def predict(self,img, faces):
analysis = []
#getting input model shapes for inference
emotion_target_size = self.emotion_classifier.input_shape[1:3]
#rgb_image = load_image(img , grayscale=False)
rgb_image = cv2.cvtColor(img.copy(),cv2.cv2.COLOR_BGR2RGB)
#gray_image = load_image(img , grayscale=True)
gray_image = cv2.cvtColor(img.copy(),cv2.cv2.COLOR_BGR2GRAY)
gray_image = np.squeeze(gray_image)
gray_image = gray_image.astype('uint8')
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, self.emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_label_arg = np.argmax(self.emotion_classifier.predict(gray_face))
emotion_text = emotion_labels[emotion_label_arg]
color = (0, 0, 255)
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1, 2)
#ouput
analysis.append([face_coordinates, emotion_text]) #Essa será a saida, externamente decidiremos para onde vai
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
return analysis, bgr_image # Aqui ele retorna lista de faces detectadas com os labels de genero e emoção
log_file_path = base_path + dataset_name + '_emotation_training.log'
csv_logger = CSVLogger(log_file_path, append=False)
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss',
fator=0.1,
patience=int(patience / 4),
verbose=1)
train_models_path = base_path + dataset_name + '_mini_XCEPTION'
model_names = train_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(model_names,
'val_loss',
verbose=1,
save_best_only=True)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]
# load_dataset:
data_loader = DataManager(dataset_name, image_size=input_shape[:2])
faces, emotions = data_loader.get_data()
faces = preprocess_input(faces)
num_samples, num_classes = emotions.shape
train_data, val_data = split_data(faces, emotions, validation_split)
train_faces, train_emotions = train_data
model.fit_generator(data_generator.flow(train_faces, train_emotions,
batch_size),
steps_per_epoch=len(train_faces) / batch_size,
epochs=num_epochs,
verbose=1,
callbacks=callbacks,
validation_data=val_data)
def process_image(image, filename, model_path):
try:
# parameters for loading data and images
detection_model_path = './trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = model_path + '/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
gender_model_path = model_path + '/gender_models/simple_CNN.81-0.96.hdf5'
emotion_labels = get_labels('fer2013')
gender_labels = get_labels('imdb')
font = cv2.FONT_HERSHEY_SIMPLEX
# hyper-parameters for bounding boxes shape
gender_offsets = (30, 60)
gender_offsets = (10, 10)
emotion_offsets = (20, 40)
emotion_offsets = (0, 0)
# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
gender_classifier = load_model(gender_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
gender_target_size = gender_classifier.input_shape[1:3]
# loading images
image_array = np.fromstring(image, np.uint8)
unchanged_image = cv2.imdecode(image_array, cv2.IMREAD_UNCHANGED)
rgb_image = cv2.cvtColor(unchanged_image, cv2.COLOR_BGR2RGB)
gray_image = cv2.cvtColor(unchanged_image, cv2.COLOR_BGR2GRAY)
faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
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
rgb_face = preprocess_input(rgb_face, False)
rgb_face = np.expand_dims(rgb_face, 0)
gender_prediction = gender_classifier.predict(rgb_face)
gender_label_arg = np.argmax(gender_prediction)
gender_text = gender_labels[gender_label_arg]
gray_face = preprocess_input(gray_face, True)
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))
emotion_text = emotion_labels[emotion_label_arg]
if gender_text == gender_labels[0]:
color = (0, 0, 255)
else:
color = (255, 0, 0)
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, gender_text, color, 0, -20, 1, 2)
draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1, 2)
except Exception as err:
logging.error('Error in emotion gender processor: "{0}"'.format(err))
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
dirname = 'result'
if not os.path.exists(dirname):
os.mkdir(dirname)
cv2.imwrite(os.path.join(dirname, filename), bgr_image)
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
if(1):
faces = detect_faces(face_detection, gray_image)
print("===bgr===")
for face_coordinates in faces:
print("===bgr1===")
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
print(gray_face.shape)
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, True) #类似于标准化的操作(64,64)
