前言

本文主要内容是将COCO格式以及VOC格式的DAGM2007数据集进行扩增

以下代码如果有不规范或错误的地方欢迎大家批评指正！

COCO以及VOC格式的DAGM2007数据集制作方法可以看上一篇博客：
DAGM2007数据集格式转换（DAGM转换为COCO格式、COCO格式转换为VOC格式）

主要包含五种扩增方法：水平翻转、垂直翻转、随机裁剪、随机旋转、随机四张图片拼接

随机裁剪与随机旋转方法主要参考：六种常见的数据扩增方式

随机旋转方法有一丢丢的小缺陷：
上面链接的大佬的思路是：图像旋转后，边界框也会随之旋转，取旋转后边界框四条边的中间点为新生成边界框的四个顶点。
该思路有一点小问题，在旋转一些细长的目标时，新得到的边界框可能会出现不完全包含目标的情况。
因此在本文中采用了opencv中的boundingRect方法得到新的边界框，该方法得到的新边界框虽然能完全包含目标，但是有些边界框会包含过多的背景。
欢迎大家一起讨论改进方法！

COCO格式的数据集扩增

在上一篇博客中因为图像名字有重复，所以为这些图像赋了新名字，这些名字就是编号。在这里也需要为扩增后的新图像赋新名字，新名字的计算方法是在编号上加一个数字，五个方法分别加5000、10000、15000、20000、25000。因为DAGM2007数据集只有一千多张图象，所以不用担心名字起冲突，但如果用于其他数据集的扩增记得修改这些数字，防止名字起冲突，而且默认是保存为.PNG格式的，这里也需要修改。

