用300行Python代码实现一个人脸识别系统
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
CSDN博客：用300行Python代码实现一个人脸识别系统_dejahu的博客-CSDN博客
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)
预编译dlib库下载地址：人脸识别系统+windows64位-dlib-19.17.0-cp37-cp37m-win_amd64.zip-深度学习文档类资源-CSDN文库
注：直接安装dlib库可能会有编译错误，可以通过下列方式获取编译好的dlib库
; 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：
- 上传人脸到数据库
- 人脸检测
- 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_dejahu的博客-CSDN博客_如何在pycharm中配置anaconda
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：
"""
"""
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]
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT))
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))
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
class MainWindow(QTabWidget):
def __init__(self):
super().__init__()
self.setWindowTitle('Target detection system')
self.resize(1200, 800)
self.setWindowIcon(QIcon("images/UI/lufei.png"))
self.output_size = 480
self.img2predict = ""
self.device = 'cpu'
self.vid_source = '0'
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")
self.initUI()
self.reset_vid()
'''
***模型初始化***
'''
@torch.no_grad()
def model_load(self, weights="",
device='',
half=False,
dnn=False,
):
device = select_device(device)
half &= device.type != 'cpu'
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 &= pt and device.type != 'cpu'
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):
font_title = QFont('楷体', 16)
font_main = QFont('楷体', 14)
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)
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)
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)
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')
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)
label_super = QLabel()
label_super.setText("或者你可以在这里找到我-->肆十二")
label_super.setFont(QFont('楷体', 16))
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):
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)
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.img2predict = fileName
self.left_img.setPixmap(QPixmap("images/tmp/upload_show_result.jpg"))
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
imgsz = 640
conf_thres = 0.25
iou_thres = 0.45
max_det = 1000
device = self.device
view_img = False
save_txt = False
save_conf = False
save_crop = False
nosave = False
classes = None
agnostic_nms = False
augment = False
visualize = False
line_thickness = 3
hide_labels = False
hide_conf = False
half = False
dnn = False
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)
save_img = not nosave and not source.endswith('.txt')
if webcam:
view_img = check_imshow()
cudnn.benchmark = True
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
bs = len(dataset)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
bs = 1
vid_path, vid_writer = [None] * bs, [None] * bs
if pt and device.type != 'cpu':
model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))
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()
im /= 255
if len(im.shape) == 3:
im = im[None]
t2 = time_sync()
dt[0] += t2 - t1
pred = model(im, augment=augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
dt[2] += time_sync() - t3
for i, det in enumerate(pred):
seen += 1
if webcam:
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)
s += '%gx%g ' % im.shape[2:]
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]
imc = im0.copy() if save_crop else im0
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum()
s += f"{n}{names[int(c)]}{'s' * (n > 1)}, "
for *xyxy, conf, cls in reversed(det):
if save_txt:
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(
-1).tolist()
line = (cls, *xywh, conf) if save_conf else (cls, *xywh)
if save_img or save_crop or view_img:
c = int(cls)
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))
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
im0 = annotator.result()
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)
self.right_img.setPixmap(QPixmap("images/tmp/single_result.jpg"))
'''
### 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_source = fileName
self.webcam = False
th = threading.Thread(target=self.detect_vid)
th.start()
'''
### 视频开启事件 ###
'''
def detect_vid(self):
model = self.model
output_size = self.output_size
imgsz = 640
conf_thres = 0.25
iou_thres = 0.45
max_det = 1000
view_img = False
save_txt = False
save_conf = False
save_crop = False
nosave = False
classes = None
agnostic_nms = False
augment = False
visualize = False
line_thickness = 3
hide_labels = False
hide_conf = False
half = False
dnn = False
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)
save_img = not nosave and not source.endswith('.txt')
if webcam:
view_img = check_imshow()
cudnn.benchmark = True
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
bs = len(dataset)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
bs = 1
vid_path, vid_writer = [None] * bs, [None] * bs
if pt and device.type != 'cpu':
model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))
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()
im /= 255
if len(im.shape) == 3:
im = im[None]
t2 = time_sync()
dt[0] += t2 - t1
pred = model(im, augment=augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
dt[2] += time_sync() - t3
for i, det in enumerate(pred):
seen += 1
if webcam:
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)
s += '%gx%g ' % im.shape[2:]
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]
imc = im0.copy() if save_crop else im0
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum()
s += f"{n}{names[int(c)]}{'s' * (n > 1)}, "
for *xyxy, conf, cls in reversed(det):
if save_txt:
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(
-1).tolist()
line = (cls, *xywh, conf) if save_conf else (cls, *xywh)
if save_img or save_crop or view_img:
c = int(cls)
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))
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
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"))
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
'''
### 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_())
Original: https://blog.csdn.net/ECHOSON/article/details/122404926
Author: 肆十二
Title: 教你用300行Python代码实现一个人脸识别系统
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