gray_face = np.expand_dims(gray_face, 0)#(1,64,64)
gray_face = np.expand_dims(gray_face, -1)#(1,64,64,1)
# emotion_prediction = emotion_classifier.predict(gray_face)
emotion_prediction=sess.run(out_softmax, feed_dict={input_x: gray_face})
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion_window.append(emotion_text)
if len(emotion_window) > frame_window:
emotion_window.pop(0)
try:
emotion_mode = mode(emotion_window)
except:
continue
def detect_face(self, img):
# workaround for CV2 bug
img = copy.deepcopy(img)
# for face detection
detector = dlib.get_frontal_face_detector()
input_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_h, img_w, _ = np.shape(input_img)
# detect faces using dlib detector
if RECOGNIZE_FACES == True:
face_bbs, identities = self.face_recognizer.identify_image_faces(
img)
else:
face_bbs = detector(input_img, 1)
expanded_face_imgs = np.empty(
(len(face_bbs), self.face_size, self.face_size, 3))
emotion2_results = []
# Get face images
for i, bb in enumerate(face_bbs):
x1, y1, x2, y2, w, h = bb.left(), bb.top(
), bb.right() + 1, bb.bottom() + 1, bb.width(), bb.height()
expanded_face_imgs[i, :, :, :] = self.get_expanded_face(img, bb)
reg_face = self.get_regular_face(img, bb)
gray_face = gray_image[y1:y2, x1:x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
#reg_face = copy.deepcopy(reg_face)
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion2_results.append(emotion_text)
# emotion2_results.append(emotion.emotionof(self.emotion_model, reg_face)[0])
if len(expanded_face_imgs) > 0:
# predict ages and genders of the detected faces
results = self.model.predict(expanded_face_imgs)
predicted_genders = results[0]
ages = np.arange(0, 101).reshape(101, 1)
predicted_ages = results[1].dot(ages).flatten()
# draw results
for i, bb in enumerate(face_bbs):
if RECOGNIZE_FACES == True:
# Display name
label12 = "{}".format(identities[i])
label1 = "{}, {}".format(
int(predicted_ages[i]),
"F" if predicted_genders[i][0] > 0.5 else "M")
self.draw_label_bottom(img, (bb.left(), bb.bottom()),
label1,
row_index=2)
self.draw_label_bottom(img, (bb.left(), bb.bottom() + 1),
label12,
row_index=0)
## Display emotion
if identities[i] == "Unknown" or "customer" in identities[i]:
label2 = "{}".format(emotion2_results[i])
else:
try:
label2 = "{}".format(emotion2_results[i])
except:
label2 = "{}".format('normal')
self.draw_label_bottom(img, (bb.left(), bb.bottom() + 1),
label2,
row_index=1)
else:
## Display age, gender and emotion
label2 = "{}".format(emotion2_results[i])
self.draw_label_bottom(img, (bb.left(), bb.bottom()),
label2,
row_index=0)
# draw face rectangles
for i, bb in enumerate(face_bbs):
x1, y1, x2, y2, w, h = bb.left(), bb.top(
), bb.right() + 1, bb.bottom() + 1, bb.width(), bb.height()
cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
return img
def detect_face_info(self, file_path):
img = cv2.imread(file_path)
print(img)
# workaround for CV2 bug
img = copy.deepcopy(img)
# for face detection
detector = dlib.get_frontal_face_detector()
input_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_h, img_w, _ = np.shape(input_img)
# detect faces using dlib detector
if RECOGNIZE_FACES == True:
face_bbs, identities = self.face_recognizer.identify_image_faces(
img)
else:
face_bbs = detector(input_img, 1)
expanded_face_imgs = np.empty(
(len(face_bbs), self.face_size, self.face_size, 3))
emotion2_results = []
# Get face images
for i, bb in enumerate(face_bbs):
x1, y1, x2, y2, w, h = bb.left(), bb.top(
), bb.right() + 1, bb.bottom() + 1, bb.width(), bb.height()
expanded_face_imgs[i, :, :, :] = self.get_expanded_face(img, bb)
reg_face = self.get_regular_face(img, bb)
gray_face = gray_image[y1:y2, x1:x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
#reg_face = copy.deepcopy(reg_face)
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion2_results.append(emotion_text)
# emotion2_results.append(emotion.emotionof(self.emotion_model, reg_face)[0])
if len(expanded_face_imgs) > 0:
# predict ages and genders of the detected faces
results = self.model.predict(expanded_face_imgs)
predicted_genders = results[0]
ages = np.arange(0, 101).reshape(101, 1)
predicted_ages = results[1].dot(ages).flatten()
all_faces_info = []
# draw results
for i, bb in enumerate(face_bbs):
face_info = {
'Name': identities[i],
'Age': int(predicted_ages[i]),
'Gender': "F" if predicted_genders[i][0] > 0.5 else "M",
'Imotion': emotion2_results[i]
}
