基于Python实现简单的人脸识别系统

目录

• 前言
• 基本原理
• 代码实现
• 创建虚拟环境
• 安装必要的库

前言

最近又多了不少朋友关注，先在这里谢谢大家。关注我的朋友大多数都是大学生，而且我简单看了一下，低年级的大学生居多，大多数都是为了完成课程设计，作为一个过来人，还是希望大家平时能多抽出点时间学习一下，这种临时抱佛脚的策略要少用嗷。今天我们来python实现一个人脸识别系统，主要是借助了dlib这个库，相当于我们直接调用现成的库来进行人脸识别，就省去了之前教程中的数据收集和模型训练的步骤了。

B站视频：用300行代码实现人脸识别系统_哔哩哔哩_bilibili

码云地址：face_dlib_py37_42: 用300行代码开发一个人脸识别系统-42 (gitee.com)

基本原理

人脸识别和目标检测这些还不太一样，比如大家传统的训练一个目标检测模型，你只有对这个目标训练了之后，你的模型才能找到这样的目标，比如你的目标检测模型如果是检测植物的，那显然就不能检测动物。但是人脸识别就不一样，以你的手机为例，你发现你只录入了一次你的人脸信息，不需要训练，他就能准确的识别你，这里识别的原理是通过人脸识别的模型提取你脸部的特征向量，然后将实时检测到的你的人脸同数据库中保存的人脸进行比对，如果相似度超过一定的阈值之后，就认为比对成功。不过我这里说的只是简化版本的人脸识别，现在手机和门禁这些要复杂和安全的多，也不是简单平面上的人脸识别。

总结下来可以分为下面的步骤：

1.上传人脸到数据库

2.人脸检测

3.数据库比对并返回结果

这里我做了一个简答的示意图，可以帮助大家简单理解一下。

代码实现

废话不多说，这里就是我们的代码实现，代码我已经上传到码云，大家直接下载就行，地址就在博客开头。

不会安装python环境的兄弟请看这里：如何在pycharm中配置anaconda的虚拟环境

创建虚拟环境

创建虚拟环境前请大家先下载博客开头的码云源码到本地。

本次我们需要使用到python3.7的虚拟环境，命令如下：

conda create -n face python==3.7.3
conda activate face

安装必要的库

pip install -r requirements.txt

愉快地开始你的人脸识别吧！

执行下面的主文件即可

python UI.py

或者在pycharm中按照下面的方式直接运行即可

首先将你需要识别的人脸上传到数据库中

通过第二个视频检测功能识别实时的人脸

详细的代码如下：

# -*- coding: utf-8 -*-
"""
-------------------------------------------------
Project Name: yolov5-jungong
File Name: window.py.py
Author: chenming
Create Date: 2021/11/8
Description：图形化界面，可以检测摄像头、视频和图片文件
-------------------------------------------------
"""
# 应该在界面启动的时候就将模型加载出来，设置tmp的目录来放中间的处理结果
import shutil
import PyQt5.QtCore
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
import threading
import argparse
import os
import sys
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
import os.path as osp
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync

# 添加一个关于界面
# 窗口主类
class MainWindow(QTabWidget):
    # 基本配置不动，然后只动第三个界面
    def __init__(self):
        # 初始化界面
        super().__init__()
        self.setWindowTitle('Target detection system')
        self.resize(1200, 800)
        self.setWindowIcon(QIcon("images/UI/lufei.png"))
        # 图片读取进程
        self.output_size = 480
        self.img2predict = ""
        self.device = 'cpu'
        # # 初始化视频读取线程
        self.vid_source = '0'  # 初始设置为摄像头
        self.stopEvent = threading.Event()
        self.webcam = True
        self.stopEvent.clear()
        self.model = self.model_load(weights="runs/train/exp_yolov5s/weights/best.pt",
                                     device="cpu")  # todo 指明模型加载的位置的设备
        self.initUI()
        self.reset_vid()

