single_File_For_ColorizationModel_For_Not_OOP_Fan.py 文件源码

def Test(image_Name,flag):
    if(flag==False):
        saver = tf.train.Saver()
        saver = tf.train.import_meta_graph('Model Directory/our_model.meta')
        saver.restore(sess, 'Model Directory/our_model')
    GreyImagesRezied_Batch = []
    OriginalImage_Batch=[]
    Original_Img = Image.open(TestingImgPath+image_Name).convert('RGB').convert('L')  
    width,height=Original_Img.size
    Original_Img = Original_Img.resize((int(width/8) * 8,int(height/8) * 8),Image.ANTIALIAS)      

    Grey_img = Original_Img.resize((224,224),Image.ANTIALIAS)      
    Original_Img = np.asanyarray(Original_Img) 
    Grey_img = np.asanyarray(Grey_img) 

    img_shape = Original_Img.shape
    Original_reshaped = Original_Img.reshape(img_shape[0],img_shape[1], GreyChannels)#[H,W,1]
    OriginalImage_Batch.append(Original_reshaped)#[#imgs,224,224,1]
    img_reshaped = Grey_img.reshape(224, 224, GreyChannels)#[224,224,1]
    GreyImagesRezied_Batch.append(img_reshaped)#[#imgs,224,224,1]

    TestImage = tf.placeholder(dtype=tf.float32,shape=[1,224,224,1])
    original = tf.placeholder(dtype=tf.float32,shape=[1,None,None,1])
    Prediction = TestModel(original,TestImage,Original_Img.shape[0],Original_Img.shape[1]) 
    Chrominance = sess.run(Prediction,feed_dict={TestImage:GreyImagesRezied_Batch,original:OriginalImage_Batch})

    NewImg = np.empty((Original_Img.shape[0],Original_Img.shape[1],3))
    for i in range(len(Original_reshaped[:,1,0])):
      for j in range(len(Original_reshaped[1,:,0])):
         NewImg[i,j,0]= 0 + ( (Original_reshaped[i,j,0] - 0) * (100 - 0) / (255 - 0) )  
    NewImg[:,:,1] = DeNormalize(Chrominance[0,:,:,0],0,1)
    NewImg[:,:,2] = DeNormalize(Chrominance[0,:,:,1],0,1)
    NewImg = color.lab2rgb(NewImg)
    plt.imsave(ResultImagePath+image_Name[0:-4]+"_Colored"+image_Name[len(image_Name)-4:],NewImg)
#------------------------------------------------