Пример #1
0
 def test_mos_subjective_model_output2(self):
     dataset = import_python_file(self.dataset_filepath)
     dataset_reader = RawDatasetReader(dataset)
     subjective_model = MosModel(dataset_reader)
     subjective_model.run_modeling()
     dataset2 = subjective_model.to_aggregated_dataset()
     dis_video = dataset2.dis_videos[0]
     self.assertTrue('groundtruth' in dis_video)
     self.assertTrue('os' not in dis_video)
     self.assertAlmostEquals(dis_video['groundtruth'], 4.884615384615385, places=4)
Пример #2
0
 def test_mos_subjective_model_output2(self):
     dataset = import_python_file(self.dataset_filepath)
     dataset_reader = RawDatasetReader(dataset)
     subjective_model = MosModel(dataset_reader)
     subjective_model.run_modeling()
     dataset2 = subjective_model.to_aggregated_dataset()
     dis_video = dataset2.dis_videos[0]
     self.assertTrue('groundtruth' in dis_video)
     self.assertTrue('os' not in dis_video)
     self.assertAlmostEquals(dis_video['groundtruth'], 4.884615384615385, places=4)
Пример #3
0
 def test_mos_subjective_model_output_custom_resampling(self):
     dataset = import_python_file(self.dataset_filepath)
     dataset_reader = RawDatasetReader(dataset)
     subjective_model = MosModel(dataset_reader)
     subjective_model.run_modeling()
     subjective_model.to_aggregated_dataset_file(self.output_dataset_filepath, resampling_type='lanczos')
     self.assertTrue(os.path.exists(self.output_dataset_filepath))
     dataset2 = import_python_file(self.output_dataset_filepath)
     self.assertFalse(hasattr(dataset2, 'quality_height'))
     self.assertFalse(hasattr(dataset2, 'quality_width'))
     self.assertEquals(dataset2.resampling_type, 'lanczos')
     dis_video = dataset2.dis_videos[0]
     self.assertTrue('groundtruth' in dis_video)
     self.assertTrue('os' not in dis_video)
     self.assertAlmostEquals(dis_video['groundtruth'], 4.884615384615385, places=4)
Пример #4
0
 def test_mos_subjective_model_output_custom_resampling(self):
     dataset = import_python_file(self.dataset_filepath)
     dataset_reader = RawDatasetReader(dataset)
     subjective_model = MosModel(dataset_reader)
     subjective_model.run_modeling()
     subjective_model.to_aggregated_dataset_file(self.output_dataset_filepath, resampling_type='lanczos')
     self.assertTrue(os.path.exists(self.output_dataset_filepath))
     dataset2 = import_python_file(self.output_dataset_filepath)
     self.assertFalse(hasattr(dataset2, 'quality_height'))
     self.assertFalse(hasattr(dataset2, 'quality_width'))
     self.assertEquals(dataset2.resampling_type, 'lanczos')
     dis_video = dataset2.dis_videos[0]
     self.assertTrue('groundtruth' in dis_video)
     self.assertTrue('os' not in dis_video)
     self.assertAlmostEquals(dis_video['groundtruth'], 4.884615384615385, places=4)
Пример #5
0
 def test_mos_subjective_model_transform_final(self):
     dataset = import_python_file(self.dataset_filepath)
     dataset_reader = RawDatasetReader(dataset)
     subjective_model = MosModel(dataset_reader)
     result = subjective_model.run_modeling(transform_final={'p1': 10, 'p0': 1})
     scores = result['quality_scores']
     self.assertAlmostEquals(scores[0], 49.84615384615385, places=4)
     self.assertAlmostEquals(scores[10], 21.769230769230771, places=4)
     self.assertAlmostEquals(np.mean(scores), 36.44790652385589, places=4)
Пример #6
0
 def test_mos_subjective_model_normalize_final(self):
     dataset = import_python_file(self.dataset_filepath)
     dataset_reader = RawDatasetReader(dataset)
     subjective_model = MosModel(dataset_reader)
     result = subjective_model.run_modeling(normalize_final=True)
     scores = result['quality_scores']
     self.assertAlmostEquals(scores[0], 1.1318646945818083, places=4)
     self.assertAlmostEquals(scores[10], -1.2400334499143002, places=4)
     self.assertAlmostEquals(np.mean(scores), 0.0, places=4)
Пример #7
0
 def test_mos_subjective_model(self):
     dataset = import_python_file(self.dataset_filepath)
     dataset_reader = RawDatasetReader(dataset)
     subjective_model = MosModel(dataset_reader)
     result = subjective_model.run_modeling()
     scores = result['quality_scores']
     self.assertAlmostEquals(scores[0], 4.884615384615385, places=4)
     self.assertAlmostEquals(scores[10], 2.0769230769230771, places=4)
     self.assertAlmostEquals(np.mean(scores), 3.544790652385589, places=4)
Пример #8
0
 def test_mos_subjective_model_normalize_final(self):
     dataset = import_python_file(self.dataset_filepath)
     dataset_reader = RawDatasetReader(dataset)
     subjective_model = MosModel(dataset_reader)
     result = subjective_model.run_modeling(normalize_final=True)
     scores = result['quality_scores']
     self.assertAlmostEquals(scores[0], 1.1318646945818083, places=4)
     self.assertAlmostEquals(scores[10], -1.2400334499143002, places=4)
     self.assertAlmostEquals(np.mean(scores), 0.0, places=4)
Пример #9
0
 def test_mos_subjective_model(self):
     dataset = import_python_file(self.dataset_filepath)
     dataset_reader = RawDatasetReader(dataset)
     subjective_model = MosModel(dataset_reader)
     result = subjective_model.run_modeling()
     scores = result['quality_scores']
     self.assertAlmostEquals(scores[0], 4.884615384615385, places=4)
     self.assertAlmostEquals(scores[10], 2.0769230769230771, places=4)
     self.assertAlmostEquals(np.mean(scores), 3.544790652385589, places=4)
Пример #10
0
 def test_mos_subjective_model_transform_final(self):
     dataset = import_python_file(self.dataset_filepath)
     dataset_reader = RawDatasetReader(dataset)
     subjective_model = MosModel(dataset_reader)
     result = subjective_model.run_modeling(transform_final={'p1': 10, 'p0': 1})
     scores = result['quality_scores']
     self.assertAlmostEquals(scores[0], 49.84615384615385, places=4)
     self.assertAlmostEquals(scores[10], 21.769230769230771, places=4)
     self.assertAlmostEquals(np.mean(scores), 36.44790652385589, places=4)
Пример #11
0
    def test_zscore_mos_subjective_model_corruptdata_subjreject(self):
        dataset = import_python_file(self.dataset_filepath)
        np.random.seed(0)
        info_dict = {
            'selected_subjects': range(5),
        }
        dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
        subjective_model = MosModel(dataset_reader)
        result = subjective_model.run_modeling(zscore_mode=True, subject_rejection=True)
        scores = result['quality_scores']

