SSD demo中详细介绍了如何在VOC数据集上使用SSD进行物体检测的训练和验证;本文介绍如何使用SSD实现对自己数据集的训练和验证过程。
pre.cjk { font-family: "Droid Sans Fallback", monospace }
p { margin-bottom: 0.25cm; line-height: 120% }
a:link { }
SSD demo中详细介绍了如何在VOC数据集上使用SSD进行物体检测的训练和验证。
本文介绍如何使用SSD实现对自己数据集的训练和验证过程,内容包括:
1 数据集的标注
2 数据集的转换
3 使用SSD如何训练
4 使用SSD如何测试
1 数据集的标注
数据的标注使用BBox-Label-Tool工具,该工具使用python实现,使用简单方便。修改后的工具支持多label的标签标注。
该工具生成的标签格式是:
object_number
className x1min y1min x1max y1max
classname x2min y2min x2max y2max
...
1.1 labelTool工具的使用说明
BBox-Label-Tool工具实现较简单,原始的git版本使用起来有一些小问题,进行了简单的修改,修改后的版本
#------------------------------------------------------------------------------- # Name: Object bounding box label tool # Purpose: Label object bboxes for ImageNet Detection data # Author: Qiushi # Created: 06/06/2014 # #------------------------------------------------------------------------------- from __future__ import division from Tkinter import * import tkMessageBox from PIL import Image, ImageTk import os import glob import random # colors for the bboxes COLORS = [\'red\', \'blue\', \'yellow\', \'pink\', \'cyan\', \'green\', \'black\'] # image sizes for the examples SIZE = 256, 256 classLabels=[\'mat\', \'door\', \'sofa\', \'chair\', \'table\', \'bed\', \'ashcan\', \'shoe\'] class LabelTool(): def __init__(self, master): # set up the main frame self.parent = master self.parent.title("LabelTool") self.frame = Frame(self.parent) self.frame.pack(fill=BOTH, expand=1) self.parent.resizable(width = False, height = False) # initialize global state self.imageDir = \'\' self.imageList= [] self.egDir = \'\' self.egList = [] self.outDir = \'\' self.cur = 0 self.total = 0 self.category = 0 self.imagename = \'\' self.labelfilename = \'\' self.tkimg = None # initialize mouse state self.STATE = {} self.STATE[\'click\'] = 0 self.STATE[\'x\'], self.STATE[\'y\'] = 0, 0 # reference to bbox self.bboxIdList = [] self.bboxId = None self.bboxList = [] self.hl = None self.vl = None self.currentClass = \'\' # ----------------- GUI stuff --------------------- # dir entry & load self.label = Label(self.frame, text = "Image Dir:") self.label.grid(row = 0, column = 0, sticky = E) self.entry = Entry(self.frame) self.entry.grid(row = 0, column = 1, sticky = W+E) self.ldBtn = Button(self.frame, text = "Load", command = self.loadDir) self.ldBtn.grid(row = 0, column = 2, sticky = W+E) # main panel for labeling self.mainPanel = Canvas(self.frame, cursor=\'tcross\') self.mainPanel.bind("<Button-1>", self.mouseClick) self.mainPanel.bind("<Motion>", self.mouseMove) self.parent.bind("<Escape>", self.cancelBBox) # press <Espace> to cancel current bbox self.parent.bind("s", self.cancelBBox) self.parent.bind("a", self.prevImage) # press \'a\' to go backforward self.parent.bind("d", self.nextImage) # press \'d\' to go forward self.mainPanel.grid(row = 1, column = 1, rowspan = 4, sticky = W+N) # showing bbox info & delete bbox self.lb1 = Label(self.frame, text = \'Bounding boxes:\') self.lb1.grid(row = 1, column = 2, sticky = W+N) self.listbox = Listbox(self.frame, width = 22, height = 12) self.listbox.grid(row = 2, column = 2, sticky = N) self.btnDel = Button(self.frame, text = \'Delete\', command = self.delBBox) self.btnDel.grid(row = 3, column = 2, sticky = W+E+N) self.btnClear = Button(self.frame, text = \'ClearAll\', command = self.clearBBox) self.btnClear.grid(row = 4, column = 2, sticky = W+E+N) #select class type self.classPanel = Frame(self.frame) self.classPanel.grid(row = 5, column = 1, columnspan = 10, sticky = W+E) label = Label(self.classPanel, text = \'class:\') label.grid(row = 5, column = 1, sticky = W+N) self.classbox = Listbox(self.classPanel, width = 4, height = 2) self.classbox.grid(row = 5,column = 2) for each in range(len(classLabels)): function = \'select\' + classLabels[each] print classLabels[each] btnMat = Button(self.classPanel, text = classLabels[each], command = getattr(self, function)) btnMat.grid(row = 5, column = each + 3) # control panel for image navigation self.ctrPanel = Frame(self.frame) self.ctrPanel.grid(row = 6, column = 1, columnspan = 2, sticky = W+E) self.prevBtn = Button(self.ctrPanel, text=\'<< Prev\', width = 10, command = self.prevImage) self.prevBtn.pack(side = LEFT, padx = 5, pady = 3) self.nextBtn = Button(self.ctrPanel, text=\'Next >>\', width = 10, command = self.nextImage) self.nextBtn.pack(side = LEFT, padx = 5, pady = 3) self.progLabel = Label(self.ctrPanel, text = "Progress: / ") self.progLabel.pack(side = LEFT, padx = 5) self.tmpLabel = Label(self.ctrPanel, text = "Go to Image No.") self.tmpLabel.pack(side = LEFT, padx = 5) self.idxEntry = Entry(self.ctrPanel, width = 5) self.idxEntry.pack(side = LEFT) self.goBtn = Button(self.ctrPanel, text = \'Go\', command = self.gotoImage) self.goBtn.pack(side = LEFT) # example pannel for illustration self.egPanel = Frame(self.frame, border = 10) self.egPanel.grid(row = 1, column = 0, rowspan = 5, sticky = N) self.tmpLabel2 = Label(self.egPanel, text = "Examples:") self.tmpLabel2.pack(side = TOP, pady = 5) self.egLabels = [] for i in range(3): self.egLabels.append(Label(self.egPanel)) self.egLabels[-1].pack(side = TOP) # display mouse position self.disp = Label(self.ctrPanel, text=\'\') self.disp.pack(side = RIGHT) self.frame.columnconfigure(1, weight = 1) self.frame.rowconfigure(10, weight = 1) # for debugging ## self.setImage() ## self.loadDir() def loadDir(self, dbg = False): if not dbg: s = self.entry.get() self.parent.focus() self.category = int(s) else: s = r\'D:\workspace\python\labelGUI\' ## if not os.path.isdir(s): ## tkMessageBox.showerror("Error!", message = "The specified dir doesn\'t exist!") ## return # get image list self.imageDir = os.path.join(r\'./Images\', \'%d\' %(self.category)) self.imageList = glob.glob(os.path.join(self.imageDir, \'*.jpg\')) if len(self.imageList) == 0: print \'No .JPEG images found in the specified dir!\' return # set up output dir self.outDir = os.path.join(r\'./Labels\', \'%d\' %(self.category)) if not os.path.exists(self.outDir): os.mkdir(self.outDir) labeledPicList = glob.glob(os.path.join(self.outDir, \'*.txt\')) for label in labeledPicList: data = open(label, \'r\') if \'0\n\' == data.read(): data.close() continue data.close() picture = label.replace(\'Labels\', \'Images\').replace(\'.txt\', \'.jpg\') if picture in self.imageList: self.imageList.remove(picture) # default to the 1st image in the collection self.cur = 1 self.total = len(self.imageList) self.loadImage() print \'%d images loaded from %s\' %(self.total, s) def loadImage(self): # load image imagepath = self.imageList[self.cur - 1] self.img = Image.open(imagepath) self.imgSize = self.img.size self.tkimg = ImageTk.PhotoImage(self.img) self.mainPanel.config(width = max(self.tkimg.width(), 400), height = max(self.tkimg.height(), 400)) self.mainPanel.create_image(0, 0, image = self.tkimg, anchor=NW) self.progLabel.config(text = "%04d/%04d" %(self.cur, self.total)) # load labels self.clearBBox() self.imagename = os.path.split(imagepath)[-1].split(\'.\')[0] labelname = self.imagename + \'.txt\' self.labelfilename = os.path.join(self.outDir, labelname) bbox_cnt = 0 if os.path.exists(self.labelfilename): with open(self.labelfilename) as f: for (i, line) in enumerate(f): if i == 0: bbox_cnt = int(line.strip()) continue tmp = [int(t.strip()) for t in line.split()] ## print tmp self.bboxList.append(tuple(tmp)) tmpId = self.mainPanel.create_rectangle(tmp[0], tmp[1], \ tmp[2], tmp[3], \ width = 2, \ outline = COLORS[(len(self.bboxList)-1) % len(COLORS)]) self.bboxIdList.append(tmpId) self.listbox.insert(END, \'(%d, %d) -> (%d, %d)\' %(tmp[0], tmp[1], tmp[2], tmp[3])) self.listbox.itemconfig(len(self.bboxIdList) - 1, fg = COLORS[(len(self.bboxIdList) - 1) % len(COLORS)]) def saveImage(self): with open(self.labelfilename, \'w\') as f: f.write(\'%d\n\' %len(self.bboxList)) for bbox in self.bboxList: f.write(\' \'.join(map(str, bbox)) + \'\n\') print \'Image No. %d saved\' %(self.cur) def mouseClick(self, event): if self.STATE[\'click\'] == 0: self.STATE[\'x\'], self.STATE[\'y\'] = event.x, event.y #self.STATE[\'x\'], self.STATE[\'y\'] = self.imgSize[0], self.imgSize[1] else: x1, x2 = min(self.STATE[\'x\'], event.x), max(self.STATE[\'x\'], event.x) y1, y2 = min(self.STATE[\'y\'], event.y), max(self.STATE[\'y\'], event.y) if x2 > self.imgSize[0]: x2 = self.imgSize[0] if y2 > self.imgSize[1]: y2 = self.imgSize[1] self.bboxList.append((self.currentClass, x1, y1, x2, y2)) self.bboxIdList.append(self.bboxId) self.bboxId = None self.listbox.insert(END, \'(%d, %d) -> (%d, %d)\' %(x1, y1, x2, y2)) self.listbox.itemconfig(len(self.bboxIdList) - 1, fg = COLORS[(len(self.bboxIdList) - 1) % len(COLORS)]) self.STATE[\'click\'] = 1 - self.STATE[\'click\'] def mouseMove(self, event): self.disp.config(text = \'x: %d, y: %d\' %(event.x, event.y)) if self.tkimg: if self.hl: self.mainPanel.delete(self.hl) self.hl = self.mainPanel.create_line(0, event.y, self.tkimg.width(), event.y, width = 2) if self.vl: self.mainPanel.delete(self.vl) self.vl = self.mainPanel.create_line(event.x, 0, event.x, self.tkimg.height(), width = 2) if 1 == self.STATE[\'click\']: if self.bboxId: self.mainPanel.delete(self.bboxId) self.bboxId = self.mainPanel.create_rectangle(self.STATE[\'x\'], self.STATE[\'y\'], \ event.x, event.y, \ width = 2, \ outline = COLORS[len(self.bboxList) % len(COLORS)]) def cancelBBox(self, event): if 1 == self.STATE[\'click\']: if self.bboxId: self.mainPanel.delete(self.bboxId) self.bboxId = None self.STATE[\'click\'] = 0 def delBBox(self): sel = self.listbox.curselection() if len(sel) != 1 : return idx = int(sel[0]) self.mainPanel.delete(self.bboxIdList[idx]) self.bboxIdList.pop(idx) self.bboxList.pop(idx) self.listbox.delete(idx) def clearBBox(self): for idx in range(len(self.bboxIdList)): self.mainPanel.delete(self.bboxIdList[idx]) self.listbox.delete(0, len(self.bboxList)) self.bboxIdList = [] self.bboxList = [] def selectmat(self): self.currentClass = \'mat\' self.classbox.delete(0,END) self.classbox.insert(0, \'mat\') self.classbox.itemconfig(0,fg = COLORS[0]) def selectdoor(self): self.currentClass = \'door\' self.classbox.delete(0,END) self.classbox.insert(0, \'door\') self.classbox.itemconfig(0,fg = COLORS[0]) def selectsofa(self): self.currentClass = \'sofa\' self.classbox.delete(0,END) self.classbox.insert(0, \'sofa\') self.classbox.itemconfig(0,fg = COLORS[0]) def selectchair(self): self.currentClass = \'chair\' self.classbox.delete(0,END) self.classbox.insert(0, \'chair\') self.classbox.itemconfig(0,fg = COLORS[0]) def selecttable(self): self.currentClass = \'table\' self.classbox.delete(0,END) self.classbox.insert(0, \'table\') self.classbox.itemconfig(0,fg = COLORS[0]) def selectbed(self): self.currentClass = \'bed\' self.classbox.delete(0,END) self.classbox.insert(0, \'bed\') self.classbox.itemconfig(0,fg = COLORS[0]) def selectashcan(self): self.currentClass = \'ashcan\' self.classbox.delete(0,END) self.classbox.insert(0, \'ashcan\') self.classbox.itemconfig(0,fg = COLORS[0]) def selectshoe(self): self.currentClass = \'shoe\' self.classbox.delete(0,END) self.classbox.insert(0, \'shoe\') self.classbox.itemconfig(0,fg = COLORS[0]) def prevImage(self, event = None): self.saveImage() if self.cur > 1: self.cur -= 1 self.loadImage() def nextImage(self, event = None): self.saveImage() if self.cur < self.total: self.cur += 1 self.loadImage() def gotoImage(self): idx = int(self.idxEntry.get()) if 1 <= idx and idx <= self.total: self.saveImage() self.cur = idx self.loadImage() ## def setImage(self, imagepath = r\'test2.png\'): ## self.img = Image.open(imagepath) ## self.tkimg = ImageTk.PhotoImage(self.img) ## self.mainPanel.config(width = self.tkimg.width()) ## self.mainPanel.config(height = self.tkimg.height()) ## self.mainPanel.create_image(0, 0, image = self.tkimg, anchor=NW) if __name__ == \'__main__\': root = Tk() tool = LabelTool(root) root.mainloop()
main.py
使用方法:
(1) 在BBox-Label-Tool/Images目录下创建保存图片的目录, 目录以数字命名(BBox-Label-Tool/Images/1), 然后将待标注的图片copy到1这个目录下;
(2) 在BBox-Label-Tool目录下执行命令 python main.py
(3) 在工具界面上, Image Dir 框中输入需要标记的目录名(比如 1), 然后点击load按钮, 工具自动将Images/1目录下的图片加载进来;
需要说明一下, 如果目录中的图片已经标注过,点击load时不会被重新加载进来.
(4) 该工具支持多类别标注, 画bounding boxs框标定之前,需要先选定类别,然后再画框.
(5) 一张图片标注完后, 点击Next>>按钮, 标注下一张图片, 图片label成功后,会在BBox-Label-Tool/Labels对应的目录下生成与图片文件名对应的label文件.
2 数据集的转换
caffe训练使用LMDB格式的数据,ssd框架中提供了voc数据格式转换成LMDB格式的脚本。
所以实践中先将BBox-Label-Tool标注的数据转换成voc数据格式,然后再转换成LMDB格式。
2.1 voc数据格式
(1)Annotations中保存的是xml格式的label信息
<?xml version="1.0" ?> <annotation> <folder>VOC2007</folder> <filename>1.jpg</filename> <source> <database>My Database</database> <annotation>VOC2007</annotation> <image>flickr</image> <flickrid>NULL</flickrid> </source> <owner> <flickrid>NULL</flickrid> <name>idaneel</name> </owner> <size> <width>320</width> <height>240</height> <depth>3</depth> </size> <segmented>0</segmented> <object> <name>door</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>109</xmin> <ymin>3</ymin> <xmax>199</xmax> <ymax>204</ymax> </bndbox> </object> </annotation>
VOC XML内容信息
(2)ImageSet目录下的Main目录里存放的是用于表示训练的图片集和测试的图片集
(3)JPEGImages目录下存放所有图片集
(4)label目录下保存的是BBox-Label-Tool工具标注好的bounding box坐标文件,
该目录下的文件就是待转换的label标签文件。
2.2 Label转换成VOC数据格式
BBox-Label-Tool工具标注好的bounding box坐标文件转换成VOC数据格式的形式.
