'''
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. 旋转（任意角度，如45°，90°，180°，270°）
3. 翻转（水平翻转，垂直翻转）
4. 明亮度改变（变亮，变暗）
5. 像素平移（往一个方向平移像素，空出部分自动填补黑色）
6. 添加噪声（椒盐噪声，高斯噪声）
'''
import os
import cv2
import numpy as np
'''
缩放
'''
# 放大缩小
def Scale(image, scale):
return cv2.resize(image,None,fx=scale,fy=scale,interpolation=cv2.INTER_LINEAR)
'''
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=0.05):
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=0.05):
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
# def TestOnePic():
# test_jpg_loc = r"data/realism/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)
# def TestOneDir():
# root_path = "data/realism"
# 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)
def AllData(rootpath):
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)
split = os.path.split(file_i_path)
dir_loc = os.path.split(split[0])[1]
save_path = os.path.join(save_loc,dir_loc)
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)
if __name__ == "__main__":
# TestOneDir()
# TestOnePic()
root_path = "data/"
AllData(root_path)