我的问题是想看到模型迭代中看到图像的渲染，这个过程需要花费一些时间，所以要分为渲染主线程和数据计算子线程。然而又希望加载模型一次，下面是我尝试成功的一个例子框架。但是想要子线程加载模型以及计算，主线程只管渲染这个方法没有尝试成功，如果有哪位仁兄成功了，可以@我一下吗，非常感谢！
在利用qt designer 设计了界面之后，会生成一个界面类；
主要包括setupUi(self, MainWindow): 和 retranslateUi(self, MainWindow): 两个函数；按照下面方式修改类，就可以像普通类一样用Ui__MainWindow了。
from PyQt5.QtWidgets import QApplication , QMainWindow
class Ui_MainWindow(QMainWindow):
def __init__(self):
super().__init__()
self.setupUi(self)
def setupUi(self,MainWindow):
...
def retranslateUi(self, MainWindow):
...
if __name__ == '__main__':
app = QApplication(sys.argv)
myWin = Ui_MainWindow()
myWin.show()
sys.exit(app.exec_())
在Ui_MainWindow 类中增加 tensorflow模型函数，因人而异的，下面仅做借鉴；另外在init(self) 函数中增加loadModel 初始化；
class Ui_MainWindow(QMainWindow):
def __init__(self):
super().__init__()
self.setupUi(self)
self.loadModel(parse_arguments(sys.argv[1:]))
def loadModel(self,args):
self.phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
self.blendshape = tf.Variable(tf.zeros([1, self.hidden_dim]), dtype=tf.float32, name="bs")
# the input for the discrimiator
self.input_Img_placeholder = tf.placeholder(tf.float32, [1, self.discri_ImgWei, self.discri_ImgWei, 3],
name="real_image")
generator = DCGAN()
self.pred = generator.g(self.blendshape, training=self.phase_train_placeholder)
pred_netfeature[args.discriminate_featuremap])
all_vars = tf.trainable_variables()
ftune_vlist = [v for v in all_vars if v.name.startswith('bs')]
self.train_op = NetUtil.train(self.loss, global_step=self.global_step, optimizer=args.optimizer,
learning_rate=self.lr, moving_average_decay=args.moving_average_decay,
update_gradient_vars=ftune_vlist)
# fixed the restore model parameters
var_to_restore = [v for v in all_vars if (v.name.startswith('g') or v.name.startswith('d'))]
gen_saver = tf.train.Saver(var_to_restore)
self.summary_op = tf.summary.merge_all()
self.sess_gen = tf.Session()
self.writer = tf.summary.FileWriter('log/train', self.sess_gen.graph)
# TODO initial the bs by different methods
self.sess_gen.run(tf.global_variables_initializer())
self.sess_gen.run(tf.local_variables_initializer())
self.gen_model = generator_model
gen_saver.restore(self.sess_gen, self.gen_model)
graph = tf.get_default_graph()
print("load the generator model success")
触发某个事件，调用子线程，传递所有模型的参数到子线程中;下面涉及到从子线程传递参数到主线程和从主线程传递参数到子线程。其中，主线程传递参数到子线程，只需要在子线程init函数中传参就可以了。从子线程传递到主线程就需要信号和槽机制，
- 首先 声明 InfoSignal = QtCore.pyqtSignal(list,list,float,int)
- 在run函数中self.InfoSignal.emit(img,bsret,loss_var,i+1)
- 子线程传回信号处理事件，在下面代码中函数 OptimEnd函数
触发事件和收到信号代码：
def on_click(self):
print("into on click")
self.pushButton.setDisabled(True)
# 得到总共优化步数 开始优化,
optimSumStep = self.stepSumspinBox.value()
optimStep = self.stepIdxspinBox.value()
self.lr = self.lrDoubleSpinBox.value()
# 一个循环每迭代一次，可视化到面板上 ，input: realImg, 和上一次得到blendshape
self.OptimThread = OptimizerThread(optimSumStep, self.sess_gen, self.RealImg,
self.last_bs, self.blendshape, self.pred,
self.train_op, self.loss, self.input_Img_placeholder,
self.phase_train_placeholder)
self.OptimThread.InfoSignal.connect(self.OptimEnd)
self.OptimThread.start()
def OptimEnd(self,gen_img,last_bs,loss,cnt):
print("loss=",loss)
self.last_bs = last_bs
print(self.last_bs)
self.gen_img = np.array(gen_img)
pix = QtGui.QPixmap(qimage2ndarray.array2qimage(self.gen_img))
self.OptimGraphicsView.setPixmap(QtGui.QPixmap(pix).scaled(580, 640))
self.stepIdxspinBox.setProperty("value", cnt)
self.pushButton.setDisabled(False)
子线程代码：
class OptimizerThread(QThread):
# 声明一个信号,接受返回值 generator_image,bs,loss
InfoSignal = QtCore.pyqtSignal(list,list,float,int)
basic_ImgWei = 256
basic_ImgHei = 256
discri_ImgWei = 224
#构造函数，增加参数sess, realImage,bs_init
def __init__(self,optimStep,sess,realImg,bs_init,blendshape,pred,train_op,loss,input_Img_placeholder,phase_train_placeholder,parent=None):
super(OptimizerThread,self).__init__(parent)
print("into the optimizer thread ")
self.optimStep = optimStep
self.sess_gen = sess
self.realImg = realImg
self.bs_init = bs_init
self.blendshape = blendshape
self.pred = pred
self.train_op = train_op
self.loss = loss
self.input_Img_placeholder = input_Img_placeholder
self.phase_train_placeholder = phase_train_placeholder
#重写run函数
def run(self):
print("into thread run")
self.sess_gen.run(tf.assign(self.blendshape, self.bs_init))
print("after blendshape")
for i in range(self.optimStep):
bs_img = imresize(self.realImg, (self.discri_ImgWei, self.discri_ImgWei))
bsimg_tensor = bs_img.reshape(-1, self.discri_ImgWei, self.discri_ImgWei, 3)
gen_img, _, input_bs, loss_var = self.sess_gen.run(
[self.pred, self.train_op, self.blendshape, self.loss],
feed_dict={self.input_Img_placeholder: bsimg_tensor, self.phase_train_placeholder: True})
self.last_bs = input_bs
img = gen_img[0].tolist()
bsret = self.last_bs[0].tolist()
# self.optimStep = self.optimStep+1
self.InfoSignal.emit(img,bsret,loss_var,i+1)
print("thread end")
遗存的问题，以上，我尝试的方案就成功了，我的问题是需要加载一张图片，然后做优化。当我点击触发优化事件之后，再点击触发优化事件ui就会退出，但是从新加载一张照片之后就不会产生这样的情况，即使存在这样的bug,也可以用。如果你发现如何改进，非常感谢你能通知我，希望对初学者有用。
