基于深度学习的图像分割python代码
时间: 2023-12-08 09:01:17 浏览: 65
深度学习在图像分割上取得了很大的成就,其中Python是一个广泛使用的编程语言。基于深度学习的图像分割python代码主要使用深度学习框架如TensorFlow、PyTorch或Keras来实现。在这些框架中,有一些已经预先训练好的模型可以用来进行图像分割,比如FCN、U-Net和Mask R-CNN等。
首先,需要导入必要的Python库和深度学习框架,然后加载需要进行分割的图像数据。接下来,可以选择一个已经训练好的模型,或者自己训练一个新的模型。如果选择预训练模型,可以直接加载并进行图像分割,如果选择自己训练模型,则需要准备好训练数据集,并进行训练。训练数据集通常需要包含带有标注的图像和对应的分割结果,以便训练模型。
在进行图像分割时,可以使用不同的损失函数和优化器来指导模型的学习过程,同时也可以对模型进行调参来提高分割的准确性和速度。此外,还可以使用一些数据增强技术来扩充训练数据集,以提高模型的泛化能力。
最后,可以将训练好的模型应用到新的图像数据上进行分割,并评估分割结果的准确性。通过调整模型的参数和训练策略,不断优化模型,可以得到更好的图像分割效果。基于深度学习的图像分割python代码在实际应用中有着广泛的用途,比如医学影像分割、自动驾驶和工业质检等领域。
相关问题
基于深度神经网络的图像分割python代码
以下是使用深度学习进行图像分割的Python代码示例,其中使用的是U-Net模型:
```python
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
from skimage import io
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from keras import backend as K
# 设置图像大小和路径
IMG_WIDTH = 256
IMG_HEIGHT = 256
IMG_CHANNELS = 3
TRAIN_PATH = 'train/'
TEST_PATH = 'test/'
# 预处理训练数据
def preprocess_train_data():
train_ids = next(os.walk(TRAIN_PATH))[1]
X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)
Y_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)
print('Preprocessing train data...')
for i, id_ in enumerate(train_ids):
path = TRAIN_PATH + id_
img = io.imread(path+'/images/'+id_+'.png')[:,:,:IMG_CHANNELS]
img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation=cv2.INTER_AREA)
X_train[i] = img
mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)
for mask_file in next(os.walk(path+'/masks/'))[2]:
mask_ = io.imread(path+'/masks/'+mask_file)
mask_ = cv2.resize(mask_, (IMG_HEIGHT, IMG_WIDTH), interpolation=cv2.INTER_AREA)
mask_ = np.expand_dims(mask_, axis=-1)
mask = np.maximum(mask, mask_)
Y_train[i] = mask
return X_train, Y_train
# 构建U-Net模型
def unet():
inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
s = inputs
c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (s)
c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c1)
p1 = MaxPooling2D((2, 2)) (c1)
c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (p1)
c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c2)
p2 = MaxPooling2D((2, 2)) (c2)
c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (p2)
c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c3)
p3 = MaxPooling2D((2, 2)) (c3)
c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (p3)
c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c4)
p4 = MaxPooling2D(pool_size=(2, 2)) (c4)
c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (p4)
c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c5)
u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (c5)
u6 = concatenate([u6, c4])
c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (u6)
c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c6)
u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c6)
u7 = concatenate([u7, c3])
c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (u7)
c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c7)
u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c7)
u8 = concatenate([u8, c2])
c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (u8)
c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c8)
u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c8)
u9 = concatenate([u9, c1], axis=3)
c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (u9)
c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same') (c9)
outputs = Conv2D(1, (1, 1), activation='sigmoid') (c9)
model = Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
model.summary()
return model
# 训练模型
def train():
print('Loading train data...')
X_train, Y_train = preprocess_train_data()
model = unet()
print('Fitting model...')
earlystopper = EarlyStopping(patience=5, verbose=1)
checkpointer = ModelCheckpoint('model.h5', verbose=1, save_best_only=True)
results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=16, epochs=50,
callbacks=[earlystopper, checkpointer])
return results
# 预测测试数据
def predict():
print('Loading test data...')
test_ids = next(os.walk(TEST_PATH))[1]
X_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)
sizes_test = []
for i, id_ in enumerate(test_ids):
path = TEST_PATH + id_
img = io.imread(path+'/images/'+id_+'.png')[:,:,:IMG_CHANNELS]
sizes_test.append([img.shape[0], img.shape[1]])
img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation=cv2.INTER_AREA)
X_test[i] = img
print('Loading model...')
model = unet()
print('Predicting masks on test data...')
preds_test = model.predict(X_test, verbose=1)
preds_test_t = (preds_test > 0.5).astype(np.uint8)
return preds_test_t, sizes_test
# 显示图像及其掩模
def show_image_mask(image, mask):
plt.figure(figsize=(10, 5))
plt.subplot(121)
plt.imshow(image)
plt.title('Image')
plt.subplot(122)
plt.imshow(mask, cmap='gray')
plt.title('Mask')
# 显示预测结果
def show_predict_results(preds_test_t, sizes_test):
print('Resizing predicted masks to original images...')
preds_test_upsampled = []
for i in range(len(preds_test_t)):
preds_test_upsampled.append(cv2.resize(np.squeeze(preds_test_t[i]),
(sizes_test[i][1], sizes_test[i][0]),
interpolation=cv2.INTER_AREA))
print('Showing prediction results...')
for i in range(len(preds_test_upsampled)):
show_image_mask(X_test[i], preds_test_upsampled[i])
# 训练和预测
results = train()
preds_test_t, sizes_test = predict()
show_predict_results(preds_test_t, sizes_test)
```
这个代码示例使用U-Net模型进行图像分割,在训练过程中使用了早期停止技术和模型检查点技术,在预测过程中使用了图像插值技术。通过修改代码中的路径和参数,可以使用自己的数据集进行训练和预测。
Python深度学习图像分割代码示例
以下是一个基于Python深度学习框架Keras的图像分割代码示例:
```python
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import *
import numpy as np
def unet(pretrained_weights = None,input_size = (256,256,1)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(inputs = inputs, outputs = conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
# 数据准备
# Load data
# ...
# 模型训练
model = unet()
model_checkpoint = ModelCheckpoint('unet.hdf5', monitor='loss',verbose=1, save_best_only=True)
model.fit(data, labels, batch_size=32, epochs=100, verbose=1,validation_split=0.2, shuffle=True, callbacks=[model_checkpoint])
# 模型预测
model = unet()
model.load_weights('unet.hdf5')
test_image = np.expand_dims(test_image, axis = 0)
predicted_mask = model.predict(test_image)
```
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