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我们将介绍如何用PIL库实现一些简单的图像增强方法。
[!NOTE] 初始化配置
import numpy as np
from PIL import Image, ImageOps, ImageEnhance
import warningswarnings.filterwarnings('ignore')
IMAGE_SIZE = 640
[!important] 辅助函数
主要用于控制增强幅度
def int_parameter(level, maxval): return int(level * maxval / 10) def float_parameter(level, maxval): return float(level) * maxval / 10.def sample_level(n): return np.random.uniform(low=0.1, high=n)level用于控制增强方法的数值强度，maxval一般取值为4，level是一个从均匀分布中采样的数值，这样让每次增强都具有随机性。
[!example] 增强方法
色彩反转
def invert(pil_img, _): return ImageOps.invert(pil_img)
镜像
def mirror(pil_img, _): return ImageOps.mirror(pil_img)
均衡化
def equalize(pil_img, _): return ImageOps.equalize(pil_img)
色彩分离
def posterize(pil_img, level): level = int_parameter(sample_level(level), 4) return ImageOps.posterize(pil_img, 4 - level)
旋转
def rotate(pil_img, level): degrees = int_parameter(sample_level(level), 30) if np.random.uniform() > 0.5: degrees = -degrees return pil_img.rotate(degrees, resample=Image.BILINEAR)
Solarize
def solarize(pil_img, level): level = int_parameter(sample_level(level), 256) return ImageOps.solarize(pil_img, 256 - level)
Shear_x
def shear_x(pil_img, level): level = float_parameter(sample_level(level), 0.3) if np.random.uniform() > 0.5: level = -level return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE), Image.AFFINE, (1, level, 0, 0, 1, 0), resample=Image.BILINEAR)
Shear_y
def shear_y(pil_img, level): level = float_parameter(sample_level(level), 0.3) if np.random.uniform() > 0.5: level = -level return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE), Image.AFFINE, (1, 0, 0, level, 1, 0), resample=Image.BILINEAR)
Translate_x
def translate_x(pil_img, level): level = int_parameter(sample_level(level), IMAGE_SIZE / 3) if np.random.random() > 0.5: level = -level return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE), Image.AFFINE, (1, 0, level, 0, 1, 0), resample=Image.BILINEAR)
Translate_y
def translate_y(pil_img, level): level = int_parameter(sample_level(level), IMAGE_SIZE / 3) if np.random.random() > 0.5: level = -level return pil_img.transform((IMAGE_SIZE, IMAGE_SIZE), Image.AFFINE, (1, 0, 0, 0, 1, level), resample=Image.BILINEAR)
Color
def color(pil_img, level): level = float_parameter(sample_level(level), 1.8) + 0.1 return ImageEnhance.Color(pil_img).enhance(level)
Contrast
def contrast(pil_img, level): level = float_parameter(sample_level(level), 1.8) + 0.1 return ImageEnhance.Contrast(pil_img).enhance(level)
AutoContrast
def autocontrast(pil_img, level): level = float_parameter(sample_level(level), 10) return ImageOps.autocontrast(pil_img, 10 - level)
Brightness
def brightness(pil_img, level): level = float_parameter(sample_level(level), 1.8) + 0.1 return ImageEnhance.Brightness(pil_img).enhance(level)
Sharpness
def sharpness(pil_img, level): level = float_parameter(sample_level(level), 1.8) + 0.1 return ImageEnhance.Sharpness(pil_img).enhance(level)
[!success] 使用案例
对于这样一张原图：
augmentations_all = { "autocontrast":autocontrast, "equalize":equalize, "posterize":posterize, "rotate":rotate, "solarize":solarize, "shear_x":shear_x, "shear_y":shear_y, "translate_x":translate_x, "translate_y":translate_y, "color":color, "contrast":contrast, "brightness":brightness, "sharpness":sharpness, "mirror":mirror, "invert":invert } import matplotlib.pyplot as plt img=Image.open(r"C:\Users\Administrator\Downloads\result1.5\result\original_resized\class0\0.jpg") def draw(plt,idx,img,title): plt.subplot(int("24"+str(idx))) plt.imshow(img) plt.xticks([]) plt.yticks([]) plt.title(title) plt.figure(figsize=(20,16))
for idx,(k,v) in enumerate(augmentations_all.items()): draw(plt,(idx)%8+1,v(img.copy(),1),k) if idx!=0 and idx % 7 == 0: plt.show() plt.figure(figsize=(20,16))
