我是一个ML新手，我试图在我的灰度Tif图像上应用图像分割。图像中的NaN值代表海洋，0到2的值代表陆地。我创建了一些真实的掩模用于训练。掩模的区域中，NaN代表大海，0代表陆地，1代表云层。我想创建一个分割掩模，它有3个等级，分别代表海洋、陆地和云层。
我指的是 TensorFlow教程 和 谷歌Colab教程 并有下面的代码。输出的分类器确实显示了一些东西，但是分段掩码总体上变成了0。请大家帮忙，谢谢。
from glob import glob
from PIL import Image
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.python.keras import layers
from tensorflow.python.keras import losses
from tensorflow.python.keras import models
#load images
img = sorted(glob('/content/drive/My Drive/train_sub_5/*.tif'))
mask = sorted(glob('/content/drive/My Drive/train_mask_sub_5/*.tif'))
#split into train and test dataset
img, img_val, mask, mask_val = train_test_split(img, mask, test_size=0.2, random_state=42)
#read images as array and make their shape (512, 512, 1)
train_image = []
for m in img[:]:
image= Image.open(m)
img_arr= np.nan_to_num(np.array(image), nan=0)
stacked_img= np.stack((img_arr,)*1, axis=-1)
train_image.append(stacked_img)
train_mask = []
for n in mask[:]:
image_mask= Image.open(n)
mask_arr= np.nan_to_num(np.array(image_mask), nan=2)
stacked_mask = np.stack((mask_arr,)*1, axis=-1)
train_mask.append(stacked_mask)
test_img = []
for o in img_val[:]:
image= Image.open(o)
img_arr = np.nan_to_num(np.array(image), nan=0)
stacked_img = np.stack((img_arr,)*1, axis=-1)
test_img.append(stacked_img)
test_mask = []
for p in mask_val[:]:
image_mask= Image.open(p)
mask_arr= np.nan_to_num(np.array(image_mask), nan=2)
stacked_mask = np.stack((mask_arr,)*1, axis=-1)
test_mask.append(stacked_mask)
#create tensorflow dataset
train= tf.data.Dataset.from_tensor_slices((train_image, train_mask))
test = tf.data.Dataset.from_tensor_slices((test_img, test_mask))
#set parameters
train_length = len(train_image)
img_shape = (512,512,1)
batch_size = 16
epochs = 20
#shuffle, batch, and repeat
train_dataset = train.cache().shuffle(train_length).batch(batch_size).repeat()
train_dataset = train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
test_dataset = test.batch(batch_size).repeat()
#build the model
def conv_block(input_tensor, num_filters):
encoder = layers.Conv2D(num_filters, (3, 3), padding='same')(input_tensor)
encoder = layers.BatchNormalization()(encoder)
encoder = layers.Activation('relu')(encoder)
encoder = layers.Conv2D(num_filters, (3, 3), padding='same')(encoder)
encoder = layers.BatchNormalization()(encoder)
encoder = layers.Activation('relu')(encoder)
return encoder
def encoder_block(input_tensor, num_filters):
encoder = conv_block(input_tensor, num_filters)
encoder_pool = layers.MaxPooling2D((2, 2), strides=(2, 2))(encoder)
return encoder_pool, encoder
def decoder_block(input_tensor, concat_tensor, num_filters):
decoder = layers.Conv2DTranspose(num_filters, (2, 2), strides=(2, 2), padding='same')(input_tensor)
decoder = layers.concatenate([concat_tensor, decoder], axis=-1)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Activation('relu')(decoder)
decoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Activation('relu')(decoder)
decoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Activation('relu')(decoder)
return decoder
inputs = layers.Input(shape=img_shape)
encoder0_pool, encoder0 = encoder_block(inputs, 32)
encoder1_pool, encoder1 = encoder_block(encoder0_pool, 64)
encoder2_pool, encoder2 = encoder_block(encoder1_pool, 128)
encoder3_pool, encoder3 = encoder_block(encoder2_pool, 256)
encoder4_pool, encoder4 = encoder_block(encoder3_pool, 512)
center = conv_block(encoder4_pool, 1024)
decoder4 = decoder_block(center, encoder4, 512)
decoder3 = decoder_block(decoder4, encoder3, 256)
decoder2 = decoder_block(decoder3, encoder2, 128)
decoder1 = decoder_block(decoder2, encoder1, 64)
decoder0 = decoder_block(decoder1, encoder0, 32)
outputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(decoder0)
#defined the model
model = models.Model(inputs=[inputs], outputs=[outputs])
#defined loss function
def dice_coeff(y_true, y_pred):
smooth = 1.
