imgaug成对图像数据增强（附代码）

男娘i 2023-10-03 11:57 25阅读 0赞

本文使用imgaug库集成了一些主要的成对图像增强方法，如图像翻转、旋转、平移、剪切等（共13中）。

### 1、增强效果部分 ###

翻转  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA_size_16_color_FFFFFF_t_70_pic_center]  
旋转  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA_size_16_color_FFFFFF_t_70_pic_center 1]  
平移  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA_size_16_color_FFFFFF_t_70]  
加噪声  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA_size_16_color_FFFFFF_t_70 1]  
剪切  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA_size_16_color_FFFFFF_t_70 2]

### 代码 ###

from imgaug import augmenters as iaa #引入数据增强的包
    import os
    import cv2
    from matplotlib import pyplot as plt
    import numpy as np
    from imgaug.augmentables.segmaps import SegmentationMapsOnImage
    
    def FlipHorizontalImage():
        """水平翻转"""
        sequential.append(iaa.Fliplr())
    
    def FlipVerticalImage():
        """垂直翻转"""
        sequential.append(iaa.Flipud())
    
    def AffineRotateImage(r=(-180,180)):
        """旋转：默认随机旋转(-180,180)"""
        sequential.append(iaa.Affine(rotate=r))
    
    def AffineTranslationImage(t=(-0.5,0.5)):
        """平移,默认平移(-50%,50%)"""
        sequential.append(iaa.Affine(translate_percent=t))
    
    def AffineScaleImage(s=(0.4,0.8)):
        """缩放,默认缩放(0.4倍,0.8倍)"""
        sequential.append(iaa.Affine(scale=s))
    
    def CropImage(p=(0, 0.5)):
        """剪切，默认剪切幅度(0,50%)"""
        sequential.append(iaa.Crop(percent=p))
    
    def AddGaussianNoiseImage(s=(0, 0.2*255)):
        """加入高斯噪声"""
        sequential.append(iaa.AdditiveGaussianNoise(scale=s))
    
    def FindMaxFile(results_file_list):
        """找到最大的文件"""
        flag = 1
        for file_name in results_file_list:
            if int(file_name[3:])>flag:
                flag = int(file_name[3:])
        return 'exp'+str(flag)
    
    def PiecewiseAffineImage(s=(0.05,0.15)):
        """对图像进行扭曲"""
        sequential.append(iaa.PiecewiseAffine(scale=s))
    
    def AffineShearImage(s=(-50, 50)):
        """进行剪切"""
        sequential.append(iaa.Affine(shear=s))
    
    def AddRungImage(i=4):
        """加横杠,每i条线加一个横杠"""
        def img_func(images, random_state, parents, hooks):
            for img in images:
                img[::i] = 0
            return images
        def keypoint_func(keypoints_on_images, random_state, parents, hooks):
            return keypoints_on_images
        
        sequential.append(iaa.Lambda(img_func, keypoint_func))
    
    def AddGaussianBlurImage(sigma=5.0):
        """高斯模糊"""
        sequential.append(iaa.GaussianBlur(sigma=(0.0, sigma)))
    
    def DropoutImage(p=0.2):
        """丢弃一些像素,p为丢弃比例"""
        sequential.append(iaa.Dropout(p=(0, p)))
    
    def AddAverageBlurImage(k=(5,5)):
        """平均模糊"""
        sequential.append(iaa.AverageBlur(k=k))
    
    if __name__ == '__main__':
        pwd = os.getcwd()
        file_path = os.path.join(pwd,'datasets','train') # 原图片存储路径
        file_name_list = os.listdir(file_path) # 该路径下所有的文件名字
        label_path = os.path.join(pwd,'datasets','label') # 标签数据
    
