Python使用 Open CV 进行口罩检测

我不是女神ヾ 2023-10-02 04:11 17阅读 0赞

## [Face Mask Detection using Open CVOur motto is to detect facial mask monitoring using Open CV and Python to stop the flow of corona virus.![W3_BYVVVh.png][]https://karanshah2910.hashnode.dev/face-mask-detection-using-open-cv][Face Mask Detection using Open CVOur motto is to detect facial mask monitoring using Open CV and Python to stop the flow of corona virus._W3_BYVVVh.png_https_karanshah2910.hashnode.dev_face-mask-detection-using-open-cv] ##

根据 Worldometer 汇编的数据（截至 2020 年 6 月 5 日），冠状病毒病已蔓延到超过 213 个国家，在全球范围内感染了超过 700 万人并造成超过 403,202 人死亡

为了限制冠状病毒的传播，保持社交距离和遵守卫生标准（例如强制佩戴口罩、使用手手套、面罩和使用消毒剂）非常重要。

许多组织强制要求遵守社交距离和戴口罩。本文介绍了如何使用 OpenCV 和 Python 检测口罩监控。

我假设您具有 OpenCV 和 Python 的基本知识。

**主要有两个步骤：**

1.  在输入视频的每一帧中识别人脸和嘴巴
2.  识别人员是否使用面具

**第 1 步：使用 Python 和 OpenCV 识别人脸和嘴巴进行人脸预测的步骤是：**

1.  使用“CascadeClassifier”函数和“haarcascade\_frontalface\_default.xml”创建HAAR Cascade对象
2.  使用功能读取的图像**“imread”（或用于视频“读” /相机输入）**功能
3.  使用**'cvtColor'**函数进行灰度转换
4.  使用**“detectMultiScale”**功能检测人脸

要获取 Open CV 如何检测人脸的详细信息，请参阅[使用 OpenCV 的人脸识别][OpenCV]链接[— OpenCV 2.4.13.7 文档][OpenCV]

据观察，戴白色面具的人，大多数时候 OpenCV 无法正确识别人脸。为了克服这个困难，使用“阈值”函数转换黑白图像，然后将此图像发送到**“detectMultiScale”**函数。

> 注意：根据相机和周围光线在**80 到 105** “阈值”范围内调整阈值 (bw\_threshold) 值很重要。

**以下代码检测图像中人脸和嘴巴：**

![1.png][]

**第 2 步：识别是否使用面具**

如上代码所示，一共有三个矩形对象：

1.  **“灰色”**图像人脸矩形。
2.  **“黑白”**图像人脸矩形。
3.  **'灰色'** 图像 嘴矩形。

添加以下代码以在**“检测嘴巴”**代码后验证掩码/无掩码。

**=> 当人戴着面具时，它将显示为检测到面具：-**

![2.png][]

**=> 当人不戴口罩时，它将显示为未检测到口罩：-**

![3.png][]

#### 质量检测标准 ####

根据矩形的数量和矩形的嘴和脸的位置，我们可以创建规则来检测面具。以下真值表将提供带面具/不带面具的正确条件。

![4.png][]

[您可以从 GitHub 下载完整的代码和 HAAR 级联 XML 文件。][GitHub _ HAAR _ XML]

import numpy as np
    import cv2
    import random
    
    # multiple cascades: https://github.com/Itseez/opencv/tree/master/data/haarcascades
    
    face_cascade = cv2.CascadeClassifier('data\\xml\\haarcascade_frontalface_default.xml')
    eye_cascade = cv2.CascadeClassifier('data\\xml\\haarcascade_eye.xml')
    mouth_cascade = cv2.CascadeClassifier('data\\xml\\haarcascade_mcs_mouth.xml')
    upper_body = cv2.CascadeClassifier('data\\xml\\haarcascade_upperbody.xml')
    
    
    
