我在训练unet时报错，原因是mygene传入的参数是folders列表，但是我的影像是来自六个不同的资料夹(五张单通道影像 一张三通道影像)，该怎么修改
unet model部分已修改成八通道 def unet(pretrained_weights = None,input_size = (256,256,8)):
参考的是zhixuhao/unet
main.py
from model import *
from data import *
#os.environ["CUDA_VISIBLE_DEVICES"] = "0"
def show_train_history(train_history, train,name):
plt.plot(train_history.history[train])
plt.title(name)
plt.ylabel('train')
plt.xlabel('Epoch')
plt.legend(['train'], loc='center right')
#plt.show()
plt.savefig(name+".png")
plt.close()
acc_name ="Accuracy"
loss_name="Loss"
data_gen_args = dict(rotation_range=0.2,
width_shift_range=0.05,
height_shift_range=0.05,
shear_range=0.05,
zoom_range=0.05,
horizontal_flip=True,
fill_mode='nearest')
# 訓練資料路徑列表
folders = [
'data/syu/train1day/auto_correlation1_reverse',
'data/syu/train1day/energy1',
'data/syu/train1day/entropy1_reverse',
'data/syu/train1day/homogeneity1',
'data/syu/train1day/temprature',
'data/syu/train1day/clahe1'
]
myGene = trainGenerator(8, folders,'image','label',data_gen_args,save_to_dir = None)
model = unet(input_size=(256, 256, 8))
model_checkpoint = ModelCheckpoint('weight/unet_8channels.hdf5', monitor='loss',verbose=1, save_best_only=True)
train_history=model.fit_generator(myGene,steps_per_epoch=200,epochs=20,callbacks=[model_checkpoint])
acc_name ="Accuracy"
loss_name="Loss"
show_train_history(train_history, '1accuracy',acc_name)
show_train_history(train_history, 'loss',loss_name)
data.py 报错的部分在def trainGenerator,
from __future__ import print_function
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
import glob
import skimage.io as io
import skimage.transform as trans
import cv2
from matplotlib import pyplot as plt
from skimage import img_as_ubyte
import tensorflow as tf
Sky = [128,128,128]
Building = [128,0,0]
Pole = [192,192,128]
Road = [128,64,128]
Pavement = [60,40,222]
Tree = [128,128,0]
SignSymbol = [192,128,128]
Fence = [64,64,128]
Car = [64,0,128]
Pedestrian = [64,64,0]
Bicyclist = [0,128,192]
Unlabelled = [0,0,0]
COLOR_DICT = np.array([Sky, Building, Pole, Road, Pavement,
Tree, SignSymbol, Fence, Car, Pedestrian, Bicyclist, Unlabelled])
def normalized_the_gray_value(img):
gray_max=img.max()
gray_min=img.min()
normalized_img= ( (img-gray_min) / (gray_max-gray_min) )*255
return normalized_img
# change the img to YUV and normalized the Y value
def RGB_Y_normalized(img,gamma_value):
#print(img_out)
#print(gamma_value)
YUV = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
Y, U, V = cv2.split(YUV)
#print(y)
y_max=Y.max()
y_min=Y.min()
#print(y_min)
#print(y_max)
temp_y= ( ( ( Y-y_min) / ( y_max - y_min ) ) **gamma_value )*255
# Y normalized and return
#YCrCb_array = np.zeros((img.shape[0], img.shape[1], 3), "uint8")
#YUV_array = np.zeros((img.shape[0], img.shape[1], 3))
YUV_array = np.zeros((img.shape[0], img.shape[1], 3), "uint8")
YUV_array[:, :, 0], YUV_array[:, :, 1], YUV_array[:, :, 2] = temp_y, YUV[:,:,1], YUV[:,:,2]
#print(YCrCb_array[:, :, 0])
# return the YUV channel to RGB channel image array
#print(np.shape(YUV_array))
final_rgb=cv2.cvtColor(YUV_array, cv2.COLOR_YUV2BGR)
