Mobilenet-SSD在widerface数据集上训练人脸检测模型

    最近一段时间一直忙着实习和在研究insightface人脸识别模型的相关事宜，上次说更新mobilenet-ssd的人脸检测，结果一拖再拖，非常抱歉。最近我也会将自己跑Insightface的实践写出来，并将以前和进后的项目上传个人Github仓库，这里先给出链接：https://github.com/Danbinabo

    VOC数据集包含20个类别的物体和1个背景，分别是——aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, foa, train, tvmonitor，有时候我们想用VOC数据集训练，但并不需要这么多类别，而caffe-ssd提供的数据处理工具create_list.sh和create_data.sh默认是处理所有的20个分类的。如果我们不想重写这些数据处理工具，可以从根源入手，也就是直接修改数据集里的标注信息，把多余分类的信息删去。

其实训练多类别检测(N+1)和人脸检测(1+1)原理是完全一样的，不同点在于要生成对应的数据集，这里先将widerface数据集转VOC格式数据集的代码贴出来，对应训练数据路径进行修改：

# -*- coding: utf-8 -*-
import os, cv2, sys, shutil
from xml.dom.minidom import Document

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('yanyu'))
    owner.appendChild(flickrid_o)
    name_o = doc.createElement('name')
    name_o.appendChild(doc.createTextNode('yanyu'))
    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('1'))
        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 = "E:/public_data/wideface"


def convertimgset(img_set):
    imgdir = rootdir + "/WIDER_" + img_set + "/images"
    gtfilepath = rootdir + "/wider_face_split/wider_face_" + img_set + "_bbx_gt.txt"

    fwrite = open(rootdir + "/ImageSets/Main/" + img_set + ".txt", 'w')

    index = 0

    with open(gtfilepath, 'r') as gtfiles:
        while (True):  # true
            filename = gtfiles.readline()[:-1]
            if filename == None or filename == "":
                break
            imgpath = imgdir + "/" + filename
            print(imgpath)
            img = cv2.imread(imgpath)
            if not img.data:
                print(imgpath)
                break
            numbbox = int(gtfiles.readline())

            bboxes = []

            print(numbbox)

            for i in range(numbbox):
                line = gtfiles.readline()
                lines = line.split(" ")
                lines = lines[0:4]

                bbox = (int(lines[0]), int(lines[1]), int(lines[2]), int(lines[3]))

                if int(lines[2]) < 40 or int(lines[3]) < 40:
                    continue

                bboxes.append(bbox)

                # cv2.rectangle(img, (bbox[0],bbox[1]),(bbox[0]+bbox[2],bbox[1]+bbox[3]),color=(255,255,0),thickness=1)

            filename = filename.replace("/", "_")

            if len(bboxes) == 0:
                print("no face")
                continue
            # cv2.imshow("img", img)
            # cv2.waitKey(0)

            cv2.imwrite("{}/JPEGImages/{}".format(rootdir, filename), img)

            fwrite.write(filename.split(".")[0] + "\n")

            xmlpath = "{}/Annotations/{}.xml".format(rootdir, filename.split(".")[0])

            writexml(filename, img, bboxes, xmlpath)

            print("success number is ", index)
            index += 1

    fwrite.close()


if __name__ == "__main__":
    img_sets = ["train", "val"]
    for img_set in img_sets:
        convertimgset(img_set)

    shutil.move(rootdir + "/ImageSets/Main/" + "train.txt", rootdir + "/ImageSets/Main/" + "trainval.txt")
    shutil.move(rootdir + "/ImageSets/Main/" + "val.txt", rootdir + "/ImageSets/Main/" + "test.txt")

这样就将wideface数据集生成了上一篇我们用于训练VOC20类物体检测的数据集格式，后面训练过程几乎同上，按照固有的流程进行建立数据软连接，网络生成，模型训练即可。

训练的模型测试可以运行mobilenet-ssd自带的测试代码demo.py,在里面修改自己模型的路径和测试图片即可，这里我在1080ti上大概跑了一个晚上，第二天进行了测试，检测效果还不错，这里主要是用mobilenet-ssd训练的模型较小，非常适合移动端模型的部署。