def test_validation_levels(caplog):
cache_default = config.get_option('cache_transformers')
config.set_option('cache_transformers', False)
ext = BrightnessExtractor()
stim = TextStim(text='hello world')
with pytest.raises(TypeError):
ext.transform(stim)
res = ext.transform(stim, validation='warn')
log_message = caplog.records[0].message
assert log_message == ("Transformers of type BrightnessExtractor can "
"only be applied to stimuli of type(s) <class 'pliers"
".stimuli.image.ImageStim'> (not type TextStim), and no "
"applicable Converter was found.")
assert not res
res = ext.transform(stim, validation='loose')
assert not res
stim2 = ImageStim(join(get_test_data_path(), 'image', 'apple.jpg'))
res = ext.transform([stim, stim2], validation='loose')
assert len(res) == 1
assert np.isclose(res[0].to_df()['brightness'][0], 0.88784294, 1e-5)
config.set_option('cache_transformers', cache_default)
def test_validation_levels(caplog):
cache_default = config.get_option('cache_transformers')
config.set_option('cache_transformers', False)
ext = BrightnessExtractor()
stim = TextStim(text='hello world')
with pytest.raises(TypeError):
ext.transform(stim)
res = ext.transform(stim, validation='warn')
log_message = caplog.records[0].message
assert log_message == (
"Transformers of type BrightnessExtractor can "
"only be applied to stimuli of type(s) <class 'pliers"
".stimuli.image.ImageStim'> (not type TextStim), and no "
"applicable Converter was found.")
assert not res
res = ext.transform(stim, validation='loose')
assert not res
stim2 = ImageStim(join(get_test_data_path(), 'image', 'apple.jpg'))
res = ext.transform([stim, stim2], validation='loose')
assert len(res) == 1
assert np.isclose(res[0].to_df()['brightness'][0], 0.88784294, 1e-5)
config.set_option('cache_transformers', cache_default)
def test_validation_levels(capsys):
ext = BrightnessExtractor()
stim = TextStim(text='hello world')
with pytest.raises(TypeError):
ext.transform(stim)
res = ext.transform(stim, validation='warn')
assert not res
out, err = capsys.readouterr()
assert err == "WARNING:root:Transformers of type BrightnessExtractor can "\
"only be applied to stimuli of type(s) <class 'pliers"\
".stimuli.image.ImageStim'> (not type TextStim), and no "\
"applicable Converter was found.\n"
res = ext.transform(stim, validation='loose')
assert not res
stim2 = ImageStim(join(get_test_data_path(), 'image', 'apple.jpg'))
res = ext.transform([stim, stim2], validation='loose')
assert len(res) == 1
assert np.isclose(res[0].to_df()['brightness'][0], 0.88784294, 1e-5)
def test_transformations_on_compound_stim():
image1 = ImageStim(join(get_test_data_path(), 'image', 'apple.jpg'))
image2 = ImageStim(join(get_test_data_path(), 'image', 'obama.jpg'))
text = ComplexTextStim(text="The quick brown fox jumped...")
stim = CompoundStim([image1, image2, text])
ext = BrightnessExtractor()
results = ext.transform(stim)
assert len(results) == 2
assert np.allclose(results[0].data[0], 0.88784294)
def test_transformations_on_compound_stim():
image1 = ImageStim(join(get_test_data_path(), 'image', 'apple.jpg'))
image2 = ImageStim(join(get_test_data_path(), 'image', 'obama.jpg'))
text = ComplexTextStim(text="The quick brown fox jumped...")
stim = CompoundStim([image1, image2, text])
ext = BrightnessExtractor()
results = ext.transform(stim)
assert len(results) == 2
assert np.allclose(results[0]._data[0], 0.88784294)
def test_parallelization():
# TODO: test that parallelization actually happened (this will likely
# require some new logging functionality, or introspection). For now we
# just make sure the parallelized version produces the same result.
default = config.get_option('parallelize')
filename = join(get_test_data_path(), 'video', 'small.mp4')
video = VideoStim(filename)
ext = BrightnessExtractor()
# With parallelization
config.set_option('parallelize', True)
result1 = ext.transform(video)
# Without parallelization
config.set_option('parallelize', False)
result2 = ext.transform(video)
assert result1 == result2
config.set_option('parallelize', default)
def test_parallelization():
