对于本地化进行图像的增强，大家都是非常好操作的。但是，对于标注信息一起增强，还是稍微有一些难度的，麻烦很多。

我是遇到一个数据集非常少的任务，只有40张图。就直接标记了去训练，发现几乎不拟合，当然这里使用的是yolo v8，而不是UNet。

于是，先本地化给增强到50倍数据集，然后再去训练，说不定是个好的方法。这里采用的就是imgaug的开源库，学习参考如下：

imgaug 地址
官方文档
Augment Polygons

一、单张实验下

1、首先，是采用labelme标注的一张图像，如下，是用labelme打开时候看到的内容：

在这里插入图片描述

用imgaug查看原图和标记内容，如下：

import imageio
import imgaug as ia
import json
import numpy as np
from imgaug.augmentables.polys import Polygon, PolygonsOnImageclass LabelJson(object):def __init__(self, abs_path=None) -> None:super().__init__()self.abs_path = abs_pathself.read()def read(self):with open(self.abs_path, 'r', encoding='utf-8') as f:lj = json.load(f)self.wh = [lj.get('imageWidth'), lj.get('imageHeight')]shapes = lj.get('shapes')self.cls = [i.get('label') for i in shapes]  # '1305' if i.get('label') == '14' elsepoints = [i.get('points') for i in shapes]points = [np.array(i, dtype=np.int32).reshape((-1, 2)) for i in points]self.loc = pointsself.box = [[j[:, 0].min(), j[:, 1].min(), j[:, 0].max(), j[:, 1].max()] for j in points]self.img_name = lj.get('imagePath')self.is_pos = bool(self.cls)return selfimg_path = r"F:\tmp\png/catDog.jpg"
json_path = r'F:\tmp\png\catDog.json'
image = imageio.imread(img_path)
json_info = LabelJson(json_path)print(image.shape)ia.imshow(image)image_polys = np.copy(image)
for point in json_info.loc:meerkat = Polygon(point)image_polys = meerkat.draw_on_image(image_polys, alpha_face=0.2, size_points=7)ia.imshow(image_polys)

显示的图片内容：

在这里插入图片描述

2、开始进行第一次数据增强，不改变形状，加入高斯噪声等等操作

#  let's convert our polygons to an PolygonsOnImage instance:
psoi = ia.PolygonsOnImage([Polygon(point) for point in json_info.loc],shape=image.shape)import imgaug.augmenters as iaa
ia.seed(1)# add aug
aug = iaa.Sequential([iaa.AdditiveGaussianNoise(scale=10),iaa.CoarseDropout(0.1, size_px=8),iaa.AddToHueAndSaturation((-50, 50))
])image_aug, psoi_aug = aug(image=image, polygons=psoi)
ia.imshow(psoi_aug.draw_on_image(image_aug, alpha_face=0.2, size_points=7))

下面就是增强后的结果：

在这里插入图片描述

3、接下来进行第二次的增强，这次加入形状和位置调整。

# add aug 2
aug = iaa.Sequential([iaa.Affine(translate_percent={"x": 0.2, "y": 0.1}),iaa.Fliplr(1.0)
])image_aug, psoi_aug = aug(image=image, polygons=psoi)
ia.imshow(psoi_aug.draw_on_image(image_aug, alpha_face=0.2, size_points=7))

