【Python数据增强】图像数据集扩充

 前言：该脚本用于图像数据增强，特别是目标检测任务中的图像和标签数据增强。通过应用一系列数据增强技术（如旋转、平移、裁剪、加噪声、改变亮度、cutout、翻转等），生成多样化的图像数据集，以提高目标检测模型的鲁棒性和准确性。

效果：img存的原始图像168张图片，img2扩充的数量为5040张图片

目录

1.环境准备

2.显示图片函数

3.数据增强类

3.1类初始化

3.2数据增强方法

3.3 数据增强主方法

4.XML解析工具类 

4.1 解析XML

4.2 保存图片 

4.3 保存XML 

5. 主函数

完整程序

---

1.环境准备

这段代码导入了脚本所需的库，用于图像处理（cv2、numpy）、随机操作（random）、文件操作（os）、XML解析（etree）等。

# -*- coding=utf-8 -*-
import time
import random
import copy
import cv2
import os
import math
import numpy as np
from skimage.util import random_noise
from lxml import etree, objectify
import xml.etree.ElementTree as ET
import argparse

2.显示图片函数

该函数用于显示图片，并在图片上绘制边界框（bounding box）。

def show_pic(img, bboxes=None):'''输入:img: 图像arraybboxes: 图像的所有bounding box list, 格式为[[x_min, y_min, x_max, y_max]....]'''for i in range(len(bboxes)):bbox = bboxes[i]x_min = bbox[0]y_min = bbox[1]x_max = bbox[2]y_max = bbox[3]cv2.rectangle(img, (int(x_min), int(y_min)), (int(x_max), int(y_max)), (0, 255, 0), 3)cv2.namedWindow('pic', 0)cv2.moveWindow('pic', 0, 0)cv2.resizeWindow('pic', 1200, 800)cv2.imshow('pic', img)cv2.waitKey(0)cv2.destroyAllWindows()

3.数据增强类

3.1类初始化

该类初始化函数设置了数据增强的各种参数和是否启用某种增强方式的标志。

class DataAugmentForObjectDetection():def __init__(self, rotation_rate=0.5, max_rotation_angle=5,crop_rate=0.5, shift_rate=0.5, change_light_rate=0.5,add_noise_rate=0.5, flip_rate=0.5,cutout_rate=0.5, cut_out_length=50, cut_out_holes=1, cut_out_threshold=0.5,is_addNoise=True, is_changeLight=True, is_cutout=True, is_rotate_img_bbox=True,is_crop_img_bboxes=True, is_shift_pic_bboxes=True, is_filp_pic_bboxes=True):self.rotation_rate = rotation_rateself.max_rotation_angle = max_rotation_angleself.crop_rate = crop_rateself.shift_rate = shift_rateself.change_light_rate = change_light_rateself.add_noise_rate = add_noise_rateself.flip_rate = flip_rateself.cutout_rate = cutout_rateself.cut_out_length = cut_out_lengthself.cut_out_holes = cut_out_holesself.cut_out_threshold = cut_out_thresholdself.is_addNoise = is_addNoiseself.is_changeLight = is_changeLightself.is_cutout = is_cutoutself.is_rotate_img_bbox = is_rotate_img_bboxself.is_crop_img_bboxes = is_crop_img_bboxesself.is_shift_pic_bboxes = is_shift_pic_bboxesself.is_filp_pic_bboxes = is_filp_pic_bboxes

3.2数据增强方法

加噪声。为图像添加高斯噪声。

def _addNoise(self, img):return random_noise(img, mode='gaussian', clip=True) * 255

