深度学习 数据集增强-代码

数据集增强-代码

"""
增强数据集
"""
from PIL import Image
from PIL import ImageEnhance
import os
import cv2
import numpy as np


def flip(root_path, img_name):  # 翻转图像
    img = Image.open(os.path.join(root_path, img_name))
    filp_img = img.transpose(Image.FLIP_LEFT_RIGHT)
    # filp_img.save(os.path.join(root_path,img_name.split(\'.\')[0] + \'_flip.jpg\'))
    return filp_img


def rotation(root_path, img_name):
    img = Image.open(os.path.join(root_path, img_name))
    rotation_img = img.rotate(20)  # 旋转角度
    # rotation_img.save(os.path.join(root_path,img_name.split(\'.\')[0] + \'_rotation.jpg\'))
    return rotation_img


def randomColor(root_path, img_name):  # 随机颜色
    """
    对图像进行颜色抖动
    :param image: PIL的图像image
    :return: 有颜色色差的图像image
    """
    image = Image.open(os.path.join(root_path, img_name))
    random_factor = np.random.randint(0, 31) / 10.  # 随机因子
    color_image = ImageEnhance.Color(image).enhance(random_factor)  # 调整图像的饱和度
    random_factor = np.random.randint(10, 21) / 10.  # 随机因子
    brightness_image = ImageEnhance.Brightness(color_image).enhance(
        random_factor)  # 调整图像的亮度
    random_factor = np.random.randint(10, 21) / 10.  # 随机因子
    contrast_image = ImageEnhance.Contrast(brightness_image).enhance(
        random_factor)  # 调整图像对比度
    random_factor = np.random.randint(0, 31) / 10.  # 随机因子
    return ImageEnhance.Sharpness(contrast_image).enhance(
        random_factor)  # 调整图像锐度


def contrastEnhancement(root_path, img_name):  # 对比度增强
    image = Image.open(os.path.join(root_path, img_name))
    enh_con = ImageEnhance.Contrast(image)
    contrast = 1.5
    image_contrasted = enh_con.enhance(contrast)
    return image_contrasted


def brightnessEnhancement(root_path, img_name):  # 亮度增强
    image = Image.open(os.path.join(root_path, img_name))
    enh_bri = ImageEnhance.Brightness(image)
    brightness = 1.5
    image_brightened = enh_bri.enhance(brightness)
    return image_brightened


def colorEnhancement(root_path, img_name):  # 颜色增强
    image = Image.open(os.path.join(root_path, img_name))
    enh_col = ImageEnhance.Color(image)
    color = 1.5
    image_colored = enh_col.enhance(color)
    return image_colored


def main():
    imageDir = r""  # 要改变的图片的路径文件夹
    maskDir = r""
    saveDir = r""  # 要保存的图片的路径文件夹
    
    # 数据集-image增强
    # for name in os.listdir(imageDir):
    #     saveName = name[:-4] + "id.jpg"
    #     image = Image.open(os.path.join(imageDir, name))
    #     image.save(os.path.join(saveDir, \'id\', saveName))
    #
    #     saveName = name[:-4] + "be.jpg"
    #     saveImage = brightnessEnhancement(imageDir, name)
    #     saveImage.save(os.path.join(saveDir, \'be\', saveName))
    #
    #     saveName = name[:-4] + "fl.jpg"
    #     saveImage = flip(imageDir, name)
    #     saveImage.save(os.path.join(saveDir,\'fl\', saveName))
    #
    #     saveName = name[:-4] + "ro.jpg"
    #     saveImage = rotation(imageDir, name)
    #     saveImage.save(os.path.join(saveDir,\'ro\', saveName))
    #
    #     saveName = name[:-4] + "co.jpg"
    #     saveImage = colorEnhancement(imageDir, name)
    #     saveImage.save(os.path.join(saveDir, \'co\', saveName))
    #
    #     saveName = name[:-4] + "ra.jpg"
    #     saveImage = randomColor(imageDir, name)
    #     saveImage.save(os.path.join(saveDir, \'ra\', saveName))
        
    # 数据集-mask增强
    for part in os.listdir(maskDir):
        part_path = os.path.join(maskDir, part)
        for name in os.listdir(part_path):
            
            # 原图
            addname = \'yuan\'
            image = Image.open(os.path.join(part_path, name))
            image.save(os.path.join(saveDir, part, addname, name))
        
            # 翻转图
            addname = \'flip\'
            saveImage = flip(part_path, name)
            saveImage.save(os.path.join(saveDir, part, addname, name))
        
            # 转动角度
            addname = \'rotation\'
            saveImage = rotation(part_path, name)
            saveImage.save(os.path.join(saveDir, part, addname, name))
        

if __name__ == \'__main__\':
    main()