代码

AugmentMethodClass.py

import os
import cv2
import random
import numpy as npclass AugmentMethod:def __init__(self, coco=None, root=None, new_root=None, model='train'):self.coco = cocoself.root = rootself.model = modelself.new_root = new_rootself.mosaic_annot_id = 0# horizion为True时水平翻转# horizion为False时垂直翻转# new_img_info: {'id': , 'width': , 'height': , 'file_name': }# new_annot_info: [{'id': , 'image_id': , ...}, ...]def flip_image(self, img_info, annot_infos, horizion=True):img_path = os.path.join(self.root, self.model, img_info['file_name'])img, (h, w) = self.get_img(img_path)# process imageif horizion:new_img = img[:, ::-1]else:new_img = img[::-1, :]# 修改图片以及标注信息new_img_info = img_info.copy()if horizion:new_img_info['id'] = new_img_info['id'] + 5000else:new_img_info['id'] = new_img_info['id'] + 10000new_img_info['file_name'] = ''.join('0' for i in range(8 - len(str(new_img_info['id'])))) \+ str(new_img_info['id']) + '.PNG'new_annot_infos = []for annot_info in annot_infos:new_annot_info = annot_info.copy()if horizion:new_annot_info['id'] = new_annot_info['id'] + 5000else:new_annot_info['id'] = new_annot_info['id'] + 10000new_annot_info['image_id'] = new_img_info['id']# 若没有下面的这个copy()# 当我尝试改变new_annot_info['bbox']中的值时，annot_info['bbox']中的值也将会改变new_annot_info['bbox'] = annot_info['bbox'].copy()if horizion:new_annot_info['bbox'][0] = w - new_annot_info['bbox'][0] - new_annot_info['bbox'][2]else:new_annot_info['bbox'][1] = h - new_annot_info['bbox'][1] - new_annot_info['bbox'][3]new_annot_infos.append(new_annot_info)# 将新图片写入到新路径new_path = os.path.join(self.new_root, self.model, new_img_info['file_name'])if not os.path.exists(new_path):cv2.imwrite(new_path, new_img)return new_img_info, new_annot_infos# 随机切割def crop_image(self, img_info, annot_infos, random_seed):random.seed(random_seed)img_path = os.path.join(self.root, self.model, img_info['file_name'])img, (h, w) = self.get_img(img_path)# 对图像进行处理# 计算出图像四条边到bbox四条边的距离d_to_left, d_to_right, d_to_top, d_to_bottom = [], [], [], []for annot_info in annot_infos:d_to_left.append(annot_info['bbox'][0])d_to_right.append(w - (annot_info['bbox'][0] + annot_info['bbox'][2]))d_to_top.append(annot_info['bbox'][1])d_to_bottom.append(h - (annot_info['bbox'][1] + annot_info['bbox'][3]))# 确定裁剪后的图片范围range_crop_left = int(random.uniform(0, min(d_to_left)))range_crop_right = w - int(random.uniform(0, min(d_to_right)))range_crop_top = int(random.uniform(0, min(d_to_top)))range_crop_bottom = h - int(random.uniform(0, min(d_to_bottom)))# 将裁剪后的图像填充为原图像大小（0填充）# np.array[行索引, 列索引]crop_img = img[range_crop_top: range_crop_bottom, range_crop_left: range_crop_right]new_img = np.zeros((h, w)).astype(img.dtype)new_img[0: (range_crop_bottom - range_crop_top), 0: (range_crop_right - range_crop_left)] = crop_img# 修改标注信息new_img_info = img_info.copy()new_img_info['id'] = new_img_info['id'] + 15000new_img_info['file_name'] = ''.join('0' for i in range(8 - len(str(new_img_info['id'])))) \+ str(new_img_info['id']) + '.PNG'new_annot_infos = []for annot_info in annot_infos:new_annot_info = annot_info.copy()new_annot_info['id'] = new_annot_info['id'] + 15000new_annot_info['image_id'] = new_img_info['id']new_annot_info['bbox'] = annot_info['bbox'].copy()new_annot_info['bbox'][0] = max(0, new_annot_info['bbox'][0] - range_crop_left)new_annot_info['bbox'][1] = max(0, new_annot_info['bbox'][1] - range_crop_top)new_annot_infos.append(new_annot_info)# 将新图片写入到新路径new_path = os.path.join(self.new_root, self.model, new_img_info['file_name'])if not os.path.exists(new_path):cv2.imwrite(new_path, new_img)return new_img_info, new_annot_infos# 随机旋转# https://www.cnblogs.com/lky-learning/p/11653861.html 大佬很厉害def rotate_image(self, img_info, annot_infos, random_seed):random.seed(random_seed)angle = int(random.uniform(0, 180))img_path = os.path.join(self.root, self.model, img_info['file_name'])img, (h, w) = self.get_img(img_path)(cx, cy) = (w // 2, h // 2)# 对图像进行旋转处理M = cv2.getRotationMatrix2D((cx, cy), angle, 1.0)new_img = cv2.warpAffine(img, M, (w, h))# cv2.imshow(f"{angle}", new_img)# cv2.waitKey(0)new_img_info = img_info.copy()new_img_info['id'] = new_img_info['id'] + 20000new_img_info['file_name'] = ''.join('0' for i in range(8 - len(str(new_img_info['id'])))) \+ str(new_img_info['id']) + '.PNG'# 先矫正bboxnew_annot_infos = []for annot_info in annot_infos:new_annot_info = annot_info.copy()new_annot_info['bbox'] = annot_info['bbox'].copy()new_annot_info['id'] = new_annot_info['id'] + 20000new_annot_info['image_id'] = new_img_info['id']left_top = np.array([new_annot_info['bbox'][0],new_annot_info['bbox'][1], 1])top_right = np.array([new_annot_info['bbox'][0] + new_annot_info['bbox'][2],new_annot_info['bbox'][1], 1])right_bottom = np.array([new_annot_info['bbox'][0] + new_annot_info['bbox'][2],new_annot_info['bbox'][1] + new_annot_info['bbox'][3], 1])bottom_left = np.array([new_annot_info['bbox'][0],new_annot_info['bbox'][1] + new_annot_info['bbox'][3], 1])left_top = np.dot(M, left_top)top_right = np.dot(M, top_right)right_bottom = np.dot(M, right_bottom)bottom_left = np.dot(M, bottom_left)concat = np.vstack((left_top, top_right, right_bottom, bottom_left))concat = concat.astype(np.int32)rx, ry, rw, rh = cv2.boundingRect(concat)if (rx >= w) or (ry >= h) or ((rx + w) <= 0) or ((ry + h) <= 0):continuerx = rx if rx > 0 else 0ry = ry if ry > 0 else 0new_annot_info['bbox'][0] = rxnew_annot_info['bbox'][1] = rynew_annot_info['bbox'][2] = w - rx - 1 if rx + rw >= w else rwnew_annot_info['bbox'][3] = h - ry - 1 if ry + rh >= h else rhnew_annot_infos.append(new_annot_info)if len(new_annot_infos) == 0:return new_img_info, new_annot_infos# 将新图片写入到新路径new_path = os.path.join(self.new_root, self.model, new_img_info['file_name'])if not os.path.exists(new_path):cv2.imwrite(new_path, new_img)return new_img_info, new_annot_infos# mosaicdef mosaic(self, index_list, id):img_infos = self.coco.loadImgs(ids=index_list)center_x = max([i['width'] for i in img_infos])center_y = max([i['height'] for i in img_infos])new_img_info = {'id': id + 25000,'width': center_x * 2,'height': center_y * 2}new_img_info['file_name'] = ''.join('0' for i in range(8 - len(str(new_img_info['id'])))) \+ str(new_img_info['id']) + '.PNG'new_annot_infos = []for i, (img_info, img_id) in enumerate(zip(img_infos, index_list)):img, (h, w) = self.get_img(os.path.join(self.root, self.model, img_info['file_name']))annot_infos = self.coco.loadAnns(self.coco.getAnnIds(imgIds=img_id))if i == 0:new_img = np.zeros((center_y * 2, center_x * 2)).astype(img.dtype)# top_leftx_min, y_min, x_max, y_max = center_x - w, center_y - h, center_x, center_yelif i == 1:# top_rightx_min, y_min, x_max, y_max = center_x, center_y - h, center_x + w, center_yelif i == 2:# bottom_leftx_min, y_min, x_max, y_max = center_x - w, center_y, center_x, center_y + helif i == 3:# bottom_rightx_min, y_min, x_max, y_max = center_x, center_y, center_x + w, center_y + hnew_img[y_min: y_max, x_min: x_max] = imgfor annot in annot_infos:new_annot_info = annot.copy()new_annot_info['id'] = self.mosaic_annot_id + 25000new_annot_info['image_id'] = new_img_info['id']new_annot_info['bbox'] = annot['bbox'].copy()new_annot_info['bbox'][0] = x_min + new_annot_info['bbox'][0]new_annot_info['bbox'][1] = y_min + new_annot_info['bbox'][1]new_annot_infos.append(new_annot_info)self.mosaic_annot_id += 1# 将新图片写入到新路径new_path = os.path.join(self.new_root, self.model, new_img_info['file_name'])if not os.path.exists(new_path):cv2.imwrite(new_path, new_img)return new_img_info, new_annot_infosdef get_img(self, path):img = cv2.imread(path)[:, :, 0]h, w = img.shapereturn img, (h, w)

