def setUpClass(cls):
cls.url = 'https://www.dropbox.com/s/tcem6metr3ejp6y/2015_07_13-10h38m15s101111ms_Juliette__full_test_calm.wav?dl=0'
cls.filename = 'test_huge_sound.wav'
cls.wav_file = wget_file(cls.url)
cls.dataset_path = _get_training_data()
cls.file_regexp = os.path.join(cls.dataset_path, '*.wav')
cls.file_regexp_bis = os.path.join(cls.dataset_path, '*/*.wav')
cls.files = glob.glob(cls.file_regexp) + glob.glob(cls.file_regexp_bis)
cls.sound_classification_obj = classification_service.SoundClassification(
wav_file_list=cls.files, calibrate_score=True)
cls.sound_classification_obj.learn()
cls.wav_duration = io_sound.duration(cls.wav_file)
def test_bell_detection(enable_calibration_of_score):
dataset_path = _get_training_data()
file_regexp = os.path.join(dataset_path, '*.wav')
files = glob.glob(file_regexp)
sound_classification_obj = classification_service.SoundClassification(wav_file_list=files, calibrate_score=enable_calibration_of_score)
sound_classification_obj.learn()
test_file_url = "https://www.dropbox.com/s/8dlr28s9gby46h1/bell_test.wav?dl=0"
test_file = wget_file(test_file_url)
test_file = os.path.abspath(test_file)
res = sound_classification_obj.processed_wav(test_file)
## TODO assert deskbell is better than doorbell
assert('DeskBell' in set([x.class_predicted for x in res]))
def setUpClass(cls):
cls.url = (
"https://www.dropbox.com/s/tcem6metr3ejp6y/2015_07_13-10h38m15s101111ms_Juliette__full_test_calm.wav?dl=0"
)
cls.filename = "test_huge_sound.wav"
cls.wav_file = wget_file(cls.url)
cls.dataset_path = _get_training_data()
cls.file_regexp = os.path.join(cls.dataset_path, "*.wav")
cls.file_regexp_bis = os.path.join(cls.dataset_path, "*/*.wav")
cls.files = glob.glob(cls.file_regexp) + glob.glob(cls.file_regexp_bis)
cls.sound_classification_obj = classification_service.SoundClassification(
wav_file_list=cls.files, calibrate_score=True
)
cls.sound_classification_obj.learn()
cls.wav_duration = io_sound.duration(cls.wav_file)
def setUpClass(cls, dataset_url=None, wav_file_url=None, csv_url=None):
cls.min_precision = 0.7
cls.min_recall = 0.7
cls.enable_calibration_of_score = True
cls.dataset_url = dataset_url
cls.dataset_path = _get_training_data(cls.dataset_url)
cls.file_regexp = os.path.join(cls.dataset_path, '*.wav')
cls.file_regexp_bis = os.path.join(cls.dataset_path, '*/*.wav')
cls.files = glob.glob(cls.file_regexp) + glob.glob(cls.file_regexp_bis)
cls.sound_classification_obj = classification_service.SoundClassification(wav_file_list=cls.files, calibrate_score=cls.enable_calibration_of_score)
cls.test_file = "test.wav"
if wav_file_url is None:
wav_file_url = 'https://www.dropbox.com/s/tcem6metr3ejp6y/2015_07_13-10h38m15s101111ms_Juliette__full_test_calm.wav?dl=0'
cls.wav_file_url = wav_file_url
cls.test_file = wget_file(cls.wav_file_url)
cls.test_file = os.path.abspath(cls.test_file)
#cls.test_file = '/home/lgeorge/tests/2015_07_13-10h38m15s101111ms_Juliette__full_test_calm.wav'
if csv_url is None:
csv_url = 'https://www.dropbox.com/s/umohtewtn6l5275/2015_07_13-10h38m15s101111ms_Juliette__full_test_calm.csv?dl=0'
cls.csv_url = csv_url
cls.csv_file = wget_file(cls.csv_url)
cls.df_expected = load_csv_annotation(cls.csv_file)
cls.csv_file = os.path.abspath(cls.csv_file)
cls.sound_classification_obj.learn()
cls.res = cls.sound_classification_obj.processed_wav(cls.test_file)
cls.df = pandas.DataFrame([rec.__dict__ for rec in cls.res])
if cls.enable_calibration_of_score:
#df.class_predicted[df.score <= 0.9] = 'NOTHING'
cls.df = cls.df[cls.df.score >= 2.5] #we drop low score
cls.df = cls.df.reset_index(drop=True)
cls.df.to_csv('output.csv') # just for later check if needed
cls.labels_present_in_wavfile = set(cls.df_expected.class_expected)
detection_dict = compute_tp_fp(cls.df, cls.df_expected)
#print("True positive count: {}".format(len(detection_dict['true_positives'])))
#print("False positive count: {}".format(len(detection_dict['false_positives'])))
#print("Silence detected as something: {}".format(len(detection_dict['silence_not_detected'])))
#print("Not detected count: {}".format(len(detection_dict['not_detected'])))
