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我对皮肤组织的多类分割感兴趣，我将 3000 个皮肤组织标签分为 4 类，我创建了一个 CNN 分类算法来训练我的分类模型。我想将分类模型用于新皮肤组织图像的分割任务，并对属于每个类的皮肤组织进行特征提取
以下是用于训练我的分类模型的代码
from tensorflow.keras.layers import input,Concatenate,Dropout,Flatten,Dense,GlobalAveragePooling2D,Conv2D
from tensorflow.keras import BACkend as K
#from tensorflow.keraS.Utils import np_utils
from tensorflow.keraS.Utils import to_categorical
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import optimizers
from tensorflow.keras.metrics import top_k_categorical_accuracy
from tensorflow.keras.models import Sequential,Model,load_model
import tensorflow as tf
from tensorflow.keras.initializers import he_uniform
from tensorflow.keras.callBACks import Modelcheckpoint,LearningRatescheduler,TensorBoard,EarlyStopPing,CSVLogger,ReduceLROnPlateau
#from tensorflow.compat.keras.BACkend import KTF
#import keras.BACkend.tensorflow_BACkend as KTF
from tensorflow.keras.applications.resnet50 import resnet50
from tensorflow.keras.applications.inception_v3 import InceptionV3
import os
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
#import numpy as np,Pillow,skimage,imageio,matplotlib
#from scipy.misc import imresize
from skimage.transform import resize
from tqdm import tqdm
from tensorflow.keras import metrics
#### PREPROCESS STAGE ####
# Path to superpixels class files
classes_file = "/home/DEV/SKIN_3000_CLASSEs.csv"
concatenated_data= pd.read_csv(classes_file,header=NonE)
# Instances with targets
targets = concatenated_data[1].toList()
# Split data according to their classes
class_0 = concatenated_data[concatenated_data[1] == 0]
class_1 = concatenated_data[concatenated_data[1] == 1]
class_2 = concatenated_data[concatenated_data[1] == 2]
class_3 = concatenated_data[concatenated_data[1] == 3]
# Holdout split Train/test set (Other options are k-folds or leave-one-out)
split_proportion = 0.8
split_size_0 = int(len(class_0)*split_proportion)
split_size_1 = int(len(class_1)*split_proportion)
split_size_2 = int(len(class_2)*split_proportion)
split_size_3 = int(len(class_3)*split_proportion)
new_class_0_Train = np.random.choice(len(class_0),split_size_0,replace=falsE)
new_class_0_Train = class_0.iloc[new_class_0_Train]
new_class_0_test = ~class_0.iloc[:][0].isin(new_class_0_Train.iloc[:][0])
new_class_0_test = class_0[new_class_0_test]
new_class_1_Train = np.random.choice(len(class_1),split_size_1,replace=falsE)
new_class_1_Train = class_1.iloc[new_class_1_Train]
new_class_1_test = ~class_1.iloc[:][0].isin(new_class_1_Train.iloc[:][0])
new_class_1_test = class_1[new_class_1_test]
new_class_2_Train = np.random.choice(len(class_2),split_size_2,replace=falsE)
new_class_2_Train = class_2.iloc[new_class_2_Train]
new_class_2_test = ~class_2.iloc[:][0].isin(new_class_2_Train.iloc[:][0])
new_class_2_test = class_2[new_class_2_test]
new_class_3_Train = np.random.choice(len(class_3),split_size_3,replace=falsE)
new_class_3_Train = class_3.iloc[new_class_3_Train]
new_class_3_test = ~class_3.iloc[:][0].isin(new_class_3_Train.iloc[:][0])
new_class_3_test = class_3[new_class_3_test]
x_Train_List = pd.concat(
[new_class_0_Train,new_class_1_Train,new_class_2_Train,new_class_3_Train])
x_test_List = pd.concat(
[new_class_0_test,new_class_1_test,new_class_2_test,new_class_3_test])
# Load superpixels files
imagePath = "/home/DEV/SKIN_SET_3000/"
x_Train = []
y_Train = []
for index,row in tqdm(x_Train_List.iterrows(),@R_631_10586@l=x_Train_List.shape[0]):
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".jpg")
x_Train.append(loadedImagE)
y_Train.append(row[1])
except:
# Try with .png file format if images are not properly loaded
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".png")
x_Train.append(loadedImagE)
y_Train.append(row[1])
except:
# Print file names whenever it is impossible to load image files
print(imagePath + str(row[0]))
x_test = []
y_test = []
for index,row in tqdm(x_test_List.iterrows(),@R_631_10586@l=x_test_List.shape[0]):
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".jpg")
x_test.append(loadedImagE)
y_test.append(row[1])
except:
# Try with .png file format if images are not properly loaded
try:
loadedImage = plt.imread(imagePath + str(row[0]) + ".png")
x_test.append(loadedImagE)
y_test.append(row[1])
except:
# Print file names whenever it is impossible to load image files
print(imagePath + str(row[0]))
# Reescaling of images
img_wIDth,img_height = 139,139
index = 0
for image in tqdm(x_Train):
#aux = resize(image,(img_wIDth,img_height,3),"bilinear")
aux = resize(image,img_height))
x_Train[index] = aux / 255.0 # normalization
index += 1
index = 0
for image in tqdm(x_test):
#aux = resize(image,img_height))
x_test[index] = aux / 255.0 # normalization
index += 1
#### TraiNING STAGE ####
os.environ["KERAS_BACKEND"] = "tensorflow"
RANDOM_STATE = 42
def get_session(gpu_fraction=0.8):
num_threads = os.environ.get('OMP_NUM_THREADS')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction)
if num_threads:
return tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options,intra_op_parallelism_threads=num_threads))
else:
return tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
#KTF.set_session(get_session())
def precision(y_true,y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred,1)))
preDicted_positives = K.sum(K.round(K.clip(y_pred,1)))
precision = true_positives / (preDicted_positives + K.epsilon())
return precision
def recall(y_true,1)))
possible_positives = K.sum(K.round(K.clip(y_true,1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def fbeta_score(y_true,y_pred,beta=1):
if beta < 0:
raise ValueError('The loWest choosable beta is zero (only precision).')
