Python imgaug库 安装与使用 （图片加模糊光雨雪雾等特效） |
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Python imgaug库 安装与使用 （图片加模糊光雨雪雾等特效）
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imgaug库
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
图片放入input main.py # ###################源代码#################### # !usr/bin/python # -*- coding: utf-8 -*- import cv2 from imgaug import augmenters as iaa import os # Sometimes(0.5, ...) 所有情况的 50% 中应用给定的增强器 # e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image. sometimes = lambda aug: iaa.Sometimes(0.5, aug) # 定义一组变换方法. seq = iaa.Sequential([ # 选择0到5种方法做变换 iaa.SomeOf((0, 5), [ iaa.Fliplr(0.5), # 对50%的图片进行水平镜像翻转 iaa.Flipud(0.5), # 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 20 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=(0, 1.0), n_segments=(20, 200) ) ), # Blur each image with varying strength using # gaussian blur (sigma between 0 and 3.0), # average/uniform blur (kernel size between 2x2 and 7x7) # median blur (kernel size between 3x3 and 11x11). iaa.OneOf([ iaa.GaussianBlur((0, 3.0)), iaa.AverageBlur(k=(2, 7)), iaa.MedianBlur(k=(3, 11)), ]), # Sharpen each image, overlay the result with the original # image using an alpha between 0 (no sharpening) and 1 # (full sharpening effect). iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), # Same as sharpen, but for an embossing effect. iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # Add gaussian noise to some images. # In 50% of these cases, the noise is randomly sampled per # channel and pixel. # In the other 50% of all cases it is sampled once per # pixel (i.e. brightness change). iaa.AdditiveGaussianNoise( loc=0, scale=(0.0, 0.05 * 255) ), # Invert each image's chanell with 5% probability. # This sets each pixel value v to 255-v. iaa.Invert(0.05, per_channel=True), # invert color channels # Add a value of -10 to 10 to each pixel. iaa.Add((-10, 10), per_channel=0.5), # Add random values between -40 and 40 to images, with each value being sampled per pixel: iaa.AddElementwise((-40, 40)), # Change brightness of images (50-150% of original value). iaa.Multiply((0.5, 1.5)), # Multiply each pixel with a random value between 0.5 and 1.5. iaa.MultiplyElementwise((0.5, 1.5)), # Improve or worsen the contrast of images. iaa.ContrastNormalization((0.5, 2.0)), iaa.imgcorruptlike.Saturate(severity=3), ], # 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 = cv2.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10次 for count in range(10): images_aug = seq.augment_images(imglist) for index in range(len(images_aug)): filename = str(count) + str(index) + '.jpg' # 保存图片 cv2.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 cv2 from imgaug import augmenters as iaa import os # Sometimes(0.5, ...) applies the given augmenter in 50% of all cases, # e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image. # sometimes = lambda aug: iaa.Sometimes(0.5, aug) # 定义一组变换方法. seq = iaa.Sequential([ iaa.MotionBlur(k=15), # 运动模糊 # iaa.Clouds(), # 云雾 # iaa.imgcorruptlike.Fog(severity=1), # 多雾/霜 # iaa.imgcorruptlike.Snow(severity=2), # 下雨、大雪 # iaa.Rain(drop_size=(0.10, 0.20), speed=(0.2, 0.3)), # 雨 # iaa.Rain(speed=(0.3, 0.5)), # 雨 # iaa.Snowflakes(flake_size=(0.6, 0.7), speed=(0.02, 0.03)), # 雪点 # iaa.imgcorruptlike.Spatter(severity=2), # 溅 123水滴、45泥 # iaa.contrast.LinearContrast((0.5, 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 # iaa.FastSnowyLandscape(lightness_threshold=(100, 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=3, 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 = cv2.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1次 for count in range(1): 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]) cv2.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 cv2 from imgaug import augmenters as iaa import os # sometimes = lambda aug: iaa.Sometimes(0.5, aug) # 所有情况的 50% 中应用给定的增强器 seq = iaa.Sequential([ # 选择2到3种方法做变换 iaa.SomeOf((2, 3), [ iaa.imgcorruptlike.MotionBlur(severity=(1, 2)), # 运动模糊 # iaa.Clouds(), # 云雾 iaa.imgcorruptlike.Fog(severity=1), # 多雾/霜 # iaa.imgcorruptlike.Snow(severity=2), # 下雨、大雪 iaa.Rain(drop_size=(0.10, 0.15), speed=(0.1, 0.2)), # 雨 iaa.Snowflakes(flake_size=(0.1, 0.4), speed=(0.01, 0.03)), # 雪点 # iaa.FastSnowyLandscape(lightness_threshold=(100, 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=5), # 溅 123水滴、45泥 # 对比度 亮度 饱和度 选其一 iaa.SomeOf((1, 1), [ iaa.imgaug.augmenters.contrast.LinearContrast((0.5, 2.0), per_channel=0.5), # 对比度变为原来的一半或者二倍 iaa.imgcorruptlike.Brightness(severity=(1, 2)), # 亮度增加 iaa.imgcorruptlike.Saturate(severity=(1, 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 = cv2.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(1): 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]) cv2.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.jpgRename0001.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)[0] filetype = os.path.splitext(file)[1] # zfill() 方法返回指定长度的字符串，原字符串右对齐，前面填充0 Newdir = os.path.join(path, filename.zfill(5) + 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.jpgRename1.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 = 0 # 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 + 1 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 input示例 00005.jpg
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output示例 00001.jpg 
output示例 00005.jpg
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