1、算法将检测的目标名称和目标位置保存到txt文本
图片名 xmin ymin xmax ymax
(4).avi237face.jpg
4
smoke 83 234 142 251
hand 119 255 271 306
eye 178 148 216 163
eye 111 156 148 173
#!/usr/bin/python
# -*- coding: UTF-8 -*- import os, h5py, cv2, sys, shutil
import numpy as np
from xml.dom.minidom import Document rootdir = "G:/MTCNNTraining/faceData/train"
convet2yoloformat = True
convert2vocformat = True
resized_dim = (48, 48) # 最小取20大小的脸，并且补齐
minsize2select = 1
usepadding = True def convertimgset(img_set="train"):
imgdir = rootdir + "/trainImages"
gtfilepath = rootdir + "/SSDSave.txt" imagesdir = rootdir + "/images"
vocannotationdir = rootdir + "/Annotations"
labelsdir = rootdir + "/labels" if not os.path.exists(imagesdir):
os.mkdir(imagesdir)
if convet2yoloformat:
if not os.path.exists(labelsdir):
os.mkdir(labelsdir)
if convert2vocformat:
if not os.path.exists(vocannotationdir):
os.mkdir(vocannotationdir) index = 0
with open(gtfilepath, 'r') as gtfile:
while (True): # and len(faces)<10
filename = gtfile.readline()[:-1]
if (filename == ""):
break
sys.stdout.write("\r" + str(index) + ":" + filename + "\t\t\t")
sys.stdout.flush()
imgpath = imgdir + "/" + filename
img = cv2.imread(imgpath)
if not img.data:
break
imgheight = img.shape[0]
imgwidth = img.shape[1]
maxl = max(imgheight, imgwidth) paddingleft = (maxl - imgwidth) >> 1
paddingright = (maxl - imgwidth) >> 1
paddingbottom = (maxl - imgheight) >> 1
paddingtop = (maxl - imgheight) >> 1
saveimg = cv2.copyMakeBorder(img, paddingtop, paddingbottom, paddingleft, paddingright, cv2.BORDER_CONSTANT,value=0)
showimg = saveimg.copy() numbbox = int(gtfile.readline())
bboxes = []
bnames=[]
for i in range(numbbox):
line_read = gtfile.readline()
line_cor = line_read.strip().split(" ")
obj_name = line_cor[0]
#line = line_cor[1:5]
line = list(map(int,line_cor[1:5])) if (int(line[3]) <= 0 or int(line[2]) <= 0):
continue
x = int(line[0]) + paddingleft #左上角顶点x
y = int(line[1]) + paddingtop #左上角顶点y
width = int(line[2]) - int(line[0]) + 1 #宽度
height = int(line[3]) - int(line[1])+ 1 #高度
bbox = (x, y, width, height)
#x2 = x + width
#y2 = y + height
# face=img[x:x2,y:y2]
if width >= minsize2select and height >= minsize2select:
bboxes.append(bbox)
bnames.append(obj_name)
#cv2.rectangle(showimg, (x, y), (x2, y2), (0, 255, 0))
# maxl=max(width,height)
# x3=(int)(x+(width-maxl)*0.5)
# y3=(int)(y+(height-maxl)*0.5)
# x4=(int)(x3+maxl)
# y4=(int)(y3+maxl)
# cv2.rectangle(img,(x3,y3),(x4,y4),(255,0,0))
#else:
#cv2.rectangle(showimg, (x, y), (x2, y2), (0, 0, 255)) #filename = filename.replace("/", "_")
if len(bboxes) == 0:
print ("warrning: no face")
continue cv2.imwrite(imagesdir + "/" + filename, saveimg) #if convet2yoloformat:
#height = saveimg.shape[0]
#width = saveimg.shape[1]
#txtpath = labelsdir + "/" + filename
#txtpath = txtpath[:-3] + "txt"
#ftxt = open(txtpath, 'w')
#for i in range(len(bboxes)):
#bbox = bboxes[i]
#xcenter = (bbox[0] + bbox[2] * 0.5) / width
#ycenter = (bbox[1] + bbox[3] * 0.5) / height
#wr = bbox[2] * 1.0 / width
#hr = bbox[3] * 1.0 / height
#txtline = "0 " + str(xcenter) + " " + str(ycenter) + " " + str(wr) + " " + str(hr) + "\n"
#ftxt.write(txtline)
#ftxt.close() if convert2vocformat:
xmlpath = vocannotationdir + "/" + filename
xmlpath = xmlpath[:-3] + "xml"
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('widerFace'))
owner.appendChild(flickrid_o)
name_o = doc.createElement('name')
name_o.appendChild(doc.createTextNode('widerFace'))
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(''))
annotation.appendChild(segmented) for i in range(len(bboxes)):
bbox = bboxes[i]
objects = doc.createElement('object')
annotation.appendChild(objects)
object_name = doc.createElement('name')
bnames_var = str(bnames[i]) object_name.appendChild(doc.createTextNode(bnames_var))
objects.appendChild(object_name)
pose = doc.createElement('pose')
pose.appendChild(doc.createTextNode('Unspecified'))
objects.appendChild(pose)
truncated = doc.createElement('truncated')
truncated.appendChild(doc.createTextNode(''))
objects.appendChild(truncated)
difficult = doc.createElement('difficult')
difficult.appendChild(doc.createTextNode(''))
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()
# cv2.imshow("img",showimg)
# cv2.waitKey()
index = index + 1 def convertdataset():
img_sets = ["train"]
for img_set in img_sets:
convertimgset(img_set) if __name__ == "__main__":
convertdataset()