        self.assertAlmostEquals(np.mean(scores), 0.0, places=4)
        self.assertAlmostEquals(np.var(scores), 0.66670826882879042, places=4)
Пример #12
0
    def test_mos_subjective_model_corruptdata_subjreject(self):
        dataset = import_python_file(self.dataset_filepath)
        np.random.seed(0)
        info_dict = {
            'selected_subjects': range(5),
        }
        dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
        subjective_model = MosModel(dataset_reader)
        result = subjective_model.run_modeling(subject_rejection=True)
        scores = result['quality_scores']

        self.assertAlmostEquals(np.mean(scores), 3.5611814345991566, places=4)
        self.assertAlmostEquals(np.var(scores), 1.1049505732699529, places=4) # 1.4012220200639218
Пример #13
0
    def test_mos_subjective_model_corruptdata(self):
        dataset = import_python_file(self.dataset_filepath)
        np.random.seed(0)
        info_dict = {
            'selected_subjects': range(5),
        }
        dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
        subjective_model = MosModel(dataset_reader)
        result = subjective_model.run_modeling()
        scores = result['quality_scores']

        self.assertAlmostEquals(np.mean(scores), 3.5447906523855899, places=4)
        self.assertAlmostEquals(np.var(scores), 0.95893305294535369, places=4) # 1.4012220200639218
Пример #14
0
    def test_zscore_mos_subjective_model_corruptdata_subjreject(self):
        dataset = import_python_file(self.dataset_filepath)
        np.random.seed(0)
        info_dict = {
            'selected_subjects': range(5),
        }
        dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
        subjective_model = MosModel(dataset_reader)
        result = subjective_model.run_modeling(zscore_mode=True, subject_rejection=True)
        scores = result['quality_scores']

        self.assertAlmostEquals(np.mean(scores), 0.0, places=4)
        self.assertAlmostEquals(np.var(scores), 0.66670826882879042, places=4)
Пример #15
0
    def test_mos_subjective_model_corruptdata_subjreject(self):
        dataset = import_python_file(self.dataset_filepath)
        np.random.seed(0)
        info_dict = {
            'selected_subjects': range(5),
        }
        dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
        subjective_model = MosModel(dataset_reader)
        result = subjective_model.run_modeling(subject_rejection=True)
        scores = result['quality_scores']

        self.assertAlmostEquals(np.mean(scores), 3.5611814345991566, places=4)
        self.assertAlmostEquals(np.var(scores), 1.1049505732699529, places=4) # 1.4012220200639218
Пример #16
0
    def test_mos_subjective_model_corruptdata(self):
        dataset = import_python_file(self.dataset_filepath)
        np.random.seed(0)
        info_dict = {
            'selected_subjects': range(5),
        }
        dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
        subjective_model = MosModel(dataset_reader)
        result = subjective_model.run_modeling()
        scores = result['quality_scores']

        self.assertAlmostEquals(np.mean(scores), 3.5447906523855899, places=4)
        self.assertAlmostEquals(np.var(scores), 0.95893305294535369, places=4) # 1.4012220200639218