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visioninfer.py
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visioninfer.py
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import sys
from PyQt5 import QtCore, QtGui,QtWidgets,QtNetwork
from Ui_visioninfer import Ui_MainWindow
import cv2
import os
import datetime
import xml_rc
import paddlex as pdx
class MainWindow(QtWidgets.QMainWindow,Ui_MainWindow):
isstop,issnap,isrec=False,False,False #中断执行标记 #拍照标记 #录制标记
isscaled,iscam,isimg,isipcam,isvideo=False,True,False,False,False #勾选标记
ip,camid,user,pwd="","","","" #录像机登录信息
cap=cv2.VideoCapture()
Video=cv2.VideoWriter()
def __init__(self):
super().__init__()
self.setupUi(self)
#状态栏
self.statusBar().showMessage("可视化推理预测系统")
#默认勾选摄像头
self.rbtcam.setChecked(True)
#槽信号
self.btnclose.clicked.connect(self.Close)
self.btnopen.clicked.connect(self.Open)
self.cboxtask.currentIndexChanged.connect(self.schange)
self.menuhelp.triggered.connect(self.winaction)
self.cboxcamid.currentIndexChanged.connect(self.changecamid)
self.btnsnap.clicked.connect(self.snap)
self.btnrec.clicked.connect(self.rec)
self.cboxtask.addItems(["请选择模型"])
if os.path.exists("models"):
lst=os.listdir("models")
self.cboxtask.addItems(lst)
else:
os.makedirs("models")
os.makedirs("models/cls-model")
os.makedirs("models/det-model")
os.makedirs("models/face-model")
#默认64通道数
self.cboxcamid.addItems([str(id).rjust(2,"0") for id in range(1,65)])
#释放资源文件
if not os.path.exists("haarcascade_frontalface_alt.xml"):
QtCore.QFile.copy(":harr/haarcascade_frontalface_alt.xml","haarcascade_frontalface_alt.xml")
os.environ["CUDA_VISIBLE_DEVICES"]="0,1"
def winaction(self,action):
q=action.text()
if q=="说明":
QtWidgets.QMessageBox.information(self,
"程序说明：",
"1、程序主要实现对推理模型可视化预测，任务模型分三大类：人脸识别、图像分类、目标检测。\n\n\
2、存放任务模型默认目录分别是：cls-model(图像分类模型)、det-model(目标检测模型)、face-model(人脸分类模型),默认目录名不能更改\n\n\
3、任务模型默认目录里可以增加用户自己的模型目录，但模型目录里的inference_model默认目录名不能更改，用来存放用户自已的推理模型文件\n")
if q=="关于":
img=QtGui.QImage(":img/start.jpg")
self.lblsrc.setPixmap(QtGui.QPixmap.fromImage(img).scaled(1024,480 , QtCore.Qt.KeepAspectRatio))
self.lblsrc.setScaledContents(True) #自适应大小
QtWidgets.QMessageBox.about(self, "关于", "可视化推理预测系统")
def changecamid(self,i):
if self.rbtipcam.isChecked():
self.Open()
def schange(self,i):
#print(i,self.cboxtask.itemText(i))
self.cboxmodel.clear()
try:
lst=os.listdir(os.path.join("models",self.cboxtask.itemText(i)))
self.cboxmodel.addItems(lst)
except:
pass
#获取录像机的信息
def getipcam(self):
self.ip=self.leip.displayText()
self.camid=self.cboxcamid.currentText()
self.user=self.leuser.displayText()
self.lepwd.setEchoMode(self.lepwd.Normal)
self.pwd=self.lepwd.displayText() #获取密码
self.lepwd.setEchoMode(self.lepwd.Password)
url="rtsp://"+self.user+":"+self.pwd+"@"+self.ip+"/Streaming/Channels/"+self.camid+"01?transportmode=multicas"
ret=cv2.VideoCapture(url)
if ret.grab():
return url
else:
self.rbtcam.setChecked(True)
return 0
#获取录像机的通道数
def getcamid(self):
self.ip=self.leip.displayText()
self.user=self.leuser.displayText()
self.lepwd.setEchoMode(self.lepwd.Normal)
self.pwd=self.lepwd.displayText() #获取密码
self.lepwd.setEchoMode(self.lepwd.Password)
