无所遁形——快把你的口罩戴上(口罩识别)

网友投稿 754 2022-05-30

人脸识别,是基于人的脸部特征信息进行身份识别的一种生物识别技术。用摄像机或摄像头采集含有人脸的图像或视频流,并自动在图像中检测和跟踪人脸,进而对检测到的人脸进行脸部识别的一系列相关技术,通常也叫做人像识别、面部识别。

疫情当下,学校封校,教室上网课,食堂就餐等等环境,口罩佩戴依旧十分有意义,单靠人员监测效率太过低下,笔者就在考虑能否让计算机完成相关工作,就查阅了相关资料,在开源训练集的基础上,设计了本款口罩识别。

图片:

视频:

口罩识别案例

配置环境:

windows10 系统

pyCharm

Anaconda环境下的python3.7

tenforflow1.15.0

cuda10.0

整体流程:

相信小伙伴们已经迫不及待了,上代码走起!

from tkinter import *

from tkinter.filedialog import askdirectory

from tkinter.messagebox import showinfo

import cv2

import numpy as np

from PIL import Image, ImageTk

from tkinter import ttk

import pygame

import time

import tensorflow_infer as flow

pygame.mixer.init(frequency=16000, size=-16, channels=2, buffer=4096)

detector = cv2.CascadeClassifier('haarcascades\haarcascade_frontalface_default.xml')

mask_detector = cv2.CascadeClassifier('xml\cascade.xml')

class GUI:

def __init__(self):

self.camera = None # 摄像头

self.root = Tk()

self.root.title('maskdetection')

self.root.geometry('%dx%d' % (800, 600))

self.createFirstPage()

mainloop()

def createFirstPage(self):

self.page1 = Frame(self.root)

self.page1.pack()

Label(self.page1, text='口罩追踪系统', font=('粗体', 20)).pack()

image = Image.open("14.jpg") # 随便使用一张图片做背景界面 不要太大

photo = ImageTk.PhotoImage(image = image)

self.data1 = Label(self.page1, width=780,image = photo)

self.data1.image = photo

self.data1.pack(padx=5, pady=5)

self.button11 = Button(self.page1, width=18, height=2, text="深度学习算法", bg='red', font=("宋", 12),

relief='raise',command = self.createSecondPage1)

self.button11.pack(side=LEFT, padx=25, pady = 10)

self.button13.pack(side=LEFT, padx=25, pady = 10)

self.button14 = Button(self.page1, width=18, height=2, text="退出系统", bg='gray', font=("宋", 12),

relief='raise',command = self.quitMain)

self.button14.pack(side=LEFT, padx=25, pady = 10)

def createSecondPage1(self):

self.camera = cv2.VideoCapture(0)

self.page1.pack_forget()

self.page2 = Frame(self.root)

self.page2.pack()

Label(self.page2, text='实时追踪口罩佩戴情况', font=('粗体', 20)).pack()

self.data2 = Label(self.page2)

self.data2.pack(padx=5, pady=5)

self.button21 = Button(self.page2, width=18, height=2, text="返回", bg='gray', font=("宋", 12),

relief='raise',command = self.backFirst)

self.button21.pack(padx=25,pady = 10)

self.video_loop1(self.data2)

def video_loop1(self, panela):

def slogan_short():

timeplay = 1.5

global playflag_short

playflag_short = 1

while playflag_short:

track = pygame.mixer.music.load(file_slogan_short)

print("------------请您戴好口罩")

pygame.mixer.music.play()

time.sleep(timeplay)

playflag_short = 0

time.sleep(0)

success, img = self.camera.read() # 从摄像头读取照片

if success:

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

num,c,img = flow.inference(img, conf_thresh=0.5, iou_thresh=0.4, target_shape=(260, 260), draw_result=True,

show_result=False)

# 语音提示

# if(isinstance(num/5,int)& (c=='NoMask')):

# slogan_short()

# cv2.imshow('image', img)

# img = flow.inference(img, show_result=True, target_shape=(260, 260))

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

cv2image = cv2.cvtColor(img, cv2.COLOR_BGR2RGBA) # 转换颜色从BGR到RGBA

current_image = Image.fromarray(cv2image) # 将图像转换成Image对象

imgtk = ImageTk.PhotoImage(image=current_image)

panela.imgtk = imgtk

panela.config(image=imgtk)

self.root.after(1, lambda: self.video_loop1(panela))

def select_path(self):

self.pash_= askdirectory()

path = StringVar()

path.set(self.pash_)

def createSecondPage(self):

self.camera = cv2.VideoCapture(0)

self.page1.pack_forget()

self.page2 = Frame(self.root)

self.page2.pack()

Label(self.page2, text='实时追踪口罩佩戴情况', font=('粗体', 20)).pack()

self.data2 = Label(self.page2)

self.data2.pack(padx=5, pady=5)

self.button21 = Button(self.page2, width=18, height=2, text="返回", bg='gray', font=("宋", 12),

relief='raise',command = self.backFirst)

self.button21.pack(padx=25,pady = 10)

self.video_loop(self.data2)

def video_loop(self, panela):

success, img = self.camera.read() # 从摄像头读取照片

if success:

faces = detector.detectMultiScale(img, 1.1, 3)

for (x, y, w, h) in faces:

# 参数分别为 图片、左上角坐标,右下角坐标,颜色,厚度

face = img[y:y + h, x:x + w] # 裁剪坐标为[y0:y1, x0:x1]

mask_face = mask_detector.detectMultiScale(img, 1.1, 5)

for (x2, y2, w2, h2) in mask_face:

cv2.putText(img, 'mask', (x2 - 2, y2 - 2),

cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255))

cv2.rectangle(img, (x2, y2), (x2 + w2, y2 + h2), (0, 0, 255), 2)

#img = mask.facesdetecter(img)

cv2image = cv2.cvtColor(img, cv2.COLOR_BGR2RGBA) # 转换颜色从BGR到RGBA

#faces = detector.detectMultiScale(cv2image, 1.1, 3)

current_image = Image.fromarray(cv2image) # 将图像转换成Image对象

imgtk = ImageTk.PhotoImage(image=current_image)

panela.imgtk = imgtk

panela.config(image=imgtk)

self.root.after(1, lambda: self.video_loop(panela))

def backFirst(self):

self.page2.pack_forget()

self.page1.pack()

# 释放摄像头资源

self.camera.release()

cv2.destroyAllWindows()

def backMain(self):

self.root.geometry('900x600')

self.page3.pack_forget()

self.page1.pack()

def quitMain(self):

sys.exit(0)

if __name__ == '__main__':

demo = GUI()

插播一句,深度学习的项目目前完全开源,大家可以先体验体验:

https://demo.aizoo.com/face-mask-detection.html

深度学习(DL, Deep Learning)是机器学习(ML, Machine Learning)领域中一个新的研究方向,它被引入机器学习使其更接近于最初的目标——人工智能(AI, Artificial Intelligence)。

深度学习是学习样本数据的内在规律和表示层次,这些学习过程中获得的信息对诸如文字,图像和声音等数据的解释有很大的帮助。它的最终目标是让机器能够像人一样具有分析学习能力,能够识别文字、图像和声音等数据。 深度学习是一个复杂的机器学习算法,在语音和图像识别方面取得的效果,远远超过先前相关技术。

深度学习在搜索技术,数据挖掘,机器学习,机器翻译,自然语言处理,多媒体学习,语音,推荐和个性化技术,以及其他相关领域都取得了很多成果。深度学习使机器模仿视听和思考等人类的活动,解决了很多复杂的模式识别难题,使得人工智能相关技术取得了很大进步。

#!/usr/bin/env python

# -*- coding:utf-8 -*-

import cv2

# 测试打开摄像头检测跟踪人脸

# 识别人脸的xml文件,构建人脸检测器

detector = cv2.CascadeClassifier('haarcascades\haarcascade_frontalface_default.xml')

# 获取0号摄像头的实例

cap = cv2.VideoCapture(0)

while True:

# 就是从摄像头获取到图像,这个函数返回了两个变量,第一个为布尔值表示成功与否,以及第二个是图像。

ret, img = cap.read()

#转为灰度图

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 获取人脸坐标

faces = detector.detectMultiScale(gray, 1.1, 3)

for (x, y, w, h) in faces:

# 参数分别为 图片、左上角坐标,右下角坐标,颜色,厚度

cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)

cv2.imshow('Mask', img)

cv2.waitKey(3)

cap.release()

cv2.destroyAllWindows()

#!/usr/bin/env python

# -*- coding:utf-8 -*-

# -*- coding:utf-8 -*-

import cv2

import time

import argparse

import pygame

import numpy as np

from PIL import Image

from tensorflow.keras.models import model_from_json

from utils.anchor_generator import generate_anchors

from utils.anchor_decode import decode_bbox

from utils.nms import single_class_non_max_suppression

from load_model.tensorflow_loader import load_tf_model, tf_inference

# sess, graph = load_tf_model('FaceMaskDetection-master\models\face_mask_detection.pb')

sess, graph = load_tf_model('models/face_mask_detection.pb')

# anchor configuration

feature_map_sizes = [[33, 33], [17, 17], [9, 9], [5, 5], [3, 3]]

anchor_sizes = [[0.04, 0.056], [0.08, 0.11], [0.16, 0.22], [0.32, 0.45], [0.64, 0.72]]

anchor_ratios = [[1, 0.62, 0.42]] * 5

file_slogan = r'video/slogan.mp3'

file_slogan_short = r'video/slogan_short.mp3'

pygame.mixer.init(frequency=16000, size=-16, channels=2, buffer=4096)

# generate anchors

anchors = generate_anchors(feature_map_sizes, anchor_sizes, anchor_ratios)