        # 创建数据增强保存的文件夹
        results_path = os.path.join(pwd,'results')
        results_file_list = os.listdir(results_path)
        if len(results_file_list) == 0:
            results_path = os.path.join(results_path, 'exp0')
            os.mkdir(os.path.join(results_path))
            os.mkdir(os.path.join(results_path,'train'))
            os.mkdir(os.path.join(results_path,'label'))
            os.mkdir(os.path.join(results_path, 'show'))
        else:
            file_name = FindMaxFile(results_file_list)
            num = int(file_name[3:])
            num += 1
            file_name = 'exp'+str(num)
            results_path = os.path.join(results_path, file_name)
            os.mkdir(results_path)
            os.mkdir(os.path.join(results_path, 'train'))
            os.mkdir(os.path.join(results_path, 'show'))
            os.mkdir(os.path.join(results_path, 'label'))
    
    
        image_result_list = [] # 放结果图片数据的集合
    
        sequential = iaa.Sequential([
        ], random_order=True)
    
        """翻转"""
        FlipHorizontalImage() # 水平翻转
        # FlipVerticalImage() # 垂直翻转
        """放射变换"""
        # AffineRotateImage() # 旋转
        # AffineTranslationImage() # 平移
        # AffineScaleImage() # 缩小
        # PiecewiseAffineImage() # 扭曲
        # AffineShearImage() # 剪切
        """裁剪"""
        # CropImage() # 裁剪
        """加噪声"""
        # AddGaussianNoiseImage() # 加入高斯噪声
        # AddRungImage() # 加横杠
        # DropoutImage() # 丢弃
        """模糊"""
        # AddGaussianBlurImage() # 加入高斯模糊
        # AddAverageBlurImage() # 平均模糊
    
        for file_name in file_name_list:
            img = cv2.imread(os.path.join(file_path, file_name))
            img_label = cv2.imread(os.path.join(label_path, file_name))
            img_label = SegmentationMapsOnImage(img_label, shape=img.shape)
    
            image_aug, label_aug = sequential(image=img, segmentation_maps=img_label)
            cv2.imwrite(os.path.join(results_path,'train',file_name), image_aug)
            cv2.imwrite(os.path.join(results_path, 'label', file_name), label_aug.get_arr_int())
    
            # BGR->RGB
            img_vstack = np.hstack((
                                               np.vstack((img[:,:,::-1], image_aug[:,:,::-1])),
                                               np.vstack((img_label.get_arr_int()[:, :, ::-1],
                                                          label_aug.get_arr_int()[:, :, ::-1]))
                                               ))
    
            cv2.imwrite(os.path.join(results_path, 'show', file_name), img_vstack[:,:,::-1])
            image_result_list.append(img_vstack)
    
        for i,img in enumerate(image_result_list):
            plt.imshow(img)
            plt.show()

### 使用方法 ###

目录结构  
![在这里插入图片描述][7dcb06435c764d7db39d96d4fe76d0ff.jpg]  
找到main.py中的这几行，你要使用哪些增强方法就不要注释那几行，例如要使用水平翻转+旋转+平移，就不要注释这几行

"""翻转"""
        FlipHorizontalImage() # 水平翻转
        # FlipVerticalImage() # 垂直翻转
        """放射变换"""
        AffineRotateImage() # 旋转
        AffineTranslationImage() # 平移
        # AffineScaleImage() # 缩小
        # PiecewiseAffineImage() # 扭曲
        # AffineShearImage() # 剪切
        """裁剪"""
        # CropImage() # 裁剪
        """加噪声"""
        # AddGaussianNoiseImage() # 加入高斯噪声
        # AddRungImage() # 加横杠
        # DropoutImage() # 丢弃
        """模糊"""
        # AddGaussianBlurImage() # 加入高斯模糊
        # AddAverageBlurImage() # 平均模糊

运行main.py，在results文件夹中看结果  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA_size_16_color_FFFFFF_t_70 3]

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/013852496df6457cb084cfcdf718ec42.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/358e8a2d91354a3ebb6b5d7d862ee5c2.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA==,size_16,color_FFFFFF,t_70
[7dcb06435c764d7db39d96d4fe76d0ff.jpg]: https://img-blog.csdnimg.cn/7dcb06435c764d7db39d96d4fe76d0ff.jpg
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA_size_16_color_FFFFFF_t_70 3]: https://img-blog.csdnimg.cn/f815f351611f4dbebb36766af32850c3.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2lpaWlpaWltcA==,size_16,color_FFFFFF,t_70