CNN神经网络彩色细胞形态图像识别数据集制作，训练，预测.zip

#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()