如何在新图像上测试复杂的CNN模型？

问题描述

我正在学习CNN，并且在网上找到了一个脚本，该脚本根据卫星图像对建筑物的屋顶进行分类。该脚本可以正常工作，但是我无法找到一种在新的单个图像上测试脚本的方法。我将简要显示代码，然后将显示我尝试过的内容：

seq = iaa.Sequential([
    iaa.imgcorruptlike.Fog(severity=1),iaa.imgcorruptlike.Spatter(severity =1),])

batch_size = 16
size = 512
epochs =50
version = 1 # version 2 for MobilV2unet
data_augmentation = True
model_type = 'UNet%d' % (version)
translearn = True

from tensorflow.keras.applications import MobileNetV2

def m_u_net(input_shape):
    inputs = Input(shape=input_shape,name="input_image")
    
    encoder = MobileNetV2(input_tensor=inputs,weights="imagenet",include_top=False,alpha=1.3)
    #encoder.trainable=False
    skip_connection_names = ["input_image","block_1_expand_relu","block_3_expand_relu","block_6_expand_relu"]
    encoder_output = encoder.get_layer("block_13_expand_relu").output
    
    f = [16,32,48,64]
    x = encoder_output
    for i in range(1,len(skip_connection_names)+1,1):
        x_skip = encoder.get_layer(skip_connection_names[-i]).output
        x = UpSampling2D((2,2))(x)
        x = Concatenate()([x,x_skip])
        
        x = Conv2D(f[-i],(3,3),padding="same")(x)
        x = Batchnormalization()(x)
        x = Activation("relu")(x)
        
        x = Conv2D(f[-i],padding="same")(x)
        x = Batchnormalization()(x)
        x = Activation("relu")(x)
        
    x = Conv2D(1,(1,1),padding="same")(x)
    x = Activation("sigmoid")(x)
    
    model = Model(inputs,x)
    return model

def load_rasters_simple(path,pathX,pathY ):  # Subset from original raster with extent and upperleft coord
    """Load training data pairs (two high resolution images and two low resolution images)"""
    pathXabs = os.path.join(path,pathX)
    pathYabs = os.path.join(path,pathY)
    le = len(os.listdir(pathXabs) )
        
    stackX = []
    stackY = []
    for i in range(0,le):
        fileX = os.path.join(pathXabs,os.listdir(pathXabs)[i])
        fileY = os.path.join(pathYabs,os.listdir(pathXabs)[i])
        datax = gdal_array.LoadFile(fileX) #.astype(np.int),ysize=extent[1],xsize=extent[0]
        stackX.append(datax)
        dataY = gdal_array.LoadFile(fileY) #.astype(np.int),xsize=extent[0]
        stackY.append(dataY)
    stackX = np.array(stackX)
    stackY = np.array(stackY)
    return stackX,stackY 
X,Y= load_rasters_simple('/Users/vaibhavsaxena/Desktop/segmentation/Classification/Satellite dataset ó± (global cities)','image','label') 

def slice (arr,size,inputsize,stride):
    result = []
    if stride is None:
        stride = size
    for i in range(0,(inputsize-size)+1,stride):
        for j in range(0,stride):
        
            s = arr[i:(i+size),j:(j+size),]
            result.append(s)
 
    result = np.array(result)
    return result

def batchslice (arr,stride,num_img):
    result = []
    for i in range(0,num_img):
        s= slice(arr[i,],stride )
        result.append(s )
    result = np.array(result)
    result = result.reshape(result.shape[0]*result.shape[1],result.shape[2],result.shape[3],-1)
    return result

Y=batchslice(Y,Y.shape[1],Y.shape[0]).squeeze()
X_cl =batchslice(X_cl,X_cl.shape[1],X_cl.shape[0]) 

X_train = X_cl[:int(X_cl.shape[0]*0.8),]
Y_train = Y[:int(Y.shape[0]*0.8),]
X_test = X_cl[int(X_cl.shape[0]*0.8)+1:,] 
Y_test = Y[int(Y.shape[0]*0.8)+1:,] 

大型的unet模型架构。整个脚本可以在here中找到。

此模型仅适用于数据集。我正在尝试使用自己的数据集图像进行测试，这就是我尝试过的：

model = load_model('no_aug_unet_model.h5',custom_objects=dependencies)
model.compile(loss='binary_crossentropy',metrics=[IoU],optimizer=Adam(learning_rate=lr_schedule(0)))

from keras.preprocessing import image

test_image= image.load_img('bangkok_noi_2.jpg',target_size = (2000,2000))  
test_image = image.img_to_array(test_image)
test_image1 = test_image.reshape((1,2000,3))
testpre = model.predict(test_image1)
img = Image.fromarray(test_image,'RGB')
img.show()

我的测试图像的原始形状为(1852,3312,3)。 我得到一张奇怪的预测图像，与预期没有任何意义。我相信，我对测试图像进​​行了错误的预处理。任何帮助将不胜感激。

可以找到整个脚本here。

解决方法

暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！

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