tensorflow|widerface转换为VOC数据集 tensorflow|widerface

注意：VM虚拟机无法使用物理机显卡，建议用docker
有大兄弟表示运行完 train.txt 没有内容，解决方法为路径不能有中文。
一、下载widerface数据集
widerface数据集下载地址：http://shuoyang1213.me/WIDERFACE/

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wider_face_split.zipwiderface的标注文件（只有train和val的标注文件）
WIDER_test.zip、WIDER_val.zip、WIDER_train.zip分别为图像文件
二、相关文件配置
1、设置一个根目录（本人：/home/roy/TF/widerface）
2、将下载的四个文件解压

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3、创建三个文件夹
【tensorflow|widerface转换为VOC数据集】Annotations 标注文件夹
ImageSets/Main/train.txt 图片文件名
JPEGImages 图片源文件

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其中ImageSets内还要创建一个Main文件夹
三、转换脚本
注意修改脚本中地址

rootdir = "/home/roy/TF/widerface" gtfile = "/home/roy/TF/widerface/wider_face_split/wider_face_train_bbx_gt.txt" im_folder = "/home/roy/TF/widerface/WIDER_train/images" fwrite = open("/home/roy/TF/widerface/ImageSets/Main/train.txt", "w")

import os,cv2,sys,shutil,numpyfrom xml.dom.minidom import Document import os# 本程序可以讲widerface转为VOC格式的数据def writexml(filename, saveimg, bboxes, xmlpath): doc = Document()annotation = doc.createElement('annotation')doc.appendChild(annotation)folder = doc.createElement('folder')folder_name = doc.createTextNode('widerface') folder.appendChild(folder_name) annotation.appendChild(folder) filenamenode = doc.createElement('filename') filename_name = doc.createTextNode(filename) filenamenode.appendChild(filename_name) annotation.appendChild(filenamenode) source = doc.createElement('source') annotation.appendChild(source) database = doc.createElement('database') database.appendChild(doc.createTextNode('wider face Database')) source.appendChild(database) annotation_s = doc.createElement('annotation') annotation_s.appendChild(doc.createTextNode('PASCAL VOC2007')) source.appendChild(annotation_s) image = doc.createElement('image') image.appendChild(doc.createTextNode('flickr')) source.appendChild(image) flickrid = doc.createElement('flickrid') flickrid.appendChild(doc.createTextNode('-1')) source.appendChild(flickrid) owner = doc.createElement('owner') annotation.appendChild(owner) flickrid_o = doc.createElement('flickrid') flickrid_o.appendChild(doc.createTextNode('muke')) owner.appendChild(flickrid_o) name_o = doc.createElement('name') name_o.appendChild(doc.createTextNode('muke')) owner.appendChild(name_o)size = doc.createElement('size') annotation.appendChild(size)width = doc.createElement('width') width.appendChild(doc.createTextNode(str(saveimg.shape[1]))) height = doc.createElement('height') height.appendChild(doc.createTextNode(str(saveimg.shape[0]))) depth = doc.createElement('depth') depth.appendChild(doc.createTextNode(str(saveimg.shape[2])))size.appendChild(width)size.appendChild(height) size.appendChild(depth) segmented = doc.createElement('segmented') segmented.appendChild(doc.createTextNode('0')) annotation.appendChild(segmented) for i in range(len(bboxes)): bbox = bboxes[i] objects = doc.createElement('object') annotation.appendChild(objects) object_name = doc.createElement('name') object_name.appendChild(doc.createTextNode('face')) objects.appendChild(object_name) pose = doc.createElement('pose') pose.appendChild(doc.createTextNode('Unspecified')) objects.appendChild(pose) truncated = doc.createElement('truncated') truncated.appendChild(doc.createTextNode('0')) objects.appendChild(truncated) difficult = doc.createElement('difficult') difficult.appendChild(doc.createTextNode('0')) objects.appendChild(difficult) bndbox = doc.createElement('bndbox') objects.appendChild(bndbox) xmin = doc.createElement('xmin') xmin.appendChild(doc.createTextNode(str(bbox[0]))) bndbox.appendChild(xmin) ymin = doc.createElement('ymin') ymin.appendChild(doc.createTextNode(str(bbox[1]))) bndbox.appendChild(ymin) xmax = doc.createElement('xmax') xmax.appendChild(doc.createTextNode(str(bbox[0] + bbox[2]))) bndbox.appendChild(xmax) ymax = doc.createElement('ymax') ymax.appendChild(doc.createTextNode(str(bbox[1] + bbox[3]))) bndbox.appendChild(ymax) f = open(xmlpath, "w") f.write(doc.toprettyxml(indent='')) f.close()rootdir = "/home/roy/TF/widerface" gtfile = "/home/roy/TF/widerface/wider_face_split/wider_face_train_bbx_gt.txt" im_folder = "/home/roy/TF/widerface/WIDER_train/images" fwrite = open("/home/roy/TF/widerface/ImageSets/Main/train.txt", "w")# wider_face_train_bbx_gt.txt的文件内容 # 第一行为名字 # 第二行为头像的数量 n # 剩下的为n行人脸数据 # 以下为示例 # 0--Parade/0_Parade_marchingband_1_117.jpg # 9 # 69 359 50 36 1 0 0 0 0 1 # 227 382 56 43 1 0 1 0 0 1 # 296 305 44 26 1 0 0 0 0 1 # 353 280 40 36 2 0 0 0 2 1 # 885 377 63 41 1 0 0 0 0 1 # 819 391 34 43 2 0 0 0 1 0 # 727 342 37 31 2 0 0 0 0 1 # 598 246 33 29 2 0 0 0 0 1 # 740 308 45 33 1 0 0 0 2 1with open(gtfile, "r") as gt: while(True): gt_con = gt.readline()[:-1] if gt_con is None or gt_con == "": break; im_path = im_folder + "/" + gt_con; print(im_path) im_data = https://www.it610.com/article/cv2.imread(im_path) if im_data is None: continue # 可视化的部分 # cv2.imshow(im_path, im_data) # cv2.waitKey(0)numbox = int(gt.readline())# 获取每一行人脸数据 bboxes = [] if numbox == 0:# numbox 为0 的情况处理 gt.readline() else: for i in range(numbox): line = gt.readline() infos = line.split(" ")# 用空格分割 # x y w h ..... bbox = (int(infos[0]), int(infos[1]), int(infos[2]), int(infos[3])) # 绘制人脸框 # cv2.rectangle(im_data, (int(infos[0]), int(infos[1])), #(int(infos[0]) + int(infos[2]), int(infos[1]) + int(infos[3])), #color=(0, 0, 255), thickness=1) bboxes.append(bbox)# 将一张图片的所有人脸数据加入bboxes # cv2.imshow(im_path, im_data) # cv2.waitKey(0) filename = gt_con.replace("/", "_")# 将存储位置作为图片名称，斜杠转为下划线 fwrite.write(filename.split(".")[0] + "\n") cv2.imwrite("{}/JPEGImages/{}".format(rootdir, filename), im_data) xmlpath = "{}/Annotations/{}.xml".format(rootdir, filename.split(".")[0]) writexml(filename, im_data, bboxes, xmlpath) fwrite.close()

四、运行脚本