改变亮度。随机改变图像亮度。

def _changeLight(self, img):alpha = random.uniform(0.35, 1)blank = np.zeros(img.shape, img.dtype)return cv2.addWeighted(img, alpha, blank, 1 - alpha, 0)

cutout。随机在图像中遮挡某些部分（cutout），避免遮挡太多目标。

def _cutout(self, img, bboxes, length=100, n_holes=1, threshold=0.5):def cal_iou(boxA, boxB):xA = max(boxA[0], boxB[0])yA = max(boxA[1], boxB[1])xB = min(boxA[2], boxB[2])yB = min(boxA[3], boxB[3])if xB <= xA or yB <= yA:return 0.0interArea = (xB - xA + 1) * (yB - yA + 1)boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)iou = interArea / float(boxBArea)return iouif img.ndim == 3:h, w, c = img.shapeelse:_, h, w, c = img.shapemask = np.ones((h, w, c), np.float32)for n in range(n_holes):chongdie = Truewhile chongdie:y = np.random.randint(h)x = np.random.randint(w)y1 = np.clip(y - length // 2, 0, h)y2 = np.clip(y + length // 2, 0, h)x1 = np.clip(x - length // 2, 0, w)x2 = np.clip(x + length // 2, 0, w)chongdie = Falsefor box in bboxes:if cal_iou([x1, y1, x2, y2], box) > threshold:chongdie = Truebreakmask[y1: y2, x1: x2, :] = 0.img = img * maskreturn img

旋转。旋转图像和对应的边界框。

def _rotate_img_bbox(self, img, bboxes, angle=5, scale=1.):w, h = img.shape[1], img.shape[0]rangle = np.deg2rad(angle)nw = (abs(np.sin(rangle) * h) + abs(np.cos(rangle) * w)) * scalenh = (abs(np.cos(rangle) * h) + abs(np.sin(rangle) * w)) * scalerot_mat = cv2.getRotationMatrix2D((nw * 0.5, nh * 0.5), angle, scale)rot_move = np.dot(rot_mat, np.array([(nw - w) * 0.5, (nh - h) * 0.5, 0]))rot_mat[0, 2] += rot_move[0]rot_mat[1, 2] += rot_move[1]rot_img = cv2.warpAffine(img, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)rot_bboxes = []for bbox in bboxes:points = np.array([[bbox[0], bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]])new_points = cv2.transform(points[None, :, :], rot_mat)[0]rx, ry, rw, rh = cv2.boundingRect(new_points)corrected_bbox = [max(0, rx), max(0, ry), min(nw, rx + rw), min(nh, ry + rh)]corrected_bbox = [int(val) for val in corrected_bbox]rot_bboxes.append(corrected_bbox)return rot_img, rot_bboxes

裁剪。随机裁剪图像，同时裁剪对应的边界框。

def _crop_img_bboxes(self, img, bboxes):w = img.shape[1]h = img.shape[0]x_min = wx_max = 0y_min = hy_max = 0for bbox in bboxes:x_min = min(x_min, bbox[0])y_min = min(y_min, bbox[1])x_max = max(x_max, bbox[2])y_max = max(y_max, bbox[3])d_to_left = x_mind_to_right = w - x_maxd_to_top = y_mind_to_bottom = h - y_maxcrop_x_min = int(x_min - random.uniform(0, d_to_left))crop_y_min = int(y_min - random.uniform(0, d_to_top))crop_x_max = int(x_max + random.uniform(0, d_to_right))crop_y_max = int(y_max + random.uniform(0, d_to_bottom))crop_x_min = max(0, crop_x_min)crop_y_min = max(0, crop_y_min)crop_x_max = min(w, crop_x_max)crop_y_max = min(h, crop_y_max)crop_img = img[crop_y_min:crop_y_max, crop_x_min:crop_x_max]crop_bboxes = list()for bbox in bboxes:crop_bboxes.append([bbox[0] - crop_x_min, bbox[1] - crop_y_min, bbox[2] - crop_x_min, bbox[3] - crop_y_min])return crop_img, crop_bboxes