    '''
    ***模型初始化***
    '''
    @torch.no_grad()
    def model_load(self, weights="",  # model.pt path(s)
                   device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
                   half=False,  # use FP16 half-precision inference
                   dnn=False,  # use OpenCV DNN for ONNX inference
                   ):
        device = select_device(device)
        half &= device.type != 'cpu'  # half precision only supported on CUDA
        device = select_device(device)
        model = DetectMultiBackend(weights, device=device, dnn=dnn)
        stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
        # Half
        half &= pt and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
        if pt:
            model.model.half() if half else model.model.float()
        print("模型加载完成!")
        return model

    '''
    ***界面初始化***
    '''
    def initUI(self):
        # 图片检测子界面
        font_title = QFont('楷体', 16)
        font_main = QFont('楷体', 14)
        # 图片识别界面, 两个按钮，上传图片和显示结果
        img_detection_widget = QWidget()
        img_detection_layout = QVBoxLayout()
        img_detection_title = QLabel("图片识别功能")
        img_detection_title.setFont(font_title)
        mid_img_widget = QWidget()
        mid_img_layout = QHBoxLayout()
        self.left_img = QLabel()
        self.right_img = QLabel()
        self.left_img.setPixmap(QPixmap("images/UI/up.jpeg"))
        self.right_img.setPixmap(QPixmap("images/UI/right.jpeg"))
        self.left_img.setAlignment(Qt.AlignCenter)
        self.right_img.setAlignment(Qt.AlignCenter)
        mid_img_layout.addWidget(self.left_img)
        mid_img_layout.addStretch(0)
        mid_img_layout.addWidget(self.right_img)
        mid_img_widget.setLayout(mid_img_layout)
        up_img_button = QPushButton("上传图片")
        det_img_button = QPushButton("开始检测")
        up_img_button.clicked.connect(self.upload_img)
        det_img_button.clicked.connect(self.detect_img)
        up_img_button.setFont(font_main)
        det_img_button.setFont(font_main)
        up_img_button.setStyleSheet("QPushButton{color:white}"
                                    "QPushButton:hover{background-color: rgb(2,110,180);}"
                                    "QPushButton{background-color:rgb(48,124,208)}"
                                    "QPushButton{border:2px}"
                                    "QPushButton{border-radius:5px}"
                                    "QPushButton{padding:5px 5px}"
                                    "QPushButton{margin:5px 5px}")
        det_img_button.setStyleSheet("QPushButton{color:white}"
                                     "QPushButton:hover{background-color: rgb(2,110,180);}"
                                     "QPushButton{background-color:rgb(48,124,208)}"
                                     "QPushButton{border:2px}"
                                     "QPushButton{border-radius:5px}"
                                     "QPushButton{padding:5px 5px}"
                                     "QPushButton{margin:5px 5px}")
        img_detection_layout.addWidget(img_detection_title, alignment=Qt.AlignCenter)
        img_detection_layout.addWidget(mid_img_widget, alignment=Qt.AlignCenter)
        img_detection_layout.addWidget(up_img_button)
        img_detection_layout.addWidget(det_img_button)
        img_detection_widget.setLayout(img_detection_layout)

