capture = cv2.VideoCapture(0)
while True:
# Capture video feed
ret, frame = capture.read()
image = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
faces = face.detect_face(image)
if faces != None:
for i in range(0, len(faces)):
x, y, w, h = faces[i]
# x und y cordinaten des Gesichts speichern um ausschnittsabsuchungen wieder zu normalisieren
x_face = x
y_face = y
if config.RECOGNITION_ALGORITHM == 1:
crop = face.crop(image, x, y, w, h)
else:
crop = face.resize(face.crop(image, x, y, w, h))
label, confidence = model.predict(crop)
cv2.rectangle(frame, (x, y), (x + w, y + h), 255)
cv2.putText(frame, str(h), (x + w, y + h + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
# print config.personen[label] + " - " + str(confidence)
if label != -1:
# If person is close to the camera use smaller POSITIVE_THRESHOLD
if h > 190 and confidence < 50:
cv2.putText(
frame, config.personen[label], (x - 3, y - 8), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 255, 255), 1
)
cv2.putText(
frame, str(confidence), (x - 2, y + h + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1
faces = face.detect_faces(image)
label = 0
if faces is not None:
for i in range(0, len(faces)):
if faces[i] is None:
print("Bad face object None")
continue
if len(faces[i]) != 4:
print("Bad face object {0}".format(faces[i]))
continue
x, y, w, h = faces[i]
# x and y coordinates of the face
x_face = x
y_face = y
if config.RECOGNITION_ALGORITHM == 1:
crop = face.crop(image, x, y, w, h)
else:
crop = face.resize(face.crop(image, x, y, w, h))
# confidence the lower the stronger the match
confidence = model.predict(crop)
label = label + 1
match = "None"
label_str = "None"
if (label != -1 and label != 0):
label_str = config.user_label(label)
print label_str, confidence
# the closer confidence is to zer the stronger the match
if confidence < 0.6 * config.POSITIVE_THRESHOLD:
label_str = 'Strong:' + label_str + str(confidence)
elif confidence < config.POSITIVE_THRESHOLD: