fopen = open(path)

path_list = fopen.readlines()

data = []

labels = []

for _file in path_list:

img_path = _file.split()[0]

label = _file.split()[1]

labels.append(eval(label))

img = cv2.imread(os.path.join(root_file,img_path))

img = cv2.resize(img,(224,224))

img = img_to_array(img)

data.append(img)

labels = np.array(labels)

data = np.array(data)

labels = to_categorical(labels, num_classes=8)

model = RN.resnet(weights=model_path,input_tensor=None,input_shape=(224,224,3),classes=8)

logging = TensorBoard(log_dir='/home/app/program/micro_emotion')

lr_decay = ReduceLROnPlateau(monitor='val_loss',factor=0.1,patience=3)

model.compile(loss="categorical_crossentropy", optimizer=Adam(lr=0.001,decay=0.001/100),

metrics=['accuracy'])

model.fit(train_data,train_labels,epochs=100,batch_size=32,

validation_data=(test_data, test_labels),callbacks=[logging,lr_decay])

train_datagen = ImageDataGenerator()

test_datagen = ImageDataGenerator()

print("获取测试数据... ...")

test_generator = test_datagen.flow_from_directory(test_path, target_size=(224, 224), batch_size=32)

print("获取训练数据... ...")

train_generator = train_datagen.flow_from_directory(train_path,target_size=(224,224),batch_size=32)

print("开始训练... ...")

logging = TensorBoard(log_dir='/home/app/program/micro_emotion/log')

early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1)

model_check = ModelCheckpoint(filepath=pre_model,monitor='val_loss',save_best_only=True)

lr_decay = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, min_delta=0.001)

model = RN.resnet10(include_top = False, weights = ori_model, pooling = 'avg', input_shape=(224, 224, 3), classes=7)

x = model.output

x = Dense(7, activation='softmax', name='fc8_5')(x)

model = Model(inputs=model.input, outputs=x)

model.compile(loss="categorical_crossentropy", optimizer=SGD(lr=0.01,momentum=0.9,decay=0.1 / 20),

metrics=['accuracy'])

history = model.fit_generator(train_generator,steps_per_epoch=346,epochs=60,

validation_data=test_generator,validation_steps=38,

emotion_list = ['Surprise', 'Fear', 'Disgust', 'Happiness', 'Sadness', 'Anger', 'Neutral']

model = RN.resnet10(include_top=False, pooling='avg',input_tensor=None, input_shape=(224, 224, 3),

classes=7)

x = model.output

x = Dense(7, activation='softmax', name='fc8_5')(x)

model = Model(inputs=model.input, outputs=x)

model.load_weights('/home/app/program/micro_emotion/resnet10/macro/model.h5', by_name=True)

dector = MTCNN()

# img_path = '/home/app/data/beiyou/basic/Image/test/train/3/test_0063.jpg'

# img = cv2.imread(img_path)

# # t = dector.detect_faces(img)

# # point = t[0]['box']

# # face = img[point[1]:point[1] + point[3], point[0]:point[0] + point[2]]

# face = cv2.resize(img, (224, 224))

# cv2.imshow('face',face)

# face = img_to_array(face)

# face = face.reshape((-1, 224, 224, 3))

# out = model.predict(face)

# print(emotion_list[out.argmax()])

# cv2.waitKey()

capture = cv2.VideoCapture(0)

while (True):

ref, frame = capture.read()

img = frame.copy()

t = dector.detect_faces(img)

point = t[0]['box']

#face = img[point[1]:point[1] + point[3], point[0]:point[0] + point[2]]

keypoint1 = np.float32([[30, 30], [70, 30], [50, 80]])

keypoint2 = []

keypoint2.append(t[0]['keypoints']['left_eye'])

keypoint2.append(t[0]['keypoints']['right_eye'])

x = np.array(t[0]['keypoints']['mouth_left'], dtype=np.float32)

y = np.array(t[0]['keypoints']['mouth_right'], dtype=np.float32)

center = (x + y) / 2

keypoint2 = np.array(keypoint2, dtype=np.float32)

keypoint2 = np.row_stack((keypoint2, center))

matrix = cv2.getAffineTransform(keypoint2, keypoint1)

output = cv2.warpAffine(img, matrix, (img.shape[1], img.shape[0]))

face = output[:100, :100]

face = cv2.resize(face, (224, 224))

face = img_to_array(face)

face = face.reshape((-1, 224, 224, 3))

start = time.clock()

out = model.predict(face)

end = time.clock()

print('耗时{}s'.format(end - start))

#print(out)

print(emotion_list[out.argmax()])

cv2.rectangle(frame, (point[0], point[1]), (point[0] + point[2], point[1] + point[3]), (0, 255, 0), 2)

cv2.imshow('1', frame)

for i in range(135):

if ((model.layers[i].name == 'concatenate_{0}'.format(count)) or (model.layers[i].name == 'average_{0}'.format(count))

or (model.layers[i].name == 'multiply_{0}'.format(count))

or (model.layers[i].name == 'add_{0}'.format((2*count + 1)))

or (model.layers[i].name == 'activation_{0}'.format((2*count + 2)))):

model.layers[i].trainable = True

# concat = 'concatenate_{0}'.format(count)

# ave = 'average_{0}'.format(count)

# mul = 'multiply_{0}'.format(count)

# add = 'add_{0}'.format((2*count + 1))

# act = 'activation_{0}'.format((2*count + 2))

# model.concat.trainable = True

# model.ave.trainable = True

# model.mul.trainable = True

# model.add.trainable = True

# model.act.trainable = True

model.trainable = False

for i in range(135):

if ((model.layers[i].name == 'concatenate') or (model.layers[i].name == 'res2a_branch2') or (model.layers[i].name == 'bn2a_branch2')

or (model.layers[i].name == 'res3a_branch2') or (model.layers[i].name == 'bn3a_branch2') or (model.layers[i].name == 'res4a_branch2')

or (model.layers[i].name == 'bn4a_branch2') or (model.layers[i].name == 'res5a_branch2') or (model.layers[i].name == 'bn5a_branch2')

or (model.layers[i].name == 'average') or (model.layers[i].name == 'multiply') or (model.layers[i].name == 'add_1')

or (model.layers[i].name == 'activation_2')):

model.layers[i].trainable = True

# model.concatenate.trainable = True

#

# model.res2a_branch2.trainable = True

# model.bn2a_branch2.trainable = True

# model.res3a_branch2.trainable = True

# model.bn3a_branch2.trainable = True

# model.res4a_branch2.trainable = True

# model.bn4a_branch2.trainable = True

# model.res5a_branch2.trainable = True

# model.bn5a_branch2.trainable = True

#

#

# model.average.trainable = True

# model.multiply.trainable = True

# model.add_1.trainable = True

# model.activation_2.trainable = True

for count in range(1,8):

model = unfreeze(model,count)

train_datagen = ImageDataGenerator()

test_datagen = ImageDataGenerator()

print("获取测试数据... ...")

test_generator = test_datagen.flow_from_directory(test_path, target_size=(224, 224), batch_size=4)

print("获取训练数据... ...")

train_generator = train_datagen.flow_from_directory(train_path, target_size=(224, 224), batch_size=4)

print("开始训练... ...")

logging = TensorBoard(log_dir='/home/app/program/micro_emotion/log')

early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1)

model_check = ModelCheckpoint(filepath=model_path, monitor='val_loss', save_best_only=True)

lr_decay = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, min_delta=0.001)

model = RN.resnet10(include_top=False, pooling='avg',

input_shape=(224, 224, 3), classes=5)

x = model.output

x = Dense(5, activation='softmax', name='fc8_micro')(x)

model = Model(inputs=model.input, outputs=x)

model.load_weights(pre_model,by_name=True)

#model = frozen(model)

model.compile(loss="categorical_crossentropy", optimizer=SGD(lr=0.01, momentum=0.9, decay=0.1 / 10),

metrics=['accuracy'])

history = model.fit_generator(train_generator, steps_per_epoch=62, epochs=30,

validation_data=test_generator, validation_steps=30,

labels = os.listdir(img_path)

labels.sort()

files = []

for label in labels:

imgpath = os.path.join(img_path,label)

imgnames = os.listdir(imgpath)

for imgname in imgnames:

s = '{0}/{1} {0}'.format(label,imgname)

files.append(s)

np.random.shuffle(files)

confusion_matrix = np.zeros((dim, dim), np.int)

confusion_matrix_float = np.zeros((dim, dim), np.float)

#构建模型

model = RN.resnet10(include_top=False, pooling='avg', input_tensor=None, input_shape=(224, 224, 3),

classes=5)

x = model.output

x = Dense(5, activation='softmax', name='fc8_micro')(x)

model = Model(inputs=model.input, outputs=x)

model.load_weights(model_path, by_name=True)

#获取混淆矩阵

for file in files:

path = file.split()[0]

sign = eval(file.split()[1])

img = cv2.imread(os.path.join(img_path,path))

face = cv2.resize(img, (224, 224))

face = img_to_array(face)

face = face.reshape((-1, 224, 224, 3))

predict_value = int(model.predict(face).argmax())

confusion_matrix[sign, predict_value] += 1

print(confusion_matrix)

c_sum = confusion_matrix.sum(axis=1)

confusion_matrix_float = confusion_matrix / c_sum