        # todo 视频识别界面
        # 视频识别界面的逻辑比较简单，基本就从上到下的逻辑
        vid_detection_widget = QWidget()
        vid_detection_layout = QVBoxLayout()
        vid_title = QLabel("视频检测功能")
        vid_title.setFont(font_title)
        self.vid_img = QLabel()
        self.vid_img.setPixmap(QPixmap("images/UI/up.jpeg"))
        vid_title.setAlignment(Qt.AlignCenter)
        self.vid_img.setAlignment(Qt.AlignCenter)
        self.webcam_detection_btn = QPushButton("摄像头实时监测")
        self.mp4_detection_btn = QPushButton("视频文件检测")
        self.vid_stop_btn = QPushButton("停止检测")
        self.webcam_detection_btn.setFont(font_main)
        self.mp4_detection_btn.setFont(font_main)
        self.vid_stop_btn.setFont(font_main)
        self.webcam_detection_btn.setStyleSheet("QPushButton{color:white}"
                                                "QPushButton:hover{background-color: rgb(2,110,180);}"
                                                "QPushButton{background-color:rgb(48,124,208)}"
                                                "QPushButton{border:2px}"
                                                "QPushButton{border-radius:5px}"
                                                "QPushButton{padding:5px 5px}"
                                                "QPushButton{margin:5px 5px}")
        self.mp4_detection_btn.setStyleSheet("QPushButton{color:white}"
                                             "QPushButton:hover{background-color: rgb(2,110,180);}"
                                             "QPushButton{background-color:rgb(48,124,208)}"
                                             "QPushButton{border:2px}"
                                             "QPushButton{border-radius:5px}"
                                             "QPushButton{padding:5px 5px}"
                                             "QPushButton{margin:5px 5px}")
        self.vid_stop_btn.setStyleSheet("QPushButton{color:white}"
                                        "QPushButton:hover{background-color: rgb(2,110,180);}"
                                        "QPushButton{background-color:rgb(48,124,208)}"
                                        "QPushButton{border:2px}"
                                        "QPushButton{border-radius:5px}"
                                        "QPushButton{padding:5px 5px}"
                                        "QPushButton{margin:5px 5px}")
        self.webcam_detection_btn.clicked.connect(self.open_cam)
        self.mp4_detection_btn.clicked.connect(self.open_mp4)
        self.vid_stop_btn.clicked.connect(self.close_vid)
        # 添加组件到布局上
        vid_detection_layout.addWidget(vid_title)
        vid_detection_layout.addWidget(self.vid_img)
        vid_detection_layout.addWidget(self.webcam_detection_btn)
        vid_detection_layout.addWidget(self.mp4_detection_btn)
        vid_detection_layout.addWidget(self.vid_stop_btn)
        vid_detection_widget.setLayout(vid_detection_layout)

        # todo 关于界面
        about_widget = QWidget()
        about_layout = QVBoxLayout()
        about_title = QLabel('欢迎使用目标检测系统\n\n 提供付费指导：有需要的好兄弟加下面的QQ即可')  # todo 修改欢迎词语
        about_title.setFont(QFont('楷体', 18))
        about_title.setAlignment(Qt.AlignCenter)
        about_img = QLabel()
        about_img.setPixmap(QPixmap('images/UI/qq.png'))
        about_img.setAlignment(Qt.AlignCenter)

        # label4.setText("<a href='https://oi.wiki/wiki/学习率的调整'>如何调整学习率</a>")
        label_super = QLabel()  # todo 更换作者信息
        label_super.setText("<a href='https://blog.csdn.net/ECHOSON'>或者你可以在这里找到我-->肆十二</a>")
        label_super.setFont(QFont('楷体', 16))
        label_super.setOpenExternalLinks(True)
        # label_super.setOpenExternalLinks(True)
        label_super.setAlignment(Qt.AlignRight)
        about_layout.addWidget(about_title)
        about_layout.addStretch()
        about_layout.addWidget(about_img)
        about_layout.addStretch()
        about_layout.addWidget(label_super)
        about_widget.setLayout(about_layout)

        self.left_img.setAlignment(Qt.AlignCenter)
        self.addTab(img_detection_widget, '图片检测')
        self.addTab(vid_detection_widget, '视频检测')
        self.addTab(about_widget, '联系我')
        self.setTabIcon(0, QIcon('images/UI/lufei.png'))
        self.setTabIcon(1, QIcon('images/UI/lufei.png'))
        self.setTabIcon(2, QIcon('images/UI/lufei.png'))