    # Adjust threshold value in range 80 to 105 based on your light.
    bw_threshold = 80
    
    # User message
    font = cv2.FONT_HERSHEY_SIMPLEX
    org = (30, 30)
    weared_mask_font_color = (255, 255, 255)
    not_weared_mask_font_color = (0, 0, 255)
    thickness = 2
    font_scale = 1
    weared_mask = "Thank You for wearing MASK"
    not_weared_mask = "Please wear MASK to defeat Corona"
    
    # Read video
    cap = cv2.VideoCapture(0)
    
    while 1:
        # Get individual frame
        ret, img = cap.read()
        img = cv2.flip(img,1)
    
        # Convert Image into gray
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
        # Convert image in black and white
        (thresh, black_and_white) = cv2.threshold(gray, bw_threshold, 255, cv2.THRESH_BINARY)
        #cv2.imshow('black_and_white', black_and_white)
    
        # detect face
        faces = face_cascade.detectMultiScale(gray, 1.1, 4)
    
        # Face prediction for black and white
        faces_bw = face_cascade.detectMultiScale(black_and_white, 1.1, 4)
    
    
        if(len(faces) == 0 and len(faces_bw) == 0):
            cv2.putText(img, "No face found...", org, font, font_scale, weared_mask_font_color, thickness, cv2.LINE_AA)
        elif(len(faces) == 0 and len(faces_bw) == 1):
            # It has been observed that for white mask covering mouth, with gray image face prediction is not happening
            cv2.putText(img, weared_mask, org, font, font_scale, weared_mask_font_color, thickness, cv2.LINE_AA)
        else:
            # Draw rectangle on gace
            for (x, y, w, h) in faces:
                cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 255), 2)
                roi_gray = gray[y:y + h, x:x + w]
                roi_color = img[y:y + h, x:x + w]
    
    
                # Detect lips counters
                mouth_rects = mouth_cascade.detectMultiScale(gray, 1.5, 5)
    
            # Face detected but Lips not detected which means person is wearing mask
            if(len(mouth_rects) == 0):
                cv2.putText(img, weared_mask, org, font, font_scale, weared_mask_font_color, thickness, cv2.LINE_AA)
            else:
                for (mx, my, mw, mh) in mouth_rects:
    
                    if(y < my < y + h):
                        # Face and Lips are detected but lips coordinates are within face cordinates which `means lips prediction is true and
                        # person is not waring mask
                        cv2.putText(img, not_weared_mask, org, font, font_scale, not_weared_mask_font_color, thickness, cv2.LINE_AA)
    
                        #cv2.rectangle(img, (mx, my), (mx + mh, my + mw), (0, 0, 255), 3)
                        break
    
        # Show frame with results
        cv2.imshow('Mask Detection', img)
        k = cv2.waitKey(30) & 0xff
        if k == 27:
            break
    
    # Release video
    cap.release()
    cv2.destroyAllWindows()

[W3_BYVVVh.png]: https://cdn.hashnode.com/res/hashnode/image/upload/v1611242155728/W3_BYVVVh.png
[Face Mask Detection using Open CVOur motto is to detect facial mask monitoring using Open CV and Python to stop the flow of corona virus._W3_BYVVVh.png_https_karanshah2910.hashnode.dev_face-mask-detection-using-open-cv]: https://karanshah2910.hashnode.dev/face-mask-detection-using-open-cv
[OpenCV]: https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html
[1.png]: https://img-blog.csdnimg.cn/img_convert/7d5108aa600b07c4072c5d33d41a28be.png
[2.png]: https://img-blog.csdnimg.cn/img_convert/839fb245a254d908460c8f6679eda4b5.png
[3.png]: https://img-blog.csdnimg.cn/img_convert/a497c2fbd5d7fcb80dd068574c3ac682.png
[4.png]: https://img-blog.csdnimg.cn/img_convert/24a6cfd51a86edb5ece4deb3dc91da6c.png
[GitHub _ HAAR _ XML]: https://github.com/karan19100/Face-Mask-Detection-using-Open-CV-