数据增强（python）

数据增强（python）"/>

数据增强（python）

'''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 matrixM = cv2.getRotationMatrix2D((w/2,h/2), angle, scale)#rotateimage = 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 darkerfor 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 brighterfor 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.shapeheight=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]=0else:SP_NoiseImg[randR,randG,randB]=255return 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_Noiseimgdef 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_pathfor 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_pathfor 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:continuesplit = 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)