数据集不够怎么办？
——数据增强
代码：
'''
这是图片数据增强的代码，可以对图片实现：
1. 尺寸放大缩小
2. 随机裁剪
3. 变形
4. 旋转（任意角度，如45°，90°，180°，270°）
5. 翻转（水平翻转，垂直翻转）
6. 明亮度改变（变亮，变暗）
7. 像素平移（往一个方向平移像素，空出部分自动填补黑色）
8. 添加噪声（椒盐噪声，高斯噪声）
'''
import os
import cv2
import numpy as np
# import tensorflow as tf
import random as rd
import matplotlib
matplotlib.use('TkAgg')
from matplotlib import pyplot as plt
'''
缩放
'''
# 放大缩小
def Scale(image, scale):
return cv2.resize(image,(500,500),fx=scale,fy=scale,interpolation=cv2.INTER_LINEAR)
'''
裁剪
'''
def crop(image, min_ratio=0.6, max_ratio=1.0):
h, w = image.shape[:2]
ratio = rd.random()
scale = min_ratio + ratio * (max_ratio - min_ratio)
new_h = int(h*scale)
new_w = int(w*scale)
y = np.random.randint(0, h - new_h)
x = np.random.randint(0, w - new_w)
image = image[y:y+new_h, x:x+new_w, :]
return image
# #随机裁剪
# def crop(image):
# x,y,z = image[:]
# return tf.random_crop(image,[x*rd.random,y*rd.random,z])
'''
变形
'''
def change(image):
x,y = image.shape[:2]
pts1 = np.float32([[50,50], [200,50], [50,200]])
pts2 = np.float32([[10,100], [200,50], [100,250]])
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(image, M,(y,x),borderValue=(255,255,255))
return dst
'''
翻转
'''
# 水平翻转
def Horizontal(image):
return cv2.flip(image,1,dst=None) #水平镜像
# 垂直翻转
def Vertical(image):
return cv2.flip(image,0,dst=None) #垂直镜像
# 旋转，R可控制图片放大缩小
def Rotate(image, angle=15, scale=0.9):
w = image.shape[1]
h = image.shape[0]
#rotate matrix
M = cv2.getRotationMatrix2D((w/2,h/2), angle, scale)
#rotate
image = cv2.warpAffine(image,M,(w,h))
return image
'''
明亮度
'''
# 变暗
def Darker(image,percetage=0.9):
image_copy = image.copy()
w = image.shape[1]
h = image.shape[0]
#get darker
for xi in range(0,w):
for xj in range(0,h):
image_copy[xj,xi,0] = int(image[xj,xi,0]*percetage)
image_copy[xj,xi,1] = int(image[xj,xi,1]*percetage)
image_copy[xj,xi,2] = int(image[xj,xi,2]*percetage)
return image_copy
# 明亮
def Brighter(image, percetage=1.1):
image_copy = image.copy()
w = image.shape[1]
h = image.shape[0]
#get brighter
for xi in range(0,w):
for xj in range(0,h):
image_copy[xj,xi,0] = np.clip(int(image[xj,xi,0]*percetage),a_max=255,a_min=0)
image_copy[xj,xi,1] = np.clip(int(image[xj,xi,1]*percetage),a_max=255,a_min=0)
image_copy[xj,xi,2] = np.clip(int(image[xj,xi,2]*percetage),a_max=255,a_min=0)
return image_copy
# 平移
def Move(img,x,y):
img_info=img.shape
height=img_info[0]
width=img_info[1]
mat_translation=np.float32([[1,0,x],[0,1,y]]) #变换矩阵：设置平移变换所需的计算矩阵：2行3列
#[[1,0,20],[0,1,50]] 表示平移变换：其中x表示水平方向上的平移距离，y表示竖直方向上的平移距离。
dst=cv2.warpAffine(img,mat_translation,(width,height)) #变换函数
return dst
'''
增加噪声
'''
# 椒盐噪声
def SaltAndPepper(src,percetage):
SP_NoiseImg=src.copy()
SP_NoiseNum=int(percetage*src.shape[0]*src.shape[1])
for i in range(SP_NoiseNum):
randR=np.random.randint(0,src.shape[0]-1)
randG=np.random.randint(0,src.shape[1]-1)
randB=np.random.randint(0,3)
if np.random.randint(0,1)==0:
SP_NoiseImg[randR,randG,randB]=0
else:
SP_NoiseImg[randR,randG,randB]=255
return SP_NoiseImg
# 高斯噪声
def GaussianNoise(image,percetage):
G_Noiseimg = image.copy()
w = image.shape[1]
h = image.shape[0]
G_NoiseNum=int(percetage*image.shape[0]*image.shape[1])
for i in range(G_NoiseNum):
temp_x = np.random.randint(0,h)
temp_y = np.random.randint(0,w)
G_Noiseimg[temp_x][temp_y][np.random.randint(3)] = np.random.randn(1)[0]
return G_Noiseimg
def Blur(img):
blur = cv2.GaussianBlur(img, (7, 7), 1.5)
# # cv2.GaussianBlur(图像，卷积核，标准差）
return blur
# 单图增强
def TestOnePic():
test_jpg_loc = r"data/daisy/1.jpg"
test_jpg = cv2.imread(test_jpg_loc)
cv2.imshow("Show Img", test_jpg)
# cv2.waitKey(0)
img1 = Blur(test_jpg)
cv2.imshow("Img 1", img1)
# cv2.waitKey(0)
# img2 = GaussianNoise(test_jpg,0.01)
# cv2.imshow("Img 2", img2)
cv2.waitKey(0)
# 多图/文件夹增强
def TestOneDir():