平移。随机平移图像和对应的边界框。

def _shift_pic_bboxes(self, img, bboxes):h, w = img.shape[:2]x = random.uniform(-w * 0.2, w * 0.2)y = random.uniform(-h * 0.2, h * 0.2)M = np.float32([[1, 0, x], [0, 1, y]])shift_img = cv2.warpAffine(img, M, (w, h))shift_bboxes = []for bbox in bboxes:new_bbox = [bbox[0] + x, bbox[1] + y, bbox[2] + x, bbox[3] + y]corrected_bbox = [max(0, new_bbox[0]), max(0, new_bbox[1]), min(w, new_bbox[2]), min(h, new_bbox[3])]corrected_bbox = [int(val) for val in corrected_bbox]shift_bboxes.append(corrected_bbox)return shift_img, shift_bboxes

 翻转。随机翻转图像和对应的边界框。

def _filp_pic_bboxes(self, img, bboxes):flipCode = random.choice([-1, 0, 1])flip_img = cv2.flip(img, flipCode)h, w, _ = img.shapeflip_bboxes = []for bbox in bboxes:x_min, y_min, x_max, y_max = bboxif flipCode == 0:new_bbox = [x_min, h - y_max, x_max, h - y_min]elif flipCode == 1:new_bbox = [w - x_max, y_min, w - x_min, y_max]else:new_bbox = [w - x_max, h - y_max, w - x_min, h - y_min]flip_bboxes.append(new_bbox)return flip_img, flip_bboxes

3.3 数据增强主方法

综合应用各种数据增强方法，对输入图像和边界框进行增强。

def dataAugment(self, img, bboxes):change_num = 0while change_num < 1:if self.is_rotate_img_bbox:if random.random() > self.rotation_rate:change_num += 1angle = random.uniform(-self.max_rotation_angle, self.max_rotation_angle)scale = random.uniform(0.7, 0.8)img, bboxes = self._rotate_img_bbox(img, bboxes, angle, scale)if self.is_shift_pic_bboxes:if random.random() < self.shift_rate:change_num += 1img, bboxes = self._shift_pic_bboxes(img, bboxes)if self.is_changeLight:if random.random() > self.change_light_rate:change_num += 1img = self._changeLight(img)if self.is_addNoise:if random.random() < self.add_noise_rate:change_num += 1img = self._addNoise(img)if self.is_cutout:if random.random() < self.cutout_rate:change_num += 1img = self._cutout(img, bboxes, length=self.cut_out_length, n_holes=self.cut_out_holes,threshold=self.cut_out_threshold)if self.is_filp_pic_bboxes:if random.random() < self.flip_rate:change_num += 1img, bboxes = self._filp_pic_bboxes(img, bboxes)return img, bboxes

4.XML解析工具类 

4.1 解析XML

从XML文件中提取边界框信息。

class ToolHelper():def parse_xml(self, path):tree = ET.parse(path)root = tree.getroot()objs = root.findall('object')coords = list()for ix, obj in enumerate(objs):name = obj.find('name').textbox = obj.find('bndbox')x_min = int(box[0].text)y_min = int(box[1].text)x_max = int(box[2].text)y_max = int(box[3].text)coords.append([x_min, y_min, x_max, y_max, name])return coords

4.2 保存图片 

保存增强后的图片。

def save_img(self, file_name, save_folder, img):cv2.imwrite(os.path.join(save_folder, file_name), img)

4.3 保存XML 

保存增强后的XML文件。

def save_xml(self, file_name, save_folder, img_info, height, width, channel, bboxs_info):folder_name, img_name = img_infoE = objectify.ElementMaker(annotate=False)anno_tree = E.annotation(E.folder(folder_name),E.filename(img_name),E.path(os.path.join(folder_name, img_name)),E.source(E.database('Unknown'),),E.size(E.width(width),E.height(height),E.depth(channel)),E.segmented(0),)labels, bboxs = bboxs_infofor label, box in zip(labels, bboxs):anno_tree.append(E.object(E.name(label),E.pose('Unspecified'),E.truncated('0'),E.difficult('0'),E.bndbox(E.xmin(box[0]),E.ymin(box[1]),E.xmax(box[2]),E.ymax(box[3]))))etree.ElementTree(anno_tree).write(os.path.join(save_folder, file_name), pretty_print=True)

5. 主函数

首先新建几个文件夹，修改主函数里相应的文件路径，即可。

• img 用于存放自己手里已有的数据集图片
• img2 用于存放增强后的数据集图片
• xml 用于存放自己手里已有的数据集图片对应的标签（这里必须是VOC格式）
• xml2 用于存放增强后的数据集图片对应的标签
• txt 用于存放将xml2中的voc格式的标签转换成txt格式（yolov识别txt格式的标签）

修改每个图片的增强次数即可决定增强图片的数量。 

主函数：

• 解析命令行参数，获取图片和XML文件路径。
• 创建保存路径文件夹（如果不存在）。
• 遍历源图片路径，读取图片和对应的XML文件。
• 应用数据增强，保存增强后的图片和XML文件。

if __name__ == '__main__':need_aug_num = 30  # 每张图片需要增强的次数is_endwidth_dot = True  # 文件是否以.jpg或者png结尾dataAug = DataAugmentForObjectDetection()  # 数据增强工具类toolhelper = ToolHelper()  # 工具# 获取相关参数parser = argparse.ArgumentParser()parser.add_argument('--source_img_path', type=str, default='D:/lenovo/Archie/shujukuochongv1.0/img')parser.add_argument('--source_xml_path', type=str, default='D:/lenovo/Archie/shujukuochongv1.0/xml')parser.add_argument('--save_img_path', type=str, default='D:/lenovo/Archie/shujukuochongv1.0/img2')parser.add_argument('--save_xml_path', type=str, default='D:/lenovo/Archie/shujukuochongv1.0/xml2')args = parser.parse_args()source_img_path = args.source_img_path  # 图片原始位置source_xml_path = args.source_xml_path  # xml的原始位置save_img_path = args.save_img_path  # 图片增强结果保存文件save_xml_path = args.save_xml_path  # xml增强结果保存文件if not os.path.exists(save_img_path):os.mkdir(save_img_path)if not os.path.exists(save_xml_path):os.mkdir(save_xml_path)for parent, _, files in os.walk(source_img_path):files.sort()for file in files:cnt = 0pic_path = os.path.join(parent, file)xml_path = os.path.join(source_xml_path, file[:-4] + '.xml')values = toolhelper.parse_xml(xml_path)coords = [v[:4] for v in values]labels = [v[-1] for v in values]if is_endwidth_dot:dot_index = file.rfind('.')_file_prefix = file[:dot_index]_file_suffix = file[dot_index:]img = cv2.imread(pic_path)while cnt < need_aug_num:auged_img, auged_bboxes = dataAug.dataAugment(img, coords)auged_bboxes_int = np.array(auged_bboxes).astype(np.int32)height, width, channel = auged_img.shapeimg_name = '{}_{}{}'.format(_file_prefix, cnt + 1, _file_suffix)tool

完整程序

该脚本用于对图像数据进行各种数据增强操作，并保存增强后的图像和标签数据。通过这些增强操作，可以生成大量多样化的训练数据，提升目标检测模型的鲁棒性和准确性。

# -*- coding=utf-8 -*-import time
import random
import copy
import cv2
import os
import math
import numpy as np
from skimage.util import random_noise
from lxml import etree, objectify
import xml.etree.ElementTree as ET
import argparse# 显示图片
def show_pic(img, bboxes=None):'''输入:img:图像arraybboxes:图像的所有boudning box list, 格式为[[x_min, y_min, x_max, y_max]....]names:每个box对应的名称'''for i in range(len(bboxes)):bbox = bboxes[i]x_min = bbox[0]y_min = bbox[1]x_max = bbox[2]y_max = bbox[3]cv2.rectangle(img, (int(x_min), int(y_min)), (int(x_max), int(y_max)), (0, 255, 0), 3)cv2.namedWindow('pic', 0)  # 1表示原图cv2.moveWindow('pic', 0, 0)cv2.resizeWindow('pic', 1200, 800)  # 可视化的图片大小cv2.imshow('pic', img)cv2.waitKey(0)cv2.destroyAllWindows()# 图像均为cv2读取
class DataAugmentForObjectDetection():def __init__(self, rotation_rate=0.5, max_rotation_angle=5,crop_rate=0.5, shift_rate=0.5, change_light_rate=0.5,add_noise_rate=0.5, flip_rate=0.5,cutout_rate=0.5, cut_out_length=50, cut_out_holes=1, cut_out_threshold=0.5,is_addNoise=True, is_changeLight=True, is_cutout=True, is_rotate_img_bbox=True,is_crop_img_bboxes=True, is_shift_pic_bboxes=True, is_filp_pic_bboxes=True):# 配置各个操作的属性self.rotation_rate = rotation_rateself.max_rotation_angle = max_rotation_angleself.crop_rate = crop_rateself.shift_rate = shift_rateself.change_light_rate = change_light_rateself.add_noise_rate = add_noise_rateself.flip_rate = flip_rateself.cutout_rate = cutout_rateself.cut_out_length = cut_out_lengthself.cut_out_holes = cut_out_holesself.cut_out_threshold = cut_out_threshold# 是否使用某种增强方式self.is_addNoise = is_addNoiseself.is_changeLight = is_changeLightself.is_cutout = is_cutoutself.is_rotate_img_bbox = is_rotate_img_bboxself.is_crop_img_bboxes = is_crop_img_bboxesself.is_shift_pic_bboxes = is_shift_pic_bboxesself.is_filp_pic_bboxes = is_filp_pic_bboxes# ----1.加噪声---- #def _addNoise(self, img):'''输入:img:图像array输出:加噪声后的图像array,由于输出的像素是在[0,1]之间,所以得乘以255'''# return cv2.GaussianBlur(img, (11, 11), 0)return random_noise(img, mode='gaussian', clip=True) * 255# ---2.调整亮度--- #def _changeLight(self, img):alpha = random.uniform(0.35, 1)blank = np.zeros(img.shape, img.dtype)return cv2.addWeighted(img, alpha, blank, 1 - alpha, 0)# ---3.cutout--- #def _cutout(self, img, bboxes, length=100, n_holes=1, threshold=0.5):'''原版本：https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.pyRandomly mask out one or more patches from an image.Args:img : a 3D numpy array,(h,w,c)bboxes : 框的坐标n_holes (int): Number of patches to cut out of each image.length (int): The length (in pixels) of each square patch.'''def cal_iou(boxA, boxB):'''boxA, boxB为两个框，返回iouboxB为bouding box'''# determine the (x, y)-coordinates of the intersection rectanglexA = max(boxA[0], boxB[0])yA = max(boxA[1], boxB[1])xB = min(boxA[2], boxB[2])yB = min(boxA[3], boxB[3])if xB <= xA or yB <= yA:return 0.0# compute the area of intersection rectangleinterArea = (xB - xA + 1) * (yB - yA + 1)# compute the area of both the prediction and