    '''
    ***上传图片***
    '''
    def upload_img(self):
        # 选择录像文件进行读取
        fileName, fileType = QFileDialog.getOpenFileName(self, 'Choose file', '', '*.jpg *.png *.tif *.jpeg')
        if fileName:
            suffix = fileName.split(".")[-1]
            save_path = osp.join("images/tmp", "tmp_upload." + suffix)
            shutil.copy(fileName, save_path)
            # 应该调整一下图片的大小，然后统一防在一起
            im0 = cv2.imread(save_path)
            resize_scale = self.output_size / im0.shape[0]
            im0 = cv2.resize(im0, (0, 0), fx=resize_scale, fy=resize_scale)
            cv2.imwrite("images/tmp/upload_show_result.jpg", im0)
            # self.right_img.setPixmap(QPixmap("images/tmp/single_result.jpg"))
            self.img2predict = fileName
            self.left_img.setPixmap(QPixmap("images/tmp/upload_show_result.jpg"))
            # todo 上传图片之后右侧的图片重置，
            self.right_img.setPixmap(QPixmap("images/UI/right.jpeg"))

    '''
    ***检测图片***
    '''
    def detect_img(self):
        model = self.model
        output_size = self.output_size
        source = self.img2predict  # file/dir/URL/glob, 0 for webcam
        imgsz = 640  # inference size (pixels)
        conf_thres = 0.25  # confidence threshold
        iou_thres = 0.45  # NMS IOU threshold
        max_det = 1000  # maximum detections per image
        device = self.device  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img = False  # show results
        save_txt = False  # save results to *.txt
        save_conf = False  # save confidences in --save-txt labels
        save_crop = False  # save cropped prediction boxes
        nosave = False  # do not save images/videos
        classes = None  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms = False  # class-agnostic NMS
        augment = False  # ugmented inference
        visualize = False  # visualize features
        line_thickness = 3  # bounding box thickness (pixels)
        hide_labels = False  # hide labels
        hide_conf = False  # hide confidences
        half = False  # use FP16 half-precision inference
        dnn = False  # use OpenCV DNN for ONNX inference
        print(source)
        if source == "":
            QMessageBox.warning(self, "请上传", "请先上传图片再进行检测")
        else:
            source = str(source)
            device = select_device(self.device)
            webcam = False
            stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
            imgsz = check_img_size(imgsz, s=stride)  # check image size
            save_img = not nosave and not source.endswith('.txt')  # save inference images
            # Dataloader
            if webcam:
                view_img = check_imshow()
                cudnn.benchmark = True  # set True to speed up constant image size inference
                dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
                bs = len(dataset)  # batch_size
            else:
                dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
                bs = 1  # batch_size
            vid_path, vid_writer = [None] * bs, [None] * bs
            # Run inference
            if pt and device.type != 'cpu':
                model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))  # warmup
            dt, seen = [0.0, 0.0, 0.0], 0
            for path, im, im0s, vid_cap, s in dataset:
                t1 = time_sync()
                im = torch.from_numpy(im).to(device)
                im = im.half() if half else im.float()  # uint8 to fp16/32
                im /= 255  # 0 - 255 to 0.0 - 1.0
                if len(im.shape) == 3:
                    im = im[None]  # expand for batch dim
                t2 = time_sync()
                dt[0] += t2 - t1
                # Inference
                # visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
                pred = model(im, augment=augment, visualize=visualize)
                t3 = time_sync()
                dt[1] += t3 - t2
                # NMS
                pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
                dt[2] += time_sync() - t3
                # Second-stage classifier (optional)
                # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
                # Process predictions
                for i, det in enumerate(pred):  # per image
                    seen += 1
                    if webcam:  # batch_size >= 1
                        p, im0, frame = path[i], im0s[i].copy(), dataset.count
                        s += f'{i}: '
                    else:
                        p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
                    p = Path(p)  # to Path
                    s += '%gx%g ' % im.shape[2:]  # print string
                    gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
                    imc = im0.copy() if save_crop else im0  # for save_crop
                    annotator = Annotator(im0, line_width=line_thickness, example=str(names))
                    if len(det):
                        # Rescale boxes from img_size to im0 size
                        det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()

                        # Print results
                        for c in det[:, -1].unique():
                            n = (det[:, -1] == c).sum()  # detections per class
                            s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                        # Write results
                        for *xyxy, conf, cls in reversed(det):
                            if save_txt:  # Write to file
                                xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(
                                    -1).tolist()  # normalized xywh
                                line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                                # with open(txt_path + '.txt', 'a') as f:
                                #     f.write(('%g ' * len(line)).rstrip() % line + '\n')

                            if save_img or save_crop or view_img:  # Add bbox to image
                                c = int(cls)  # integer class
                                label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                                annotator.box_label(xyxy, label, color=colors(c, True))
                                # if save_crop:
                                #     save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg',
                                #                  BGR=True)
                    # Print time (inference-only)
                    LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
                    # Stream results
                    im0 = annotator.result()
                    # if view_img:
                    #     cv2.imshow(str(p), im0)
                    #     cv2.waitKey(1)  # 1 millisecond
                    # Save results (image with detections)
                    resize_scale = output_size / im0.shape[0]
                    im0 = cv2.resize(im0, (0, 0), fx=resize_scale, fy=resize_scale)
                    cv2.imwrite("images/tmp/single_result.jpg", im0)
                    # 目前的情况来看，应该只是ubuntu下会出问题，但是在windows下是完整的，所以继续
                    self.right_img.setPixmap(QPixmap("images/tmp/single_result.jpg"))

    # 视频检测，逻辑基本一致，有两个功能，分别是检测摄像头的功能和检测视频文件的功能，先做检测摄像头的功能。

    '''
    ### 界面关闭事件 ###
    '''
    def closeEvent(self, event):
        reply = QMessageBox.question(self,
                                     'quit',
                                     "Are you sure?",
                                     QMessageBox.Yes | QMessageBox.No,
                                     QMessageBox.No)
        if reply == QMessageBox.Yes:
            self.close()
            event.accept()
        else:
            event.ignore()

    '''
    ### 视频关闭事件 ###
    '''

    def open_cam(self):
        self.webcam_detection_btn.setEnabled(False)
        self.mp4_detection_btn.setEnabled(False)
        self.vid_stop_btn.setEnabled(True)
        self.vid_source = '0'
        self.webcam = True
        th = threading.Thread(target=self.detect_vid)
        th.start()

    '''
    ### 开启视频文件检测事件 ###
    '''

    def open_mp4(self):
        fileName, fileType = QFileDialog.getOpenFileName(self, 'Choose file', '', '*.mp4 *.avi')
        if fileName:
            self.webcam_detection_btn.setEnabled(False)
            self.mp4_detection_btn.setEnabled(False)
            # self.vid_stop_btn.setEnabled(True)
            self.vid_source = fileName
            self.webcam = False
            th = threading.Thread(target=self.detect_vid)
            th.start()

    '''
    ### 视频开启事件 ###
    '''