cls.expected, cls.predicted, cls.labels = convert_tp_fp_to_confusion_matrix(detection_dict['true_positives'], detection_dict['false_positives'], detection_dict['not_detected'], detection_dict['silence_not_detected'])
report = sklearn.metrics.classification_report(cls.expected, cls.predicted, labels=cls.labels, target_names=None, sample_weight=None, digits=2)
matrix = sklearn.metrics.confusion_matrix(cls.expected, cls.predicted, cls.labels)
print("Confusion Matrix")
pprint.pprint(matrix)
#res.savefig('confusion_mat.png')
print(report)
cls.precisions = sklearn.metrics.precision_score(cls.expected, cls.predicted, labels=cls.labels, average=None)
cls.recalls = sklearn.metrics.recall_score(cls.expected, cls.predicted, labels=cls.labels, average=None)
cls.labels_to_consider = [l for l in cls.labels if l in cls.sound_classification_obj.clf.classes_]
cls.labels_to_ignore = [l for l in cls.labels if l not in cls.sound_classification_obj.clf.classes_]
cls.labels_to_consider_index = [num for (num, val) in enumerate(cls.labels) if val in cls.labels_to_consider]
def setUpClass(cls, dataset_url=None, wav_file_url=None, csv_url=None):
cls.min_precision = 0.7
cls.min_recall = 0.7
cls.enable_calibration_of_score = True
cls.dataset_url = dataset_url
cls.dataset_path = _get_training_data(cls.dataset_url)
cls.file_regexp = os.path.join(cls.dataset_path, '*.wav')
cls.file_regexp_bis = os.path.join(cls.dataset_path, '*/*.wav')
cls.files = glob.glob(cls.file_regexp) + glob.glob(cls.file_regexp_bis)
cls.sound_classification_obj = classification_service.SoundClassification(
wav_file_list=cls.files,
calibrate_score=cls.enable_calibration_of_score)
cls.test_file = "test.wav"
if wav_file_url is None:
wav_file_url = 'https://www.dropbox.com/s/tcem6metr3ejp6y/2015_07_13-10h38m15s101111ms_Juliette__full_test_calm.wav?dl=0'
cls.wav_file_url = wav_file_url
cls.test_file = wget_file(cls.wav_file_url)
cls.test_file = os.path.abspath(cls.test_file)
#cls.test_file = '/home/lgeorge/tests/2015_07_13-10h38m15s101111ms_Juliette__full_test_calm.wav'
if csv_url is None:
csv_url = 'https://www.dropbox.com/s/umohtewtn6l5275/2015_07_13-10h38m15s101111ms_Juliette__full_test_calm.csv?dl=0'
cls.csv_url = csv_url
cls.csv_file = wget_file(cls.csv_url)
cls.df_expected = load_csv_annotation(cls.csv_file)
cls.csv_file = os.path.abspath(cls.csv_file)
cls.sound_classification_obj.learn()
cls.res = cls.sound_classification_obj.processed_wav(cls.test_file)
cls.df = pandas.DataFrame([rec.__dict__ for rec in cls.res])
if cls.enable_calibration_of_score:
#df.class_predicted[df.score <= 0.9] = 'NOTHING'
cls.df = cls.df[cls.df.score >= 2.5] #we drop low score
cls.df = cls.df.reset_index(drop=True)
cls.df.to_csv('output.csv') # just for later check if needed
cls.labels_present_in_wavfile = set(cls.df_expected.class_expected)
detection_dict = compute_tp_fp(cls.df, cls.df_expected)
#print("True positive count: {}".format(len(detection_dict['true_positives'])))
#print("False positive count: {}".format(len(detection_dict['false_positives'])))
#print("Silence detected as something: {}".format(len(detection_dict['silence_not_detected'])))
#print("Not detected count: {}".format(len(detection_dict['not_detected'])))
cls.expected, cls.predicted, cls.labels = convert_tp_fp_to_confusion_matrix(
detection_dict['true_positives'],
detection_dict['false_positives'], detection_dict['not_detected'],
detection_dict['silence_not_detected'])
report = sklearn.metrics.classification_report(cls.expected,
cls.predicted,
labels=cls.labels,
target_names=None,
sample_weight=None,
digits=2)
matrix = sklearn.metrics.confusion_matrix(cls.expected, cls.predicted,
cls.labels)
print("Confusion Matrix")
pprint.pprint(matrix)
#res.savefig('confusion_mat.png')
print(report)
cls.precisions = sklearn.metrics.precision_score(cls.expected,
cls.predicted,
labels=cls.labels,
average=None)
cls.recalls = sklearn.metrics.recall_score(cls.expected,
cls.predicted,
labels=cls.labels,
average=None)
cls.labels_to_consider = [
l for l in cls.labels
if l in cls.sound_classification_obj.clf.classes_
]
cls.labels_to_ignore = [
l for l in cls.labels
if l not in cls.sound_classification_obj.clf.classes_
]
cls.labels_to_consider_index = [
num for (num, val) in enumerate(cls.labels)
if val in cls.labels_to_consider
]