DatasetAugment.py

from pycocotools.coco import COCO
import numpy as np
import os
import json
from AugmentMethodClass import AugmentMethod
import shutil
import random
from tqdm import tqdm# roots
root = '/DAGM2007-COCO'
set_name = 'train'
new_root = '/DAGM2007-COCO-v2'
if not os.path.exists(new_root):print("The path: '" + new_root + "' doesn't exist! Need to create new path")os.makedirs(new_root)os.makedirs(os.path.join(new_root, 'annotations'))os.makedirs(os.path.join(new_root, 'train'))dagm_coco = COCO(os.path.join(root, 'annotations', 'instances_' + set_name + '.json'))
augment = AugmentMethod(dagm_coco, root, new_root)
image_ids = dagm_coco.getImgIds()dagm_images = dagm_coco.loadImgs(image_ids)
dagm_categories = dagm_coco.loadCats(dagm_coco.getCatIds())
dagm_annotations = dagm_coco.loadAnns(dagm_coco.getAnnIds(imgIds=image_ids))# 遍历图像，将每张图像进行扩增
for i, index in enumerate(tqdm(image_ids)):img_info = dagm_coco.loadImgs(ids=index)[0]annot_infos = dagm_coco.loadAnns(dagm_coco.getAnnIds(imgIds=index))# 原训练集复制到新路径下image_path = os.path.join(root, set_name, img_info['file_name'])new_image_path = os.path.join(new_root, set_name, img_info['file_name'])shutil.copy(image_path, new_image_path)# 原训练集水平翻转flipped_img_info, flipped_annot_infos = augment.flip_image(img_info, annot_infos)dagm_images.append(flipped_img_info)for annot in flipped_annot_infos:dagm_annotations.append(annot)# 原训练集竖直翻转vertical_flipped_img_info, vertical_flipped_annot_infos = augment.flip_image(img_info, annot_infos, horizion=False)dagm_images.append(vertical_flipped_img_info)for annot in vertical_flipped_annot_infos:dagm_annotations.append(annot)# 原训练集随机裁剪crop_img_info, crop_annot_infos = augment.crop_image(img_info, annot_infos, i)dagm_images.append(crop_img_info)for annot in crop_annot_infos:dagm_annotations.append(annot)# 原训练集随机旋转rotate_img_info, rotate_annot_infos = augment.rotate_image(img_info, annot_infos, i)if len(rotate_annot_infos) != 0:dagm_images.append(rotate_img_info)for annot in rotate_annot_infos:dagm_annotations.append(annot)print('数据集已复制到新路径下，并进行（水平翻转、竖直翻转、随机裁剪、随机旋转）等方式的扩增')
print('现采用yolo中的mosaic方法对原数据集进行扩增')# mosaic ing
for random_seed in tqdm(range(4)):random.seed(random_seed)random.shuffle(image_ids)num_yolo = int(len(image_ids) / 4)yolo_image_ids = np.resize(image_ids[: num_yolo * 4], (num_yolo, 4))for i, index_list in enumerate(yolo_image_ids):mosaic_img_info, mosaic_annot_infos = augment.mosaic(index_list, i + random_seed * len(yolo_image_ids))dagm_images.append(mosaic_img_info)for annot in mosaic_annot_infos:dagm_annotations.append(annot)coco = {'images': dagm_images, 'annotations': dagm_annotations, 'categories': dagm_categories}file_name = f'{new_root}/annotations/instances_{set_name}.json'
if os.path.exists(file_name):os.remove(file_name)
json.dump(coco, open(file_name, 'w'))print('数据集扩增完成！！！！')

VOC格式的数据集扩增

思路和COCO格式的扩增方法一样，只是针对格式不同对代码做了一些调整
因为这个是用来扩增博主的其他数据集，扩增后图像的命名方式同上面的不太一样。这里是在原图像名称上增加_000、_011、_001、_010（水平翻转、垂直翻转、随机切割、随机旋转），随机四张拼接的图像名称是直接将原四张图象的名字拼接起来。默认是保存为.jpg格式的。原数据集图像格式也需要为.jpg，若为其他格式同样需要修改代码。

