【OpenCV】Pyqt界面+摄像头实现人脸检测+安全帽识别，最终导出为exe文件
发布时间：2023-10-30 16:30
文章目录
- 前言
- 一、界面设计
- 二、相关代码
- 三、导出exe文件
- 总结
前言
接上节的内容。设计好的界面如下：
![\"【OpenCV】Pyqt界面+摄像头实现人脸检测+安全帽识别，最终导出为exe文件_第1张图片\"](\"https://img.it610.com/image/info8/f61a604743904e488b76471bb9db0e43.jpg\")
实现的功能：
1.通过摄像头进行截图，把截图上传到OneNetAI服务器进行人脸检测。
2.通过打开图片文件，上传图片到OneNetAI服务器进行安全帽识别检测。
一、界面设计
参考我博客的另外一篇博客。【OpenCV】Pyqt5界面设计+USB摄像头
二、相关代码
文件结构如下：
![\"【OpenCV】Pyqt界面+摄像头实现人脸检测+安全帽识别，最终导出为exe文件_第2张图片\"](\"https://img.it610.com/image/info8/4c6afd9bc5ca480e944224da41433397.jpg\")
demo.ui是界面设计文件，使用pyqt的designer.exe实现的。
demo.py是界面设计文件通过pyuic5.exe转化的python代码。
at.jpg是戴安全帽的一个测试图片。
myqtdemo.py是编写逻辑控制代码的文件。
upload.jpg是截图按钮截图以后保存的文件。
demo.py代码如下：
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWindow.setObjectName(\"MainWindow\")
MainWindow.resize(933, 727)
self.centralwidget = QtWidgets.QWidget(MainWindow)
self.centralwidget.setObjectName(\"centralwidget\")
self.label_Camera = QtWidgets.QLabel(self.centralwidget)
self.label_Camera.setGeometry(QtCore.QRect(60, 50, 200, 200))
self.label_Camera.setObjectName(\"label_Camera\")
self.Button_OpenCamera = QtWidgets.QPushButton(self.centralwidget)
self.Button_OpenCamera.setGeometry(QtCore.QRect(120, 290, 100, 50))
self.Button_OpenCamera.setObjectName(\"Button_OpenCamera\")
self.label_Capture = QtWidgets.QLabel(self.centralwidget)
self.label_Capture.setGeometry(QtCore.QRect(360, 50, 200, 200))
self.label_Capture.setObjectName(\"label_Capture\")
self.Button_Faces = QtWidgets.QPushButton(self.centralwidget)
self.Button_Faces.setGeometry(QtCore.QRect(720, 290, 100, 50))
self.Button_Faces.setObjectName(\"Button_Faces\")
self.Button_Capture = QtWidgets.QPushButton(self.centralwidget)
self.Button_Capture.setGeometry(QtCore.QRect(420, 290, 100, 50))
self.Button_Capture.setObjectName(\"Button_Capture\")
self.label_Result = QtWidgets.QLabel(self.centralwidget)
self.label_Result.setGeometry(QtCore.QRect(660, 50, 200, 200))
self.label_Result.setObjectName(\"label_Result\")
self.Button_ReadImage = QtWidgets.QPushButton(self.centralwidget)
self.Button_ReadImage.setGeometry(QtCore.QRect(110, 600, 100, 50))
self.Button_ReadImage.setObjectName(\"Button_ReadImage\")
self.label_ReadImage = QtWidgets.QLabel(self.centralwidget)
self.label_ReadImage.setGeometry(QtCore.QRect(60, 380, 200, 200))
self.label_ReadImage.setObjectName(\"label_ReadImage\")
self.Button_hat = QtWidgets.QPushButton(self.centralwidget)
self.Button_hat.setGeometry(QtCore.QRect(400, 600, 100, 50))
self.Button_hat.setObjectName(\"Button_hat\")
self.label_HatResult = QtWidgets.QLabel(self.centralwidget)
self.label_HatResult.setGeometry(QtCore.QRect(350, 380, 200, 200))
self.label_HatResult.setObjectName(\"label_HatResult\")
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 933, 23))
self.menubar.setObjectName(\"menubar\")
MainWindow.setMenuBar(self.menubar)
self.statusbar = QtWidgets.QStatusBar(MainWindow)
self.statusbar.setObjectName(\"statusbar\")
MainWindow.setStatusBar(self.statusbar)
self.retranslateUi(MainWindow)
self.Button_OpenCamera.clicked.connect(MainWindow.Button_OpenCamera_Clicked)
self.Button_Capture.clicked.connect(MainWindow.Button_Capture_Clicked)
self.Button_ReadImage.clicked.connect(MainWindow.Button_ReadImage_Clicked)
self.Button_Faces.clicked.connect(MainWindow.Button_faces_Clicked)
self.Button_hat.clicked.connect(MainWindow.Button_hat_Clicked)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
def retranslateUi(self, MainWindow):
_translate = QtCore.QCoreApplication.translate
MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MainWindow\"))
self.label_Camera.setText(_translate(\"MainWindow\", \"摄像头图片显示\"))
self.Button_OpenCamera.setText(_translate(\"MainWindow\", \"打开摄像头\"))
self.label_Capture.setText(_translate(\"MainWindow\", \"截图结果显示\"))
self.Button_Faces.setText(_translate(\"MainWindow\", \"人脸检测\"))
self.Button_Capture.setText(_translate(\"MainWindow\", \"截图\"))
self.label_Result.setText(_translate(\"MainWindow\", \"上传结果显示\"))
self.Button_ReadImage.setText(_translate(\"MainWindow\", \"打开图片\"))
self.label_ReadImage.setText(_translate(\"MainWindow\", \"打开图片显示\"))
self.Button_hat.setText(_translate(\"MainWindow\", \"安全帽识别\"))
self.label_HatResult.setText(_translate(\"MainWindow\", \"打开图片显示\"))
myqtdemo.py代码如下：
import sys
import cv2
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import QFileDialog, QMainWindow
from demo import Ui_MainWindow
import requests
import json
import base64
class PyQtMainEntry(QMainWindow, Ui_MainWindow):
def __init__(self):
super().__init__()
self.setupUi(self)
self.camera = cv2.VideoCapture(0)
self.is_camera_opened = False # 摄像头有没有打开标记
# 定时器：30ms捕获一帧
self._timer = QtCore.QTimer(self)
self._timer.timeout.connect(self._queryFrame)
self._timer.setInterval(30)
def Button_OpenCamera_Clicked(self): #打开和关闭摄像头
self.is_camera_opened = ~self.is_camera_opened
if self.is_camera_opened:
self.Button_OpenCamera.setText(\"关闭摄像头\")
self._timer.start()
else:
self.Button_OpenCamera.setText(\"打开摄像头\")
self._timer.stop()
def Button_Capture_Clicked(self):# 捕获图片
# 摄像头未打开，不执行任何操作
if not self.is_camera_opened:
return
self.captured = self.frame
# ===================先截图保存起来====================================
self.captured = cv2.cvtColor(self.captured, cv2.COLOR_BGR2RGB)
cv2.imwrite(\'upload.jpg\', self.captured)
# =====================================================================
self.captured = cv2.cvtColor(self.captured, cv2.COLOR_BGR2RGB)
rows, cols, channels = self.captured.shape
bytesPerLine = channels * cols
# Qt显示图片时，需要先转换成QImgage类型
QImg = QImage(self.captured.data, cols, rows, bytesPerLine, QImage.Format_RGB888)
self.label_Capture.setPixmap(QPixmap.fromImage(QImg).scaled(self.label_Capture.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation))
def Button_faces_Clicked(self):# 上传数据到OneNetAI平台 人脸检测
# 如果没有捕获图片，则不执行操作
if not hasattr(self, \"captured\"):
return
# self.cpatured = cv2.cvtColor(self.captured, cv2.COLOR_RGB2GRAY)
url = \'http://ai.heclouds.com:9090/v1/aiApi/picture/FACE_RECO\'
headers = {\'Content-Type\': \'application/json\',
\'token\': \'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpZCI6ImQ1N2NlYmIwMWZkYTRkZjM4N2EzM2IwMWQ5OTFlY2Y4IiwiZXhwIjoxNjUxMTM4MTU4LCJ2YWx1ZSI6IntcImFpS2V5XCI6XCI0MWU3YjQ3MTA4ZWM0YjZkYWRmMGE1MTNiNDhlYmQwM1wiLFwiYXBwSWRcIjpcIjkxNjMwNTY0NzgzMjY2MjAxNlwiLFwicHVycG9zZVwiOlwiYXBpXCIsXCJzZWNyZXRLZXlcIjpcImI2NWQ1NDk1Zjk0MjRiNjE5YWI5NmEyYTg4NDEyZTljXCIsXCJ1c2VySWRcIjpcIjAxMHUwMDE1NDg0MjM5MTY4NTg2NzY0XCJ9In0.zIArmy1JaMeM2WeclSDp_G2SgdoBb0T16oznRKwIQvQ\'}
file = open(\'upload.jpg\', \'rb\') # 打开图片文件
base64Str = base64.b64encode(file.read()).decode() # 将其转为base64信息
file.close() # 关闭打开的文件
data = {\'picture\': [base64Str]} # 构造接口调用参数
response = requests.request(\"POST\", url, headers=headers, data=json.dumps(data)) # POST 方式调用
strdata = response.text # 打印结果
print(strdata)
mydata = json.loads(strdata) # 字符串转字典
print(mydata[\"data\"][0][\'confidence\']) #字典下data列表的第1个confidence值
pointx = int(mydata[\"data\"][0][\'box\'][\'x\'])
pointy = int(mydata[\"data\"][0][\'box\'][\'y\'])
point_width = pointx + int(mydata[\"data\"][0][\'box\'][\'width\'])
point_height = pointy + int(mydata[\"data\"][0][\'box\'][\'height\'])
self.captured = cv2.imread(\'upload.jpg\')
self.captured = cv2.cvtColor(self.captured, cv2.COLOR_BGR2RGB)
self.captured = cv2.rectangle(self.captured, (pointx, pointy), (point_width, point_height), (0, 255, 0), 4)
rows, cols, channels = self.captured.shape
print(self.captured.shape) # 480*640
bytesPerLine = channels * cols
QImg = QImage(self.captured.data, cols, rows, bytesPerLine, QImage.Format_RGB888)
self.label_Result.setPixmap(QPixmap.fromImage(QImg).scaled(self.label_Result.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation))
def Button_ReadImage_Clicked(self): # 从本地读取图片 文件路径不能有中文
# 打开文件选取对话框
filename, _ = QFileDialog.getOpenFileName(self, \'打开图片\')
self.myfilename = str(filename)
if filename:
self.captured = cv2.imread(str(filename))
# OpenCV图像以BGR通道存储，显示时需要从BGR转到RGB
self.captured = cv2.cvtColor(self.captured, cv2.COLOR_BGR2RGB)
rows, cols, channels = self.captured.shape
bytesPerLine = channels * cols
QImg = QImage(self.captured.data, cols, rows, bytesPerLine, QImage.Format_RGB888)
self.label_ReadImage.setPixmap(QPixmap.fromImage(QImg).scaled(self.label_ReadImage.size(), Qt.KeepAspectRatio,Qt.SmoothTransformation))
def Button_hat_Clicked(self):# 上传数据到OneNetAI平台 安全帽检测
# if not hasattr(self, \"captured\"):
# return
url = \'http://ai.heclouds.com:9090/v1/aiApi/picture/HAT_RECO\'
headers = {\'Content-Type\': \'application/json\',
\'token\': \'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpZCI6ImQ1N2NlYmIwMWZkYTRkZjM4N2EzM2IwMWQ5OTFlY2Y4IiwiZXhwIjoxNjUxMTM4MTU4LCJ2YWx1ZSI6IntcImFpS2V5XCI6XCI0MWU3YjQ3MTA4ZWM0YjZkYWRmMGE1MTNiNDhlYmQwM1wiLFwiYXBwSWRcIjpcIjkxNjMwNTY0NzgzMjY2MjAxNlwiLFwicHVycG9zZVwiOlwiYXBpXCIsXCJzZWNyZXRLZXlcIjpcImI2NWQ1NDk1Zjk0MjRiNjE5YWI5NmEyYTg4NDEyZTljXCIsXCJ1c2VySWRcIjpcIjAxMHUwMDE1NDg0MjM5MTY4NTg2NzY0XCJ9In0.zIArmy1JaMeM2WeclSDp_G2SgdoBb0T16oznRKwIQvQ\'}
file = open(self.myfilename, \'rb\') # 打开图片文件
base64Str = base64.b64encode(file.read()).decode() # 将其转为base64信息
file.close() # 关闭打开的文件
data = {\'picture\': [base64Str]} # 构造接口调用参数
response = requests.request(\"POST\", url, headers=headers, data=json.dumps(data)) # POST 方式调用
# if response:
# print(response.json())
# json_dict = response.json()
# print(json_dict)
# guid = json_dict.get(\"data\")
# print(guid[0][\'confidence\'])
strdata=response.text # 打印结果
print(strdata)
mydata = json.loads(strdata)#字符串转字典
print(mydata[\"data\"][0][\'confidence\'])
pointx=int(mydata[\"data\"][0][\'box\'][\'x\'])
pointy=int(mydata[\"data\"][0][\'box\'][\'y\'])
point_width=pointx+int(mydata[\"data\"][0][\'box\'][\'width\'])
point_height=pointy+int(mydata[\"data\"][0][\'box\'][\'height\'])
self.captured = cv2.imread(self.myfilename)
self.captured = cv2.cvtColor(self.captured, cv2.COLOR_BGR2RGB)
self.captured=cv2.rectangle(self.captured, (pointx,pointy), (point_width,point_height),(0, 255, 0), 4)
rows, cols, channels = self.captured.shape
print(self.captured.shape) # 480*640
bytesPerLine = channels * cols
QImg = QImage(self.captured.data, cols, rows, bytesPerLine, QImage.Format_RGB888)
self.label_HatResult.setPixmap(QPixmap.fromImage(QImg).scaled(self.label_HatResult.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation))
@QtCore.pyqtSlot()
def _queryFrame(self): #循环捕获图片
ret, self.frame = self.camera.read()
img_rows, img_cols, channels = self.frame.shape
bytesPerLine = channels * img_cols
cv2.cvtColor(self.frame, cv2.COLOR_BGR2RGB, self.frame)
QImg = QImage(self.frame.data, img_cols, img_rows, bytesPerLine, QImage.Format_RGB888)
self.label_Camera.setPixmap(QPixmap.fromImage(QImg).scaled(self.label_Camera.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation))
if __name__ == \"__main__\":
app = QtWidgets.QApplication(sys.argv)
window = PyQtMainEntry()
window.show()
sys.exit(app.exec_())
三、导出exe文件
1.pyinstaller是将Python脚本打包成可执行程序的一个包，必须先安装一下。在Anaconda Prompt环境下输入pip install pyinstaller
![\"【OpenCV】Pyqt界面+摄像头实现人脸检测+安全帽识别，最终导出为exe文件_第3张图片\"](\"https://img.it610.com/image/info8/e263d0303ef04e34acea3c0a96109e63.png\")
2.进入当前项目的目录，在途径栏位置输入cmd。
![\"【OpenCV】Pyqt界面+摄像头实现人脸检测+安全帽识别，最终导出为exe文件_第4张图片\"](\"https://img.it610.com/image/info8/155daceaecc74a518695d5af9d716bca.jpg\")
3.命令行界面出现后，输入 pyinstaller -F -w myqtdemo.py myqtdemo.py是你的py文件名称，注意，你的文件名称不一定和我的一样。
如果想加个图标，则输入pyinstaller -F --icon=my.ico yourfile.py
4.最终会在当前项目目录下生成build和dist文件夹，这一步得耐心等待，时间比较久。打开dist文件夹，内部即是exe程序。
![\"【OpenCV】Pyqt界面+摄像头实现人脸检测+安全帽识别，最终导出为exe文件_第5张图片\"](\"https://img.it610.com/image/info8/2e4247c6350f402480e4774dafde11de.jpg\")
总结
视频动态识别的话，受限于网络到服务器传输的速度，需要使用深度学习的方法实现。