Python imgaug库安装与使用教程(图片加模糊光雨雪雾等特效)

简介

imgaug：机器学习实验中的图像增强库，特别是卷积神经网络。支持以多种不同方式增强图像、关键点/地标、边界框、热图和分割图。

安装

在anaconda prompt里进行

 pip install imgaug

看了几篇文章，出错的话可以先安装依赖库shapely

Overview

特效

官网网址

在这里插入图片描述

Project 结构

在这里插入图片描述

程序

图片放入input

在这里插入图片描述

参考的源代码（来源于网络）

main.py

 
# ###################源代码####################
# !usr/bin/python
# -*- coding: utf- -*-
import cv
from imgaug import augmenters as iaa
import os
 
# Sometimes(.5, ...) 所有情况的 50% 中应用给定的增强器
# e.g. Sometimes(.5, GaussianBlur(0.3)) would blur roughly every second image.
sometimes = lambda aug: iaa.Sometimes(.5, aug)
 
# 定义一组变换方法.
seq = iaa.Sequential([
 
    # 选择到5种方法做变换
    iaa.SomeOf((, 5),
               [
                   iaa.Fliplr(.5),  # 对50%的图片进行水平镜像翻转
                   iaa.Flipud(.5),  # 对50%的图片进行垂直镜像翻转
 
                   # superpixel representation 将一些图像转换为它们的超像素表示，每张图像采样 到 200 个超像素，但不要用它们的平均值替换所有超像素，只替换其中的一些（p_replace）。
                   sometimes(
                       iaa.Superpixels(
                           p_replace=(, 1.0),
                           n_segments=(, 200)
                       )
                   ),
 
                   # Blur each image with varying strength using
                   # gaussian blur (sigma between and 3.0),
                   # average/uniform blur (kernel size betweenx2 and 7x7)
                   # median blur (kernel size betweenx3 and 11x11).
                   iaa.OneOf([
                       iaa.GaussianBlur((, 3.0)),
                       iaa.AverageBlur(k=(, 7)),
                       iaa.MedianBlur(k=(, 11)),
                   ]),
 
                   # Sharpen each image, overlay the result with the original
                   # image using an alpha between (no sharpening) and 1
                   # (full sharpening effect).
                   iaa.Sharpen(alpha=(, 1.0), lightness=(0.75, 1.5)),
 
                   # Same as sharpen, but for an embossing effect.
                   iaa.Emboss(alpha=(, 1.0), strength=(0, 2.0)),
 
                   # Add gaussian noise to some images.
                   # In% of these cases, the noise is randomly sampled per
                   # channel and pixel.
                   # In the other% of all cases it is sampled once per
                   # pixel (i.e. brightness change).
                   iaa.AdditiveGaussianNoise(
                       loc=, scale=(0.0, 0.05 * 255)
                   ),
 
                   # Invert each image's chanell with% probability.
                   # This sets each pixel value v to-v.
                   iaa.Invert(.05, per_channel=True),  # invert color channels
 
                   # Add a value of - to 10 to each pixel.
                   iaa.Add((-, 10), per_channel=0.5),
 
                   # Add random values between - and 40 to images, with each value being sampled per pixel:
                   iaa.AddElementwise((-, 40)),
 
                   # Change brightness of images (-150% of original value).
                   iaa.Multiply((.5, 1.5)),
 
                   # Multiply each pixel with a random value between.5 and 1.5.
                   iaa.MultiplyElementwise((.5, 1.5)),
 
                   # Improve or worsen the contrast of images.
                   iaa.ContrastNormalization((.5, 2.0)),
                   iaa.imgcorruptlike.Saturate(severity=),
 
               ],
               # do all of the above augmentations in random order
               random_order=True
               )
 
], random_order=True)  # apply augmenters in random order
 
# 图片文件相关路径
path = './input/'
savedpath = './output/'
 
imglist = []
filelist = os.listdir(path)
 
# 遍历要增强的文件夹，把所有的图片保存在imglist中
for item in filelist:
    img = cv.imread(path + item)
    # print('item is ',item)
    # print('img is ',img)
    # images = load_batch(batch_idx)
    imglist.append(img)
# print('imglist is ' ,imglist)
print('all the picture have been appent to imglist')
 