代码

AugmentMethodClass.py

import os
import copy
import random
import xml.etree.ElementTree as ET
from lxml import etree, objectify
import cv2.cv2 as cv2
import numpy as np
import shutilclass AugmentMethod:def __init__(self, root, new_root, set='train'):self.root = rootself.new_root = new_rootself.xml_root = os.path.join(root, 'Annotations')self.images_root = os.path.join(root, 'JPEGImages')self.names_root = os.path.join(root, 'ImageSets', 'Main')self.name_list = list()with open(os.path.join(self.names_root, f'{set}.txt'), 'r') as f:for i in f.readlines():self.name_list.append(i.split('\n')[0])f.close()def parseXmlFiles(self):infos = list()for name in self.name_list:info = dict()info['folder'] = self.new_rootinfo['filename'] = name + '.jpg'xml_path = os.path.join(self.xml_root, name + '.xml')tree = ET.parse(xml_path)root = tree.getroot()size = dict()xml_size = root.find('size')size['width'] = xml_size.find('width').textsize['height'] = xml_size.find('height').textsize['depth'] = xml_size.find('depth').textinfo['size'] = sizeobjects = list()xml_object_set = root.findall('object')for xml_object in xml_object_set:object = dict()object['name'] = xml_object.find('name').textobject['pose'] = xml_object.find('pose').textobject['truncated'] = xml_object.find('truncated').textobject['difficult'] = xml_object.find('difficult').textobject['bndbox'] = dict()for i in xml_object.find('bndbox'):object['bndbox'][i.tag] = i.textobjects.append(object)info['object'] = objectsinfos.append(info)return infos# 水平翻转新图像名字后增加"_000"# 垂直翻转新图像名字后增加"_011"def flip_image(self, infos, horizion=True):infos_ori = copy.deepcopy(infos)name_list = list()for info in infos_ori:# 处理图像img = cv2.imread(os.path.join(self.images_root, info['filename']), -1)if horizion:new_img = img[:, ::-1]info['filename'] = info['filename'].split('.tif')[0] + "_000" + ".jpg"else:new_img = img[::-1, :]info['filename'] = info['filename'].split('.tif')[0] + "_011" + ".jpg"# 处理标注信息for object in info['object']:x_min = int(object['bndbox']['xmin'])x_max = int(object['bndbox']['xmax'])y_min = int(object['bndbox']['ymin'])y_max = int(object['bndbox']['ymax'])w = x_max - x_minh = y_max - y_minif horizion:object['bndbox']['xmin'] = int(info['size']['width']) - x_min - wobject['bndbox']['xmax'] = int(object['bndbox']['xmin']) + welse:object['bndbox']['ymin'] = int(info['size']['height']) - y_min - hobject['bndbox']['ymax'] = int(object['bndbox']['ymin']) + hnew_path = os.path.join(self.new_root, 'JPEGImages', info['filename'])cv2.imwrite(new_path, new_img)self.write_xml(info)name_list.append(info['filename'].split('.jpg')[0])return name_list# 新图像名字后增加"_001"def crop_image(self, infos, random_seed):name_list = list()random.seed(random_seed)infos_ori = copy.deepcopy(infos)for info in infos_ori:# 处理图像img = cv2.imread(os.path.join(self.images_root, info['filename']), -1)d_to_left, d_to_right, d_to_top, d_to_bottom = [], [], [], []for obj in info['object']:d_to_left.append(int(obj['bndbox']['xmin']))d_to_right.append(int(info['size']['width']) - int(obj['bndbox']['xmax']))d_to_top.append(int(obj['bndbox']['ymin']))d_to_bottom.append(int(info['size']['height']) - int(obj['bndbox']['ymax']))# 确定裁剪后的图片范围range_crop_left = int(random.uniform(0, min(d_to_left)))range_crop_right = int(info['size']['width']) - int(random.uniform(0, min(d_to_right)))range_crop_top = int(random.uniform(0, min(d_to_top)))range_crop_bottom = int(info['size']['height']) - int(random.uniform(0, min(d_to_bottom)))# 将裁剪后的图像填充为原图像大小（0填充）# np.array[行索引, 列索引]crop_img = img[range_crop_top: range_crop_bottom, range_crop_left: range_crop_right]new_img = np.zeros((int(info['size']['height']), int(info['size']['width']))).astype(img.dtype)new_img[0: (range_crop_bottom - range_crop_top), 0: (range_crop_right - range_crop_left)] = crop_img# 处理标注信息info['filename'] = info['filename'].split('.jpg')[0] + "_001" + ".jpg"for obj in info['object']:x_min = int(obj['bndbox']['xmin'])x_max = int(obj['bndbox']['xmax'])y_min = int(obj['bndbox']['ymin'])y_max = int(obj['bndbox']['ymax'])w = x_max - x_minh = y_max - y_minobj['bndbox']['xmin'] = max(0, x_min - range_crop_left)obj['bndbox']['ymin'] = max(0, y_min - range_crop_top)obj['bndbox']['xmax'] = obj['bndbox']['xmin'] + wobj['bndbox']['ymax'] = obj['bndbox']['ymin'] + hname_list.append(info['filename'].split('.tif')[0])new_path = os.path.join(self.new_root, 'JPEGImages', info['filename'])cv2.imwrite(new_path, new_img)self.write_xml(info)return name_list# 新图像名字后增加"_010"def rotate_image(self, infos, random_seed):infos_ori = copy.deepcopy(infos)name_list = list()random.seed(random_seed)angle = int(random.uniform(0, 180))for info in infos_ori:# 处理图像img = cv2.imread(os.path.join(self.images_root, info['filename']), -1)cx, cy = int(info['size']['width']) // 2, int(info['size']['height']) // 2M = cv2.getRotationMatrix2D((cx, cy), angle, 1.0)new_img = cv2.warpAffine(img, M, (int(info['size']['width']), int(info['size']['height'])))# 处理标注信息info['filename'] = info['filename'].split('.jpg')[0] + "_010" + ".jpg"for obj in info['object']:x_min = int(obj['bndbox']['xmin'])x_max = int(obj['bndbox']['xmax'])y_min = int(obj['bndbox']['ymin'])y_max = int(obj['bndbox']['ymax'])w = x_max - x_minh = y_max - y_minleft_top = np.array([x_min, y_min, 1])top_right = np.array([x_max, y_min, 1])right_bottom = np.array([x_max, y_max, 1])bottom_left = np.array([x_min, y_max, 1])left_top = np.dot(M, left_top)top_right = np.dot(M, top_right)right_bottom = np.dot(M, right_bottom)bottom_left = np.dot(M, bottom_left)concat = np.vstack((left_top, top_right, right_bottom, bottom_left))concat = concat.astype(np.int32)rx, ry, rw, rh = cv2.boundingRect(concat)if (rx >= int(info['size']['width'])) \or (ry >= int(info['size']['height'])) \or ((rx + int(info['size']['width'])) <= 0) \or ((ry + int(info['size']['height'])) <= 0):info['object'].remove(obj)rx = rx if rx > 0 else 0ry = ry if ry > 0 else 0obj['bndbox']['xmin'] = rxobj['bndbox']['ymin'] = ryrw = int(info['size']['width']) - rx - 1 if rx + rw >= int(info['size']['width']) else rwrh = int(info['size']['height']) - ry - 1 if ry + rh >= int(info['size']['height']) else rhobj['bndbox']['xmax'] = rx + rwobj['bndbox']['ymax'] = ry + rhif len(info['object']) == 0:continuename_list.append(info['filename'].split('.jpg')[0])new_path = os.path.join(self.new_root, 'JPEGImages', info['filename'])cv2.imwrite(new_path, new_img)self.write_xml(info)return name_list# 四张图象名字拼接def mosaic(self, all_infos):name_list = list()for random_seed in range(4):infos_ori = copy.deepcopy(all_infos)random.seed(random_seed)random.shuffle(infos_ori)num_info = int(len(infos_ori) / 4)resized_infos = np.resize(infos_ori[: num_info * 4], (num_info, 4))for dfhhedd, infos in enumerate(resized_infos):center_x = max([int(i['size']['width']) for i in infos])center_y = max([int(i['size']['height']) for i in infos])new_info = dict()new_info['folder'] = self.new_rootnew_info['filename'] = infos[0]['filename'].split('.jpg')[0] + '_' + \infos[1]['filename'].split('.jpg')[0] + '_' + \infos[2]['filename'].split('.jpg')[0] + '_' + \infos[3]['filename'].split('.jpg')[0] + '.jpg'new_info['size'] = {'width': center_x * 2, 'height': center_y * 2, 'depth': 1}new_info['object'] = list()for j, info in enumerate(infos):img = cv2.imread(os.path.join(self.images_root, info['filename']), -1)w = int(info['size']['width'])h = int(info['size']['height'])if j == 0:new_img = np.zeros((center_y * 2, center_x * 2)).astype(img.dtype)# top_leftx_min, y_min, x_max, y_max = center_x - w, center_y - h, center_x, center_yelif j == 1:# top_rightx_min, y_min, x_max, y_max = center_x, center_y - h, center_x + w, center_yelif j == 2:# bottom_leftx_min, y_min, x_max, y_max = center_x - w, center_y, center_x, center_y + helif j == 3:# bottom_rightx_min, y_min, x_max, y_max = center_x, center_y, center_x + w, center_y + hnew_img[y_min: y_max, x_min: x_max] = imgfor obj in info['object']:obj['bndbox']['xmin'] = x_min + int(obj['bndbox']['xmin'])obj['bndbox']['ymin'] = y_min + int(obj['bndbox']['ymin'])obj['bndbox']['xmax'] = x_min + int(obj['bndbox']['xmax'])obj['bndbox']['ymax'] = y_min + int(obj['bndbox']['ymax'])new_info['object'].append(obj)name_list.append(new_info['filename'].split('.jpg')[0])new_path = os.path.join(self.new_root, 'JPEGImages', new_info['filename'])cv2.imwrite(new_path, new_img)self.write_xml(new_info)return name_listdef write_xml(self, info):E = objectify.ElementMaker(annotate=False)anno_tree = E.annotation(E.folder(self.new_root),E.filename(info['filename']),E.size(E.width(info['size']['width']),E.height(info['size']['height']),E.depth(info['size']['depth'])),E.segmented(0))for obj in info['object']:E2 = objectify.ElementMaker(annotate=False)anno_tree2 = E2.object(E.name(obj['name']),E.pose(obj['pose']),E.truncated(obj['truncated']),E.difficult(obj['difficult']),E.bndbox(E.xmin(obj['bndbox']['xmin']),E.ymin(obj['bndbox']['ymin']),E.xmax(obj['bndbox']['xmax']),E.ymax(obj['bndbox']['ymax'])))anno_tree.append(anno_tree2)save_path = os.path.join(self.new_root, 'Annotations', info['filename'].split('.jpg')[0] + '.xml')etree.ElementTree(anno_tree).write(save_path, pretty_print=True)def copy(self, infos):name_list = list()for info in infos:shutil.copy(os.path.join(self.images_root, info['filename']),os.path.join(self.new_root, 'JPEGImages', info['filename']))self.write_xml(info)name_list.append(info['filename'].split('.jpg')[0])return name_list

