class FacialExpressionRecognizerLayout(BaseLayout):
def __init__(self, *args, svm_path=None, **kwargs):
super().__init__(*args, **kwargs)
self.clf = cv2.ml.SVM_load(svm_path)
self.face_detector = FaceDetector(
face_cascade='params/haarcascade_frontalface_default.xml',
eye_cascade='params/haarcascade_lefteye_2splits.xml')
def augment_layout(self):
pass
def process_frame(self, frame_rgb: np.ndarray) -> np.ndarray:
success, frame, self.head, (
x, y) = self.face_detector.detect_face(frame_rgb)
if not success:
return frame
success, head = self.face_detector.align_head(self.head)
if not success:
return frame
# We have to pass [1 x n] array predict.
features = featurize(head)[None]
result = self.clf.predict(features)
label = int(result[1][0])
# Draw predicted label above the bounding box.
cv2.putText(frame, decode(label), (x, y - 20),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
return frame
class FacialExpressionRecognizerLayout(BaseLayout):
def __init__(self, *args, clf_path=None, **kwargs):
super().__init__(*args, **kwargs)
self.clf = cv2.ml.ANN_MLP_load(str(clf_path / 'mlp.xml'))
self.index_to_label = pickle_load(clf_path / 'index_to_label')
self.pca_args = pickle_load(clf_path / 'pca_args')
self.face_detector = FaceDetector(
face_cascade='params/haarcascade_frontalface_default.xml',
eye_cascade='params/haarcascade_lefteye_2splits.xml')
def featurize_head(self, head):
return _pca_featurize(head[None], *self.pca_args)
def augment_layout(self):
pass
def process_frame(self, frame_rgb: np.ndarray) -> np.ndarray:
success, frame, self.head, (
x, y) = self.face_detector.detect_face(frame_rgb)
if not success:
return frame
success, head = self.face_detector.align_head(self.head)
if not success:
return frame
# We have to pass [1 x n] array predict.
_, output = self.clf.predict(self.featurize_head(head))
label = self.index_to_label[np.argmax(output)]
# Draw predicted label above the bounding box.
cv2.putText(frame, label, (x, y - 20), cv2.FONT_HERSHEY_COMPLEX, 1,
(0, 255, 0), 2)
return frame
return plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), vmin=0, vmax=255)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--image', default='media/B04521_07_01.png')
args = parser.parse_args()
face_detector = FaceDetector(eye_cascade='../.py3.7-cv-blueprints/lib/python3.7/site-packages/cv2/data/haarcascade_eye.xml')
img = cv2.resize(cv2.imread(args.image), DIMENSIONS)
fig = plt.figure(figsize=(19, 6))
fig.add_subplot(1, 3, 1)
success, frame, head, (x, y) = face_detector.detect_face(img)
assert success, 'Face was not detected'
_imshow(frame)
plt.title('Detected Face')
fig.add_subplot(1, 3, 2)
head_copy = head.copy()
eyes = face_detector.eye_centers(head_copy, outline=True)
_imshow(head_copy)
plt.title('Detected Eyes')
fig.add_subplot(1, 3, 3)
success, trainig_image = face_detector.align_head(head)
assert success, 'Eyes were not detected.'
_imshow(trainig_image)
plt.title('Training Image')
class DataCollectorLayout(BaseLayout):
def __init__(self,
*args,
training_data='data/cropped_faces.csv',
**kwargs):
super().__init__(*args, **kwargs)
self.face_detector = FaceDetector(
face_cascade='params/haarcascade_frontalface_default.xml',
eye_cascade='params/haarcascade_lefteye_2splits.xml')
self.training_data = training_data
def augment_layout(self):
"""Initializes GUI"""
# initialize data structure
self.samples = []
self.labels = []
# create a horizontal layout with all buttons
pnl2 = wx.Panel(self, -1)
self.neutral = wx.RadioButton(pnl2,
-1,
'neutral', (10, 10),
style=wx.RB_GROUP)
self.happy = wx.RadioButton(pnl2, -1, 'happy')
self.sad = wx.RadioButton(pnl2, -1, 'sad')
self.surprised = wx.RadioButton(pnl2, -1, 'surprised')
self.angry = wx.RadioButton(pnl2, -1, 'angry')
self.disgusted = wx.RadioButton(pnl2, -1, 'disgusted')
hbox2 = wx.BoxSizer(wx.HORIZONTAL)
hbox2.Add(self.neutral, 1)
hbox2.Add(self.happy, 1)
hbox2.Add(self.sad, 1)
hbox2.Add(self.surprised, 1)
hbox2.Add(self.angry, 1)
hbox2.Add(self.disgusted, 1)
pnl2.SetSizer(hbox2)
# create horizontal layout with single snapshot button
pnl3 = wx.Panel(self, -1)
self.snapshot = wx.Button(pnl3, -1, 'Take Snapshot')
self.Bind(wx.EVT_BUTTON, self._on_snapshot, self.snapshot)
hbox3 = wx.BoxSizer(wx.HORIZONTAL)
hbox3.Add(self.snapshot, 1)
pnl3.SetSizer(hbox3)
# arrange all horizontal layouts vertically
self.panels_vertical.Add(pnl2, flag=wx.EXPAND | wx.BOTTOM, border=1)
self.panels_vertical.Add(pnl3, flag=wx.EXPAND | wx.BOTTOM, border=1)
def process_frame(self, frame_rgb: np.ndarray) -> np.ndarray:
"""
Add a bounding box around the face if a face is detected.
"""
_, frame, self.head, _ = self.face_detector.detect_face(frame_rgb)
return frame
def _on_snapshot(self, evt):
"""Takes a snapshot of the current frame
This method takes a snapshot of the current frame, preprocesses
it to extract the head region, and upon success adds the data
sample to the training set.
"""
if self.neutral.GetValue():
label = 'neutral'
elif self.happy.GetValue():
label = 'happy'
elif self.sad.GetValue():
label = 'sad'
elif self.surprised.GetValue():
label = 'surprised'
elif self.angry.GetValue():
label = 'angry'
elif self.disgusted.GetValue():
label = 'disgusted'
if self.head is None:
print("No face detected")
else:
success, aligned_head = self.face_detector.align_head(self.head)
if success:
save_datum(self.training_data, label, aligned_head)
print(f"Saved {label} training datum.")
else:
print("Could not align head (eye detection failed?)")
