图像处理课程的一个课程设计，基于深度学习的细胞数目识别与计数.zip

# -*- coding: UTF-8 -*- import cv2 import os from keras.callbacks import ModelCheckpoint from keras.layers import * from keras.models import * from keras.optimizers import * import keras from data_strength import Augmentation os.environ["CUDA_VISIBLE_DEVICES"] = "0" #指定运行在编号为0的GPU，若没有GPU加速，可以注释掉，其计算会自动运行在CPU上 class myUnet(keras.Model): def __init__(self, img_rows=256, img_cols=256): self.img_rows = img_rows self.img_cols = img_cols # 载入数据 def unet(self, pretrained_weights=None): ''' unet网络，卷积核是3*3 :return: ''' inputs = Input((self.img_rows, self.img_cols, 1)) 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']) print('model compile') if (pretrained_weights): model.load_weights(pretrained_weights) return model def train(self): print("got unet") model = self.unet() data = Augmentation() model_checkpoint = ModelCheckpoint('my_unet.hdf5', monitor='loss', verbose=1, save_best_only=True) print('Fitting model...') myunet= data.trainGenerator(batch_size=2, image_path='./data_set/merge', image_folder='train', mask_folder='label') model.fit_generator( myunet, steps_per_epoch=300, epochs=10, verbose=1, callbacks=[model_checkpoint]) #训练集训练网络模型 print('predict test data') testGene = data.testGenerator("./data_set/test") model = load_model('my_unet.hdf5') imgs_mask_test = model.predict_generator(testGene, 30, verbose=1) #测试集加载已训练好的模型 np.save('./data_set/test/imgs_mask_test_1.npy', imgs_mask_test) data.saveResult() def Cell_conut(self): ''' 计算图像中的细胞个数 :return: ''' print("图像中的细胞个数：") img = cv2.imread('./data_set/result/12.tif') show_img = cv2.imread('./data_set/test/12.tif') binaryimg = cv2.Canny(img, 50, 200) #边缘检测 h = cv2.findContours(binaryimg, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) #连同区域计数 contours = h[1] print(len(contours)) cv2.imshow('img', img) cv2.imshow("first-image", show_img) cv2.waitKey(0) if __name__ == '__main__': myunet = myUnet() myunet.train() myunet.Cell_conut()