import os, h5py, cv2, sys, shutil
import numpy as np
from xml.dom.minidom import Document
rootdir = "G:/MTCNNTraining/faceData/widerFace"
convet2yoloformat = True
convert2vocformat = True
resized_dim = (48, 48)
# 最小取1大小的脸，并且补齐
minsize2select = 1
usepadding = True
datasetprefix = "G:/MTCNNTraining/faceData/widerFace" #
def gen_hdf5():
imgdir = rootdir + "/WIDER_train/images"
gtfilepath = rootdir + "/wider_face_split/wider_face_train_bbx_gt.txt"
index = 0
with open(gtfilepath, 'r') as gtfile:
faces = []
labels = []
while (True): # and len(faces)<10
imgpath = gtfile.readline()[:-1]
if (imgpath == ""):
break
print (index, imgpath)
img = cv2.imread(imgdir + "/" + imgpath)
numbbox = int(gtfile.readline())
bbox = []
for i in range(numbbox):
line = gtfile.readline()
line = line.split()
line = line[0:4]
if (int(line[3]) <= 0 or int(line[2]) <= 0):
continue
bbox = (int(line[0]), int(line[1]), int(line[2]), int(line[3]))
face = img[int(line[1]):int(line[1]) + int(line[3]), int(line[0]):int(line[0]) + int(line[2])]
face = cv2.resize(face, resized_dim)
faces.append(face)
labels.append(1)
cv2.rectangle(img, (int(line[0]), int(line[1])),
(int(line[0]) + int(line[2]), int(line[1]) + int(line[3])), (255, 0, 0))
# cv2.imshow("img",img)
# cv2.waitKey(1)
index = index + 1
faces = np.asarray(faces)
labels = np.asarray(labels)
f = h5py.File('train.h5', 'w')
f['data'] = faces.astype(np.float32)
f['label'] = labels.astype(np.float32)
f.close()
def viewginhdf5():
f = h5py.File('train.h5', 'r')
f.keys()
faces = f['data'][:]
for face in faces:
face = face.astype(np.uint8)
cv2.imshow("img", face)
cv2.waitKey(1)
f.close()
def convertimgset(img_set="train"):
imgdir = rootdir + "/WIDER_" + img_set + "/images"
gtfilepath = rootdir + "/wider_face_split/wider_face_" + img_set + "_bbx_gt.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 = []
for i in range(numbbox):
line = gtfile.readline()
line = line.split()
line = line[0:4]
if (int(line[3]) <= 0 or int(line[2]) <= 0):
continue
x = int(line[0]) + paddingleft
y = int(line[1]) + paddingtop
width = int(line[2])
height = int(line[3])
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)
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')
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(''))
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 generatetxt(img_set="train"):
gtfilepath = rootdir + "/wider_face_split/wider_face_" + img_set + "_bbx_gt.txt"
f = open(rootdir + "/" + img_set + ".txt", "w")
with open(gtfilepath, 'r') as gtfile:
while (True): # and len(faces)<10
filename = gtfile.readline()[:-1]
if (filename == ""):
break
filename = filename.replace("/", "_")
imgfilepath = datasetprefix + "/images/" + filename
f.write(imgfilepath + '\n')
numbbox = int(gtfile.readline())
for i in range(numbbox):
line = gtfile.readline()
f.close()
def generatevocsets(img_set="train"):
if not os.path.exists(rootdir + "/ImageSets"):
os.mkdir(rootdir + "/ImageSets")
if not os.path.exists(rootdir + "/ImageSets/Main"):
os.mkdir(rootdir + "/ImageSets/Main")
gtfilepath = rootdir + "/wider_face_split/wider_face_" + img_set + "_bbx_gt.txt"
f = open(rootdir + "/ImageSets/Main/" + img_set + ".txt", 'w')
with open(gtfilepath, 'r') as gtfile:
while (True): # and len(faces)<10
filename = gtfile.readline()[:-1]
if (filename == ""):
break
filename = filename.replace("/", "_")
imgfilepath = filename[:-4]
f.write(imgfilepath + '\n')
numbbox = int(gtfile.readline())
for i in range(numbbox):
line = gtfile.readline()
f.close()
def convertdataset():
img_sets = ["train", "val"]
for img_set in img_sets:
convertimgset(img_set)
generatetxt(img_set)
generatevocsets(img_set)
if __name__ == "__main__":
convertdataset()
shutil.move(rootdir + "/" + "train.txt", rootdir + "/" + "trainval.txt")
shutil.move(rootdir + "/" + "val.txt", rootdir + "/" + "test.txt")
shutil.move(rootdir + "/ImageSets/Main/" + "train.txt", rootdir + "/ImageSets/Main/" + "trainval.txt")
shutil.move(rootdir + "/ImageSets/Main/" + "val.txt", rootdir + "/ImageSets/Main/" + "test.txt")