具体的转换过程包括了两个步骤:
(1)将BBox-Label-Tool下的txt格式保存的bounding box信息转换成VOC数据格式下以xml方式表示;
(2)生成用于训练的数据集和用于测试的数据集。
用python实现了上述两个步骤的换转。
createXml.py 完成txt到xml的转换; 执行脚本./createXml.py
#!/usr/bin/env python import os import sys import cv2 from itertools import islice from xml.dom.minidom import Document labels=\'label\' imgpath=\'JPEGImages/\' xmlpath_new=\'Annotations/\' foldername=\'VOC2007\' def insertObject(doc, datas): obj = doc.createElement(\'object\') name = doc.createElement(\'name\') name.appendChild(doc.createTextNode(datas[0])) obj.appendChild(name) pose = doc.createElement(\'pose\') pose.appendChild(doc.createTextNode(\'Unspecified\')) obj.appendChild(pose) truncated = doc.createElement(\'truncated\') truncated.appendChild(doc.createTextNode(str(0))) obj.appendChild(truncated) difficult = doc.createElement(\'difficult\') difficult.appendChild(doc.createTextNode(str(0))) obj.appendChild(difficult) bndbox = doc.createElement(\'bndbox\') xmin = doc.createElement(\'xmin\') xmin.appendChild(doc.createTextNode(str(datas[1]))) bndbox.appendChild(xmin) ymin = doc.createElement(\'ymin\') ymin.appendChild(doc.createTextNode(str(datas[2]))) bndbox.appendChild(ymin) xmax = doc.createElement(\'xmax\') xmax.appendChild(doc.createTextNode(str(datas[3]))) bndbox.appendChild(xmax) ymax = doc.createElement(\'ymax\') if \'\r\' == str(datas[4])[-1] or \'\n\' == str(datas[4])[-1]: data = str(datas[4])[0:-1] else: data = str(datas[4]) ymax.appendChild(doc.createTextNode(data)) bndbox.appendChild(ymax) obj.appendChild(bndbox) return obj def create(): for walk in os.walk(labels): for each in walk[2]: fidin=open(walk[0] + \'/\'+ each,\'r\') objIndex = 0 for data in islice(fidin, 1, None): objIndex += 1 data=data.strip(\'\n\') datas = data.split(\' \') if 5 != len(datas): print \'bounding box information error\' continue pictureName = each.replace(\'.txt\', \'.jpg\') imageFile = imgpath + pictureName img = cv2.imread(imageFile) imgSize = img.shape if 1 == objIndex: xmlName = each.replace(\'.txt\', \'.xml\') f = open(xmlpath_new + xmlName, "w") doc = Document() annotation = doc.createElement(\'annotation\') doc.appendChild(annotation) folder = doc.createElement(\'folder\') folder.appendChild(doc.createTextNode(foldername)) annotation.appendChild(folder) filename = doc.createElement(\'filename\') filename.appendChild(doc.createTextNode(pictureName)) annotation.appendChild(filename) source = doc.createElement(\'source\') database = doc.createElement(\'database\') database.appendChild(doc.createTextNode(\'My Database\')) source.appendChild(database) source_annotation = doc.createElement(\'annotation\') source_annotation.appendChild(doc.createTextNode(foldername)) source.appendChild(source_annotation) image = doc.createElement(\'image\') image.appendChild(doc.createTextNode(\'flickr\')) source.appendChild(image) flickrid = doc.createElement(\'flickrid\') flickrid.appendChild(doc.createTextNode(\'NULL\')) source.appendChild(flickrid) annotation.appendChild(source) owner = doc.createElement(\'owner\') flickrid = doc.createElement(\'flickrid\') flickrid.appendChild(doc.createTextNode(\'NULL\')) owner.appendChild(flickrid) name = doc.createElement(\'name\') name.appendChild(doc.createTextNode(\'idaneel\')) owner.appendChild(name) annotation.appendChild(owner) size = doc.createElement(\'size\') width = doc.createElement(\'width\') width.appendChild(doc.createTextNode(str(imgSize[1]))) size.appendChild(width) height = doc.createElement(\'height\') height.appendChild(doc.createTextNode(str(imgSize[0]))) size.appendChild(height) depth = doc.createElement(\'depth\') depth.appendChild(doc.createTextNode(str(imgSize[2]))) size.appendChild(depth) annotation.appendChild(size) segmented = doc.createElement(\'segmented\') segmented.appendChild(doc.createTextNode(str(0))) annotation.appendChild(segmented) annotation.appendChild(insertObject(doc, datas)) else: annotation.appendChild(insertObject(doc, datas)) try: f.write(doc.toprettyxml(indent = \' \')) f.close() fidin.close() except: pass if __name__ == \'__main__\': create()
createXml.py
createTest.py 生成训练集和测试集标识文件; 执行脚本
./createTest.py %startID% %endID% %testNumber%
#!/usr/bin/env python import os import sys import random try: start = int(sys.argv[1]) end = int(sys.argv[2]) test = int(sys.argv[3]) allNum = end-start+1 except: print \'Please input picture range\' print \'./createTest.py 1 1500 500\' os._exit(0) b_list = range(start,end) blist_webId = random.sample(b_list, test) blist_webId = sorted(blist_webId) allFile = [] testFile = open(\'ImageSets/Main/test.txt\', \'w\') trainFile = open(\'ImageSets/Main/trainval.txt\', \'w\') for i in range(allNum): allFile.append(i+1) for test in blist_webId: allFile.remove(test) testFile.write(str(test) + \'\n\') for train in allFile: trainFile.write(str(train) + \'\n\') testFile.close() trainFile.close()
createTest.py
说明: 由于BBox-Label-Tool实现相对简单,该工具每次只能对一个类别进行打标签,所以转换脚本