root_path = "data/daisy"
save_path = root_path
for a, b, c in os.walk(root_path):
for file_i in c:
file_i_path = os.path.join(a, file_i)
print(file_i_path)
img_i = cv2.imread(file_i_path)
# img_scale = Scale(img_i,1.5)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_scale.jpg"), img_scale)
# img_horizontal = Horizontal(img_i)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_horizontal.jpg"), img_horizontal)
#
# img_vertical = Vertical(img_i)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_vertical.jpg"), img_vertical)
#
# img_rotate = Rotate(img_i,90)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate90.jpg"), img_rotate)
#
# img_rotate = Rotate(img_i, 180)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate180.jpg"), img_rotate)
#
# img_rotate = Rotate(img_i, 270)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate270.jpg"), img_rotate)
#
# img_move = Move(img_i,15,15)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_move.jpg"), img_move)
#
# img_darker = Darker(img_i)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_darker.jpg"), img_darker)
#
# img_brighter = Brighter(img_i)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_brighter.jpg"), img_brighter)
#
# img_blur = Blur(img_i)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_blur.jpg"), img_blur)
#
# img_salt = SaltAndPepper(img_i,0.05)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_salt.jpg"), img_salt)
# img_salt = GaussianNoise(img_i,0.05)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_GaussianNoise.jpg"), img_salt)
# 多图/文件夹增强
def AllData(root_path):
#root_path = "data/"
save_loc = root_path
for a,b,c in os.walk(root_path):
for file_i in c:
file_i_path = os.path.join(a,file_i)
#print(file_i_path)
if '.DS_Store' in file_i_path:
continue
split = os.path.split(file_i_path)
#print('split',split)
dir_loc = os.path.split(split[0])[1]
#print('dir_loc',dir_loc)
save_path = os.path.join(save_loc,dir_loc)
#查看保存文件地址，缺失文件夹需手动创建。
print('save_path',save_path)
img_i = cv2.imread(file_i_path)
# img_scale = Scale(img_i,1.5)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_scale.jpg"), img_scale)
# img_crop = crop(img_i)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_crop.jpg"), img_crop)
# img_change = change(img_i)
# cv2.imwrite(os.path.join(save_path,file_i[:-4] + "_change.jpg"),img_change)
# img_horizontal = Horizontal(img_i)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_horizontal.jpg"), img_horizontal)
# #
# img_vertical = Vertical(img_i)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_vertical.jpg"), img_vertical)
# #
# img_rotate = Rotate(img_i, 90)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate90.jpg"), img_rotate)
# #
# img_rotate = Rotate(img_i, 180)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate180.jpg"), img_rotate)
# #
# img_rotate = Rotate(img_i, 270)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_rotate270.jpg"), img_rotate)
# #
# img_move = Move(img_i, 15, 15)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_move.jpg"), img_move)
# #
# img_darker = Darker(img_i)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_darker.jpg"), img_darker)
# #
# img_brighter = Brighter(img_i)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_brighter.jpg"), img_brighter)
# #
# img_blur = Blur(img_i)
# cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_blur.jpg"), img_blur)
# #
img_salt = SaltAndPepper(img_i, 0.05)
cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_salt.jpg"), img_salt)
img_salt = GaussianNoise(img_i,0.1)
cv2.imwrite(os.path.join(save_path, file_i[:-4] + "_GaussianNoise.jpg"), img_salt)
if __name__ == "__main__":
# TestOneDir()
# TestOnePic()
root_path = "/Users/alanchris/Desktop/pic"
AllData(root_path)
