1 Wider Face标注格式转成YOLO格式

1.1 Wider Face标注介绍

The format of txt ground truth.
File name
Number of bounding box
x1, y1, w, h, blur, expression, illumination, invalid, occlusion, pose
'''
0--Parade/0_Parade_marchingband_1_849.jpg
1
449 330 122 149 0 0 0 0 0 0 
0--Parade/0_Parade_Parade_0_904.jpg
1
361 98 263 339 0 0 0 0 0 0
'''

1.2 YOLO标注介绍

The format of txt ground truth.
class, cx, cy, w, h(坐标为归一化后的数值)
'''
0 0.04248046875 0.5455729166666666 0.0283203125 0.046875
0 0.2646484375 0.505859375 0.03125 0.05078125
0 0.33642578125 0.50390625 0.0185546875 0.028645833333333332
'''

1.3 数据格式转换

1.3.1 Wider Face格式转成VOC格式

首先创建文件夹1_face2voc，在该文件夹下创建Annotations、JPEGImages、Labels文件夹

格式转换代码需要运行两次(将路径中的train换成val即可)，分别生成对应的train和val文件，转换代码如下：

# coding:utf-8
import cv2
from xml.dom.minidom import Documentdef 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 = "/kaxier01/projects/FAS/yolov7/wider_face/1_face2voc"  # 根目录
gtfile = "/kaxier01/projects/FAS/yolov7/wider_face/wider_face_split/wider_face_val_bbx_gt.txt"  # Wider Face原始标注
im_folder = "/kaxier01/projects/FAS/yolov7/wider_face/WIDER_val/images"
fwrite = open("/kaxier01/projects/FAS/yolov7/wider_face/1_face2voc/Labels/val.txt", "w")with open(gtfile, "r") as gt:while(True):gt_con = gt.readline()[:-1]if gt_con is None or gt_con == "":breakim_path = im_folder + "/" + gt_conprint(im_path)im_data = cv2.imread(im_path)if im_data is None:continuenumbox = 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bbox = (int(infos[0]), int(infos[1]), int(infos[2]), int(infos[3]))bboxes.append(bbox)  # 将一张图片的所有人脸数据加入bboxesfilename = 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])  # xml文件保存路径writexml(filename, im_data, bboxes, xmlpath)
fwrite.close()

1.3.2 VOC格式转成COCO格式

首先创建2_voc2coco文件夹，在该文件夹下创建wider_coco文件夹，在wider_coco文件夹下创建images和xml_annotations文件夹

格式转换：1）利用1.3.1步骤中生成的xml文件(生成的train和val的xml文件合并放在一个文件夹内，路径为'1_face2voc/Annotations')以及'1_face2voc/Labels'下的train.txt和val.txt，将图片和xml文件分为训练集和验证集，

代码如下：

# coding:utf-8
import os
import shutil
from tqdm import tqdmSPLIT_PATH = "/kaxier01/projects/FAS/yolov7/wider_face/1_face2voc/Labels"
IMGS_PATH = "/kaxier01/projects/FAS/yolov7/wider_face/1_face2voc/JPEGImages"
TXTS_PATH = "/kaxier01/projects/FAS/yolov7/wider_face/1_face2voc/Annotations"TO_IMGS_PATH = '/kaxier01/projects/FAS/yolov7/wider_face/2_voc2coco/wider_coco/images'
TO_TXTS_PATH = '/kaxier01/projects/FAS/yolov7/wider_face/2_voc2coco/wider_coco/xml_annotations'data_split = ['train.txt', 'val.txt']
to_split = ['train', 'val']train_file = '/kaxier01/projects/FAS/yolov7/wider_face/3_voc2yolo/images_train.txt'
val_file = '/kaxier01/projects/FAS/yolov7/wider_face/3_voc2yolo/images_val.txt'
train_file_txt = ''
val_file_txt = ''for index, split in enumerate(data_split):split_path = os.path.join(SPLIT_PATH, split)to_imgs_path = os.path.join(TO_IMGS_PATH, to_split[index])if not os.path.exists(to_imgs_path):os.makedirs(to_imgs_path)to_txts_path = os.path.join(TO_TXTS_PATH, to_split[index])if not os.path.exists(to_txts_path):os.makedirs(to_txts_path)f = open(split_path, 'r')count = 1for line in tqdm(f.readlines(), desc="{} is copying".format(to_split[index])):# 复制图片src_img_path = os.path.join(IMGS_PATH, line.strip() + '.jpg')dst_img_path = os.path.join(to_imgs_path, line.strip() + '.jpg')if os.path.exists(src_img_path):shutil.copyfile(src_img_path, dst_img_path)else:print("error file: {}".format(src_img_path))if to_split[index] == 'train':train_file_txt = train_file_txt + dst_img_path + '\n'elif to_split[index] == 'val':val_file_txt = val_file_txt + dst_img_path + '\n'# 复制txt标注文件src_txt_path = os.path.join(TXTS_PATH, line.strip() + '.xml')dst_txt_path = os.path.join(to_txts_path, line.strip() + '.xml')if os.path.exists(src_txt_path):shutil.copyfile(src_txt_path, dst_txt_path)else:print("error file: {}".format(src_txt_path))with open(train_file, 'w') as out_train:out_train.write(train_file_txt)with open(val_file, 'w') as out_val:out_val.write(val_file_txt)