每一次也是对一个类别进行数据的转换,这个问题后续需要优化改进。
优化后的BBox-Label-Tool工具,支持多类别标定,生成的label文件中增加了类别名称信息。
使用时修改classLabels,改写成自己的类别, 修改后的工具代码参见1.1中的main.py
2.3 VOC数据转换成LMDB数据
SSD提供了VOC数据到LMDB数据的转换脚本 data/VOC0712/create_list.sh 和 ./data/VOC0712/create_data.sh,这两个脚本是完全针对VOC0712目录下的数据进行的转换。
实现中为了不破坏VOC0712目录下的数据内容,针对我们自己的数据集,修改了上面这两个脚本,
将脚本中涉及到VOC0712的信息替换成我们自己的目录信息。
在处理我们的数据集时,将VOC0712替换成indoor。
具体的步骤如下:
(1) 在 $HOME/data/VOCdevkit目录下创建indoor目录,该目录中存放自己转换完成的VOC数据集;
(2) $CAFFE_ROOT/examples目录下创建indoor目录;
(3) $CAFFE_ROOT/data目录下创建indoor目录,同时将data/VOC0712下的create_list.sh,create_data.sh,labelmap_voc.prototxt
这三个文件copy到indoor目录下,分别重命名为create_list_indoor.sh,create_data_indoor.sh, labelmap_indoor.prototxt
(4)对上面新生成的两个create文件进行修改,主要修改是将VOC0712相关的信息替换成indoor
修改后的这两个文件分别为:
#!/bin/bash root_dir=$HOME/data/VOCdevkit/ sub_dir=ImageSets/Main bash_dir="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" for dataset in trainval test do dst_file=$bash_dir/$dataset.txt if [ -f $dst_file ] then rm -f $dst_file fi for name in indoor do if [[ $dataset == "test" && $name == "VOC2012" ]] then continue fi echo "Create list for $name $dataset..." dataset_file=$root_dir/$name/$sub_dir/$dataset.txt img_file=$bash_dir/$dataset"_img.txt" cp $dataset_file $img_file sed -i "s/^/$name\/JPEGImages\//g" $img_file sed -i "s/$/.jpg/g" $img_file label_file=$bash_dir/$dataset"_label.txt" cp $dataset_file $label_file sed -i "s/^/$name\/Annotations\//g" $label_file sed -i "s/$/.xml/g" $label_file paste -d\' \' $img_file $label_file >> $dst_file rm -f $label_file rm -f $img_file done # Generate image name and size infomation. if [ $dataset == "test" ] then $bash_dir/../../build/tools/get_image_size $root_dir $dst_file $bash_dir/$dataset"_name_size.txt" fi # Shuffle trainval file. if [ $dataset == "trainval" ] then rand_file=$dst_file.random cat $dst_file | perl -MList::Util=shuffle -e \'print shuffle(<STDIN>);\' > $rand_file mv $rand_file $dst_file fi done
create_list_indoor.sh
cur_dir=$(cd $( dirname ${BASH_SOURCE[0]} ) && pwd ) root_dir=$cur_dir/../.. cd $root_dir redo=1 data_root_dir="$HOME/data/VOCdevkit" dataset_name="indoor" mapfile="$root_dir/data/$dataset_name/labelmap_indoor.prototxt" anno_type="detection" db="lmdb" min_dim=0 max_dim=0 width=0 height=0 extra_cmd="--encode-type=jpg --encoded" if [ $redo ] then extra_cmd="$extra_cmd --redo" fi for subset in test trainval do python $root_dir/scripts/create_annoset.py --anno-type=$anno_type --label-map-file=$mapfile --min-dim=$min_dim --max-dim=$max_dim --resize-width=$width --resize-height=$height --check-label $extra_cmd $data_root_dir $root_dir/data/$dataset_name/$subset.txt $data_root_dir/$dataset_name/$db/$dataset_name"_"$subset"_"$db examples/$dataset_name done
create_data_indoor.sh
(5)修改labelmap_indoor.prototxt,将该文件中的类别修改成和自己的数据集相匹配,注意需要保留一个label 0 , background类别
item {
name: "none_of_the_above"
label: 0
display_name: "background"
}
item {
name: "door"
label: 1
display_name: "door"
}
labelmap_indoor.prototxt
完成上面步骤的修改后,可以开始LMDB数据数据的制作,在$CAFFE_ROOT目录下分别运行:
./data/indoor/create_list_indoor.sh
./data/indoor/create_data_indoor.sh
命令执行完毕后,可以在$CAFFE_ROOT/indoor目录下查看转换完成的LMDB数据数据。
3 使用SSD进行自己数据集的训练
训练时使用ssd demo中提供的预训练好的VGGnet model : VGG_ILSVRC_16_layers_fc_reduced.caffemodel
将该模型保存到$CAFFE_ROOT/models/VGGNet下。
将ssd_pascal.py copy一份 ssd_pascal_indoor.py文件, 根据自己的数据集修改ssd_pascal_indoor.py
主要修改点:
(1)train_data和test_data修改成指向自己的数据集LMDB
train_data = "examples/indoor/indoor_trainval_lmdb"
test_data = "examples/indoor/indoor_test_lmdb"
(2) num_test_image该变量修改成自己数据集中测试数据的数量
(3)num_classes 该变量修改成自己数据集中 标签类别数量数 + 1
针对我的数据集,ssd_pascal_indoor.py
的内容为:
from __future__ import print_function import caffe from caffe.model_libs import * from google.protobuf import text_format import math import os import shutil import stat import subprocess import sys # Add extra layers on top of a "base" network (e.g. VGGNet or Inception). def AddExtraLayers(net, use_batchnorm=True): use_relu = True # Add additional convolutional layers. from_layer = net.keys()[-1] # TODO(weiliu89): Construct the name using the last layer to avoid duplication. out_layer = "conv6_1" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 1, 0, 1) from_layer = out_layer out_layer = "conv6_2" ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 512, 3, 1, 2) for i in xrange(7, 9): from_layer = out_layer out_layer = "conv{}_1".format(i) ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 128, 1, 0, 1) from_layer = out_layer out_layer = "conv{}_2".format(i) ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 3, 1, 2) # Add global pooling layer. name = net.keys()[-1] net.pool6 = L.Pooling(net[name], pool=P.Pooling.AVE, global_pooling=True) return net ### Modify the following parameters accordingly ### # The directory which contains the caffe code. # We assume you are running the script at the CAFFE_ROOT. caffe_root = os.getcwd() # Set true if you want to start