# TODO: test that parallelization actually happened (this will likely
# require some new logging functionality, or introspection). For now we
# just make sure the parallelized version produces the same result.
default = config.get_option('parallelize')
cache_default = config.get_option('cache_transformers')
config.set_option('cache_transformers', True)
filename = join(get_test_data_path(), 'video', 'small.mp4')
video = VideoStim(filename)
ext = BrightnessExtractor()
# With parallelization
config.set_option('parallelize', True)
result1 = ext.transform(video)
# Without parallelization
config.set_option('parallelize', False)
result2 = ext.transform(video)
assert result1 == result2
config.set_option('parallelize', default)
config.set_option('cache_transformers', cache_default)
def test_transform_with_string_input():
ext = BrightnessExtractor()
res = ext.transform(join(get_test_data_path(), 'image', 'apple.jpg'))
np.testing.assert_almost_equal(res.to_df()['brightness'].values[0],
0.887842942)
def extract_visual_features(video_file):
"""
This function extracts luminance, vibrance, saliency, and sharpness from the frames of a video
using the pliers library. If you use this function, please cite the pliers library directly:
https://github.com/PsychoinformaticsLab/pliers#how-to-cite
Parameters
----------
video_file: str
Path to video file to analyze.
Returns
-------
low_level_video_df: DataFrame
Pandas dataframe with a column per low-level feature.py (index is time).
"""
# extract video luminance
print('Extracting brightness...')
from pliers.extractors import BrightnessExtractor
brightext = BrightnessExtractor()
brightres = brightext.transform(video_file)
brightres_df = pd.DataFrame(columns=brightres[0].to_df().columns)
for a, ob in enumerate(brightres):
t = ob.to_df()
t['order'] = a
brightres_df = brightres_df.append(t, ignore_index=True)
# extract saliency
print('Extracting saliency...')
from pliers.extractors import SaliencyExtractor
salext = SaliencyExtractor()
salres = salext.transform(video_file)
salres_df = pd.DataFrame(columns=salres[0].to_df().columns)
for a, ob in enumerate(salres):
t = ob.to_df()
t['order'] = a
salres_df = salres_df.append(t, ignore_index=True)
# extract sharpness
print('Extracting sharpness...')
from pliers.extractors import SharpnessExtractor
sharpext = SharpnessExtractor()
sharpres = sharpext.transform(video_file)
sharpres_df = pd.DataFrame(columns=sharpres[0].to_df().columns)
for a, ob in enumerate(sharpres):
t = ob.to_df()
t['order'] = a
sharpres_df = sharpres_df.append(t, ignore_index=True)
# extract vibrance
print('Extracting vibrance...')
from pliers.extractors import VibranceExtractor
vibext = VibranceExtractor()
vibres = vibext.transform(video_file)
vibres_df = pd.DataFrame(columns=vibres[0].to_df().columns)
for a, ob in enumerate(vibres):
t = ob.to_df()
t['order'] = a
vibres_df = vibres_df.append(t, ignore_index=True)
# combine into 1 dataframe
print('Combining data...')
low_level_video_df = brightres_df.merge(salres_df[salres_df.columns[4:]],
left_index=True,
right_index=True)
low_level_video_df = low_level_video_df.merge(
sharpres_df[sharpres_df.columns[4:]],
left_index=True,
right_index=True)
low_level_video_df = low_level_video_df.merge(
vibres_df[vibres_df.columns[4:]], left_index=True, right_index=True)
low_level_video_df['onset_ms'] = low_level_video_df['onset'] * 1000
low_level_video_df.index = pd.to_datetime(low_level_video_df['onset_ms'],
unit='ms')
low_level_video_df = low_level_video_df.drop(
['max_saliency', 'max_y', 'max_x', 'onset', 'object_id', 'order'],
axis=1)
low_level_video_df.index.name = None
print('Visual feature extraction complete.')
return low_level_video_df
def test_transform_with_string_input():
ext = BrightnessExtractor()
res = ext.transform(join(get_test_data_path(), 'image', 'apple.jpg'))
np.testing.assert_almost_equal(res.to_df()['brightness'].values[0], 0.887842942)