DatasetAugment.py

import os
import random
from AugmentMethodClass import AugmentMethodroot = '/lalala_VOC'
new_root = '/lalala_VOC_v2'
if not os.path.exists(new_root):os.makedirs(os.path.join(new_root, 'Annotations'))os.makedirs(os.path.join(new_root, 'JPEGImages'))os.makedirs(os.path.join(new_root, 'ImageSets', 'Main'))
else:os.remove(new_root)VOC_dataset = AugmentMethod(root, new_root)
train_infos = VOC_dataset.parseXmlFiles()
val_VOC_dataset = AugmentMethod(root, new_root, 'val')
val_infos = val_VOC_dataset.parseXmlFiles()train_name_list = list()
random_seed = 815
name_list0 = VOC_dataset.copy(train_infos)
name_list1 = VOC_dataset.flip_image(train_infos)
name_list2 = VOC_dataset.flip_image(train_infos, False)
name_list3 = VOC_dataset.crop_image(train_infos, random_seed)
name_list4 = VOC_dataset.rotate_image(train_infos, random_seed)
name_list5 = VOC_dataset.mosaic(train_infos)train_name_list.extend(name_list0)
train_name_list.extend(name_list1)
train_name_list.extend(name_list2)
train_name_list.extend(name_list3)
train_name_list.extend(name_list4)
train_name_list.extend(name_list5)with open(os.path.join(new_root, 'ImageSets', 'Main', 'train.txt'), 'w') as f:for i, name in enumerate(train_name_list):f.write(name)f.write('\n')# print(i)val_name_list = val_VOC_dataset.copy(val_infos)
with open(os.path.join(new_root, 'ImageSets', 'Main', 'val.txt'), 'w') as f:for i, name in enumerate(val_name_list):f.write(name)f.write('\n')# print(i)

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DAGM2007数据集扩增（COCO格式以及VOC格式的灰度图数据集都可用）

文章目录

前言

COCO格式的数据集扩增

代码

AugmentMethodClass.py

DatasetAugment.py

VOC格式的数据集扩增

代码

AugmentMethodClass.py

DatasetAugment.py

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