training right after generating all files. run_soon = True # Set true if you want to load from most recently saved snapshot. # Otherwise, we will load from the pretrain_model defined below. resume_training = True # If true, Remove old model files. remove_old_models = False # The database file for training data. Created by data/VOC0712/create_data.sh train_data = "examples/indoor/indoor_trainval_lmdb" # The database file for testing data. Created by data/VOC0712/create_data.sh test_data = "examples/indoor/indoor_test_lmdb" # Specify the batch sampler. resize_width = 300 resize_height = 300 resize = "{}x{}".format(resize_width, resize_height) batch_sampler = [ { \'sampler\': { }, \'max_trials\': 1, \'max_sample\': 1, }, { \'sampler\': { \'min_scale\': 0.3, \'max_scale\': 1.0, \'min_aspect_ratio\': 0.5, \'max_aspect_ratio\': 2.0, }, \'sample_constraint\': { \'min_jaccard_overlap\': 0.1, }, \'max_trials\': 50, \'max_sample\': 1, }, { \'sampler\': { \'min_scale\': 0.3, \'max_scale\': 1.0, \'min_aspect_ratio\': 0.5, \'max_aspect_ratio\': 2.0, }, \'sample_constraint\': { \'min_jaccard_overlap\': 0.3, }, \'max_trials\': 50, \'max_sample\': 1, }, { \'sampler\': { \'min_scale\': 0.3, \'max_scale\': 1.0, \'min_aspect_ratio\': 0.5, \'max_aspect_ratio\': 2.0, }, \'sample_constraint\': { \'min_jaccard_overlap\': 0.5, }, \'max_trials\': 50, \'max_sample\': 1, }, { \'sampler\': { \'min_scale\': 0.3, \'max_scale\': 1.0, \'min_aspect_ratio\': 0.5, \'max_aspect_ratio\': 2.0, }, \'sample_constraint\': { \'min_jaccard_overlap\': 0.7, }, \'max_trials\': 50, \'max_sample\': 1, }, { \'sampler\': { \'min_scale\': 0.3, \'max_scale\': 1.0, \'min_aspect_ratio\': 0.5, \'max_aspect_ratio\': 2.0, }, \'sample_constraint\': { \'min_jaccard_overlap\': 0.9, }, \'max_trials\': 50, \'max_sample\': 1, }, { \'sampler\': { \'min_scale\': 0.3, \'max_scale\': 1.0, \'min_aspect_ratio\': 0.5, \'max_aspect_ratio\': 2.0, }, \'sample_constraint\': { \'max_jaccard_overlap\': 1.0, }, \'max_trials\': 50, \'max_sample\': 1, }, ] train_transform_param = { \'mirror\': True, \'mean_value\': [104, 117, 123], \'resize_param\': { \'prob\': 1, \'resize_mode\': P.Resize.WARP, \'height\': resize_height, \'width\': resize_width, \'interp_mode\': [ P.Resize.LINEAR, P.Resize.AREA, P.Resize.NEAREST, P.Resize.CUBIC, P.Resize.LANCZOS4, ], }, \'emit_constraint\': { \'emit_type\': caffe_pb2.EmitConstraint.CENTER, } } test_transform_param = { \'mean_value\': [104, 117, 123], \'resize_param\': { \'prob\': 1, \'resize_mode\': P.Resize.WARP, \'height\': resize_height, \'width\': resize_width, \'interp_mode\': [P.Resize.LINEAR], }, } # If true, use batch norm for all newly added layers. # Currently only the non batch norm version has been tested. use_batchnorm = False # Use different initial learning rate. if use_batchnorm: base_lr = 0.0004 else: # A learning rate for batch_size = 1, num_gpus = 1. base_lr = 0.00004 # Modify the job name if you want. job_name = "SSD_{}".format(resize) # The name of the model. Modify it if you want. model_name = "VGG_VOC0712_{}".format(job_name) # Directory which stores the model .prototxt file. save_dir = "models/VGGNet/VOC0712/{}".format(job_name) # Directory which stores the snapshot of models. snapshot_dir = "models/VGGNet/VOC0712/{}".format(job_name) # Directory which stores the job script and log file. job_dir = "jobs/VGGNet/VOC0712/{}".format(job_name) # Directory which stores the detection results. output_result_dir = "{}/data/VOCdevkit/results/VOC2007/{}/Main".format(os.environ[\'HOME\'], job_name) # model definition files. train_net_file = "{}/train.prototxt".format(save_dir) test_net_file = "{}/test.prototxt".format(save_dir) deploy_net_file = "{}/deploy.prototxt".format(save_dir) solver_file = "{}/solver.prototxt".format(save_dir) # snapshot prefix. snapshot_prefix = "{}/{}".format(snapshot_dir, model_name) # job script path. job_file = "{}/{}.sh".format(job_dir, model_name) # Stores the test image names and sizes. Created by data/VOC0712/create_list.sh name_size_file = "data/indoor/test_name_size.txt" # The pretrained model. We use the Fully convolutional reduced (atrous) VGGNet. pretrain_model = "models/VGGNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel" # Stores LabelMapItem. label_map_file = "data/indoor/labelmap_indoor.prototxt" # MultiBoxLoss parameters. num_classes = 2 share_location = True background_label_id=0 train_on_diff_gt = True normalization_mode = P.Loss.VALID code_type = P.PriorBox.CENTER_SIZE neg_pos_ratio = 3. loc_weight = (neg_pos_ratio + 1.) / 4. multibox_loss_param = { \'loc_loss_type\': P.MultiBoxLoss.SMOOTH_L1, \'conf_loss_type\': P.MultiBoxLoss.SOFTMAX, \'loc_weight\': loc_weight, \'num_classes\': num_classes, \'share_location\': share_location, \'match_type\': P.MultiBoxLoss.PER_PREDICTION, \'overlap_threshold\': 