def test_check_target_type():
stim = ComplexTextStim(join(TEXT_DIR, 'sample_text.txt'),
columns='to',
default_duration=1)
td = SharpnessExtractor()
with pytest.raises(TypeError):
td.transform(stim)
def test_sharpness_extractor():
pytest.importorskip('cv2')
image_dir = join(get_test_data_path(), 'image')
stim = ImageStim(join(image_dir, 'apple.jpg'))
result = SharpnessExtractor().transform(stim).to_df()
sharpness = result['sharpness'][0]
assert np.isclose(sharpness, 1.0, 1e-5)
def test_sharpness_extractor():
pytest.importorskip('cv2')
stim = ImageStim(join(IMAGE_DIR, 'apple.jpg'), onset=4.2, duration=1)
result = SharpnessExtractor().transform(stim).to_df()
sharpness = result['sharpness'][0]
assert np.isclose(sharpness, 1.0, 1e-5)
assert result['onset'][0] == 4.2
assert result['duration'][0] == 1
def test_within_pipeline():
pytest.importorskip('cv2')
pytest.importorskip('sklearn')
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Normalizer
stim = join(get_test_data_path(), 'image', 'apple.jpg')
graph = Graph([BrightnessExtractor(), SharpnessExtractor()])
trans = PliersTransformer(graph)
normalizer = Normalizer()
pipeline = Pipeline([('pliers', trans), ('normalizer', normalizer)])
res = pipeline.fit_transform(stim)
assert res.shape == (1, 2)
assert np.isclose(res[0][0], 0.66393, 1e-5)
assert np.isclose(res[0][1], 0.74780, 1e-5)
meta = trans.metadata_
assert 'onset' in meta.columns
assert meta['class'][0] == 'ImageStim'
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_check_target_type():
stim = ComplexTextStim(join(TEXT_DIR, 'sample_text.txt'),
columns='to', default_duration=1)
td = SharpnessExtractor()
with pytest.raises(TypeError):
td.transform(stim)