from tensorboardX import SummaryWriter
import numpy as np
from PIL import Image
#创建类的实例
writer = SummaryWriter("logs")#这个文件夹底下
image_path = "dataset//train//ants//0013035.jpg"
img_PIL = Image.open(image_path)
img_array = np.array(img_PIL)
#两个方法
# writer.add_image()
# writer.add_scalar()
writer.add_image("test",img_array,1,dataformats='HWC')
#y=2x
for i in range(100):
writer.add_scalar("y=2x",i*2,i)
#查看图像
writer.close() #tensorboard --logdir=E:\notebookpytorch\pyTorch学习\TensorBoard\logs
image_path = "dataset//train//ants//0013035.jpg"
from PIL import Image
img = Image.open(image_path)
print(type(img)) #图片类型为PIL类型
#转化图片类型
import numpy as np
img_array = np.array(img)
print(type(img_array)) #图片类型为numpy类型
from torchvision import transforms
from PIL import Image
import cv2
from torch.utilstransboard import SummaryWriter
#python的用法-》tensor 数据类型
#通过transforms.ToTensor去看两个问题
#1.transforms该如何使用
#2.为什么我们需要Tensor数据类型
#绝对路径:E:\notebookpytorch\pyTorch学习\TensorBoard\dataset\train\ants\0013035.jpg
#相对路径:dataset/train/ants/0013035.jpg
img_path = "dataset/train/ants/0013035.jpg"
img = Image.open(img_path)
#print(img) #可以看一下图片的类型
writer = SummaryWriter("logs")
#将图片类型转换为tensor类型
tensor_trans = transforms.ToTensor() #1.transforms该如何使用
tensor_img = tensor_trans(img) #先进行创建,需要什么创建什么工具transforms.ToTensor
#print(tensor_img)
writer.add_image("Tensor_img",tensor_img)
writer.close()
#在黑框框运行 tensorboard --logdir=logs 查看图片 或者tensorboard --logdir=E:\notebookpytorch\pyTorch学习\TensorBoard\logs