# 用于推断,批大小为1,模型输出形状为[1,N,4],因此将锚点的dim扩展为[1,anchor_num,4]

anchors_exp = np.expand_dims(anchors, axis=0)

id2class = {0: 'Mask', 1: 'NoMask'}

def inference(image, conf_thresh=0.5, iou_thresh=0.4, target_shape=(160, 160), draw_result=True, show_result=True):

n = 0

n = n+1

''' 检测推理的主要功能

# :param image:3D numpy图片数组

# :param conf_thresh:分类概率的最小阈值。

# :param iou_thresh:网管的IOU门限

# :param target_shape:模型输入大小。

# :param draw_result:是否将边框拖入图像。

# :param show_result:是否显示图像。

'''

# image = np.copy(image)

output_info = []

height, width, _ = image.shape

image_resized = cv2.resize(image, target_shape)

image_np = image_resized / 255.0 # 归一化到0~1

image_exp = np.expand_dims(image_np, axis=0)

y_bboxes_output, y_cls_output = tf_inference(sess, graph, image_exp)

# remove the batch dimension, for batch is always 1 for inference.

y_bboxes = decode_bbox(anchors_exp, y_bboxes_output)[0]

y_cls = y_cls_output[0]

# 为了加快速度,请执行单类NMS,而不是多类NMS。

bbox_max_scores = np.max(y_cls, axis=1)

bbox_max_score_classes = np.argmax(y_cls, axis=1)

# keep_idx是nms之后的活动边界框。

keep_idxs = single_class_non_max_suppression(y_bboxes, bbox_max_scores, conf_thresh=conf_thresh,

iou_thresh=iou_thresh)

for idx in keep_idxs:

conf = float(bbox_max_scores[idx])

class_id = bbox_max_score_classes[idx]

bbox = y_bboxes[idx]

# 裁剪坐标,避免该值超出图像边界。

xmin = max(0, int(bbox[0] * width))

ymin = max(0, int(bbox[1] * height))

xmax = min(int(bbox[2] * width), width)

ymax = min(int(bbox[3] * height), height)

if draw_result:

if class_id == 0:

color = (0, 255, 0)

else:

color = (255, 0, 0)

cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)

cv2.putText(image, "%s: %.2f" % (id2class[class_id], conf), (xmin + 2, ymin - 2),

cv2.FONT_HERSHEY_SIMPLEX, 1, color)

output_info.append([class_id, conf, xmin, ymin, xmax, ymax])

if show_result:

Image.fromarray(image).show()

# return output_info

return n,id2class,image

# 读取摄像头或者本地视频路径并处理

def run_on_video(video_path, output_video_name, conf_thresh):

cap = cv2.VideoCapture(video_path)

height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)

width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)

fps = cap.get(cv2.CAP_PROP_FPS)

fourcc = cv2.VideoWriter_fourcc(*'XVID')

# writer = cv2.VideoWriter(output_video_name, fourcc, int(fps), (int(width), int(height)))

total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)

if not cap.isOpened():

raise ValueError("Video open failed.")

return

status = True

idx = 0

while status:

start_stamp = time.time()

status, img_raw = cap.read()

img_raw = cv2.cvtColor(img_raw, cv2.COLOR_BGR2RGB)

read_frame_stamp = time.time()

if (status):

inference(img_raw,

conf_thresh,

iou_thresh=0.5,

target_shape=(260, 260),

draw_result=True,

show_result=False)

cv2.imshow('image', img_raw[:, :, ::-1])

cv2.waitKey(1)

inference_stamp = time.time()

# writer.write(img_raw)

write_frame_stamp = time.time()

idx += 1

print("%d of %d" % (idx, total_frames))

print("read_frame:%f, infer time:%f, write time:%f" % (read_frame_stamp - start_stamp,

inference_stamp - read_frame_stamp,

write_frame_stamp - inference_stamp))

# writer.release()

'''

if __name__ == "__main__":

parser = argparse.ArgumentParser(description="Face Mask Detection")

parser.add_argument('--img-mode', type=int, default=0,

help='set 1 to run on image, 0 to run on video.') # 这里设置为1:检测图片;还是设置为0:视频文件(实时图像数据)检测

parser.add_argument('--img-path', type=str, help='path to your image.')

parser.add_argument('--video-path', type=str, default='0', help='path to your video, `0` means to use camera.')

# parser.add_argument('--hdf5', type=str, help='keras hdf5 file')

args = parser.parse_args()

if args.img_mode:

imgPath = args.img_path

# img = cv2.imread("imgPath")

img = cv2.imread(imgPath)

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

inference(img, show_result=True, target_shape=(260, 260))

else:

video_path = args.video_path

无所遁形——快把你的口罩戴上(口罩识别)

if args.video_path == '0':

video_path = 0

run_on_video(video_path, '', conf_thresh=0.5)

'''

由于代码过多无法详细展开,如有疑问欢迎大家在评论区留言,共同探讨问题。

代码源码地址:

基于tenforflow的口罩识别项目-Python文档类资源-CSDN下载

本项目仅供学习参考,如有侵权告知立删

机器学习 深度学习

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