all_faces_info.append(face_info)
return all_faces_info
while True:
bgr_image = video_capture.read()[1]
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY) # 灰度图
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB) # 彩色图
# 人脸
faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
# 人脸表情预测 -- 输入灰度图 ---》 7个label不同分数
emotion_prediction = emotion_classifier.predict(gray_face) #
# print('emotion_predicted:',emotion_prediction)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion_window.append(emotion_text)
if len(emotion_window) > frame_window:
emotion_window.pop(0)
def image_emotion_gender(image):
bgr_image = image
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
detection_model_path = 'trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = 'trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
gender_model_path = 'trained_models/gender_models/simple_CNN.81-0.96.hdf5'
emotion_labels = get_labels('fer2013')
gender_labels = get_labels('imdb')
font = cv2.FONT_HERSHEY_SIMPLEX
# hyper-parameters for bounding boxes shape
gender_offsets = (30, 60)
gender_offsets = (10, 10)
emotion_offsets = (20, 40)
emotion_offsets = (0, 0)
# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
gender_classifier = load_model(gender_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
gender_target_size = gender_classifier.input_shape[1:3]
faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
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
rgb_face = preprocess_input(rgb_face, False)
rgb_face = np.expand_dims(rgb_face, 0)
gender_prediction = gender_classifier.predict(rgb_face)
gender_label_arg = np.argmax(gender_prediction)
gender_text = gender_labels[gender_label_arg]
gray_face = preprocess_input(gray_face, True)
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))
emotion_text = emotion_labels[emotion_label_arg]
if gender_text == gender_labels[0]:
color = (0, 0, 255)
else:
color = (255, 0, 0)
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, gender_text, color, 0, -20, 1, 2)
draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1, 2)
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
return bgr_image
for face_coordinates in faces:
# 对每一个人脸，分别找到 gender 和 emotion 坐标
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]
# 调整大小
try:
rgb_face = cv2.resize(rgb_face,(gender_target_size))
gray_face = cv2.resize(gray_face,(emotion_target_size))
except:
continue
########## emotion ##########
gray_face = preprocess_input(gray_face,False) # 预处理 (64,64)
# print('gray_face0:', gray_face.shape)
gray_face = np.expand_dims(gray_face,0) #(1,64,64)
# print('gray_face1:', gray_face.shape)
gray_face = np.expand_dims(gray_face,-1)#(1,64,64,1)
# print('gray_face2:',gray_face.shape)
# emotion_label
# predict:每一个label的得分值
emotion_prediction = emotion_classifier.predict(gray_face)
# print('pre_label:',emotion_prediction)
# argmax取最大概率
emotion_label_arg = np.argmax(emotion_prediction) #最大值的索引
# print('emotion_label_arg',emotion_label_arg)
# emotion-text
emotion_text = emotion_labels[emotion_label_arg] # 由索引拿到真实标签
def process_image(image):
K.clear_session()
# parameters for loading data and images
if sys.path[1] == '/app':
#load model for heroku
detection_model_path = sys.path[
1] + '/trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = sys.path[
1] + '/trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
else:
detection_model_path = sys.path[
-1] + '/trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = sys.path[
-1] + '/trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')
font = cv2.FONT_HERSHEY_SIMPLEX
# hyper-parameters for bounding boxes shape
emotion_offsets = (20, 40)
emotion_offsets = (0, 0)
# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
# loading images
image_array = np.fromstring(image, np.uint8)
unchanged_image = cv2.imdecode(image_array, cv2.IMREAD_UNCHANGED)
rgb_image = cv2.cvtColor(unchanged_image, cv2.COLOR_BGR2RGB)
gray_image = cv2.cvtColor(unchanged_image, cv2.COLOR_BGR2GRAY)
gray_image = np.squeeze(gray_image)
gray_image = gray_image.astype('uint8')
faces = detect_faces(face_detection, gray_image)
emotion_text_arr = []
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, True)
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))
emotion_text = emotion_labels[emotion_label_arg]
emotion_text_arr.append(emotion_text)
color = (255, 0, 0)
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1,