# 对文件夹中的图片进行增强操作，循环次
for count in range():
    images_aug = seq.augment_images(imglist)
    for index in range(len(images_aug)):
        filename = str(count) + str(index) + '.jpg'
        # 保存图片
        cv.imwrite(savedpath + filename, images_aug[index])
        print('image of count%s index%s has been writen' % (count, index))

简易变换试效果

test01.py

 
# ##############简易变换#################
 
# https://imgaug.readthedocs.io/en/latest/source/overview_of_augmenters.html
import cv
from imgaug import augmenters as iaa
import os
 
# Sometimes(.5, ...) applies the given augmenter in 50% of all cases,
# e.g. Sometimes(.5, GaussianBlur(0.3)) would blur roughly every second image.
# sometimes = lambda aug: iaa.Sometimes(.5, aug)
 
# 定义一组变换方法.
seq = iaa.Sequential([
    iaa.MotionBlur(k=),  # 运动模糊
    # iaa.Clouds(),  # 云雾
    # iaa.imgcorruptlike.Fog(severity=),  # 多雾/霜
    # iaa.imgcorruptlike.Snow(severity=),  # 下雨、大雪
    # iaa.Rain(drop_size=(.10, 0.20), speed=(0.2, 0.3)),  # 雨
    # iaa.Rain(speed=(.3, 0.5)),  # 雨
    # iaa.Snowflakes(flake_size=(.6, 0.7), speed=(0.02, 0.03)), # 雪点
    # iaa.imgcorruptlike.Spatter(severity=),  # 溅 123水滴、45泥
    # iaa.contrast.LinearContrast((.5, 2.0), per_channel=0.5),# 对比度变为原来的一半或者二倍
    # iaa.imgcorruptlike.Brightness(severity=),  # 亮度增加
    # iaa.imgcorruptlike.Saturate(severity=),  # 色彩饱和度
    # iaa.FastSnowyLandscape(lightness_threshold=(, 255),lightness_multiplier=(1.5, 2.0)), # 雪地   亮度阈值是从 uniform(100, 255)（每张图像）和来自 uniform(1.5, 2.0)（每张图像）的乘数采样的。
    # iaa.Cartoon(blur_ksize=, segmentation_size=1.0, saturation=2.0, edge_prevalence=1.0), # 卡通
 
])
 
# 图片文件相关路径
path = './input/'
savedpath = './output_show/'
 
imglist = []
filelist = os.listdir(path)
 
# 遍历要增强的文件夹，把所有的图片保存在imglist中
for item in filelist:
    img = cv.imread(path + item)
    # print('item is ',item)
    # print('img is ',img)
    # images = load_batch(batch_idx)
    imglist.append(img)
# print('imglist is ' ,imglist)
print('all the picture have been appent to imglist')
 
# 对文件夹中的图片进行增强操作，循环次
for count in range():
    images_aug = seq.augment_images(imglist)
    for index in range(len(images_aug)):
        # filename = str(count) + str(index) + '.jpg'
        # 保存图片
        filename = str(filelist[index])
        cv.imwrite(savedpath + filename, images_aug[index])
        print('image of count%s index%s has been writen' % (count, index))

使用模糊光雨雪雾

运动模糊+雨雪雾天气 2-3种

对比度亮度饱和度选其一

my_augmentation.py

 
import cv
from imgaug import augmenters as iaa
import os
 
# sometimes = lambda aug: iaa.Sometimes(.5, aug)   # 所有情况的 50% 中应用给定的增强器
 
seq = iaa.Sequential([
    # 选择到3种方法做变换
    iaa.SomeOf((, 3),
               [
                   iaa.imgcorruptlike.MotionBlur(severity=(, 2)),  # 运动模糊
                   # iaa.Clouds(),  # 云雾
                   iaa.imgcorruptlike.Fog(severity=),  # 多雾/霜
                   # iaa.imgcorruptlike.Snow(severity=),  # 下雨、大雪
                   iaa.Rain(drop_size=(.10, 0.15), speed=(0.1, 0.2)),  # 雨
                   iaa.Snowflakes(flake_size=(.1, 0.4), speed=(0.01, 0.03)), # 雪点
                   # iaa.FastSnowyLandscape(lightness_threshold=(, 255),lightness_multiplier=(1.5, 2.0)), # 雪地   亮度阈值是从 uniform(100, 255)（每张图像）和来自 uniform(1.5, 2.0)（每张图像）的乘数采样的。 这似乎产生了良好而多样的结果。
                   # iaa.imgcorruptlike.Spatter(severity=),  # 溅 123水滴、45泥
 
