深度学习数据增强方法 - 包括（亮度增强、对比度增强、旋转映射图像、翻转图像、仿射变化扩展图像、误切变化扩展图像、HSV 数据增强）七种增强方法 - 每种扩展一种，实现 7 倍扩展）+ 图像缩放代码 - 批量生产
最编程
2024-06-26 10:43:04
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import math
import cv2
import numpy
from PIL import ImageEnhance
import os
import numpy as np
from PIL import Image
def brightnessEnhancement(root_path,img_name):#亮度增强
image = Image.open(os.path.join(root_path, img_name))
enh_bri = ImageEnhance.Brightness(image)
# brightness = 1.1+0.4*np.random.random()#取值范围1.1-1.5
brightness = 1.5
image_brightened = enh_bri.enhance(brightness)
return image_brightened
def contrastEnhancement(root_path, img_name): # 对比度增强
image = Image.open(os.path.join(root_path, img_name))
enh_con = ImageEnhance.Contrast(image)
# contrast = 1.1+0.4*np.random.random()#取值范围1.1-1.5
contrast = 1.5
image_contrasted = enh_con.enhance(contrast)
return image_contrasted
def rotation(root_path, img_name):
img = Image.open(os.path.join(root_path, img_name))
random_angle = np.random.randint(-2, 2)*90
if random_angle==0:
rotation_img = img.rotate(-90) #旋转角度
else:
rotation_img = img.rotate( random_angle) # 旋转角度
# rotation_img.save(os.path.join(root_path,img_name.split('.')[0] + '_rotation.jpg'))
return rotation_img
def flip(root_path,img_name): #翻转图像
img = Image.open(os.path.join(root_path, img_name))
filp_img = img.transpose(Image.FLIP_LEFT_RIGHT)
# filp_img.save(os.path.join(root_path,img_name.split('.')[0] + '_flip.jpg'))
return filp_img
def fangshe_bianhuan(root_path,img_name): #仿射变化扩充图像
img = Image.open(os.path.join(root_path, img_name))
img = cv2.cvtColor(numpy.asarray(img) , cv2.COLOR_RGB2BGR)
h, w = img.shape[0], img.shape[1]
m = cv2.getRotationMatrix2D(center=(w // 2, h // 2), angle=-30, scale=0.5)
r_img = cv2.warpAffine(src=img, M=m, dsize=(w, h), borderValue=(0, 0, 0))
r_img = Image.fromarray(cv2.cvtColor(r_img, cv2.COLOR_BGR2RGB))
return r_img
def cuoqie(root_path,img_name): #错切变化扩充图像
img = Image.open(os.path.join(root_path, img_name))
img = cv2.cvtColor(numpy.asarray(img) , cv2.COLOR_RGB2BGR)
h, w = img.shape[0], img.shape[1]
origin_coord = np.array([[0, 0, 1], [w, 0, 1], [w, h, 1], [0, h, 1]])
theta = 30 # shear角度
tan = math.tan(math.radians(theta))
# x方向错切
m = np.eye(3)
m[0, 1] = tan
shear_coord = (m @ origin_coord.T).T.astype(np.int)
shear_img = cv2.warpAffine(src=img, M=m[:2],
dsize=(np.max(shear_coord[:, 0]), np.max(shear_coord[:, 1])),
borderValue=(0, 0, 0))
c_img = Image.fromarray(cv2.cvtColor(shear_img, cv2.COLOR_BGR2RGB))
return c_img
def hsv(root_path,img_name):#HSV数据增强
h_gain , s_gain , v_gain = 0.5 , 0.5 , 0.5
img = Image.open(os.path.join(root_path, img_name))
img = cv2.cvtColor(numpy.asarray(img) , cv2.COLOR_RGB2BGR)
r = np.random.uniform(-1, 1, 3) * [h_gain, s_gain, v_gain] + 1 # random gains
hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
dtype = img.dtype # uint8
x = np.arange(0, 256, dtype=np.int16)
lut_hue = ((x * r[0]) % 180).astype(dtype)
lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
aug_img = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)
aug_img = Image.fromarray(cv2.cvtColor(aug_img, cv2.COLOR_BGR2RGB))
return aug_img
def createImage(imageDir,saveDir):#主函数，7种数据扩充方式，每种扩充一张
i=0
for name in os.listdir(imageDir):
i=i+1
saveName="cesun"+str(i)+".jpg"
saveImage=contrastEnhancement(imageDir,name)
saveImage.save(os.path.join(saveDir,saveName))
saveName1 = "flip" + str(i) + ".jpg"
saveImage1 = flip(imageDir,name)
saveImage1.save(os.path.join(saveDir, saveName1))
saveName2 = "brightnessE" + str(i) + ".jpg"
saveImage2 = brightnessEnhancement(imageDir, name)
saveImage2.save(os.path.join(saveDir, saveName2))
saveName3 = "rotate" + str(i) + ".jpg"
saveImage = rotation(imageDir, name)
saveImage.save(os.path.join(saveDir, saveName3))
saveName4 = "fangshe" + str(i) + ".jpg"
saveImage = fangshe_bianhuan(imageDir, name)
saveImage.save(os.path.join(saveDir, saveName4))
saveName5 = "cuoqie" + str(i) + ".jpg"
saveImage = cuoqie(imageDir, name)
saveImage.save(os.path.join(saveDir, saveName5))
saveName6 = "hsv" + str(i) + ".jpg"
saveImage = hsv(imageDir, name)
saveImage.save(os.path.join(saveDir, saveName6))
imageDir="jpg" #要改变的图片的路径文件夹 在当前文件夹下，建立文件夹即可
saveDir="kuochong" #数据增强生成图片的路径文件夹
print('文件的初始文件夹为：' + imageDir)
print('----------------------------------------')
print('文件的转换后存入的文件夹为：' + saveDir)
print('----------------------------------------')
print('开始转换')
print('----------------------------------------')
createImage(imageDir,saveDir)
print('----------------------------------------')
print("数据扩充完成")