展示如下所示：

在这里插入图片描述

4、增强后的坐标，转化为labelme可以读取的json形式保存，再打开查看，这也是大多数我们需要做的目的。如下操作：

import base64, os
from PIL import Image
import io
import json
import cv2
def base64encode_img(src_image):# src_image = Image.open(image_path)src_image = Image.fromarray(cv2.cvtColor(src_image, cv2.COLOR_BGR2RGB))output_buffer = io.BytesIO()src_image.save(output_buffer, format='JPEG')byte_data = output_buffer.getvalue()base64_str = base64.b64encode(byte_data).decode('utf-8')return base64_strdef savejson(points_list, clses_list, img_tmp, filename, save_dir):A = dict()listbigoption = []for cls, points in zip(clses_list, points_list):listobject = dict()listobject['points'] = pointslistobject['line_color'] = 'null'listobject['label'] = clslistobject['fill_color'] = 'null'listbigoption.append(listobject)A['imageData'] = base64encode_img(img_tmp)A['imagePath'] = filenameA['shapes'] = listbigoptionA['flags'] = {}print(A)saveJson_path = os.path.join(save_dir, 'json')os.makedirs(saveJson_path, exist_ok=True)suffix = os.path.splitext(filename)[-1]with open(saveJson_path + "/" + filename.replace(suffix, ".json"), 'w', encoding='utf-8') as f:json.dump(A, f, indent=2, ensure_ascii=False)image_aug, psoi_aug = aug(image=image, polygons=psoi)
print(psoi_aug)points_list = []
for pos in psoi_aug:print('pos:', pos)points = [list(xy.astype(np.float64)) for xy in pos]print('points:', points)points_list.append(points)image_augRGB = cv2.cvtColor(image_aug, cv2.COLOR_BGR2RGB)
savejson(points_list, json_info.cls, image_augRGB, os.path.basename(img_path), r'F:\tmp\png\aug')
# ia.imshow(psoi_aug.draw_on_image(image_aug, alpha_face=0.2, size_points=7))
ia.imshow(image_aug)
cv2.imwrite(os.path.join(r'F:\tmp\png\aug', os.path.basename(img_path)), image_augRGB)

直接保存的是BGR，需要转到RGB进行保存

在这里插入图片描述
增强后，存储到本地，用labelme再次打开查看，如下（暂未做小于0的截断处理）：

在这里插入图片描述

加入截断操作，如下：

image_aug, psoi_aug = aug(image=image, polygons=psoi)
print(psoi_aug)
nw, nh, _ = image_aug.shapepoints_list = []
for pos in psoi_aug:print('pos:', pos)points = [list(xy.astype(np.float64)) for xy in pos]print('points:', points)for p in points:if p[0]<0:p[0] = 0elif p[0]>nh:p[0] = nhif p[1]<0:p[1] = 0elif p[1]>nw:p[1] = nwpoints_list.append(points)

展示如下：

在这里插入图片描述

数据增强，随机的产生2*4个图像，用于展示：

ia.seed(2)
aug = iaa.Sequential([iaa.OneOf([iaa.AdditiveGaussianNoise(scale=10),iaa.GaussianBlur(sigma=(0.0, 3.0)),]),iaa.Affine(rotate=(-20, 20), translate_percent=(-0.2, 0.2), scale=(0.8, 1.2),mode=["constant", "edge"], cval=0),  # 放射变换iaa.OneOf([iaa.Fliplr(0.5),    # 水平翻转iaa.Flipud(0.5),    # 上下翻转]),iaa.OneOf([iaa.GammaContrast((0.5, 2.0)),iaa.LinearContrast((0.8, 1.2), per_channel=0.5),]),iaa.AddToHueAndSaturation((-20, 20)),   # 通过随机值增加或减少色调和饱和度。iaa.Sometimes(0.75, iaa.Snowflakes())
])images_polys_aug = []
for _ in range(2*4):image_aug, psoi_aug = aug(image=image, polygons=psoi)image_polys_aug = psoi_aug.draw_on_image(image_aug, alpha_face=0.2, size_points=11)images_polys_aug.append(ia.imresize_single_image(image_polys_aug, 0.5))ia.imshow(ia.draw_grid(images_polys_aug, cols=2))