return final_rgb
# change the img to YCbCr and normalized the Y value
def YCbCr_normalized(img,gamma_value):
#print(img_out)
#print(gamma_value)
YCrCb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)
Y, Cr, Cb = cv2.split(YCrCb)
#print(y)
y_max=Y.max()
y_min=Y.min()
#print(y_min)
#print(y_max)
temp_y= ( ( ( Y-y_min) / ( y_max - y_min ) ) **gamma_value )*255
# Y normalized and return
#YCrCb_array = np.zeros((img.shape[0], img.shape[1], 3), "uint8")
YCrCb_array = np.zeros((img.shape[0], img.shape[1], 3))
YCrCb_array[:, :, 0], YCrCb_array[:, :, 1], YCrCb_array[:, :, 2] = temp_y, YCrCb[:,:,1], YCrCb[:,:,2]
#print(YCrCb_array[:, :, 0])
return YCrCb_array
def adjustData(img,mask,flag_multi_class,num_class):
if(flag_multi_class):
img = img / 255
mask = mask[:,:,:,0] if(len(mask.shape) == 4) else mask[:,:,0]
new_mask = np.zeros(mask.shape + (num_class,))
for i in range(num_class):
#for one pixel in the image, find the class in mask and convert it into one-hot vector
#index = np.where(mask == i)
#index_mask = (index[0],index[1],index[2],np.zeros(len(index[0]),dtype = np.int64) + i) if (len(mask.shape) == 4) else (index[0],index[1],np.zeros(len(index[0]),dtype = np.int64) + i)
#new_mask[index_mask] = 1
new_mask[mask == i,i] = 1
new_mask = np.reshape(new_mask,(new_mask.shape[0],new_mask.shape[1]*new_mask.shape[2],new_mask.shape[3])) if flag_multi_class else np.reshape(new_mask,(new_mask.shape[0]*new_mask.shape[1],new_mask.shape[2]))
mask = new_mask
elif(np.max(img) > 1):
img = img / 255
mask = mask /255
mask[mask > 0.5] = 1
mask[mask <= 0.5] = 0
return (img,mask)
def trainGenerator(batch_size,train_path,image_folder,mask_folder,aug_dict,image_color_mode = "rgba",
mask_color_mode = "grayscale",image_save_prefix = "image",mask_save_prefix = "mask",
flag_multi_class = False,num_class = 2,save_to_dir = "data_train/test/",target_size = (256,256),seed = 1):
'''
can generate image and mask at the same time
use the same seed for image_datagen and mask_datagen to ensure the transformation for image and mask is the same
if you want to visualize the results of generator, set save_to_dir = "your path"
'''
image_datagen = ImageDataGenerator(**aug_dict)
mask_datagen = ImageDataGenerator(**aug_dict)
image_generator = image_datagen.flow_from_directory(
train_path,
classes = [image_folder],
class_mode = None,
color_mode = image_color_mode,
target_size = target_size,
batch_size = batch_size,
save_to_dir = save_to_dir,
save_prefix = image_save_prefix,
seed = seed)
mask_generator = mask_datagen.flow_from_directory(
train_path,
classes = [mask_folder],
class_mode = None,
color_mode = mask_color_mode,
target_size = target_size,
batch_size = batch_size,
save_to_dir = save_to_dir,
save_prefix = mask_save_prefix,
seed = seed)
train_generator = zip(image_generator, mask_generator)
for (img,mask) in train_generator:
img,mask = adjustData(img,mask,flag_multi_class,num_class)
yield (img,mask)
save_to_dir = "C:/Labbb" #橘色字部分 可視化結果
def testGenerator(test_path,num_image = 30,target_size = (256,256),flag_multi_class = False,as_gray = True):
for i in range(num_image):