ground-truth# rectanglesboxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)iou = interArea / float(boxBArea)return iou# 得到h和wif img.ndim == 3:h, w, c = img.shapeelse:_, h, w, c = img.shapemask = np.ones((h, w, c), np.float32)for n in range(n_holes):chongdie = True  # 看切割的区域是否与box重叠太多while chongdie:y = np.random.randint(h)x = np.random.randint(w)y1 = np.clip(y - length // 2, 0,h)  # numpy.clip(a, a_min, a_max, out=None), clip这个函数将将数组中的元素限制在a_min, a_max之间，大于a_max的就使得它等于 a_max，小于a_min,的就使得它等于a_miny2 = np.clip(y + length // 2, 0, h)x1 = np.clip(x - length // 2, 0, w)x2 = np.clip(x + length // 2, 0, w)chongdie = Falsefor box in bboxes:if cal_iou([x1, y1, x2, y2], box) > threshold:chongdie = Truebreakmask[y1: y2, x1: x2, :] = 0.img = img * maskreturn img# ---4.旋转--- #def _rotate_img_bbox(self, img, bboxes, angle=5, scale=1.):w, h = img.shape[1], img.shape[0]rangle = np.deg2rad(angle)  # angle in radiansnw = (abs(np.sin(rangle) * h) + abs(np.cos(rangle) * w)) * scalenh = (abs(np.cos(rangle) * h) + abs(np.sin(rangle) * w)) * scalerot_mat = cv2.getRotationMatrix2D((nw * 0.5, nh * 0.5), angle, scale)rot_move = np.dot(rot_mat, np.array([(nw - w) * 0.5, (nh - h) * 0.5, 0]))rot_mat[0, 2] += rot_move[0]rot_mat[1, 2] += rot_move[1]rot_img = cv2.warpAffine(img, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)rot_bboxes = []for bbox in bboxes:points = np.array([[bbox[0], bbox[1]], [bbox[2], bbox[1]], [bbox[2], bbox[3]], [bbox[0], bbox[3]]])new_points = cv2.transform(points[None, :, :], rot_mat)[0]rx, ry, rw, rh = cv2.boundingRect(new_points)corrected_bbox = [max(0, rx), max(0, ry), min(nw, rx + rw), min(nh, ry + rh)]corrected_bbox = [int(val) for val in corrected_bbox]  # Convert to int and correct order if necessaryrot_bboxes.append(corrected_bbox)return rot_img, rot_bboxes# ---5.裁剪--- #def _crop_img_bboxes(self, img, bboxes):'''裁剪后的图片要包含所有的框输入:img:图像arraybboxes:该图像包含的所有boundingboxs,一个list,每个元素为[x_min, y_min, x_max, y_max],要确保是数值输出:crop_img:裁剪后的图像arraycrop_bboxes:裁剪后的bounding box的坐标list'''# 裁剪图像w = img.shape[1]h = img.shape[0]x_min = w  # 裁剪后的包含所有目标框的最小的框x_max = 0y_min = hy_max = 0for bbox in bboxes:x_min = min(x_min, bbox[0])y_min = min(y_min, bbox[1])x_max = max(x_max, bbox[2])y_max = max(y_max, bbox[3])d_to_left = x_min  # 包含所有目标框的最小框到左边的距离d_to_right = w - x_max  # 包含所有目标框的最小框到右边的距离d_to_top = y_min  # 包含所有目标框的最小框到顶端的距离d_to_bottom = h - y_max  # 包含所有目标框的最小框到底部的距离# 随机扩展这个最小框crop_x_min = int(x_min - random.uniform(0, d_to_left))crop_y_min = int(y_min - random.uniform(0, d_to_top))crop_x_max = int(x_max + random.uniform(0, d_to_right))crop_y_max = int(y_max + random.uniform(0, d_to_bottom))# 随机扩展这个最小框 , 防止别裁的太小# crop_x_min = int(x_min - random.uniform(d_to_left//2, d_to_left))# crop_y_min = int(y_min - random.uniform(d_to_top//2, d_to_top))# crop_x_max = int(x_max + random.uniform(d_to_right//2, d_to_right))# crop_y_max = int(y_max + random.uniform(d_to_bottom//2, d_to_bottom))# 确保不要越界crop_x_min = max(0, crop_x_min)crop_y_min = max(0, crop_y_min)crop_x_max = min(w, crop_x_max)crop_y_max = min(h, crop_y_max)crop_img = img[crop_y_min:crop_y_max, crop_x_min:crop_x_max]# 裁剪boundingbox# 裁剪后的boundingbox坐标计算crop_bboxes = list()for bbox in bboxes:crop_bboxes.append([bbox[0] - crop_x_min, bbox[1] - crop_y_min, bbox[2] - crop_x_min, bbox[3] - crop_y_min])return crop_img, crop_bboxes# ---6.