camidlst=[]
for camid in range(64):
url="rtsp://"+self.user+":"+self.pwd+"@"+self.ip+"：554/Streaming/Channels/"+str(camid).rjust(2,"0")+"01?transportmode=multicas"
ret=cv2.VideoCapture(url)
grabbed=ret.grab()
if grabbed:
camidlst.append(str(camid).rjust(2,"0"))
QtWidgets.QApplication.processEvents()
return camidlst
#开启拍照或录制
def snap(self):
if self.cap.isOpened():
self.issnap=True
def rec(self):
if self.cap.isOpened():
self.isrec =not self.isrec
if self.isrec:
#创建video writer
ret,frame=self.cap.read()
self.createVideo(frame)
self.btnrec.setText("停止录制")
self.statusBar().showMessage("文件录制中…………\n")
else:
self.video.release() #释放video writer
self.btnrec.setText("开始录制")
self.statusBar().showMessage("文件保存在output目录里\n")
#摄像头或监控拍照
def imgsave(self,frame):
if self.rbtcam.isChecked() or self.rbtipcam.isChecked():
fname=datetime.datetime.now().strftime("%Y%m%d%H%M%S")+".jpg"
if not os.path.exists("output"):
os.makedirs("output")
cv2.imwrite(os.path.join("output",fname),frame)
self.statusBar().showMessage(fname+"文件保存在output目录里\n")
self.issnap=False
#摄像头或监控录制
def createVideo(self,frame):
if self.rbtcam.isChecked() or self.rbtipcam.isChecked():
fname=datetime.datetime.now().strftime("%Y%m%d%H%M%S")+".mp4"
if not os.path.exists("output"):
os.makedirs("output")
self.video=cv2.VideoWriter(
filename=os.path.join("output",fname),
fourcc=cv2.VideoWriter_fourcc(*"mp4v"),
fps=15,
frameSize=(frame.shape[1],frame.shape[0])
)
#video.write(frame)
#video.release()
#在lable上显示图像
def imgshow(self,frame,lbl):
#img=QtGui.QImage(frame.data,frame.shape[1],frame.shape[0],QtGui.QImage.Format_BGR888)
#重载修复图像显示变形问题
img = QtGui.QImage(frame.data, frame.shape[1], frame.shape[0], frame.shape[1] * 3,QtGui.QImage.Format_BGR888)
#按比例缩放
if self.isscaled:
lbl.setPixmap(QtGui.QPixmap.fromImage(img).scaled(480, 480, QtCore.Qt.KeepAspectRatio))
else:
lbl.setPixmap(QtGui.QPixmap.fromImage(img))
lbl.setScaledContents(True) #自适应大小
QtWidgets.QApplication.processEvents()
#打开输入数据类型
def Open(self):
self.Close()
self.isstop=False
self.isscaled=self.cboxscaled.isChecked()
#获取选项按钮状态
self.iscam=self.rbtcam.isChecked()
self.isimg=self.rbtimg.isChecked()
self.isvideo=self.rbtvideo.isChecked()
self.isipcam=self.rbtipcam.isChecked()
taskid=self.cboxtask.currentIndex()
task=self.cboxtask.currentText()
model_dir=os.path.join("models",self.cboxtask.currentText(),self.cboxmodel.currentText(),"inference_model")
#print(model_dir)
#图像缩放
self.isscaled=self.cboxscaled.isChecked()
if taskid==0:#预览
if self.isimg:
self.fileName, self.fileType = QtWidgets.QFileDialog.getOpenFileName(self, '选择','', "图像文件(*.jpg *.png)")
if self.fileName!="":
frame=cv2.imread(self.fileName)
self.imgshow(frame,self.lblsrc)
else:
self.Display()
else:#预测
if self.iscam or self.isipcam:
if task in ["face-model"] :
self.cls_videodetect(model_dir,isface=True)
if task in ["cls-model"]:
self.cls_videodetect(model_dir,isface=False)
if task in ["det-model"]:
self.det_videodetect(model_dir)
if self.isimg:
self.fileName, self.fileType = QtWidgets.QFileDialog.getOpenFileName(self, '选择','', "图像文件(*.jpg *.png)")
if self.fileName!="":
frame=cv2.imread(self.fileName)
self.imgshow(frame,self.lblsrc)