img = io.imread(os.path.join(test_path,"%d.png"%i),as_gray = False)
img = img / 255
img = trans.resize(img,target_size)
#img = np.reshape(img,img.shape+(1,)) if (not flag_multi_class) else img
img = np.reshape(img,(1,)+img.shape)
yield img
def geneTrainNpy(image_path,mask_path,gamma_value,flag_multi_class = False,num_class = 2,image_prefix = "image",mask_prefix = "mask",image_as_gray = True,mask_as_gray = True):
# image_name_arr = glob.glob(os.path.join(image_path,"%s*.png"%image_prefix))
image_name_arr = glob.glob(os.path.join(image_path,"*.png"))
# mask name array ---- get the mask path
mask_name_arr = glob.glob(os.path.join(mask_path,"*.png"))
target_size = (256,256)
image_arr = []
mask_arr = []
for index,item in enumerate(image_name_arr):
#img = io.imread(item,as_gray = image_as_gray)
img = cv2.imdecode(np.fromfile(item, dtype=np.uint8), 1)
#plt.subplot(1, 2, 1)
#plt.title('img')
#plt.imshow(img)
# Rerurn the Y normalized rgb channel array
img =RGB_Y_normalized(img,gamma_value)
#print("take RGB to YCbCr " ,end=" ")
# resize the image
img = cv2.resize(img, (256, 256), interpolation=cv2.INTER_LINEAR)
#img = trans.resize(img,target_size)
#img = normalized_the_gray_value(img)
#plt.subplot(1, 2, 2)
#plt.title('img')
#plt.imshow(img)
#plt.imshow(img,cmap='gray')
img = np.reshape(img,img.shape + (1,)) if image_as_gray else img
#mask = io.imread(item.replace(image_path,mask_path).replace(image_prefix,mask_prefix),as_gray = mask_as_gray)
mask = cv2.imdecode(np.fromfile(mask_name_arr[index], dtype=np.uint8), 0)
#mask = io.imread(item,as_gray = mask_as_gray)
# resize the mask
mask = cv2.resize(mask, (256, 256), interpolation=cv2.INTER_LINEAR)
#mask = trans.resize(mask,target_size)
#plt.subplot(1, 2, 2)
#plt.title('mask')
#plt.imshow(mask,cmap='gray')
mask = np.reshape(mask,mask.shape + (1,)) if mask_as_gray else mask
#plt.show()
#print(index ,item , mask_name_arr[index],img.shape)
img,mask = adjustData(img,mask,flag_multi_class,num_class)
image_arr.append(img)
mask_arr.append(mask)
image_arr = np.array(image_arr)
mask_arr = np.array(mask_arr)
return image_arr,mask_arr
'''def labelVisualize(num_class,color_dict,img):
img = img[:,:,0] if len(img.shape) == 3 else img
img_out = np.zeros(img.shape + (3,))
for i in range(num_class):
img_out[img == i,:] = color_dict[i]
return img_out / 255'''
'''def saveResult(save_path,npyfile,flag_multi_class = False,num_class = 2):
for i,item in enumerate(npyfile):
img = labelVisualize(num_class,COLOR_DICT,item) if flag_multi_class else item[:,:,0]
io.imsave(os.path.join(save_path,"%d_predict.jpg"%i),img)'''
def predict_RGBcolor_img(test_path,save_path,model,test_pic_num):
target_size = (256,256)
files_name=os.listdir(test_path)
for i in range(len(files_name)):
img = io.imread(os.path.join(test_path,"%s"%files_name[i]))
#print(files_name)
# io.imsave(os.path.join(save_path,"%s"%files_name[i]),img_as_ubyte(img))
img = img / 255
img = trans.resize(img,target_size)
img = np.reshape(img,(1,)+img.shape)
results = model.predict(img)
a=results[0][:,:,0]
'''for i in range(len(a)):
for j in range(len(a[i])):
if a[i][j]>0.5:
a[i][j]=255
else:
a[i][j]=0'''
a[a<=0.5]=0
a[a>0.5]=255
a=a.astype(np.uint8)
#print(a.shape)
#a[a<0.5]=0
#a[a>=0.5]=255
#plt.imshow(results[0][:,:,0],cmap="gray")
#plt.show()
io.imsave(os.path.join(save_path,"%s"%files_name[i]),img_as_ubyte(a))
根据你提供的代码，如果你想将来自六个不同文件夹的影像输入到 myGene 中，你需要对 trainGenerator 函数进行修改，以接受多个文件夹路径。以下是对代码进行修改的示例：
# 导入必要的库
from model import *
from data import *
# 定义训练数据的文件夹路径列表
folders = [
'data/syu/train1day/auto_correlation1_reverse',
'data/syu/train1day/energy1',
'data/syu/train1day/entropy1_reverse',
'data/syu/train1day/homogeneity1',
'data/syu/train1day/temprature',
'data/syu/train1day/clahe1'
]
# 将文件夹列表传递给 trainGenerator 函数
myGene = trainGenerator(8, folders, 'image', 'label', data_gen_args, save_to_dir=None)
# 其他代码不变
model = unet(input_size=(256, 256, 8))
model_checkpoint = ModelCheckpoint('weight/unet_8channels.hdf5', monitor='loss', verbose=1, save_best_only=True)
train_history = model.fit_generator(myGene, steps_per_epoch=200, epochs=20, callbacks=[model_checkpoint])
acc_name = "Accuracy"
loss_name = "Loss"
show_train_history(train_history, '1accuracy', acc_name)
show_train_history(train_history, 'loss', loss_name)
通过将文件夹路径列表作为参数传递给 trainGenerator 函数，你可以实现从多个文件夹加载影像的功能。请确保文件夹路径列表中的顺序与影像对应，并根据需要进行调整。
根据您提供的代码，出现错误的部分是在trainGenerator函数中，因为它无法处理多个文件夹的情况。为了修改这个问题，您可以对trainGenerator函数进行一些修改，使其接受一个文件夹列表作为输入。以下是修改后的trainGenerator函数的代码：
def trainGenerator(batch_size, folders, image_folder, mask_folder, aug_dict, image_color_mode="rgba",
mask_color_mode="grayscale", image_save_prefix="image", mask_save_prefix="mask",
flag_multi_class=False, num_class=2, save_to_dir="data_train/test/", target_size=(256, 256), seed=1):
'''
can generate image and mask at the same time
use the same seed for image_datagen and mask_datagen to ensure the transformation for image and mask is the same
if you want to visualize the results of generator, set save_to_dir = "your path"
'''
image_datagen = ImageDataGenerator(**aug_dict)
mask_datagen = ImageDataGenerator(**aug_dict)
image_generator = image_datagen.flow_from_directory(
folders,
classes=[image_folder],
class_mode=None,
color_mode=image_color_mode,
target_size=target_size,
batch_size=batch_size,
save_to_dir=save_to_dir,
save_prefix=image_save_prefix,
seed=seed)
mask_generator = mask_datagen.flow_from_directory(
folders,
classes=[mask_folder],
class_mode=None,
color_mode=mask_color_mode,
target_size=target_size,
batch_size=batch_size,
save_to_dir=save_to_dir,
save_prefix=mask_save_prefix,
seed=seed)
train_generator = zip(image_generator, mask_generator)
for (img, mask) in train_generator:
img, mask = adjustData(img, mask, flag_multi_class, num_class)
yield (img, mask)
然后，将myGene的定义更改为：
folders = [
'data/syu/train1day/auto_correlation1_reverse',
'data/syu/train1day/energy1',
'data/syu/train1day/entropy1_reverse',
'data/syu/train1day/homogeneity1',
'data/syu/train1day/temprature',
'data/syu/train1day/clahe1'
]
myGene = trainGenerator(8, folders, 'image', 'label', data_gen_args, save_to_dir=None)
这样，您应该可以处理来自六个不同文件夹的影像数据了。请确保文件夹路径和文件名正确，并且影像和标签文件名匹配。如果仍然遇到错误，请提供完整的错误消息以供进一步帮助。