y_true_f = tf.reshape(y_true, [-1])
y_pred_f = tf.reshape(y_pred, [-1])
intersection = tf.reduce_sum(y_true_f * y_pred_f)
score = (2.*intersection+smooth)/(tf.reduce_sum(y_true_f)+tf.reduce_sum(y_pred_f)+smooth)
return score
def dice_loss(y_true, y_pred):
loss = 1 - dice_coeff(y_true, y_pred)
return loss
def bce_dice_loss(y_true, y_pred):
loss = losses.binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred)
return loss
#compiled the model
model.compile(optimizer='adam', loss=bce_dice_loss, metrics=[dice_loss])
model.summary()
save_model_path = '/content/drive/My Drive/tmp/weights.hdf5'
cp = tf.keras.callbacks.ModelCheckpoint(filepath=save_model_path, monitor='val_dice_loss', mode='max', save_best_only=True)
#trained the model
history = model.fit(train_dataset, steps_per_epoch=int(np.ceil(train_length / float(batch_size))), epochs=epochs, validation_data=test_dataset, validation_steps=int(np.ceil(len(test_img) / float(batch_size))), callbacks=[cp])
#visualize training process
dice = history.history['dice_loss']
val_dice = history.history['val_dice_loss']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(epochs)
plt.figure(figsize=(16, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, dice, label='Training Dice Loss')
plt.plot(epochs_range, val_dice, label='Validation Dice Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Dice Loss')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
#visualize the output
def display(display_list):
plt.figure(figsize=(15, 15))
title = ['Input Image', 'True Mask', 'Predicted Mask']
for i in range(len(display_list)):
plt.subplot(1, len(display_list), i+1)
plt.title(title[i])
plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))
plt.axis('off')
plt.show()
def show_predictions(dataset=None, num=1):
for image, mask in dataset.take(num):
pred_mask = model.predict(image)
display([image[0,:,:,0], mask[0,:,:,0], create_mask(pred_mask)[:,:,0]]) #1
display([image[0,:,:,0], mask[0,:,:,0], pred_mask[0,:,:,0]]) #2
def create_mask(pred_mask):
pred_mask = tf.argmax(pred_mask, axis=-1)
pred_mask = pred_mask[..., tf.newaxis]
return pred_mask[0]
show_predictions(test_dataset, 3)
输出的分类器pred_mask确实显示了一些有意义的结构，下面是代码#1输出的一些例子。例一. 例二. 例三.
当我试图用代码#2创建一个像TensorFlow教程一样的分割掩模时，它的分割掩模全部返回0。例1.
我找到了如何制作遮罩。下面是需要修改的地方。
#read images as array and make their shape
train_image = []
for m in img[:]:
image= Image.open(m)
img_arr= np.nan_to_num(np.array(image), nan=0)
stacked_img= np.stack((img_arr,)*3, axis=-1)
train_image.append(stacked_img)
test_img = []
for o in img_val[:]:
image= Image.open(o)
img_arr = np.nan_to_num(np.array(image), nan=0)
stacked_img = np.stack((img_arr,)*3, axis=-1)
test_img.append(stacked_img)
#set parameters
img_shape = (512,512,3) #if I want to produce a 3-class mask, then set the third channel as 3
#build the model
outputs = layers.Conv2D(3, (1, 1), activation='softmax')(decoder0) #if I want to produce 3-class mask, then set layers.Conv2D(3,(1,1)) and use softmax; if 2-class mask is required, then set (2,(1,1)) and use sigmoid.
虽然现在它的工作，我很困惑的图像通道和参数layer.Conv2D。如果我有一个RGB图像，我想制作一个5级蒙版，我应该如何设置参数img_shape为输入，layer.Conv2D(?,(1,1))为输出？