平移--- #def _shift_pic_bboxes(self, img, bboxes):h, w = img.shape[:2]x = random.uniform(-w * 0.2, w * 0.2)y = random.uniform(-h * 0.2, h * 0.2)M = np.float32([[1, 0, x], [0, 1, y]])shift_img = cv2.warpAffine(img, M, (w, h))shift_bboxes = []for bbox in bboxes:new_bbox = [bbox[0] + x, bbox[1] + y, bbox[2] + x, bbox[3] + y]corrected_bbox = [max(0, new_bbox[0]), max(0, new_bbox[1]), min(w, new_bbox[2]), min(h, new_bbox[3])]corrected_bbox = [int(val) for val in corrected_bbox]  # Convert to int and correct order if necessaryshift_bboxes.append(corrected_bbox)return shift_img, shift_bboxes# ---7.镜像--- #def _filp_pic_bboxes(self, img, bboxes):# Randomly decide the flip methodflipCode = random.choice([-1, 0, 1])  # -1: both; 0: vertical; 1: horizontalflip_img = cv2.flip(img, flipCode)  # Apply the fliph, w, _ = img.shapeflip_bboxes = []for bbox in bboxes:x_min, y_min, x_max, y_max = bboxif flipCode == 0:  # Vertical flipnew_bbox = [x_min, h - y_max, x_max, h - y_min]elif flipCode == 1:  # Horizontal flipnew_bbox = [w - x_max, y_min, w - x_min, y_max]else:  # Both flipsnew_bbox = [w - x_max, h - y_max, w - x_min, h - y_min]flip_bboxes.append(new_bbox)return flip_img, flip_bboxes# 图像增强方法def dataAugment(self, img, bboxes):'''图像增强输入:img:图像arraybboxes:该图像的所有框坐标输出:img:增强后的图像bboxes:增强后图片对应的box'''change_num = 0  # 改变的次数# print('------')while change_num < 1:  # 默认至少有一种数据增强生效if self.is_rotate_img_bbox:if random.random() > self.rotation_rate:  # 旋转change_num += 1angle = random.uniform(-self.max_rotation_angle, self.max_rotation_angle)scale = random.uniform(0.7, 0.8)img, bboxes = self._rotate_img_bbox(img, bboxes, angle, scale)if self.is_shift_pic_bboxes:if random.random() < self.shift_rate:  # 平移change_num += 1img, bboxes = self._shift_pic_bboxes(img, bboxes)if self.is_changeLight:if random.random() > self.change_light_rate:  # 改变亮度change_num += 1img = self._changeLight(img)if self.is_addNoise:if random.random() < self.add_noise_rate:  # 加噪声change_num += 1img = self._addNoise(img)if self.is_cutout:if random.random() < self.cutout_rate:  # cutoutchange_num += 1img = self._cutout(img, bboxes, length=self.cut_out_length, n_holes=self.cut_out_holes,threshold=self.cut_out_threshold)if self.is_filp_pic_bboxes:if random.random() < self.flip_rate:  # 翻转change_num += 1img, bboxes = self._filp_pic_bboxes(img, bboxes)return img, bboxes# xml解析工具
class ToolHelper():# 从xml文件中提取bounding box信息, 格式为[[x_min, y_min, x_max, y_max, name]]def parse_xml(self, path):'''输入：xml_path: xml的文件路径输出：从xml文件中提取bounding box信息, 格式为[[x_min, y_min, x_max, y_max, name]]'''tree = ET.parse(path)root = tree.getroot()objs = root.findall('object')coords = list()for ix, obj in enumerate(objs):name = obj.find('name').textbox = obj.find('bndbox')x_min = int(box[0].text)y_min = int(box[1].text)x_max = int(box[2].text)y_max = int(box[3].text)coords.append([x_min, y_min, x_max, y_max, name])return