展示如下：
在这里插入图片描述

二、汇总

最后，做下汇总：

目标：根据采用labelme标注的pylygons标记信息，批量对图像和标注信息同时增强变换
步骤：

读取图像和json文件信息
增强操作
保存到本地
再次采用labelme，查看生成的结果，是否正常

代码如下：

import imageio
import imgaug as ia
import numpy as np
from imgaug.augmentables.polys import Polygon, PolygonsOnImage
import imgaug.augmenters as iaaclass LabelJson(object):def __init__(self, abs_path=None) -> None:super().__init__()self.abs_path = abs_pathself.read()def read(self):with open(self.abs_path, 'r', encoding='utf-8') as f:lj = json.load(f)self.wh = [lj.get('imageWidth'), lj.get('imageHeight')]shapes = lj.get('shapes')self.cls = [i.get('label') for i in shapes]  # '1305' if i.get('label') == '14' elsepoints = [i.get('points') for i in shapes]points = [np.array(i, dtype=np.int32).reshape((-1, 2)) for i in points]self.loc = pointsself.box = [[j[:, 0].min(), j[:, 1].min(), j[:, 0].max(), j[:, 1].max()] for j in points]self.img_name = lj.get('imagePath')self.is_pos = bool(self.cls)return selfimport base64, os
from PIL import Image
import io
import json
import cv2
def base64encode_img(src_image):# src_image = Image.open(image_path)src_image = Image.fromarray(cv2.cvtColor(src_image, cv2.COLOR_BGR2RGB))output_buffer = io.BytesIO()src_image.save(output_buffer, format='JPEG')byte_data = output_buffer.getvalue()base64_str = base64.b64encode(byte_data).decode('utf-8')return base64_strdef savejson(points_list, clses_list, img_tmp, filename, save_dir):A = dict()listbigoption = []for cls, points in zip(clses_list, points_list):listobject = dict()listobject['points'] = pointslistobject['line_color'] = 'null'listobject['label'] = clslistobject['fill_color'] = 'null'listbigoption.append(listobject)A['imageData'] = base64encode_img(img_tmp)A['imagePath'] = filenameA['shapes'] = listbigoptionA['flags'] = {}suffix = os.path.splitext(filename)[-1]with open(save_dir + "/" + filename.replace(suffix, ".json"), 'w', encoding='utf-8') as f:json.dump(A, f, indent=2, ensure_ascii=False)def saveJsonImg_main(image_aug, psoi_aug, cls_list, img_path, num, save_dir):nw, nh, _ = image_aug.shapepoints_list = []for pos in psoi_aug:points = [list(xy.astype(np.float64)) for xy in pos]for p in points:if p[0] < 0:p[0] = 0elif p[0] > nh:p[0] = nhif p[1] < 0:p[1] = 0elif p[1] > nw:p[1] = nwpoints_list.append(points)image_augRGB = cv2.cvtColor(image_aug, cv2.COLOR_BGR2RGB)savejson(points_list, cls_list, image_augRGB, str(num)+'_'+os.path.basename(img_path), save_dir)# ia.imshow(psoi_aug.draw_on_image(image_aug, alpha_face=0.2, size_points=7))cv2.imwrite(os.path.join(save_dir, str(num)+'_'+os.path.basename(img_path)), image_augRGB)ia.seed(2)
aug = iaa.Sequential([iaa.OneOf([iaa.SaltAndPepper(0.01),iaa.AdditiveGaussianNoise(scale=5),iaa.GaussianBlur(sigma=(0.0, 3.0)),]),iaa.OneOf([iaa.Affine(rotate=(-20, 20), translate_percent=(-0.2, 0.2), scale=(0.8, 1.2),mode=["constant", "edge"], cval=0),  # 放射变换iaa.Affine(scale={"x": (0.5, 1.5), "y": (0.5, 1.5)}),iaa.Affine(translate_px={"x": (-20, 20), "y": (-20, 20)}),iaa.TranslateX(px=(-20, 20)),iaa.Rotate((-45, 45))]),iaa.OneOf([iaa.Fliplr(0.7),    # 水平翻转iaa.Flipud(0.7),    # 上下翻转]),iaa.OneOf([iaa.GammaContrast((0.5, 2.0)),iaa.LinearContrast((0.8, 1.2), per_channel=0.5),iaa.WithBrightnessChannels(iaa.Add((-50, 50))),     # Augmenter to apply child augmenters to brightness-related image channels.iaa.AddToHueAndSaturation((-20, 20)),   # 通过随机值增加或减少色调和饱和度。]),iaa.Sometimes(0.75, iaa.Snowflakes())
])def main():img_dir = r"./images"json_dir = r'./label'save_dir = r'./aug'for file in os.listdir(img_dir):img_path = os.path.join(img_dir, file)json_path = os.path.join(json_dir, file.replace('.jpg', '.json'))# read image and get json infoimage = imageio.imread(img_path)json_info = LabelJson(json_path)#  let's convert our polygons to an PolygonsOnImage instance:psoi = ia.PolygonsOnImage([Polygon(point) for point in json_info.loc],shape=image.shape)# one labelme image aug to 50 imagefor num in range(50):# augimage_aug, psoi_aug = aug(image=image, polygons=psoi)# save json and imagesaveJsonImg_main(image_aug, psoi_aug, json_info.cls, img_path, num, save_dir)if __name__ == '__main__':main()

至此结束，感兴趣的可以赶紧去学习下。如果恰好对你也有帮助，点个赞👍，再走啦。

imgaug Augment Polygons 对标注图片和polygons的数据增强

一、单张实验下

二、汇总

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