问题
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()
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)
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.
Thank you!
回答1:
I think that Keras is expecting a "channel last" while you are passing images in "channel first mode".
There are different ways to change this setting, please refer to this: https://keras.io/backend/
回答2:
You need to reshape your input data to the way keras is expecting: (number of images, row, col, 1)
Add the printouts of the image shapes, so it will be more clear.
I think the class is redundant here. just use functions, easier to debug. Maybe you reed the images into a list, then when you transform it to array, you get this 1 as the zero shape.
image = []
label = []
for file in os.listdir(image):
if file.endswith(".tif"):
image.append(cv2.imread(image+"/"+file,0).reshape((row,col,1))
for file in os.listdir(label):
if file.endswith(".tif"):
label.append(cv2.imread(label+"/"+file,0)).reshape((row,col,1))
Then
unet.fit(images, label, batch_size=16, epochs=10)
来源：https://stackoverflow.com/questions/47645797/keras-model-unet-image-segmentation