#print(task)
if task in ["face-model"]:#调用图像分类-人脸检测与识别任务,结果显示在dst上
self.cls_imgpredict(model_dir,self.fileName,isface=True)#预测返回结果
if task in ["cls-model"]: #调用图像分类-猫狗分类
self.cls_imgpredict(model_dir,self.fileName,isface=False)#预测返回结果
if task in ["det-model"]:#调用目标检测-人头检测
self.det_imgpredict(model_dir,self.fileName)
if self.isvideo:
self.fileName, self.fileType = QtWidgets.QFileDialog.getOpenFileName(self, '选择视频文件','', '*.mp4')
if self.fileName!="":
if task in ["face-model"] :
self.cls_videodetect(model_dir,self.fileName,isface=True)
if task in ["cls-model"]:
self.cls_videodetect(model_dir,self.fileName,isface=False)
if task in ["det-model"]:
self.det_videodetect(model_dir,self.fileName)
#显示数据
def Display(self):
isscaled=self.cboxscaled.isChecked()
if self.isipcam:
self.cap=cv2.VideoCapture(self.getipcam())
if self.iscam:
self.cap = cv2.VideoCapture(0)
if self.isvideo:
self.fileName, self.fileType = QtWidgets.QFileDialog.getOpenFileName(self, '选择视频文件','', '*.mp4')
self.cap=cv2.VideoCapture(self.fileName)
while self.cap.isOpened():
ret,frame=self.cap.read()
if not ret:
break
if self.issnap:
self.imgsave(frame)
if self.isrec:#开始录制
self.video.write(frame)
if self.rbtipcam.isChecked():
(h,w)= frame.shape[:-1] #获取图片大小
frame=cv2.resize(frame, (int(w/2), int(h/2)))#缩小图像
self.imgshow(frame,self.lblsrc)
cv2.waitKey(0)
if self.isstop:
if self.isrec:
self.video.release()
self.btnrec.setText("开始录制")
self.statusBar().showMessage("文件保存在output目录里\n")
self.isrec=False
self.cap.release()
break
self.cap.release()
#关闭显示
def Close(self):
if self.rbtcam.isChecked() or self.rbtvideo.isChecked() or self.rbtipcam.isChecked():
self.isstop=True
try:
self.cap.release()
except:
pass
self.lblsrc.clear()
self.lbldst.clear()
self.resize(1024,768)
#图像文件分类检测（人脸检测与识别）
def cls_imgpredict(self,model_dir,imgfile:str,isface=True):
#创建推理
self.predictor=pdx.deploy.Predictor(model_dir,use_gpu=True)
if isface:
#加载人脸检测分类器
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
img=cv2.imread(imgfile)
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces=face_detector.detectMultiScale(gray,1.3,5)
# 如果有检测有结果，画框
if len(faces)>0:
for (x,y,w,h) in faces:
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),1)
faceimg=img[y:y+h,x:x+w] #人脸图像
res=self.predictor.predict(faceimg) #人脸识别
if res:
score="score:"+str(round(res[0]["score"],2))
category="Faceid:"+res[0]["category"]
cv2.putText(img,category+" "+score,(x,y),cv2.FONT_HERSHEY_SIMPLEX,0.5,(255,255,0),1)
else:
img=cv2.imread(imgfile)
result=self.predictor.predict(img)
cv2.putText(img,result[0]["category"]+":"+str(round(result[0]["score"],2)),(10,20),cv2.FONT_HERSHEY_SIMPLEX,0.6,(0,255,0),1)
self.imgshow(img,self.lbldst)
#图像分类-视频-人脸检测并识别
def cls_videodetect(self,model_dir,*videofile:str,isface=True):
self.isscaled=self.cboxscaled.isChecked()
self.predictor=pdx.deploy.Predictor(model_dir,use_gpu=True)
if isface:
#加载人脸检测分类器
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
if videofile:
self.cap=cv2.VideoCapture(videofile[0])
else:
if self.isipcam:
self.cap=cv2.VideoCapture(self.getipcam())
if self.iscam:
self.cap = cv2.VideoCapture(0)
while self.cap.isOpened:
#读取数据
ret,frame=self.cap.read()
