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：

1. imgaug 地址
2. 官方文档
3. 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：

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：

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：

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：

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

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）：

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：

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：

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：

1. 读取图像和json文件信息
2. 增强操作
3. 保存到本地
4. 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()

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