#coding:utf-8
import os
import numpy as np
from PIL import Image
import tensorflow
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Convolution2D, Flatten, Dropout, MaxPooling2D,Dense,Activation
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorical
#Pre process images
class PreFile(object):
def __init__(self,FilePath,Celltype):
self.FilePath = FilePath
self.Celltype = Celltype
def FileReName(self):
count = 0
for type in self.CellType:
subfolder = os.listdir(self.FilePath+type) # list up all folder
for subclass in subfolder: #output name of folder
print ('count_classese:->>' , count)
print (subclass)
print (self.FilePath+type+'/'+subclass)
os.rename(self.FilePath+type+'/'+subclass, self.FilePath+type+'/'+str(count)+'_'+subclass.split('.')[0]+".jpg")
count+=1
def FileResize(self,Width,Height,Output_folder):
for type in self.CellType:
print (type)
files = os.listdir(self.FilePath+type)
for i in files:
img_open = Image.open(self.FilePath + type+'/' + i)
conv_RGB = img_open.convert('RGB') #统一转换一下RGB格式 统一化
new_img = conv_RGB.resize((Width,Height),Image.BILINEAR)
new_img.save(os.path.join(Output_folder,os.path.basename(i)))
#main Training program
class Training(object):
def __init__(self,batch_size,number_batch,categories,train_folder):
self.batch_size = batch_size
self.number_batch = number_batch
self.categories = categories
self.train_folder = train_folder
#Read image and return Numpy array
def read_train_images(self,filename):
img = Image.open(self.train_folder+filename)
return np.array(img)
def train(self):
train_img_list = []
train_label_list = []
for file in os.listdir(self.train_folder):
files_img_in_array = self.read_train_images(filename=file)
train_img_list.append(files_img_in_array) #Image list add up
train_label_list.append(int(file.split('_')[0]))
print(train_label_list)#lable list addup
train_label_list = to_categorical(train_label_list,4)
train_img_list = np.array(train_img_list)
train_label_list = np.array(train_label_list)
print(train_label_list)
#train_label_list = to_categorical(train_label_list,4) #format into binary [0,0,0,0,1,0,0]
train_img_list = train_img_list.astype('float32')
train_img_list /= 255
#-- setup Neural network CNN
model = Sequential()
#CNN Layer - 1
model.add(Convolution2D(
filters=32, #Output for next later layer output (100,100,32)
kernel_size= (5,5) , #size of each filter in pixel
padding= 'same', #边距处理方法 padding method
input_shape=(100,100,3) , #input shape ** channel last(TensorFlow)
))
model.add(Activation('relu'))
model.add(MaxPooling2D(
pool_size=(2,2), #Output for next layer (50,50,32)
strides=(2,2),
padding='same',
))
#CNN Layer - 2
model.add(Convolution2D(
filters=64, #Output for next layer (50,50,64)
kernel_size=(2,2),
padding='same',
))
model.add(Activation('relu'))
model.add(MaxPooling2D( #Output for next layer (25,25,64)
pool_size=(2,2),
strides=(2,2),
padding='same',
))
#Fully connected Layer -1
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
# Fully connected Layer -2
model.add(Dense(512))
model.add(Activation('relu'))
# Fully connected Layer -3
model.add(Dense(256))
model.add(Activation('relu'))
# Fully connected Layer -4
model.add(Dense(self.categories))
model.add(Activation('softmax'))
# Define Optimizer
adam = Adam(lr = 0.0001)
#Compile the model
model.compile(optimizer=adam,
loss="categorical_crossentropy",
metrics=['accuracy']
)
# Fire up the network
model.fit(
train_img_list,
train_label_list,
epochs=self.number_batch,
batch_size=self.batch_size,
verbose=1,
)
#SAVE your work -model
model.save('./cellfinder.h5')
def MAIN():
CellType = ['嗜酸性粒细胞','嗜碱性粒细胞','中性粒细胞']
#Trainning Neural Network
Train = Training(batch_size=128,number_batch=60,categories=3,train_folder='train_img/')
Train.train()
if __name__ == "__main__":
MAIN()