0.5, \'use_prior_for_matching\': True, \'background_label_id\': background_label_id, \'use_difficult_gt\': train_on_diff_gt, \'do_neg_mining\': True, \'neg_pos_ratio\': neg_pos_ratio, \'neg_overlap\': 0.5, \'code_type\': code_type, } loss_param = { \'normalization\': normalization_mode, } # parameters for generating priors. # minimum dimension of input image min_dim = 300 # conv4_3 ==> 38 x 38 # fc7 ==> 19 x 19 # conv6_2 ==> 10 x 10 # conv7_2 ==> 5 x 5 # conv8_2 ==> 3 x 3 # pool6 ==> 1 x 1 mbox_source_layers = [\'conv4_3\', \'fc7\', \'conv6_2\', \'conv7_2\', \'conv8_2\', \'pool6\'] # in percent % min_ratio = 20 max_ratio = 95 step = int(math.floor((max_ratio - min_ratio) / (len(mbox_source_layers) - 2))) min_sizes = [] max_sizes = [] for ratio in xrange(min_ratio, max_ratio + 1, step): min_sizes.append(min_dim * ratio / 100.) max_sizes.append(min_dim * (ratio + step) / 100.) min_sizes = [min_dim * 10 / 100.] + min_sizes max_sizes = [[]] + max_sizes aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]] # L2 normalize conv4_3. normalizations = [20, -1, -1, -1, -1, -1] # variance used to encode/decode prior bboxes. if code_type == P.PriorBox.CENTER_SIZE: prior_variance = [0.1, 0.1, 0.2, 0.2] else: prior_variance = [0.1] flip = True clip = True # Solver parameters. # Defining which GPUs to use. gpus = "0" gpulist = gpus.split(",") num_gpus = len(gpulist) # Divide the mini-batch to different GPUs. batch_size = 4 accum_batch_size = 32 iter_size = accum_batch_size / batch_size solver_mode = P.Solver.CPU device_id = 0 batch_size_per_device = batch_size if num_gpus > 0: batch_size_per_device = int(math.ceil(float(batch_size) / num_gpus)) iter_size = int(math.ceil(float(accum_batch_size) / (batch_size_per_device * num_gpus))) solver_mode = P.Solver.GPU device_id = int(gpulist[0]) if normalization_mode == P.Loss.NONE: base_lr /= batch_size_per_device elif normalization_mode == P.Loss.VALID: base_lr *= 25. / loc_weight elif normalization_mode == P.Loss.FULL: # Roughly there are 2000 prior bboxes per image. # TODO(weiliu89): Estimate the exact # of priors. base_lr *= 2000. # Which layers to freeze (no backward) during training. freeze_layers = [\'conv1_1\', \'conv1_2\', \'conv2_1\', \'conv2_2\'] # Evaluate on whole test set. num_test_image = 800 test_batch_size = 1 test_iter = num_test_image / test_batch_size solver_param = { # Train parameters \'base_lr\': base_lr, \'weight_decay\': 0.0005, \'lr_policy\': "step", \'stepsize\': 40000, \'gamma\': 0.1, \'momentum\': 0.9, \'iter_size\': iter_size, \'max_iter\': 60000, \'snapshot\': 40000, \'display\': 10, \'average_loss\': 10, \'type\': "SGD", \'solver_mode\': solver_mode, \'device_id\': device_id, \'debug_info\': False, \'snapshot_after_train\': True, # Test parameters \'test_iter\': [test_iter], \'test_interval\': 10000, \'eval_type\': "detection", \'ap_version\': "11point", \'test_initialization\': False, } # parameters for generating detection output. det_out_param = { \'num_classes\': num_classes, \'share_location\': share_location, \'background_label_id\': background_label_id, \'nms_param\': {\'nms_threshold\': 0.45, \'top_k\': 400}, \'save_output_param\': { \'output_directory\': output_result_dir, \'output_name_prefix\': "comp4_det_test_", \'output_format\': "VOC", \'label_map_file\': label_map_file, \'name_size_file\': name_size_file, \'num_test_image\': num_test_image, }, \'keep_top_k\': 200, \'confidence_threshold\': 0.01, \'code_type\': code_type, } # parameters for evaluating detection results. det_eval_param = { \'num_classes\': num_classes, \'background_label_id\': background_label_id, \'overlap_threshold\': 0.5, \'evaluate_difficult_gt\': False, \'name_size_file\': name_size_file, } ### Hopefully you don\'t need to change the following ### # Check file. check_if_exist(train_data) check_if_exist(test_data) check_if_exist(label_map_file) check_if_exist(pretrain_model) make_if_not_exist(save_dir) make_if_not_exist(job_dir) make_if_not_exist(snapshot_dir) # Create train net. net = caffe.NetSpec() net.data, net.label = CreateAnnotatedDataLayer(train_data, batch_size=batch_size_per_device, train=True, output_label=True, label_map_file=label_map_file, transform_param=train_transform_param, batch_sampler=batch_sampler) VGGNetBody(net, from_layer=\'data\', fully_conv=True, reduced=True, dilated=True, dropout=False, freeze_layers=freeze_layers) AddExtraLayers(net, use_batchnorm) mbox_layers = CreateMultiBoxHead(net, data_layer=\'data\', from_layers=mbox_source_layers, use_batchnorm=use_batchnorm, min_sizes=min_sizes, max_sizes=max_sizes, aspect_ratios=aspect_ratios, normalizations=normalizations, num_classes=num_classes, share_location=share_location, flip=flip, clip=clip, prior_variance=prior_variance, kernel_size=3, pad=1) # Create the MultiBoxLossLayer. name = "mbox_loss" mbox_layers.append(net.label) net[name] = L.MultiBoxLoss(*mbox_layers, multibox_loss_param=multibox_loss_param, loss_param=loss_param, include=dict(phase=caffe_pb2.Phase.Value(\'TRAIN\')), propagate_down=[True, True, False, False]) with