(results[1]['Moment_noref_feature_2nd_score'],
4696.668388042271)
self.assertAlmostEqual(results[1]['Moment_noref_feature_var_score'],
1121.519917231207)
def test_to_normalized_dict(self):
asset = Asset(dataset="test", content_id=0, asset_id=0,
ref_path="dir/refvideo.yuv", dis_path="dir/disvideo.yuv",
asset_dict={'width':720, 'height':480,
'start_frame':2, 'end_frame':2})
self.assertEquals(
asset.to_normalized_dict(),
{'asset_dict': {'end_frame': 2, 'height': 480,
'start_frame': 2, 'width': 720},
'asset_id': 0,
'content_id': 0,
'dataset': 'test',
'dis_path': 'disvideo.yuv',
'ref_path': 'refvideo.yuv',
'workdir': ''
}
)
def test_executor_id(self):
asset = Asset(dataset="test",
content_id=0,
asset_id=1,
ref_path="dir/refvideo.yuv",
dis_path="dir/disvideo.yuv",
asset_dict={})
runner = VmafLegacyQualityRunner([asset], None)
self.assertEquals(runner.executor_id, 'VMAF_legacy_V1.0')
def test_executor_id(self):
asset = Asset(dataset="test",
content_id=0,
asset_id=1,
ref_path="dir/refvideo.yuv",
dis_path="dir/disvideo.yuv",
asset_dict={})
fextractor = VmafFeatureExtractor([asset], None)
self.assertEquals(fextractor.executor_id, "VMAF_feature_V0.2.1")
def test_get_log_file_path(self):
asset = Asset(dataset="test", content_id=0, asset_id=1,
ref_path="dir/refvideo.yuv", dis_path="dir/disvideo.yuv",
asset_dict={'width':720, 'height':480,
'start_frame':2, 'end_frame':2},
workdir_root="my_workdir_root")
fextractor = VmafFeatureExtractor([asset], None)
log_file_path = fextractor._get_log_file_path(asset)
self.assertTrue(re.match(r"^my_workdir_root/[a-zA-Z0-9-]+/VMAF_feature_V0.2.1_test_0_1_refvideo_720x480_2to2_vs_disvideo_720x480_2to2_q_720x480$", log_file_path))
def test_workdir(self):
import re
asset = Asset(dataset="test",
content_id=0,
asset_id=0,
ref_path="",
dis_path="",
asset_dict={},
workdir_root="my_workdir_root")
workdir = asset.workdir
self.assertTrue(re.match(r"^my_workdir_root/[a-zA-Z0-9-]+$", workdir))
def test_use_path_as_workpath(self):
asset = Asset(dataset="test", content_id=0, asset_id=0,
ref_path="dir/refvideo.yuv", dis_path="dir/disvideo.yuv",
asset_dict={'width':720, 'height':480,
'start_frame':2, 'end_frame':2,
'quality_width':1920, 'quality_height':1080},
workdir_root="workdir")
expected_ref_workfile_path_re = \
r"^workdir/[a-zA-Z0-9-]+/" \
r"ref_test_0_0_refvideo_720x480_2to2_vs_disvideo_720x480_2to2_q_1920x1080"
expected_dis_workfile_path_re = \
r"^workdir/[a-zA-Z0-9-]+/" \
r"dis_test_0_0_refvideo_720x480_2to2_vs_disvideo_720x480_2to2_q_1920x1080"
self.assertTrue(re.match(expected_ref_workfile_path_re, asset.ref_workfile_path))
self.assertTrue(re.match(expected_dis_workfile_path_re, asset.dis_workfile_path))
self.assertFalse('use_path_as_workpath' in asset.asset_dict)
asset.use_path_as_workpath = True
self.assertTrue('use_path_as_workpath' in asset.asset_dict)
self.assertTrue(asset.asset_dict['use_path_as_workpath'])
self.assertEquals(asset.ref_workfile_path, 'dir/refvideo.yuv')
self.assertEquals(asset.dis_workfile_path, 'dir/disvideo.yuv')
def from_dataframe(cls, df):
# first, make sure the df conform to the format for a single asset
cls._assert_asset_dataframe(df)
asset_repr = df.iloc[0]['asset']
asset = Asset.from_repr(asset_repr)
executor_id = df.iloc[0]['executor_id']
result_dict = {}
for _, row in df.iterrows():
result_dict[row['scores_key']] = row['scores']
return Result(asset, executor_id, result_dict)
def test_str_repr(self):