                   # 对比度 亮度 饱和度 选其一
                   iaa.SomeOf((, 1),
                       [
                           iaa.imgaug.augmenters.contrast.LinearContrast((.5, 2.0), per_channel=0.5),  # 对比度变为原来的一半或者二倍
                           iaa.imgcorruptlike.Brightness(severity=(, 2)),  # 亮度增加
                           iaa.imgcorruptlike.Saturate(severity=(, 3)),  # 色彩饱和度
                       ]
                   )
               ],
               # 随机顺序运行augmentations
               random_order=True
               )
], random_order=True)  # 随机运行augmenters数量
 
# 图片文件相关路径
path = './input/'
savedpath = './output/'
 
imglist = []
filelist = os.listdir(path)
 
# 遍历要增强的文件夹，把所有的图片保存在imglist中
for item in filelist:
    img = cv.imread(path + item)
    # print('item is ',item)
    # print('img is ',img)
    # images = load_batch(batch_idx)
    imglist.append(img)
# print('imglist is ' ,imglist)
print('all the picture have been appent to imglist')
 
 
for count in range():
    images_aug = seq.augment_images(imglist)
    for index in range(len(images_aug)):
        # 保存图片 文件名和源文件相同
        filename = str(filelist[index])
        cv.imwrite(savedpath + filename, images_aug[index])
        print('image of count%s index%s has been writen' % (count, index))

else

对input里的原图像重命名：00001.jpg或者1.jpg

重命名00001.jpg

Rename0001.py

 
# ###################文件重命名#################
 
import os
import re
import sys
 
path = r"./input"
filelist = os.listdir(path)
filetype = '.jpg'
for file in filelist:
    print(file)
for file in filelist:
    Olddir = os.path.join(path, file)
    print(Olddir)
    if os.path.isdir(Olddir):
        continue
 
    # os.path.splitext("path")：分离文件名与扩展名
    filename = os.path.splitext(file)[]
    filetype = os.path.splitext(file)[]
 
    # zfill() 方法返回指定长度的字符串，原字符串右对齐，前面填充
    Newdir = os.path.join(path, filename.zfill() + filetype)  # 数字5是定义为5位数，可随意修改需要的
    os.rename(Olddir, Newdir)

重命名1.jpg

Rename1.py

 
# ###################文件重命名################
import os
class BatchRename():
 
    def __init__(self):
        self.path = './input/'  # 图片的路径
 
    def rename(self):
        filelist = os.listdir(self.path)
        filelist.sort()
        total_num = len(filelist)  # 获取文件中有多少图片
        i =  # 文件命名从哪里开始（即命名从哪里开始）
        for item in filelist:
            if item.endswith('.png'):
                src = os.path.join(self.path, item)
                dst = os.path.join(os.path.abspath(self.path), str(i) + '.png')
 
                try:
                    os.rename(src, dst)
                    print('converting %s to %s ...' % (src, dst))
                    i = i +
                except Exception as e:
                    print(e)
                    print('rename dir fail\r\n')
 
        print('total %d to rename & converted %d jpgs' % (total_num, i))
 
 
if __name__ == '__main__':
    demo = BatchRename()  # 创建对象
    demo.rename()  # 调用对象的方法

效果图

input示例 00001.jpg

请添加图片描述

output示例 00001.jpg

在这里插入图片描述

input示例 00005.jpg

在这里插入图片描述

output示例 00005.jpg

在这里插入图片描述

	# ###################源代码####################
	# !usr/bin/python
	# -- coding: utf- --
	import cv
	from imgaug import augmenters as iaa
	import os

