pytorch(10)transform模块(进阶)
2024-09-13 09:08:43
图像变换
- Pad
对图片边缘进行填充
transforms.Pad(padding,fill=0,padding_mode='constant')
- padding:设置填充大小,(a,b,c,d)左上右下填充abcd
- padding_mode:填充模式,4种模式,constant,edge,reflect,symmetric
- fill:constant时,设置填充的像素值,(r,g,b) or (gray)
# ==================01 pad==================
flag = True
# flag = False
if flag:
x_img = Image.open("face.jpg")
x_img_handled = transforms.Pad(padding=150, fill=200, padding_mode='constant')(x_img)
x_img_handled.show()
- ColorJitter
调整亮度、对比度、饱和度和色相
transforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0)
- brightness:亮度调整因子
为a时,从[max(0,1-a),1+a]中随机选择
为(a,b)时,从[a,b]中选择 - contrast:对比度参数,同brightness
- saturation:饱和度参数,同brightness
- hue:色相参数
当为a时,从[-a,a]中选择参数,注:0<=a<=0.5
为(a,b)时,从[a,b]中选择参数,注:-0.5<= a <=b<=0.5
# ==================02 ColorJitter==========
flag = True
# flag = False
if flag:
x_img = Image.open("face.jpg")
x_img_handled = transforms.ColorJitter(brightness=(1.5, 1.5),
contrast=(1.5,1.5),
saturation=(1.5,1.5),
hue=0.7)(x_img)
x_img_handled.show()
- Grayscale
Grayscale(num_output_channels)
# ==================03 Grayscale==========
flag = True
# flag = False
if flag:
x_img = Image.open("face.jpg")
x_img_handled = transforms.Grayscale(3)(x_img)
x_img_handled.show()
- RandomGrayscale
RandomGrayscale(num_output_channels,p=0.1)
依概率将图片转换为灰度图
- num_output_channels:输出通道数,1 or 3
- p:概率值,图像被转换为灰度图的概率
# ==================04 RandomGrayscale==========
flag = True
# flag = False
if flag:
x_img = Image.open("face.jpg")
x_img_handled = transforms.RandomGrayscale(p=0.7)(x_img)
x_img_handled.show()
- RandomAffine
RandomAffine(degrees, translate=None, scale=None, shear=None,resample=False,fillcoloor=0)
对图像进行仿射变换,仿射变换是二维的线性变换,旋转、评议、缩放、错切、翻转
- shear:错切角度设置,有水平错切和垂直错切
参数为a,仅在x轴错切,错切角度在(-a,a)之间
参数为(a,b),a设置x轴角度,b设置y轴角度
为(a,b,c,d),则a,b设置x轴角度,c,d设置y轴角度 - resample:重采样方式,NEAREST,BILINEAR,BICUBIC
# ==================05 RandomAffine=============
flag = True
# flag = False
if flag:
x_img = Image.open("face.jpg")
x_img_handled = transforms.RandomAffine(30,shear=(30,50))(x_img)
plt.imshow(x_img_handled)
plt.show()
- RandomErasing
RandomErasing(p=0.5, scale=(0.02,0.33),ratio=(0.3,0.3),value=0,inplace=False)
功能:对图像进行随机遮挡
- p:概率值,执行该操作的概率
- scale:遮挡区域的面积
- ratio:遮挡区域的长宽比
- value:设置遮挡区域的像素值,(R,G,B) OR (GRAY)
# ==================06 RandomErasing=============
flag = True
# flag = False
if flag:
x_img = Image.open("face.jpg")
x_img = transforms.ToTensor()(x_img)
x_img_handled = transforms.RandomErasing(p=1,scale=(0.04,0.04),ratio=(0.3,3.3),value="random",inplace=False)(x_img)
x_img_handled = transform_invert(x_img_handled, transforms.ToTensor)
x_img_handled.show()
# plt.imshow(x_img_handled)
# plt.show()
- transforms.Lambda
用户自定义lambda方法
transforms.Lambda(lambd)
lambd: lambda匿名函数
transforms.TenCrop(200, vertical_flip=True),
transforms.Lambda(lambda crops: torch.stack([transforms.Totensor()(crop) for crop in crops]))
transforms
- transforms.RandomChoice
从一系列transforms方法中随机挑选一个
transforms.RandomChoice([transforms1,transforms2,transforms3]) - transforms.RandomApply
依概率执行一组transforms操作
transforms.RandomApply([transforms1,transforms2,transforms3], p=0.5) - transforms.RandomOrder
对一组transforms操作打乱顺序
transforms.RandomOrder([transforms1,transforms2,transforms3
自定义transforms
自定义transforms要素:
- 仅接收一个参数,返回一个参数
- 注意上下游的输出与输入(上一个的输出是下一个输入)
class Compose(object):
def __call__(self, img):
for t in self.transforms:
img = t(img)
return img
设置多个参数(通过类方法):
class YourTransforms(object):
def __init__(self,...):
...
def __call__(self,img):
...
return img
椒盐噪声
脉冲噪声,随机出现的白点或者黑点,白点成为盐噪声,黑色为椒噪声
信噪比(Signal-Noise Rate,SNR)是衡量噪声的比例,图像中为图像像素的占比
add salt pepper noise
基本结构
class AddPepperNoise(object):
def __init__(self,snr,p):
self.snr = snr
self.p = p
def __call__(self,img):
...
# 具体实现过程
...
return img
class Compose(object):
def __call__(self,img):
for t in self.transforms:
img = t(img)
return img
from PIL import Image
from torchvision import transforms
import cv2
from common_tools import transform_invert
import random
import numpy as np
snr = 0.9
img = Image.open("face.jpg")
imgdeld = np.array(img).copy()
h, w, c = imgdeld.shape
signal_pic = snr
noise_pic = 1-snr
mask = np.random.choice((0,1,2),size=(h,w,1),p=[signal_pic,noise_pic/2.,noise_pic/2])
mask = np.repeat(mask, c, axis=2)
imgdeld[mask == 1] =255
imgdeld[mask == 2] =0
masked = Image.fromarray(mask.astype('uint8')).convert('RGB')
imgdeld=Image.fromarray(imgdeld.astype('uint8')).convert('RGB')
imgdeld.show()
# print(mask.shape)
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