asset = Asset(dataset="test", content_id=0, asset_id=0,
ref_path="dir/refvideo.yuv", dis_path="dir/disvideo.yuv",
asset_dict={'width':720, 'height':480,
'start_frame':2, 'end_frame':2})
self.assertEquals(
str(asset),
"test_0_0_refvideo_720x480_2to2_vs_disvideo_720x480_2to2_q_720x480"
)
expected_repr = '{"asset_dict": {"end_frame": 2, "height": 480, "start_frame": 2, "width": 720}, "asset_id": 0, "content_id": 0, "dataset": "test", "dis_path": "disvideo.yuv", "ref_path": "refvideo.yuv", "workdir": ""}'
self.assertEquals(repr(asset), expected_repr)
recon_asset = Asset.from_repr(expected_repr)
self.assertEquals(asset, recon_asset)
self.assertTrue(asset == recon_asset)
self.assertFalse(asset != recon_asset)
self.assertEquals(asset.to_normalized_repr(), expected_repr)
asset = Asset(dataset="test", content_id=0, asset_id=1,
ref_path="dir/refvideo.yuv", dis_path="dir/disvideo.yuv",
asset_dict={'width':720, 'height':480,})
self.assertEquals(
str(asset),
"test_0_1_refvideo_720x480_vs_disvideo_720x480_q_720x480"
)
expected_repr = '{"asset_dict": {"height": 480, "width": 720}, "asset_id": 1, "content_id": 0, "dataset": "test", "dis_path": "disvideo.yuv", "ref_path": "refvideo.yuv", "workdir": ""}'
self.assertEquals(repr(asset), expected_repr)
recon_asset = Asset.from_repr(expected_repr)
self.assertEquals(asset, recon_asset)
asset = Asset(dataset="test", content_id=0, asset_id=2,
ref_path="dir/refvideo.yuv", dis_path="dir/disvideo.yuv",
asset_dict={'width':720, 'height':480,
'quality_width':1920, 'quality_height':1080})
self.assertEquals(
str(asset),
"test_0_2_refvideo_720x480_vs_disvideo_720x480_q_1920x1080"
)
expected_repr = '{"asset_dict": {"height": 480, "quality_height": 1080, "quality_width": 1920, "width": 720}, "asset_id": 2, "content_id": 0, "dataset": "test", "dis_path": "disvideo.yuv", "ref_path": "refvideo.yuv", "workdir": ""}'
self.assertEquals(repr(asset), expected_repr)
recon_asset = Asset.from_repr(expected_repr)
self.assertEquals(asset, recon_asset)
asset = Asset(dataset="test", content_id=0, asset_id=2,
ref_path="dir/refvideo.yuv", dis_path="dir/disvideo.yuv",
asset_dict={'width':720, 'height':480,
'quality_width':1920, 'quality_height':1080,
'yuv_type':'yuv422p'})
self.assertEquals(
str(asset),
"test_0_2_refvideo_720x480_yuv422p_vs_disvideo_720x480_yuv422p_q_1920x1080"
)
expected_repr = '{"asset_dict": {"height": 480, "quality_height": 1080, "quality_width": 1920, "width": 720, "yuv_type": "yuv422p"}, "asset_id": 2, "content_id": 0, "dataset": "test", "dis_path": "disvideo.yuv", "ref_path": "refvideo.yuv", "workdir": ""}'
self.assertEquals(repr(asset), expected_repr)
recon_asset = Asset.from_repr(expected_repr)
self.assertEquals(asset, recon_asset)
asset = Asset(dataset="test", content_id=0, asset_id=2,
ref_path="dir/refvideo.yuv", dis_path="dir/disvideo.yuv",
asset_dict={'width':720, 'height':480,
'quality_width':1920, 'quality_height':1080,
'resampling_type':'lanczos'})
self.assertEquals(
str(asset),
"test_0_2_refvideo_720x480_vs_disvideo_720x480_q_1920x1080_lanczos"
)
expected_repr = '{"asset_dict": {"height": 480, "quality_height": 1080, "quality_width": 1920, "resampling_type": "lanczos", "width": 720}, "asset_id": 2, "content_id": 0, "dataset": "test", "dis_path": "disvideo.yuv", "ref_path": "refvideo.yuv", "workdir": ""}'
self.assertEquals(repr(asset), expected_repr)
recon_asset = Asset.from_repr(expected_repr)
self.assertEquals(asset, recon_asset)