Face recognition is a kind of biometric identification technology based on human facial feature information. A series of related technologies that use cameras or cameras to collect images or video streams containing faces, and automatically detect and track faces in the images, and then perform face recognition on the detected faces. .
At the moment of the epidemic, schools are closed, online classes in classrooms, dining in canteens, etc., wearing masks is still very meaningful. The efficiency of monitoring personnel alone is too low. The author was considering whether to let the computer complete the relevant work, so I checked the relevant information. , On the basis of the open source training set, this mask recognition is designed.
picture:
video:
Mask recognition case
Configuration Environment:
windows10 system
pyCharm
python3.7 in Anaconda environment
tenforflow1.15.0
cuda10.0
Overall process:
I believe that the little friends can't wait, let's start with the code!
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()
An interjection, the deep learning project is currently completely open source, you can experience the experience first:
https://demo.aizoo.com/face-mask-detection.html
Deep learning (DL, Deep Learning) is a new research direction in the field of machine learning (ML, Machine Learning), it is introduced into machine learning to make it closer to the original goal - artificial intelligence (AI, Artificial Intelligence).
Deep learning is to learn the inherent laws and representation levels of sample data, and the information obtained during these learning processes is of great help to the interpretation of data such as text, images, and sounds. Its ultimate goal is to enable machines to have the ability to analyze and learn like humans, and to recognize data such as words, images, and sounds. Deep learning is a complex machine learning algorithm that has achieved results in speech and image recognition far exceeding previous related technologies.
Deep learning has achieved many results in search technology, data mining, machine learning, machine translation, natural language processing, multimedia learning, speech, recommendation and personalization technology, and other related fields. Deep learning enables machines to imitate human activities such as audio-visual and thinking, solves many complex pattern recognition problems, and makes great progress in artificial intelligence-related technologies.
#!/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
if args.video_path == '0':
video_path = 0
run_on_video(video_path, '', conf_thresh=0.5)
'''Since there are too many codes to expand in detail, if you have any questions, please leave a message in the comment area to discuss the problem together.
Code source address:
Mask recognition project based on tenforflow - Python documentation resources - CSDN download
This project is for learning reference only, if there is any infringement, please inform and delete it immediately