def test_plot_face():
# test plotting method
fx = Fex(
filename=join(get_test_data_path(), "iMotions_Test_v6.txt"),
sampling_freq=30,
detector="FACET",
)
fx = fx.read_file()
ax = fx.plot_aus(row_n=0)
assert_plot_shape(ax)
plt.close()
fx = Fex(
filename=join(get_test_data_path(), "OpenFace_Test.csv"),
sampling_freq=30,
detector="OpenFace",
)
fx = fx.read_file()
ax = fx.plot_aus(row_n=0)
assert_plot_shape(ax)
plt.close()
fx = Fex(
filename=join(get_test_data_path(),
"sample_affectiva-api-app_output.json"),
sampling_freq=30,
detector="Affectiva",
)
fx = fx.read_file(orig_cols=False)
ax = fx.plot_aus(row_n=0)
assert_plot_shape(ax)
plt.close()
# test plot in util
plot_face()
assert_plot_shape(plt.gca())
plt.close()
plot_face(au=au,
vectorfield={"reference": predict(au2)},
feature_range=(0, 1))
assert_plot_shape(plt.gca())
plt.close()
with pytest.raises(ValueError):
plot_face(model=au, au=au, vectorfield={"reference": predict(au2)})
with pytest.raises(ValueError):
plot_face(model=au, au=au, vectorfield=[])
with pytest.raises(ValueError):
plot_face(model=au, au=au, vectorfield={"noreference": predict(au2)})
def plot(self,
row_n,
model=None,
vectorfield=None,
muscles=None,
ax=None,
color='k',
linewidth=1,
linestyle='-',
gaze=None,
*args,
**kwargs):
""" Plot facial representation of data
Args:
row_n: the row of data to use
model: sklearn PLSRegression instance
vectorfield: (dict) {'target':target_array,'reference':reference_array}
muscles: (dict) {'muscle': color}
ax: matplotlib axis handle
color: matplotlib color
linewidth: matplotlib linewidth
linestyle: matplotlib linestyle
gaze: array of gaze vectors (len(5))
"""
feats = [
'AU1', 'AU2', 'AU4', 'AU5', 'AU6', 'AU7', 'AU9', 'AU10', 'AU12',
'AU14', 'AU15', 'AU17', 'AU18', 'AU20', 'AU23', 'AU24', 'AU25',
'AU26', 'AU28', 'AU43', 'Pitch', 'Roll', 'Yaw'
]
if (row_n > len(self)):
raise ValueError("Row number out of range.")
try:
au = []
for feat in feats:
aun = self[feat]
au.append(aun.copy()[row_n])
au = np.array(au)
if model is None:
model = load_h5('facet.h5')
if muscles is not None:
muscles['facet'] = 1
ax = plot_face(model=model,
au=au,
vectorfield=vectorfield,
muscles=muscles,
ax=ax,
color=color,
linewidth=linewidth,
linestyle=linestyle,
gaze=gaze,
*args,
**kwargs)
return ax
except Exception as e:
print('Unable to plot data:', e)
def test_plot_face():
# test plotting method
fx = Fex(filename=join(get_test_data_path(), 'iMotions_Test_v6.txt'),
sampling_freq=30,
detector='FACET')
fx = fx.read_file()
ax = fx.plot_aus(row_n=0)
assert_plot_shape(ax)
plt.close()
fx = Fex(filename=join(get_test_data_path(), 'OpenFace_Test.csv'),
sampling_freq=30,
detector='OpenFace')
fx = fx.read_file()
ax = fx.plot_aus(row_n=0)
assert_plot_shape(ax)
plt.close()
fx = Fex(filename=join(get_test_data_path(),
'sample_affectiva-api-app_output.json'),
sampling_freq=30,
detector='Affectiva')
fx = fx.read_file(orig_cols=False)
ax = fx.plot_aus(row_n=0)
assert_plot_shape(ax)
plt.close()
# test plot in util
plot_face()
assert_plot_shape(plt.gca())
plt.close()
plot_face(au=au, vectorfield={'reference': predict(au2)})
assert_plot_shape(plt.gca())
plt.close()
with pytest.raises(ValueError):
plot_face(model=au, au=au, vectorfield={'reference': predict(au2)})
with pytest.raises(ValueError):
plot_face(model=au, au=au, vectorfield=[])
with pytest.raises(ValueError):
plot_face(model=au, au=au, vectorfield={'noreference': predict(au2)})
# Plotting examples *written by Jin Hyun Cheong* Included in the toolbox are two models for Action Units to landmark visualization. The 'pyfeat_aus_to_landmarks.h5' model was created by using landmarks extracted using Py-FEAT to align each face in the dataset to a neutral face with numpy's least squares function. Then the PLS model was trained using Action Unit labels to predict transformed landmark data. Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended. # Load modules %matplotlib inline from feat.plotting import plot_face import numpy as np import matplotlib.pyplot as plt plot_face(au=np.zeros(20)) ## Draw lineface using input vector Affectiva vectors should be divided by twenty for use with our 'blue' model. from feat.plotting import plot_face import numpy as np import matplotlib.pyplot as plt # Add data, AU is ordered as such: # AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43 # Activate AU1: Inner brow raiser au = [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # Plot face fig, axes = plt.subplots(1,2)
f, axes = plt.subplots(5, 4, figsize=(12, 18))
axes = axes.flatten()
