Emotion detection using facial landmarks - image

Question

I plan on using scikit svm for class prediction.
I have been trying this :
Get images from a webcam
Detect Facial Landmarks
Train a machine learning algorithm (we will use a linear SVM)
Predict emotions
I have a problem in this line : clf.fit(npar_train, training_labels)
also I have a problem in site-packages\sklearn\svm_base.py and in site-packages\sklearn\utils\validation.py
How can I remove this error?
thank you in advance
python script
emotions = ['neutral', 'sad', 'happy', 'anger']
data={}
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
clf = SVC(kernel='linear', probability=True, tol=1e-3)
def get_files(emotion):
files = glob.glob('img\\datasets\\%s\\*' %emotion)
random.shuffle(files)
training = files[:int(len(files)*0.8)]
prediction = files[-int(len(files)*0.2)]
return training, prediction
def get_landmarks(image):
detections = detector(image, 1)
for k, d in enumerate(detections): # For all detected face instances individually
shape = predictor(image, d) # Draw Facial Landmarks with the predictor class
xlist = []
ylist = []
for i in range(1, 68): # Store X and Y coordinates in two lists
xlist.append(float(shape.part(i).x))
ylist.append(float(shape.part(i).y))
xmean = np.mean(xlist)
ymean = np.mean(ylist)
xcentral = [(x - xmean) for x in xlist]
ycentral = [(y - ymean) for y in ylist]
landmarks_vectorised = []
for x, y, w, z in zip(xcentral, ycentral, xlist, ylist):
landmarks_vectorised.append(w)
landmarks_vectorised.append(z)
meannp = np.asarray((ymean, xmean))
coornp = np.asarray((z, w))
dist = np.linalg.norm(coornp - meannp)
landmarks_vectorised.append(dist)
landmarks_vectorised.append((math.atan2(y, x) * 360) / (2 * math.pi))
data['landmarks_vectorised'] = landmarks_vectorised
if len(detections) < 1:
data['landmarks_vestorised'] = "error"
def make_sets():
training_data = []
training_labels = []
prediction_data = []
prediction_labels = []
for emotion in emotions:
print("Working on %s emotion" %emotion)
training, prediction = get_files(emotion)
for item in training:
image = cv2.imread(item)
try:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
except:
print()
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
clahe_image = clahe.apply(image)
get_landmarks(clahe_image)
if data['landmarks_vectorised'] == "error":
print("no face detected on this one")
else:
training_data.append(data['landmarks_vectorised']) # append image array to training data list
training_labels.append(emotions.index(emotion))
for item in prediction:
image = cv2.imread(item)
try:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
except:
print()
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
clahe_image = clahe.apply(image)
get_landmarks(clahe_image)
if data['landmarks_vectorised'] == "error":
print("no face detected on this one")
else:
prediction_data.append(data['landmarks_vectorised'])
prediction_labels.append(emotions.index(emotion))
return training_data, training_labels, prediction_data, prediction_labels
accur_lin = []
for i in range(0,10):
print("Making sets %s" % i) # Make sets by random sampling 80/20%
training_data, training_labels, prediction_data, prediction_labels = make_sets()
npar_train = np.array(training_data)
npar_trainlabs = np.array(training_labels)
print("training SVM linear %s" % i) # train SVM
clf.fit(npar_train, training_labels)
print("getting accuracies %s" % i)
npar_pred = np.array(prediction_data)
pred_lin = clf.score(npar_pred, prediction_labels)
print("Mean value lin svm: %s" % np.mean(accur_lin))