int main()
{
std::ifstream fin("D:\\Data\\MyAnnoData\\视频\\0.txt");//打开原始样本图片文件列表
Mat colorImage, detectSquareImg;
string videoReadPath;
int videoCnt = 0;//视频计数
FILE* f = fopen("F:\\videoList.txt", "wt");
if (f == NULL)
{
printf("文件打开失败!\n");
}
else
{
printf("文件打开成功!\n");
}
while (getline(fin, videoReadPath)) //一行一行读取文件列表
{
videoCnt++;
string videoPath = videoReadPath;
cout << videoPath << endl;
VideoCapture capture(videoPath);//读入视频
if (!capture.isOpened())//判断是否打开视频文件
{
cout << "video not open..." << endl;
continue;
}
else
{
int imgCnt = 0;//图片计数
while (1)
{
imgCnt++;
cout << imgCnt << endl;
if (imgCnt % 4 != 0)
{
continue;
}
capture >> colorImage;//读入图片
if (colorImage.empty())
{
break;
}
else
{
if (colorImage.channels() != 3)
{
cvtColor(colorImage, colorImage, CV_GRAY2BGR);
}
//调用算法进行检测
DetectNcnn detectNcnn;
int imgheight = colorImage.rows;
int imgwidth = colorImage.cols;
int maxl = imgheight > imgwidth ? imgheight : imgwidth;
int paddingleft = (maxl - imgwidth) >> 1;
int paddingright = (maxl - imgwidth) >> 1;
int paddingbottom = (maxl - imgheight) >> 1;
int paddingtop = (maxl - imgheight) >> 1;
copyMakeBorder(colorImage, detectSquareImg, paddingtop, paddingbottom, paddingleft, paddingright, cv::BORDER_CONSTANT, 0);
objects.clear(); classes.clear(); confidences.clear();
detectNcnn.detectObjNcnn(detectSquareImg.clone(), objects, classes, confidences);
int cntObject = classes.size();//目标个数，需要写入txt
string saveImgName = "F:\\dmsImg\\20190605" + to_string(videoCnt) + "to"+to_string(imgCnt) + ".jpg";
if (cntObject > 0) //有目标，保存图片,把图片名和图片中目标个数写入txt
{
fprintf(f, "%s%d%s%d%s", "20190605", videoCnt, "to", imgCnt, ".jpg"); //图片名写入txt
fprintf(f, "%s", "\n"); //写入换行
fprintf(f, "%d", cntObject);//目标个数写入txt
fprintf(f, "%s", "\n"); //写入换行
imwrite(saveImgName, detectSquareImg);
}
//检测结果分析
for (int i = 0; i < classes.size(); i++)
{
if (classes[i] == ObjectType::EYE)
{
//把目标名称、xmin,ymin,xmax,ymax写入txt
int x = objects[i].x;
int y = objects[i].y;
int w = objects[i].width;
int h = objects[i].height;
fprintf(f, "%s %d %d %d %d", "eye", x, y, w, h);//写入目标名和目标位置
fprintf(f, "%s", "\n"); //写入换行
}
if (classes[i] == ObjectType::CLOSED)
{
//把目标名称、xmin,ymin,xmax,ymax写入txt
int x = objects[i].x;
int y = objects[i].y;
int w = objects[i].width;
int h = objects[i].height;
fprintf(f, "%s %d %d %d %d", "closed", x, y, w, h);//写入目标名和目标位置
fprintf(f, "%s", "\n"); //写入换行
}
if (classes[i] == ObjectType::HAND)
{
//把目标名称、xmin,ymin,xmax,ymax写入txt
int x = objects[i].x;
int y = objects[i].y;
int w = objects[i].width;
int h = objects[i].height;
fprintf(f, "%s %d %d %d %d", "hand", x, y, w, h);//写入目标名和目标位置
fprintf(f, "%s", "\n"); //写入换行
}if (classes[i] == ObjectType::MOUTH)
{
//把目标名称、xmin,ymin,xmax,ymax写入txt
int x = objects[i].x;
int y = objects[i].y;
int w = objects[i].width;
int h = objects[i].height;
fprintf(f, "%s %d %d %d %d", "mouth", x, y, w, h);//写入目标名和目标位置
fprintf(f, "%s", "\n"); //写入换行
}
}
}
}
}
}
fclose(f);
return 0;
}
#!/usr/bin/python
# -*- coding: UTF-8 -*-
import os, h5py, cv2, sys, shutil
import numpy as np
from xml.dom.minidom import Document
rootdir = "F:/zidongbiaozhuSample"
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 + "/dmsImg"
gtfilepath = rootdir + "/dms20190605.txt"
#imagesdir = rootdir + "/images"
vocannotationdir = rootdir + "/dmsXml"
#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 img is None:
bboxCnt = int(gtfile.readline())
for i in range(bboxCnt):
line_read = gtfile.readline()
continue
if not img.data:
continue
#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
x = int(line[0])
#y = int(line[1]) + paddingtop #左上角顶点y
y = int(line[1])
#width = int(line[2]) - int(line[0]) + 1 #宽度
width = int(line[2])
#height = int(line[3]) - int(line[1])+ 1 #高度
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)
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])))
width.appendChild(doc.createTextNode(str(img.shape[1])))
height = doc.createElement('height')
#height.appendChild(doc.createTextNode(str(saveimg.shape[0])))
height.appendChild(doc.createTextNode(str(img.shape[0])))
depth = doc.createElement('depth')
#depth.appendChild(doc.createTextNode(str(saveimg.shape[2])))
depth.appendChild(doc.createTextNode(str(img.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')
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('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()
# 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()
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