def get_biggest_face(self, gray_image, emotion_offsets):
detected_faces, score, idx = self.detect_faces(gray_image)
size = 0
coord = [0, 0, 0, 0]
# 选出最大的人脸
for detected_face in detected_faces:
face_coordinates = make_face_coordinates(detected_face)
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
new_size = abs((x1 - x2) * (y1 - y2))
if new_size > size:
coord[0] = x1
coord[1] = x2
coord[2] = y1
coord[3] = y2
size = new_size
return gray_image[coord[2]:coord[3], coord[0]:coord[1]], coord
gender_window = []
emotion_window = []
# starting video streaming
cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(0)
while True:
bgr_image = video_capture.read()[1]
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
detected_faces, score, idx = detect_faces(face_detection, gray_image)
for detected_face in detected_faces:
face_coordinates = make_face_coordinates(detected_face)
x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)
rgb_face = rgb_image[y1:y2, x1:x2]
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
rgb_face = cv2.resize(rgb_face, (gender_target_size))
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, False)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))