基于Python实现简单的人脸识别系统

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：

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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