# Exaggerate the intensity of the expression for clearer visualization.
# We do not recommend exceeding 2.
intensity = 2
for aui, auname in enumerate(axes):
try:
auname = au_cols[aui]
au = np.zeros(clf.n_components)
au[aui] = intensity
predicted = clf.predict([au]).reshape(2, 68)
plot_face(au=au,
model=clf,
vectorfield={
"reference": neutral.T,
'target': predicted,
'color': 'r',
'alpha': .6
},
ax=axes[aui])
axes[aui].set(title=auname)
except:
pass
finally:
ax = axes[aui]
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
# Here is how we would export our model into an h5 format which can be loaded using our load_h5 function.
# In[ ]:
Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended. %load_ext autoreload %autoreload 2 %config InlineBackend.figure_format = 'retina' intensity = 2 # Load modules %matplotlib inline from feat.plotting import plot_face, predict import numpy as np import pandas as pd import matplotlib.pyplot as plt plot_face(au=np.zeros(20)) ## Draw lineface using input vector Affectiva vectors should be divided by twenty for use with our 'blue' model. from feat.plotting import plot_face, predict import numpy as np import matplotlib.pyplot as plt # Add data, AU is ordered as such: # AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43 # Activate AU1: Inner brow raiser
def plot(self,
row_n,
model=None,
vectorfield=None,
muscles=None,
ax=None,
color='k',
linewidth=1,
linestyle='-',
gaze=False,
gaze_vecs=False,
*args,
**kwargs):
""" Plot facial representation of data
Args:
row_n: the row of data to use
model: sklearn PLSRegression instance
vectorfield: (dict) {'target':target_array,'reference':reference_array}
muscles: (dict) {'muscle': color}
ax: matplotlib axis handle
color: matplotlib color
linewidth: matplotlib linewidth
linestyle: matplotlib linestyle
gaze: (bool) whether to draw gaze based on data
"""
feats = [
'AU01_r', 'AU02_r', 'AU04_r', 'AU05_r', 'AU06_r', 'AU07_r',
'AU09_r', 'AU10_r', 'AU12_r', 'AU14_r', 'AU15_r', 'AU17_r',
'AU20_r', 'AU23_r', 'AU25_r', 'AU26_r', 'AU45_r'
]
if (row_n > len(self)):
raise ValueError("Row number out of range.")
try:
au = []
for feat in feats:
aun = self[feat]
au.append(aun.copy()[row_n])
au = np.array(au + [0, 0, 0])
if gaze:
gaze_dat = ['gaze_0_x', 'gaze_0_y', 'gaze_1_x', 'gaze_1_y']
gaze = []
for i in range(4):
gaze.append(self[gaze_dat[i]][row_n])
if gaze_vecs:
gaze.append(1)
else:
gaze.append(0)
else:
gaze = None
ax = plot_face(model=model,
au=au,
vectorfield=vectorfield,
muscles=muscles,
ax=ax,
color=color,
linewidth=linewidth,
linestyle=linestyle,
gaze=gaze,
*args,
**kwargs)
return ax
except Exception as e:
print('Unable to plot data:', e)
def plot(self,
row_n,
model=None,
vectorfield=None,
muscles=None,
ax=None,
color='k',
linewidth=1,
linestyle='-',
gaze=None,
*args,
**kwargs):
""" Plot facial representation of data
Args:
row_n: the row of data to use
model: sklearn PLSRegression instance
vectorfield: (dict) {'target':target_array,'reference':reference_array}
muscles: (dict) {'muscle': color}
ax: matplotlib axis handle
color: matplotlib color
linewidth: matplotlib linewidth
linestyle: matplotlib linestyle
gaze: array of gaze vectors (len(5))
"""
if "AU01" not in self:
feats = [
"innerBrowRaise", "browRaise", "browFurrow", "eyeWiden",
"cheekRaise", "lidTighten", "noseWrinkle", "upperLipRaise",
"smile", "dimpler", "lipCornerDepressor", "chinRaise",
"lipStretch", "lipPress", "mouthOpen", "jawDrop", "eyeClosure"
]
else:
feats = [
"AU01", "AU02", "AU04", "AU05", "AU06", "AU07", "AU9", "AU10",
"AU12", "AU14", "AU15", "AU17", "AU20", "AU24", "AU25", "AU26",
"AU43"
]
if (row_n > len(self)):
raise ValueError("Row number out of range.")
try:
au = []
for feat in feats:
aun = self[feat]
au.append(aun.copy()[row_n] / 20)
au = np.array(au + [0, 0, 0])
ax = plot_face(model=model,
au=au,
vectorfield=vectorfield,
muscles=muscles,
ax=ax,
color=color,
linewidth=linewidth,
linestyle=linestyle,
gaze=gaze,
*args,
**kwargs)
return ax
except Exception as e:
print('Unable to plot data:', e)