2)
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
K.clear_session()
return (bgr_image, emotion_text_arr)
def race_emotion(image_path, save_path=None, task='save', faces):
image_path = image_path #'../test_images'#sys.argv[1]
detection_model_path = '../trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = '../trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
gender_model_path = '../trained_models/gender_models/simple_CNN.81-0.96.hdf5'
emotion_labels = get_labels('../trained_models/fer2013')
gender_labels = get_labels('../trained_models/imdb')
font = cv2.FONT_HERSHEY_SIMPLEX
base = os.path.basename(image_path)
name = os.path.splitext(base)[0]
# hyper-parameters for bounding boxes shape
gender_offsets = (30, 60)
gender_offsets = (10, 10)
emotion_offsets = (20, 40)
emotion_offsets = (0, 0)
# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
gender_classifier = load_model(gender_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
gender_target_size = gender_classifier.input_shape[1:3]
# loading images
rgb_image = load_image(image_path, grayscale=False)
gray_image = load_image(image_path, grayscale=True)
gray_image = np.squeeze(gray_image)
gray_image = gray_image.astype('uint8')
#faces = detect_faces(face_detection, gray_image)
faces = faces
i = 0
for face_coordinates in faces:
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:
print '=' * 10 + 'exception in resize'
continue
#print 'exception in resize' #continue
rgb_face = preprocess_input(rgb_face, False)
rgb_face = np.expand_dims(rgb_face, 0)
gender_prediction = gender_classifier.predict(rgb_face)
gender_label_arg = np.argmax(gender_prediction)
gender_text = gender_labels[gender_label_arg]
gray_face = preprocess_input(gray_face, True)
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))
emotion_text = emotion_labels[emotion_label_arg]
if gender_text == gender_labels[0]:
color = (0, 0, 255)
else:
color = (255, 0, 0)
crop = rgb_image[y1:y2, x1:x2]
crop = cv2.cvtColor(crop, cv2.COLOR_RGB2BGR)
#race=find_race(crop,gender_text)
print('emotion and race are:', emotion_text, race)
#cv2.imwrite('images/'+str(i)+'.png', crop)
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, color, 0, -20, 1, 2)
#draw_text(face_coordinates, rgb_image, gender_text, color, 0, -20, 1, 2)
draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1,
2)
i = i + 1
if task == 'save':
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
cv2.imwrite('../OUT/' + name + '.png', bgr_image)
def main():
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)
faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion_window.append(emotion_text)
if len(emotion_window) > frame_window:
emotion_window.pop(0)
try:
emotion_mode = mode(emotion_window)
except:
continue
if emotion_text == 'angry':
color = emotion_probability * np.asarray((255, 0, 0))
print(emoji.emojize(emoji.demojize("😠")))
elif emotion_text == 'sad':
color = emotion_probability * np.asarray((0, 0, 255))
print(emoji.emojize(emoji.demojize("😰")))
elif emotion_text == 'happy':
color = emotion_probability * np.asarray((255, 255, 0))
print(emoji.emojize(emoji.demojize("😃")))
elif emotion_text == 'surprise':
color = emotion_probability * np.asarray((0, 255, 255))
print(emoji.emojize(emoji.demojize("😯")))
elif emotion_text == 'disgusted':
color = emotion_probability * np.asarray((0, 255, 255))
print(emoji.emojize(emoji.demojize("😣")))
elif emotion_text == 'fearful':
color = emotion_probability * np.asarray((0, 255, 255))
print(emoji.emojize(emoji.demojize("😟")))
else:
color = emotion_probability * np.asarray((0, 255, 0))
print(emotion_text)
color = color.astype(int)
color = color.tolist()
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, emotion_mode, color, 0, -45,
1, 1)
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
cv2.imshow('window_frame', bgr_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
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
rgb_face = preprocess_input(rgb_face, False)
rgb_face = np.expand_dims(rgb_face, 0)
gender_prediction = gender_classifier.predict(rgb_face)
gender_label_arg = np.argmax(gender_prediction)
gender_text = gender_labels[gender_label_arg]
gray_face = preprocess_input(gray_face, True)
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))
emotion_text = emotion_labels[emotion_label_arg]
if gender_text == gender_labels[0]:
color = (0, 0, 255)
else:
color = (255, 0, 0)