2）将VOC标注格式转换成COCO格式，生成的json文件用于评估模型性能，代码如下：

import sys
import os
import json
import xml.etree.ElementTree as ET
import globSTART_BOUNDING_BOX_ID = 1
PRE_DEFINE_CATEGORIES = {"face" : 0}def get(root, name):vars = root.findall(name)return varsdef get_and_check(root, name, length):vars = root.findall(name)if len(vars) == 0:raise ValueError("Can not find %s in %s." % (name, root.tag))if length > 0 and len(vars) != length:raise ValueError("The size of %s is supposed to be %d, but is %d."% (name, length, len(vars)))if length == 1:vars = vars[0]return varsdef get_filename_as_int(filename):try:# print(filename,filename[6:])filename = filename.replace("\\", "/")filename = os.path.splitext(os.path.basename(filename))[0]if filename[:5] == "India" :  return  int("2"+filename[6:])elif filename[:5] == "Japan" :  return  int("3"+filename[6:])else : return int("1"+filename[6:])#return int(filename[6:])except:raise ValueError("Filename %s is supposed to be an integer." % (filename))def get_categories(xml_files):"""Generate category name to id mapping from a list of xml files.Arguments:xml_files {list} -- A list of xml file paths.Returns:dict -- category name to id mapping."""acceptable_classes = ["car","truck","bus"]classes_names = []for xml_file in xml_files:tree = ET.parse(xml_file)root = tree.getroot()for member in root.findall("object"):classes_names.append(member[0].text)classes_names = list(set(classes_names))return {name: i for i, name in enumerate(classes_names)}def convert(xml_files, json_file):json_dict = {"images": [], "type": "instances", "annotations": [], "categories": []}if PRE_DEFINE_CATEGORIES is not None:categories = PRE_DEFINE_CATEGORIESelse:categories = get_categories(xml_files)bnd_id = START_BOUNDING_BOX_IDfor xml_file in xml_files:tree = ET.parse(xml_file)root = tree.getroot()path = get(root, "path")if len(path) == 1:filename = os.path.basename(path[0].text)elif len(path) == 0:filename = get_and_check(root, "filename", 1).textelse:raise ValueError("%d paths found in %s" % (len(path), xml_file))image_id = filename[:-4]size = get_and_check(root, "size", 1)width = int(get_and_check(size, "width", 1).text)height = int(get_and_check(size, "height", 1).text)image = {"file_name": filename,"height": height,"width": width,"id": filename[:-4],}json_dict["images"].append(image)for obj in get(root, "object"):category = get_and_check(obj, "name", 1).textif category not in categories:continuecategory_id = categories[category]bndbox = get_and_check(obj, "bndbox", 1)xmin = int(get_and_check(bndbox, "xmin", 1).text) - 1ymin = int(get_and_check(bndbox, "ymin", 1).text) - 1xmax = int(get_and_check(bndbox, "xmax", 1).text)ymax = int(get_and_check(bndbox, "ymax", 1).text)assert xmax > xminassert ymax > ymino_width = abs(xmax - xmin)o_height = abs(ymax - ymin)ann = {"area": o_width * o_height,"iscrowd": 0,"image_id": image_id,"bbox": [xmin, ymin, o_width, o_height],"category_id": category_id,"id": bnd_id,"ignore": 0,"segmentation": [],}json_dict["annotations"].append(ann)bnd_id = bnd_id + 1for cate, cid in categories.items():cat = {"supercategory": "none", "id": cid, "name": cate}json_dict["categories"].append(cat)os.makedirs(os.path.dirname(json_file), exist_ok=True)json_fp = open(json_file, "w")json_str = json.dumps(json_dict, indent=4)json_fp.write(json_str)json_fp.close()if __name__ == "__main__":import argparseparser = argparse.ArgumentParser(description="Convert Pascal VOC annotation to COCO format.")xml_path = '/kaxier01/projects/FAS/yolov7/wider_face/2_voc2coco/wider_coco/xml_annotations/val'  # 这是xml文件所在的地址json_file = '/kaxier01/projects/FAS/yolov7/wider_face/3_voc2yolo/annotations/val.json'  # 这是你要生成的json文件xml_files = glob.glob(os.path.join(xml_path, "*.xml"))# If you want to do train/test split, you can pass a subset of xml files to convert function.print("Number of xml files: {}".format(len(xml_files)))convert(xml_files, json_file)print("Success: {}".format(json_file))