# Set F-score as 0 if there are no true positives (sklearn-likE).
if K.sum(K.round(K.clip(y_true,1))) == 0:
return 0.0
p = precision(y_true,y_pred)
r = recall(y_true,y_pred)
bb = beta ** 2
fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
return fbeta_score
nb_classes = 4
final_model = []
# Option = InceptionV3
model = InceptionV3(weights="imagenet",include_top=false,input_shape=(img_wIDth,3))
# Option = resnet
# model = resnet50(weights="imagenet",input_shape=(3,img_wIDth,img_height))
# CreaTing new outputs for the model
x = model.output
x = Flatten()(X)
x = Dense(512,activation="relu")(X)
x = Dropout(0.5)(X)
x = Dense(512,activation="relu")(X)
x = Dropout(0.5)(X)
preDictions = Dense(nb_classes,activation='softmax')(X)
#preDictions = Dense(nb_classes,activation='sigmoID')(X)
final_model = Model(inputs=model.input,outputs=preDictions)
# Metrics
learningRate = 0.001
optimizer = optimizers.SGD(learning_rate=learningRate,momentum=0.88,nesterov=TruE)
# Compiling the model...
final_model.compile(loss="categorical_crossentropy",optimizer=optimizer,metrics=["accuracy",fbeta_score])
final_model.sumMary()
#final_model.compile(loss = 'categorical_crossentropy',optimizer = 'adam',metrics = ['accuracy'])
#model.compile(loss = 'sparse_categorical_crossentropy',metrics = ['accuracy'])
#x_Train = np.array(x_Train)
#x_test = np.array(x_test)
x_Train = np.asarray(x_Train).astype(np.float32)
#x_test = np.array(x_test)
x_test = np.asarray(x_test).astype(np.float32)
# Defining targets...
y_Train = np.concatenate([np.full((new_class_0_Train.shape[0]),0),np.full((new_class_1_Train.shape[0]),1),np.full((new_class_2_Train.shape[0]),2),np.full((new_class_3_Train.shape[0]),3)])
y_test = np.concatenate([np.full((new_class_0_test.shape[0]),np.full((new_class_1_test.shape[0]),np.full((new_class_2_test.shape[0]),np.full((new_class_3_test.shape[0]),3)])
y_Train = to_categorical(y_Train)
y_test = to_categorical(y_test)
modelfilename = "/home/DEV/SKIN_SET_3000/model_inception.h5"
Trainingfilename = "/home/DEV/SKIN_SET_3000/Training.csv"
nb_Train_samples = y_Train.shape[0]
nb_test_samples = y_test.shape[0]
#epochs = 10000
epochs = 100
batch_size = 24
TrainingPatIEnce = 200
decayPatIEnce = TrainingPatIEnce / 4
# SetTing the data generator...
Train_datagen = ImageDataGenerator(
horizontal_flip=True,fill_mode="reflect",zoom_range=0.2
)
Train_generator = Train_datagen.flow(x_Train,y_Train,batch_size=batch_sizE)
# Saving the model
checkpoint = Modelcheckpoint(modelfilename,monitor='val_accuracy',verbose=1,save_best_only=True,save_weights_only=false,mode='auto',save_freq=1)
adaptativeLearningRate = ReduceLROnPlateau(monitor='val_accuracy',factor=0.5,patIEnce=decayPatIEnce,min_delta=0.0001,cooldown=0,min_lr=1e-8)
early = EarlyStopPing(monitor='val_accuracy',min_delta=0,patIEnce=TrainingPatIEnce,mode='auto')
csv_logger = CSVLogger(Trainingfilename,separator=",",append=falsE)
# CallBACks
callBACks = [checkpoint,early,csv_logger,adaptativeLearningRate]
# Training of the model
final_model.fit(Train_generator,steps_per_epoch=nb_Train_samples / batch_size,epochs=epochs,shuffle=True,valIDation_data=(x_test,y_test),valIDation_steps=nb_test_samples / batch_size,callBACks=callBACks)
final_model.save('/home/DEV/SKIN_SET_3000/model_inception.h5')
#compile metrics
为了分割我的图像，首先我使用 SliC 将我的输入图像转换为超像素
from skimage.segmentation import slic
from skimage.segmentation import mark_boundarIEs
from skimage.util import img_as_float
from skimage import io; io.use_plugin('matplotlib')
import cv2 as cv
from skimage.color import label2rgb
img_wIDth,139
# load the model we saved
model = load_model('/home/DEV/SKIN_SET_3000/model_inception.h5',compile=falsE)
# Get test image ready
img = skimage.img_as_float(skimage.io.imread('/home/DEV/SKIN_ulCER.jpg')).astype(np.float32)
plt.imshow(img)
test_image_slic = slic(img,n_segments=500,compactness=10.0)
test_image_slic_out = mark_boundarIEs(img,test_image_sliC)
plt.imshow(test_image_slic_out)
#test_image=test_image/255
test_image_array = np.array(test_image_slic_out)
test_image_resize = cv2.resize(test_image_array,img_height))
test_image_reshape = test_image_resize.reshape(1,3)
我想检查我输入的每个超像素是否被标记为 4 个组织类中的目标类之一，并提取属于每个类的特征作为掩码并量化掩码的总表面积。 任何关于如何实施这种方法的建议将不胜感激。
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