基于Python實現(xiàn)簡單的人臉識別系統(tǒng)
更新時間：2024年04月08日 11:51:50 作者：肆十二
這篇文章主要介紹了如何通過Python實現(xiàn)一個簡單的人臉識別系統(tǒng)，文中的示例代碼講解詳細，對我們學習Python有一定的幫助，感興趣的可以跟隨小編一起試一試
前言
最近又多了不少朋友關注，先在這里謝謝大家。關注我的朋友大多數(shù)都是大學生，而且我簡單看了一下，低年級的大學生居多，大多數(shù)都是為了完成課程設計，作為一個過來人，還是希望大家平時能多抽出點時間學習一下，這種臨時抱佛腳的策略要少用嗷。今天我們來python實現(xiàn)一個人臉識別系統(tǒng)，主要是借助了dlib這個庫，相當于我們直接調用現(xiàn)成的庫來進行人臉識別，就省去了之前教程中的數(shù)據(jù)收集和模型訓練的步驟了。
B站視頻：用300行代碼實現(xiàn)人臉識別系統(tǒng)_嗶哩嗶哩_bilibili
碼云地址：face_dlib_py37_42: 用300行代碼開發(fā)一個人臉識別系統(tǒng)-42 (gitee.com)
基本原理
人臉識別和目標檢測這些還不太一樣，比如大家傳統(tǒng)的訓練一個目標檢測模型，你只有對這個目標訓練了之后，你的模型才能找到這樣的目標，比如你的目標檢測模型如果是檢測植物的，那顯然就不能檢測動物。但是人臉識別就不一樣，以你的手機為例，你發(fā)現(xiàn)你只錄入了一次你的人臉信息，不需要訓練，他就能準確的識別你，這里識別的原理是通過人臉識別的模型提取你臉部的特征向量，然后將實時檢測到的你的人臉同數(shù)據(jù)庫中保存的人臉進行比對，如果相似度超過一定的閾值之后，就認為比對成功。不過我這里說的只是簡化版本的人臉識別，現(xiàn)在手機和門禁這些要復雜和安全的多，也不是簡單平面上的人臉識別。
總結下來可以分為下面的步驟：
1.上傳人臉到數(shù)據(jù)庫
2.人臉檢測
3.數(shù)據(jù)庫比對并返回結果
這里我做了一個簡答的示意圖，可以幫助大家簡單理解一下。
代碼實現(xiàn)
廢話不多說，這里就是我們的代碼實現(xiàn)，代碼我已經(jīng)上傳到碼云，大家直接下載就行，地址就在博客開頭。
不會安裝python環(huán)境的兄弟請看這里：如何在pycharm中配置anaconda的虛擬環(huán)境
創(chuàng)建虛擬環(huán)境
創(chuàng)建虛擬環(huán)境前請大家先下載博客開頭的碼云源碼到本地。
本次我們需要使用到python3.7的虛擬環(huán)境，命令如下：
conda create -n face python==3.7.3 conda activate face
安裝必要的庫
pip install -r requirements.txt
愉快地開始你的人臉識別吧！
執(zhí)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運行即可
首先將你需要識別的人臉上傳到數(shù)據(jù)庫中
通過第二個視頻檢測功能識別實時的人臉
詳細的代碼如下：
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
Project Name: yolov5-jungong
File Name: window.py.py
Author: chenming
Create Date: 2021/11/8
Description：圖形化界面，可以檢測攝像頭、視頻和圖片文件
-------------------------------------------------
"""
# 應該在界面啟動的時候就將模型加載出來，設置tmp的目錄來放中間的處理結果
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
# 添加一個關于界面
# 窗口主類
class MainWindow(QTabWidget):
# 基本配置不動，然后只動第三個界面
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"))
# 圖片讀取進程
self.output_size = 480
self.img2predict = ""
self.device = 'cpu'
# # 初始化視頻讀取線程
self.vid_source = '0' # 初始設置為攝像頭
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 指明模型加載的位置的設備
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("模型加載完成!")
return model
'''
***界面初始化***
'''
def initUI(self):
# 圖片檢測子界面
font_title = QFont('楷體', 16)
font_main = QFont('楷體', 14)
# 圖片識別界面, 兩個按鈕，上傳圖片和顯示結果
img_detection_widget = QWidget()
img_detection_layout = QVBoxLayout()
img_detection_title = QLabel("圖片識別功能")
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("上傳圖片")
det_img_button = QPushButton("開始檢測")
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 視頻識別界面
# 視頻識別界面的邏輯比較簡單，基本就從上到下的邏輯
vid_detection_widget = QWidget()
vid_detection_layout = QVBoxLayout()
vid_title = QLabel("視頻檢測功能")
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("攝像頭實時監(jiān)測")
self.mp4_detection_btn = QPushButton("視頻文件檢測")
self.vid_stop_btn = QPushButton("停止檢測")
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)
# 添加組件到布局上
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 關于界面
about_widget = QWidget()
about_layout = QVBoxLayout()
about_title = QLabel('歡迎使用目標檢測系統(tǒng)\n\n 提供付費指導：有需要的好兄弟加下面的QQ即可') # todo 修改歡迎詞語
about_title.setFont(QFont('楷體', 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 )
label_super = QLabel() # todo 更換作者信息
label_super.setText("<a )
label_super.setFont(QFont('楷體', 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, '圖片檢測')
self.addTab(vid_detection_widget, '視頻檢測')
self.addTab(about_widget, '聯(lián)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'))
'''
***上傳圖片***
'''
def upload_img(self):
# 選擇錄像文件進行讀取
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)
# 應該調整一下圖片的大小，然后統(tǒng)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 上傳圖片之后右側的圖片重置，
self.right_img.setPixmap(QPixmap("images/UI/right.jpeg"))
'''
***檢測圖片***
'''
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, "請上傳", "請先上傳圖片再進行檢測")
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)
# 目前的情況來看，應該只是ubuntu下會出問題，但是在windows下是完整的，所以繼續(xù)
self.right_img.setPixmap(QPixmap("images/tmp/single_result.jpg"))
# 視頻檢測，邏輯基本一致，有兩個功能，分別是檢測攝像頭的功能和檢測視頻文件的功能，先做檢測攝像頭的功能。
'''
### 界面關閉事件 ###
'''
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()
'''
### 視頻關閉事件 ###
'''
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()
'''
### 開啟視頻文件檢測事件 ###
'''
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()
'''
### 視頻開啟事件 ###
'''
# 視頻和攝像頭的主函數(shù)是一樣的，不過是傳入的source不同罷了
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
'''
### 視頻重置事件 ###
'''
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_())
以上就是基于Python實現(xiàn)簡單的人臉識別系統(tǒng)的詳細內容，更多關于Python人臉識別系統(tǒng)的資料請關注腳本之家其它相關文章！
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