	# Sometimes(.5, ...) 所有情况的 50% 中应用给定的增强器
	# e.g. Sometimes(.5, GaussianBlur(0.3)) would blur roughly every second image.
	sometimes = lambda aug: iaa.Sometimes(.5, aug)

	# 定义一组变换方法.
	seq = iaa.Sequential([

	# 选择到5种方法做变换
	iaa.SomeOf((, 5),
	[
	iaa.Fliplr(.5), # 对50%的图片进行水平镜像翻转
	iaa.Flipud(.5), # 对50%的图片进行垂直镜像翻转

	# superpixel representation 将一些图像转换为它们的超像素表示，每张图像采样到 200 个超像素，但不要用它们的平均值替换所有超像素，只替换其中的一些（p_replace）。
	sometimes(
	iaa.Superpixels(
	p_replace=(, 1.0),
	n_segments=(, 200)
	)
	),

	# Blur each image with varying strength using
	# gaussian blur (sigma between and 3.0),
	# average/uniform blur (kernel size betweenx2 and 7x7)
	# median blur (kernel size betweenx3 and 11x11).
	iaa.OneOf([
	iaa.GaussianBlur((, 3.0)),
	iaa.AverageBlur(k=(, 7)),
	iaa.MedianBlur(k=(, 11)),
	]),

	# Sharpen each image, overlay the result with the original
	# image using an alpha between (no sharpening) and 1
	# (full sharpening effect).
	iaa.Sharpen(alpha=(, 1.0), lightness=(0.75, 1.5)),

	# Same as sharpen, but for an embossing effect.
	iaa.Emboss(alpha=(, 1.0), strength=(0, 2.0)),

	# Add gaussian noise to some images.
	# In% of these cases, the noise is randomly sampled per
	# channel and pixel.
	# In the other% of all cases it is sampled once per
	# pixel (i.e. brightness change).
	iaa.AdditiveGaussianNoise(
	loc=, scale=(0.0, 0.05 * 255)
	),

	# Invert each image's chanell with% probability.
	# This sets each pixel value v to-v.
	iaa.Invert(.05, per_channel=True), # invert color channels

	# Add a value of - to 10 to each pixel.
	iaa.Add((-, 10), per_channel=0.5),

	# Add random values between - and 40 to images, with each value being sampled per pixel:
	iaa.AddElementwise((-, 40)),

	# Change brightness of images (-150% of original value).
	iaa.Multiply((.5, 1.5)),

	# Multiply each pixel with a random value between.5 and 1.5.
	iaa.MultiplyElementwise((.5, 1.5)),

	# Improve or worsen the contrast of images.
	iaa.ContrastNormalization((.5, 2.0)),
	iaa.imgcorruptlike.Saturate(severity=),

	],
	# do all of the above augmentations in random order
	random_order=True
	)

	], random_order=True) # apply augmenters in random order

	# 图片文件相关路径
	path = './input/'
	savedpath = './output/'

	imglist = []
	filelist = os.listdir(path)

	# 遍历要增强的文件夹，把所有的图片保存在imglist中
	for item in filelist:
	img = cv.imread(path + item)
	# print('item is ',item)
	# print('img is ',img)
	# images = load_batch(batch_idx)
	imglist.append(img)
	# print('imglist is ' ,imglist)
	print('all the picture have been appent to imglist')

	# 对文件夹中的图片进行增强操作，循环次
	for count in range():
	images_aug = seq.augment_images(imglist)
	for index in range(len(images_aug)):
	filename = str(count) + str(index) + '.jpg'
	# 保存图片
	cv.imwrite(savedpath + filename, images_aug[index])
	print('image of count%s index%s has been writen' % (count, index))

	# ##############简易变换#################

	# https://imgaug.readthedocs.io/en/latest/source/overview_of_augmenters.html
	import cv
	from imgaug import augmenters as iaa
	import os

	# Sometimes(.5, ...) applies the given augmenter in 50% of all cases,
	# e.g. Sometimes(.5, GaussianBlur(0.3)) would blur roughly every second image.
	# sometimes = lambda aug: iaa.Sometimes(.5, aug)

