【python】批量实现图像数据增强（扩增）并复制xml标签文件

迷南。 2023-01-16 12:27 192阅读 0赞

**数据扩增的概念**

数据扩增是指不实际增加原始数据，只是对原始数据做一些变换，从而创造出更多的数据。

**数据扩增的目的**

数据扩增的目的是增加数据量、丰富数据多样性、提高模型的泛化能力。

**数据扩增的方法**

单样本扩增包括：图像翻转、图像旋转、图像扭曲、图像仿射变换、图像缩放、图像压缩、图像随机crop、图像随机padding、图像对比度调整、亮度调整、色度调整、饱和度调整、色彩抖动、添加噪声、图像模糊、图像区域随机擦除、风格转换、生成对抗网络生成等。

多样本扩增包括：SamplePairing和mixup，二者思路很相近

**本文扩增的方法**

校验噪声、高斯噪声、调节亮度。

# 代码 #

# -*- coding: utf-8 -*-
    
    import cv2
    import numpy as np
    import os.path
    import shutil
    
    
    # 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 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
    
    
    # 亮度
    def brightness(image, percetage):
        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
    
    
    if __name__ == '__main__':
        # 图片文件夹路径
        input_jpg = '../dataset/JPEGImages'
        input_xml = '../dataset/Annotations'
        output_jpg = '../augmentation_data/JPEGImages'
        output_xml = '../augmentation_data/Annotations'
    
        for img_name in os.listdir(input_jpg):
            name = img_name.split('.')[0]
            print(name)
            print(img_name)
            img_path = os.path.join(input_jpg, img_name)
            img = cv2.imread(img_path)
            xml_src_path = os.path.join(input_xml, name + '.xml')
            xml_dst_path = os.path.join(output_xml, name)
    
            # 增加噪声
            img_gauss = addGaussianNoise(img, 0.3)
            cv2.imwrite(os.path.join(output_jpg, name + '_noise.jpg'), img_gauss)
            shutil.copyfile(xml_src_path, xml_dst_path + '_noise.xml')
            print("Save " + os.path.join(output_jpg, name + '_noise.jpg') + " Successfully!")
    
            # 变暗
            img_darker = brightness(img, 0.6)
            cv2.imwrite(os.path.join(output_jpg, name + '_darker.jpg'), img_darker)
            shutil.copyfile(xml_src_path, xml_dst_path + '_darker.xml')
            print("Save " + os.path.join(output_jpg, name + '_darker.jpg') + " Successfully!")
            
            # 变亮
            img_brighter = brightness(img, 1.5)
            cv2.imwrite(os.path.join(output_jpg, name + '_brighter.jpg'), img_brighter)
            shutil.copyfile(xml_src_path, xml_dst_path + '_brighter.xml')
            print("Save " + os.path.join(output_jpg, name + '_brighter.jpg') + " Successfully!")
    
            blur = cv2.GaussianBlur(img, (7, 7), 1.5)
            # cv2.GaussianBlur(图像，卷积核，标准差）
            cv2.imwrite(os.path.join(output_jpg, name + '_blur.jpg'), blur)
            shutil.copyfile(xml_src_path, xml_dst_path + '_blur.xml')
            print("Save " + os.path.join(output_jpg, name + '_blur.jpg') + " Successfully!")