python图像数据增强包括图片旋转、翻转、模糊、增加噪声、亮度几种处理

import cv2 import numpy as np import os.path import copy 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 def addGaussianNoise(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 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 def brighter(image, percetage=1.5): 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 def rotate(image, angle, center=None, scale=1.0): (h, w) = image.shape[:2] # If no rotation center is specified, the center of the image is set as the rotation center if center is None: center = (w / 2, h / 2) m = cv2.getRotationMatrix2D(center, angle, scale) rotated = cv2.warpAffine(image, m, (w, h)) return rotated def flip(image): flipped_image = np.fliplr(image) return flipped_image file_dir = "img/" for img_name in os.listdir(file_dir): img_path = file_dir + img_name img = cv2.imread(img_path) # cv2.imshow("1",img) # cv2.waitKey(5000) # 旋转 rotated_90 = rotate(img, 90) cv2.imwrite(file_dir + img_name[0:-4] + '_r90.jpg', rotated_90) rotated_180 = rotate(img, 180) cv2.imwrite(file_dir + img_name[0:-4] + '_r180.jpg', rotated_180) for img_name in os.listdir(file_dir): img_path = file_dir + img_name img = cv2.imread(img_path) # 镜像 flipped_img = flip(img) cv2.imwrite(file_dir +img_name[0:-4] + '_fli.jpg', flipped_img) # 增加噪声 # img_salt = SaltAndPepper(img, 0.3) # cv2.imwrite(file_dir + img_name[0:7] + '_salt.jpg', img_salt) img_gauss = addGaussianNoise(img, 0.3) cv2.imwrite(file_dir + img_name[0:-4] + '_noise.jpg',img_gauss) #变亮、变暗 img_darker = darker(img) cv2.imwrite(file_dir + img_name[0:-4] + '_darker.jpg', img_darker) img_brighter = brighter(img) cv2.imwrite(file_dir + img_name[0:-4] + '_brighter.jpg', img_brighter) blur = cv2.GaussianBlur(img, (7, 7), 1.5) # cv2.GaussianBlur(图像，卷积核，标准差） cv2.imwrite(file_dir + img_name[0:-4] + '_blur.jpg',blur)