open(train_net_file, \'w\') as f: print(\'name: "{}_train"\'.format(model_name), file=f) print(net.to_proto(), file=f) shutil.copy(train_net_file, job_dir) # Create test net. net = caffe.NetSpec() net.data, net.label = CreateAnnotatedDataLayer(test_data, batch_size=test_batch_size, train=False, output_label=True, label_map_file=label_map_file, transform_param=test_transform_param) VGGNetBody(net, from_layer=\'data\', fully_conv=True, reduced=True, dilated=True, dropout=False, freeze_layers=freeze_layers) AddExtraLayers(net, use_batchnorm) mbox_layers = CreateMultiBoxHead(net, data_layer=\'data\', from_layers=mbox_source_layers, use_batchnorm=use_batchnorm, min_sizes=min_sizes, max_sizes=max_sizes, aspect_ratios=aspect_ratios, normalizations=normalizations, num_classes=num_classes, share_location=share_location, flip=flip, clip=clip, prior_variance=prior_variance, kernel_size=3, pad=1) conf_name = "mbox_conf" if multibox_loss_param["conf_loss_type"] == P.MultiBoxLoss.SOFTMAX: reshape_name = "{}_reshape".format(conf_name) net[reshape_name] = L.Reshape(net[conf_name], shape=dict(dim=[0, -1, num_classes])) softmax_name = "{}_softmax".format(conf_name) net[softmax_name] = L.Softmax(net[reshape_name], axis=2) flatten_name = "{}_flatten".format(conf_name) net[flatten_name] = L.Flatten(net[softmax_name], axis=1) mbox_layers[1] = net[flatten_name] elif multibox_loss_param["conf_loss_type"] == P.MultiBoxLoss.LOGISTIC: sigmoid_name = "{}_sigmoid".format(conf_name) net[sigmoid_name] = L.Sigmoid(net[conf_name]) mbox_layers[1] = net[sigmoid_name] net.detection_out = L.DetectionOutput(*mbox_layers, detection_output_param=det_out_param, include=dict(phase=caffe_pb2.Phase.Value(\'TEST\'))) net.detection_eval = L.DetectionEvaluate(net.detection_out, net.label, detection_evaluate_param=det_eval_param, include=dict(phase=caffe_pb2.Phase.Value(\'TEST\'))) with open(test_net_file, \'w\') as f: print(\'name: "{}_test"\'.format(model_name), file=f) print(net.to_proto(), file=f) shutil.copy(test_net_file, job_dir) # Create deploy net. # Remove the first and last layer from test net. deploy_net = net with open(deploy_net_file, \'w\') as f: net_param = deploy_net.to_proto() # Remove the first (AnnotatedData) and last (DetectionEvaluate) layer from test net. del net_param.layer[0] del net_param.layer[-1] net_param.name = \'{}_deploy\'.format(model_name) net_param.input.extend([\'data\']) net_param.input_shape.extend([ caffe_pb2.BlobShape(dim=[1, 3, resize_height, resize_width])]) print(net_param, file=f) shutil.copy(deploy_net_file, job_dir) # Create solver. solver = caffe_pb2.SolverParameter( train_net=train_net_file, test_net=[test_net_file], snapshot_prefix=snapshot_prefix, **solver_param) with open(solver_file, \'w\') as f: print(solver, file=f) shutil.copy(solver_file, job_dir) max_iter = 0 # Find most recent snapshot. for file in os.listdir(snapshot_dir): if file.endswith(".solverstate"): basename = os.path.splitext(file)[0] iter = int(basename.split("{}_iter_".format(model_name))[1]) if iter > max_iter: max_iter = iter train_src_param = \'--weights="{}" \\\n\'.format(pretrain_model) if resume_training: if max_iter > 0: train_src_param = \'--snapshot="{}_iter_{}.solverstate" \\\n\'.format(snapshot_prefix, max_iter) if remove_old_models: # Remove any snapshots smaller than max_iter. for file in os.listdir(snapshot_dir): if file.endswith(".solverstate"): basename = os.path.splitext(file)[0] iter = int(basename.split("{}_iter_".format(model_name))[1]) if max_iter > iter: os.remove("{}/{}".format(snapshot_dir, file)) if file.endswith(".caffemodel"): basename = os.path.splitext(file)[0] iter = int(basename.split("{}_iter_".format(model_name))[1]) if max_iter > iter: os.remove("{}/{}".format(snapshot_dir, file)) # Create job file. with open(job_file, \'w\') as f: f.write(\'cd {}\n\'.format(caffe_root)) f.write(\'./build/tools/caffe train \\\n\') f.write(\'--solver="{}" \\\n\'.format(solver_file)) f.write(train_src_param) if solver_param[\'solver_mode\'] == P.Solver.GPU: f.write(\'--gpu {} 2>&1 | tee {}/{}.log\n\'.format(gpus, job_dir, model_name)) else: f.write(\'2>&1 | tee {}/{}.log\n\'.format(job_dir, model_name)) # Copy the python script to job_dir. py_file = os.path.abspath(__file__) shutil.copy(py_file, job_dir) # Run the job. os.chmod(job_file, stat.S_IRWXU) if run_soon: subprocess.call(job_file, shell=True)
ssd_pascal_indoor.py
训练命令:
python examples/ssd/
ssd_pascal_indoor.py
4 测试
SSD框架中提供了测试代码,有C++版本和python版本
4.1
c++版本
编译完SSD后,C++版本的的可执行文件存放目录: .build_release/examples/ssd/ssd_detect.bin
测试命令 ./.build_release/examples/ssd/ssd_detect.bin
models/VGGNet/indoor/
deploy.prototxt models/VGGNet/indoor/VGG_VOC0712_SSD_300x300_iter_60000.caffemodel pictures.txt
其中pictures.txt中保存的是待测试图片的list
4.2
python版本
python 版本的测试过程参见examples/detection.ipynb
参考:
1 将数据集做成VOC2007格式用于Faster-RCNN训练
2 SSD的配置安装与测试