coords# 保存图片结果def save_img(self, file_name, save_folder, img):cv2.imwrite(os.path.join(save_folder, file_name), img)# 保持xml结果def save_xml(self, file_name, save_folder, img_info, height, width, channel, bboxs_info):''':param file_name:文件名:param save_folder:#保存的xml文件的结果:param height:图片的信息:param width:图片的宽度:param channel:通道:return:'''folder_name, img_name = img_info  # 得到图片的信息E = objectify.ElementMaker(annotate=False)anno_tree = E.annotation(E.folder(folder_name),E.filename(img_name),E.path(os.path.join(folder_name, img_name)),E.source(E.database('Unknown'),),E.size(E.width(width),E.height(height),E.depth(channel)),E.segmented(0),)labels, bboxs = bboxs_info  # 得到边框和标签信息for label, box in zip(labels, bboxs):anno_tree.append(E.object(E.name(label),E.pose('Unspecified'),E.truncated('0'),E.difficult('0'),E.bndbox(E.xmin(box[0]),E.ymin(box[1]),E.xmax(box[2]),E.ymax(box[3]))))etree.ElementTree(anno_tree).write(os.path.join(save_folder, file_name), pretty_print=True)if __name__ == '__main__':need_aug_num = 30  # 每张图片需要增强的次数is_endwidth_dot = True  # 文件是否以.jpg或者png结尾dataAug = DataAugmentForObjectDetection()  # 数据增强工具类toolhelper = ToolHelper()  # 工具# 获取相关参数parser = argparse.ArgumentParser()parser.add_argument('--source_img_path', type=str, default='D:/lenovo/Archie/shujukuochongv1.0/img')parser.add_argument('--source_xml_path', type=str, default='D:/lenovo/Archie/shujukuochongv1.0/xml')parser.add_argument('--save_img_path', type=str, default='D:/lenovo/Archie/shujukuochongv1.0/img2')parser.add_argument('--save_xml_path', type=str, default='D:/lenovo/Archie/shujukuochongv1.0/xml2')args = parser.parse_args()source_img_path = args.source_img_path  # 图片原始位置source_xml_path = args.source_xml_path  # xml的原始位置save_img_path = args.save_img_path  # 图片增强结果保存文件save_xml_path = args.save_xml_path  # xml增强结果保存文件# 如果保存文件夹不存在就创建if not os.path.exists(save_img_path):os.mkdir(save_img_path)if not os.path.exists(save_xml_path):os.mkdir(save_xml_path)for parent, _, files in os.walk(source_img_path):files.sort()for file in files:cnt = 0pic_path = os.path.join(parent, file)xml_path = os.path.join(source_xml_path, file[:-4] + '.xml')values = toolhelper.parse_xml(xml_path)  # 解析得到box信息，格式为[[x_min,y_min,x_max,y_max,name]]coords = [v[:4] for v in values]  # 得到框labels = [v[-1] for v in values]  # 对象的标签# 如果图片是有后缀的if is_endwidth_dot:# 找到文件的最后名字dot_index = file.rfind('.')_file_prefix = file[:dot_index]  # 文件名的前缀_file_suffix = file[dot_index:]  # 文件名的后缀img = cv2.imread(pic_path)# show_pic(img, coords)  # 显示原图while cnt < need_aug_num:  # 继续增强auged_img, auged_bboxes = dataAug.dataAugment(img, coords)auged_bboxes_int = np.array(auged_bboxes).astype(np.int32)height, width, channel = auged_img.shape  # 得到图片的属性img_name = '{}_{}{}'.format(_file_prefix, cnt + 1, _file_suffix)  # 图片保存的信息toolhelper.save_img(img_name, save_img_path,auged_img)  # 保存增强图片toolhelper.save_xml('{}_{}.xml'.format(_file_prefix, cnt + 1),save_xml_path, (save_img_path, img_name), height, width, channel,(labels, auged_bboxes_int))  # 保存xml文件# show_pic(auged_img, auged_bboxes)  # 强化后的图print(img_name)cnt += 1  # 继续增强下一张