Keras模型-Unet图像分割

本文介绍了Keras模型-Unet图像分割的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我正在尝试使用Keras模型API重新创建UNet，我已经收集了单元格的图像以及它的分段版本，并试图使用它来训练模型.这样，我可以上传其他单元格并获取图像的分段版本作为预测.

I am attempting to recreate a UNet using the Keras model API, I have collected images of cells, and the segmented version of it and I am attempting to train a model with it. In doing so I could upload a different cell and get the segmented version of the image as a prediction.

https://github.com/JamilGafur/Unet

from __future__ import print_function

from matplotlib import pyplot as plt
from keras import losses
import os
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.optimizers import Adam
import cv2
import numpy as np
# training data 
image_location = "C:/Users/JamilG-Lenovo/Desktop/train/"
image = image_location+"image"
label = image_location +"label"


class train_data():

    def __init__(self, image, label):
        self.image = []
        self.label = []
        for file in os.listdir(image):
            if file.endswith(".tif"):
                self.image.append(cv2.imread(image+"/"+file,0))

        for file in os.listdir(label):
            if file.endswith(".tif"):
                #print(label+"/"+file)
                self.label.append(cv2.imread(label+"/"+file,0))

    def get_image(self):
        return np.array(self.image)

    def get_label(self):
        return np.array(self.label)



def get_unet(rows, cols):
    inputs = Input((rows, cols, 1))
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)

    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)

    up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)

    up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)

    up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)

    up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)

    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)

    model = Model(inputs=[inputs], outputs=[conv10])
    model.compile(optimizer=Adam(lr=1e-5), loss = losses.mean_squared_error)

    return model



def main():
    # load all the training images
    train_set = train_data(image, label)
    # get the training image
    train_images = train_set.get_image()
    # get the segmented image
    train_label = train_set.get_label()
    print("type of train_images" + str(type(train_images[0])))
    print("type of train_label" + str(type(train_label[0])))
    print('\n')
    print("shape of train_images" + str(train_images[0].shape))
    print("shape of train_label" + str(train_label[0].shape))


    plt.imshow(train_images[0], interpolation='nearest')
    plt.title("actual image")
    plt.show()

    plt.imshow(train_label[0], interpolation='nearest')
    plt.title("segmented image")
    plt.show()
    # create a UNet (512,512)
    unet = get_unet(train_label[0].shape[0],
                    train_label[0].shape[1])

    # look at the summary of the unet
    unet.summary()
    #-----------errors start here-----------------

    # fit the unet with the actual image, train_images
    # and the output, train_label
    unet.fit(train_images, train_label, batch_size=16, epochs=10)

main()

当我尝试运行它时，我希望它可以适应10个纪元，但是却抛出了以下错误:

When I am attempting to run it I expect it to be fitting over 10 epochs, but instead, it is throwing the following error:

File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", 
line 144, in _standardize_input_data str(array.shape))

ValueError: Error when checking input: expected input_5 to have shape (None, 
512, 512, 1) but got array with shape (1, 30, 512, 512)

如果有人可以告诉我我做错了什么，代码应该是什么，或者指出正确的方向，我将不胜感激.

If someone could tell me what I did wrong, what the code should be, or point me in the right direction, I would much appreciate it.

谢谢！

keras

Keras模型-Unet图像分割

问题描述

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