	# 定义一组变换方法.
	seq = iaa.Sequential([
	iaa.MotionBlur(k=), # 运动模糊
	# iaa.Clouds(), # 云雾
	# iaa.imgcorruptlike.Fog(severity=), # 多雾/霜
	# iaa.imgcorruptlike.Snow(severity=), # 下雨、大雪
	# iaa.Rain(drop_size=(.10, 0.20), speed=(0.2, 0.3)), # 雨
	# iaa.Rain(speed=(.3, 0.5)), # 雨
	# iaa.Snowflakes(flake_size=(.6, 0.7), speed=(0.02, 0.03)), # 雪点
	# iaa.imgcorruptlike.Spatter(severity=), # 溅 123水滴、45泥
	# iaa.contrast.LinearContrast((.5, 2.0), per_channel=0.5),# 对比度变为原来的一半或者二倍
	# iaa.imgcorruptlike.Brightness(severity=), # 亮度增加
	# iaa.imgcorruptlike.Saturate(severity=), # 色彩饱和度
	# iaa.FastSnowyLandscape(lightness_threshold=(, 255),lightness_multiplier=(1.5, 2.0)), # 雪地亮度阈值是从 uniform(100, 255)（每张图像）和来自 uniform(1.5, 2.0)（每张图像）的乘数采样的。
	# iaa.Cartoon(blur_ksize=, segmentation_size=1.0, saturation=2.0, edge_prevalence=1.0), # 卡通

	])

	# 图片文件相关路径
	path = './input/'
	savedpath = './output_show/'

	imglist = []
	filelist = os.listdir(path)

	# 遍历要增强的文件夹，把所有的图片保存在imglist中
	for item in filelist:
	img = cv.imread(path + item)
	# print('item is ',item)
	# print('img is ',img)
	# images = load_batch(batch_idx)
	imglist.append(img)
	# print('imglist is ' ,imglist)
	print('all the picture have been appent to imglist')

	# 对文件夹中的图片进行增强操作，循环次
	for count in range():
	images_aug = seq.augment_images(imglist)
	for index in range(len(images_aug)):
	# filename = str(count) + str(index) + '.jpg'
	# 保存图片
	filename = str(filelist[index])
	cv.imwrite(savedpath + filename, images_aug[index])
	print('image of count%s index%s has been writen' % (count, index))

	# ###################文件重命名#################

	import os
	import re
	import sys

	path = r"./input"
	filelist = os.listdir(path)
	filetype = '.jpg'
	for file in filelist:
	print(file)
	for file in filelist:
	Olddir = os.path.join(path, file)
	print(Olddir)
	if os.path.isdir(Olddir):
	continue

	# os.path.splitext("path")：分离文件名与扩展名
	filename = os.path.splitext(file)[]
	filetype = os.path.splitext(file)[]

	# zfill() 方法返回指定长度的字符串，原字符串右对齐，前面填充
	Newdir = os.path.join(path, filename.zfill() + filetype) # 数字5是定义为5位数，可随意修改需要的
	os.rename(Olddir, Newdir)

	# ###################文件重命名################
	import os
	class BatchRename():

	def __init__(self):
	self.path = './input/' # 图片的路径

	def rename(self):
	filelist = os.listdir(self.path)
	filelist.sort()
	total_num = len(filelist) # 获取文件中有多少图片
	i = # 文件命名从哪里开始（即命名从哪里开始）
	for item in filelist:
	if item.endswith('.png'):
	src = os.path.join(self.path, item)
	dst = os.path.join(os.path.abspath(self.path), str(i) + '.png')

	try:
	os.rename(src, dst)
	print('converting %s to %s ...' % (src, dst))
	i = i +
	except Exception as e:
	print(e)
	print('rename dir fail\r\n')

	print('total %d to rename & converted %d jpgs' % (total_num, i))


	if __name__ == '__main__':
	demo = BatchRename() # 创建对象
	demo.rename() # 调用对象的方法

Python imgaug库安装与使用教程(图片加模糊光雨雪雾等特效)

目录

简介

安装

Overview

特效

Project 结构

程序

参考的源代码（来源于网络）

简易变换 试效果

使用 模糊光雨雪雾

else

重命名00001.jpg

重命名1.jpg

效果图

简易变换试效果

使用模糊光雨雪雾