    # 视频和摄像头的主函数是一样的，不过是传入的source不同罢了
    def detect_vid(self):
        # pass
        model = self.model
        output_size = self.output_size
        # source = self.img2predict  # file/dir/URL/glob, 0 for webcam
        imgsz = 640  # inference size (pixels)
        conf_thres = 0.25  # confidence threshold
        iou_thres = 0.45  # NMS IOU threshold
        max_det = 1000  # maximum detections per image
        # device = self.device  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img = False  # show results
        save_txt = False  # save results to *.txt
        save_conf = False  # save confidences in --save-txt labels
        save_crop = False  # save cropped prediction boxes
        nosave = False  # do not save images/videos
        classes = None  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms = False  # class-agnostic NMS
        augment = False  # ugmented inference
        visualize = False  # visualize features
        line_thickness = 3  # bounding box thickness (pixels)
        hide_labels = False  # hide labels
        hide_conf = False  # hide confidences
        half = False  # use FP16 half-precision inference
        dnn = False  # use OpenCV DNN for ONNX inference
        source = str(self.vid_source)
        webcam = self.webcam
        device = select_device(self.device)
        stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
        imgsz = check_img_size(imgsz, s=stride)  # check image size
        save_img = not nosave and not source.endswith('.txt')  # save inference images
        # Dataloader
        if webcam:
            view_img = check_imshow()
            cudnn.benchmark = True  # set True to speed up constant image size inference
            dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
            bs = len(dataset)  # batch_size
        else:
            dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
            bs = 1  # batch_size
        vid_path, vid_writer = [None] * bs, [None] * bs
        # Run inference
        if pt and device.type != 'cpu':
            model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))  # warmup
        dt, seen = [0.0, 0.0, 0.0], 0
        for path, im, im0s, vid_cap, s in dataset:
            t1 = time_sync()
            im = torch.from_numpy(im).to(device)
            im = im.half() if half else im.float()  # uint8 to fp16/32
            im /= 255  # 0 - 255 to 0.0 - 1.0
            if len(im.shape) == 3:
                im = im[None]  # expand for batch dim
            t2 = time_sync()
            dt[0] += t2 - t1
            # Inference
            # visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
            pred = model(im, augment=augment, visualize=visualize)
            t3 = time_sync()
            dt[1] += t3 - t2
            # NMS
            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
            dt[2] += time_sync() - t3
            # Second-stage classifier (optional)
            # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
            # Process predictions
            for i, det in enumerate(pred):  # per image
                seen += 1
                if webcam:  # batch_size >= 1
                    p, im0, frame = path[i], im0s[i].copy(), dataset.count
                    s += f'{i}: '
                else:
                    p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
                p = Path(p)  # to Path
                # save_path = str(save_dir / p.name)  # im.jpg
                # txt_path = str(save_dir / 'labels' / p.stem) + (
                #     '' if dataset.mode == 'image' else f'_{frame}')  # im.txt
                s += '%gx%g ' % im.shape[2:]  # print string
                gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
                imc = im0.copy() if save_crop else im0  # for save_crop
                annotator = Annotator(im0, line_width=line_thickness, example=str(names))
                if len(det):
                    # Rescale boxes from img_size to im0 size
                    det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()

                    # Print results
                    for c in det[:, -1].unique():
                        n = (det[:, -1] == c).sum()  # detections per class
                        s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                    # Write results
                    for *xyxy, conf, cls in reversed(det):
                        if save_txt:  # Write to file
                            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(
                                -1).tolist()  # normalized xywh
                            line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                            # with open(txt_path + '.txt', 'a') as f:
                            #     f.write(('%g ' * len(line)).rstrip() % line + '\n')

                        if save_img or save_crop or view_img:  # Add bbox to image
                            c = int(cls)  # integer class
                            label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                            annotator.box_label(xyxy, label, color=colors(c, True))
                            # if save_crop:
                            #     save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg',
                            #                  BGR=True)
                # Print time (inference-only)
                LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
                # Stream results
                # Save results (image with detections)
                im0 = annotator.result()
                frame = im0
                resize_scale = output_size / frame.shape[0]
                frame_resized = cv2.resize(frame, (0, 0), fx=resize_scale, fy=resize_scale)
                cv2.imwrite("images/tmp/single_result_vid.jpg", frame_resized)
                self.vid_img.setPixmap(QPixmap("images/tmp/single_result_vid.jpg"))
                # self.vid_img
                # if view_img:
                # cv2.imshow(str(p), im0)
                # self.vid_img.setPixmap(QPixmap("images/tmp/single_result_vid.jpg"))
                # cv2.waitKey(1)  # 1 millisecond
            if cv2.waitKey(25) & self.stopEvent.is_set() == True:
                self.stopEvent.clear()
                self.webcam_detection_btn.setEnabled(True)
                self.mp4_detection_btn.setEnabled(True)
                self.reset_vid()
                break
        # self.reset_vid()

    '''
    ### 界面重置事件 ###
    '''

    def reset_vid(self):
        self.webcam_detection_btn.setEnabled(True)
        self.mp4_detection_btn.setEnabled(True)
        self.vid_img.setPixmap(QPixmap("images/UI/up.jpeg"))
        self.vid_source = '0'
        self.webcam = True

    '''
    ### 视频重置事件 ###
    '''

    def close_vid(self):
        self.stopEvent.set()
        self.reset_vid()

if __name__ == "__main__":
    app = QApplication(sys.argv)
    mainWindow = MainWindow()
    mainWindow.show()
    sys.exit(app.exec_())

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