1.3.3 VOC格式转成YOLO格式

首先创建3_voc2yolo文件夹，在该文件夹下创建images和labels文件夹

1）提取由1.3.2步骤划分好的xml文件的文件名('2_voc2coco/wider_coco/xml_annotations/val/***.xml')并将文件名保存在'2_voc2coco/name_val.txt or name_train.txt '，代码需要执行两次，代码如下：

import osfile_path = "/kaxier01/projects/FAS/yolov7/wider_face/2_voc2coco/wider_coco/xml_annotations/val/"
path_list = os.listdir(file_path)  # os.listdir(file)会历遍文件夹内的文件并返回一个列表
path_name = []  # 把文件列表写入save.txt中def saveList(pathName):for file_name in pathName:with open("/kaxier01/projects/FAS/yolov7/wider_face/2_voc2coco/name_val.txt", "a") as f:f.write(file_name.split(".")[0] + "\n")def dirList(path_list):for i in range(0, len(path_list)):path = os.path.join(file_path, path_list[i])if os.path.isdir(path):saveList(os.listdir(path))dirList(path_list)
saveList(path_list)

2）将xml格式转成YOLO格式，代码需要执行两次，代码如下：

import xml.etree.ElementTree as ET
import osclasses = ['face']train_file = '/kaxier01/projects/FAS/yolov7/wider_face/3_voc2yolo/wider_val.txt'  
train_file_txt = ''wd = os.getcwd()def convert(size, box):dw = 1. / size[0]dh = 1. / size[1]box = list(box)box[1] = min(box[1], size[0])   # 限制目标的范围在图片尺寸内box[3] = min(box[3], size[1])x = ((box[0] + box[1]) / 2.0) * dwy = ((box[2] + box[3]) / 2.0) * dhw = (box[1] - box[0]) * dwh = (box[3] - box[2]) * dhreturn (x, y, w, h)   def convert_annotation(image_id):in_file = open('/kaxier01/projects/FAS/yolov7/wider_face/2_voc2coco/wider_coco/xml_annotations/val/%s.xml' % (image_id))  # 读取xml文件路径out_file = open('/kaxier01/projects/FAS/yolov7/wider_face/3_voc2yolo/labels/val/%s.txt' % (image_id), 'w')  # 需要保存的txt格式文件路径tree = ET.parse(in_file)root = tree.getroot()size = root.find('size')w = int(size.find('width').text)h = int(size.find('height').text)for obj in root.iter('object'):cls = obj.find('name').textif cls not in classes:continuecls_id = classes.index(cls)xmlbox = obj.find('bndbox')b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),float(xmlbox.find('ymax').text))bb = convert((w, h), b)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')image_ids_train = open('/kaxier01/projects/FAS/yolov7/wider_face/2_voc2coco/name_val.txt').read().strip().split()  # 读取xml文件名索引for image_id in image_ids_train:convert_annotation(image_id)anns = os.listdir('/kaxier01/projects/FAS/yolov7/wider_face/2_voc2coco/wider_coco/xml_annotations/val/')
for ann in anns:ans = ''outpath = '/kaxier01/projects/FAS/yolov7/wider_face/3_voc2yolo/labels/val/' + annif ann[-3:] != 'xml':continuetrain_file_txt = train_file_txt + '/kaxier01/projects/FAS/yolov7/wider_face/3_voc2yolo/images/val/' + ann[:-3] + 'jpg\n'with open(train_file, 'w') as outfile:outfile.write(train_file_txt)

至此，便把Wider Face标注格式转换成了YOLO格式，对应的图片和标签分别保存在'wider_face/3_voc2yolo/images/'和'wider_face/3_voc2yolo/labels/'，

转换后的标注如下：

2 使用Wider Face数据集训练YOLOV7

模仿coco.yaml生成wider_face.yaml文件，文件内如如下：

# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
# 以下两个txt文件由步骤1.3.2生成
train: /kaxier01/projects/FAS/yolov7/wider_face/3_voc2yolo/wider_train.txt  # 12876 images
val: /kaxier01/projects/FAS/yolov7/wider_face/3_voc2yolo/wider_val.txt  # 3226 images# number of classes
nc: 1# class names
names: [ 'face' ]

配置文件'cfg/training/yolov7.yaml'中的anchors大小不需要修改，如果anchors不合适，算法会重新聚类anchors

修改YOLOV7工程中的test.py脚本中的anno_json(Line 257)：

anno_json = '/kaxier01/projects/FAS/yolov7/wider_face/3_voc2yolo/annotations/val.json'  # 该文件由1.3.2中格式转换的步骤2生成

测试结果

目前模型训练到了46epoch(总共300epoch)，性能如下：

可视化结果如下：

标注格式转换参考：

https://blog.csdn.net/mary_0830/article/details/116589279