if not ret:
break
gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
if self.rbtipcam.isChecked():
(h,w)= frame.shape[:-1] #获取图片大小
frame=cv2.resize(frame, (int(w/2), int(h/2)))#缩小图像
self.imgshow(frame,self.lblsrc)
faces=face_detector.detectMultiScale(gray,1.3,5)
# 如果有检测有结果，画框
if len(faces)>0:
for (x,y,w,h) in faces:
cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),1)
faceimg=frame[y:y+h,x:x+w] #人脸图像
res=self.predictor.predict(faceimg) #人脸识别
if res:
score="score:"+str(round(res[0]["score"],2))
category="Faceid:"+res[0]["category"]
cv2.putText(frame,category+" "+score,(x,y),cv2.FONT_HERSHEY_SIMPLEX,0.5,(255,255,0),1)
self.imgshow(frame,self.lbldst)
cv2.waitKey(15)
#
if self.isstop:
break
self.cap.release()
else:#不是人脸检测识别，是普通图像分类
if videofile:
self.cap=cv2.VideoCapture(videofile[0])
else:
if self.isipcam:
self.cap=cv2.VideoCapture(self.getipcam())
if self.iscam:
self.cap = cv2.VideoCapture(0)
while self.cap.isOpened:
#读取数据
ret,frame=self.cap.read()
if not ret:
break
if self.rbtipcam.isChecked():
(h,w)= frame.shape[:-1] #获取图片大小
frame=cv2.resize(frame, (int(w/2), int(h/2)))#缩小图像
self.imgshow(frame,self.lblsrc)
res=self.predictor.predict(frame)
if res:
score="score:"+str(round(res[0]["score"],2))
category="category:"+res[0]["category"]
cv2.putText(frame,category+" "+score,(20,30),cv2.FONT_HERSHEY_SIMPLEX,0.5,(255,255,0),1)
self.imgshow(frame,self.lbldst)
cv2.waitKey(15)
#
if self.isstop:
break
self.cap.release()
#目标检测-图象
def det_imgpredict(self,model_dir,imgfile:str):
self.predictor=pdx.deploy.Predictor(model_dir,use_gpu=True)
img=cv2.imread(imgfile)
result=self.predictor.predict(img)
#print(result)
vis_img=pdx.det.visualize(img,result,threshold=0.5,save_dir=None)
self.imgshow(vis_img,self.lbldst)
#目标检测-视频
def det_videodetect(self,model_dir,*videofile:str):
self.isscaled=self.cboxscaled.isChecked()
self.predictor=pdx.deploy.Predictor(model_dir,use_gpu=True)
if videofile:
self.cap=cv2.VideoCapture(videofile[0])
else:
if self.isipcam:
self.cap=cv2.VideoCapture(self.getipcam())
if self.iscam:
self.cap = cv2.VideoCapture(0)
while self.cap.isOpened():
ret, frame = self.cap.read()
if self.rbtipcam.isChecked():
(h,w)= frame.shape[:-1] #获取图片大小
frame=cv2.resize(frame, (int(w/2), int(h/2)))#缩小图像
self.imgshow(frame,self.lblsrc)
if ret:
result = self.predictor.predict(frame)
if result:
score = result[0]['score']
if score >= 0.5:
pass
frame = pdx.det.visualize(frame, result, threshold=0.5, save_dir=None)
self.imgshow(frame,self.lbldst)
else:
break
cv2.waitKey(15)
if self.isstop:
break
self.cap.release()
if __name__ == '__main__':
try:
app=QtWidgets.QApplication(sys.argv)
app.processEvents()
serverName="AppServer"
socket=QtNetwork.QLocalSocket()
socket.connectToServer(serverName)
#防止程序实例重复启动
if socket.waitForConnected(500):
app.quit()
else:
localServer=QtNetwork.QLocalServer()
localServer.listen(serverName)
splash = QtWidgets.QSplashScreen(QtGui.QPixmap(':img/start.jpg'))
splash.show()
QtWidgets.QApplication.processEvents()
# 可以显示启动信息
splash.showMessage('正在加载……')
# 关闭启动画面
splash.close()
mywin=MainWindow()
mywin.setWindowTitle("可视化推理预测系统")
mywin.show()
sys.exit(app.exec())
except:
pass