论文：https://arxiv.org/pdf/1708.04896v2.pdf
代码：https://github.com/zhunzhong07/Random-Erasing
1. 论文核心
训练模型时，随机选取一个图片的矩形区域，将这个矩形区域的像素值用随机值或者平均像素值代替，产生局部遮挡的效果。该数据增强可以与随机切除、随机翻转等数据增强结合起来使用。
2. 代码实现
代码中：
Sl、Sh分别是需要随机擦除的矩形面积大小上下阈值；
r1是限制矩形长宽比r的阈值 ；
import random
import math
import cv2
import numpy as np
class RandomErasing:
"""Random erasing the an rectangle region in Image.
Class that performs Random Erasing in Random Erasing Data Augmentation by Zhong et al.
Args:
sl: min erasing area region
sh: max erasing area region
r1: min aspect ratio range of earsing region
p: probability of performing random erasing
"""
def __init__(self, p=0.5, sl=0.02, sh=0.4, r1=0.3):
self.p = p
self.s = (sl, sh)
self.r = (r1, 1/r1)
def __call__(self, img):
"""
perform random erasing
Args:
img: opencv numpy array in form of [w, h, c] range
from [0, 255]
Returns:
erased img
"""
assert len(img.shape) == 3, 'image should be a 3 dimension numpy array'
if random.random() > self.p:
return img
else:
while True:
Se = random.uniform(*self.s) * img.shape[0] * img.shape[1]
re = random.uniform(*self.r)
He = int(round(math.sqrt(Se * re)))
We = int(round(math.sqrt(Se / re)))
xe = random.randint(0, img.shape[1])
ye = random.randint(0, img.shape[0])
if xe + We <= img.shape[1] and ye + He <= img.shape[0]:
img[ye : ye + He, xe : xe + We, :] = np.random.randint(low=0, high=255, size=(He, We, img.shape[2]))
return img
if __name__ == "__main__":
img = cv2.imread("test.jpg")
RE = RandomErasing(p=1)
for i in range(20):
img1 = RE(img.copy())
cv2.imshow("test", img1)
cv2.waitKey(1000) 3. 实验效果
从实验可以看出，Random Erasing 可以降低识别的错误率，与Random flipping、Random cropping结合起来用效果更好。
从实验可以看出，用上Random erasing可以提高目标检测的map。