一天到晚闲的没事干，用Raspberry Pi + OpenCV + Streamlit 做了一个人脸识别

2023-12-14 15:23:22

介绍

如何创建面部识别应用程序的记录，具体来说，所做如下：

尝试使用Raspberry Pi 4 + CameraModule3 进行拍摄
尝试使用 OpenCV 对相机拍摄的图像进行面部识别处理
尝试使用 Streamlit 将以上内容转换为 Web 应用程序

开发环境

Raspberry Pi 4
Bullseye 64bit
Python 3.9.2
CameraModule3

使用 Raspberry Pi 和相机拍摄

在这个步骤中遇到了很多麻烦，过几天再出篇文章，单独进行总结

使用 OpenCV 对拍摄图像进行人脸识别处理

安装包

安装 picamera2 和 OpenCV。

pip install picamera2
pip install opencv-python

下载训练好的模型

yunet_n_640_640.onnx
face_recognizer_fast.onnx

保存面部图像

从图像中检测人脸，将其剪切出来并保存为人脸图像。

源代码

import os
import argparse
import cv2

def main():

    parser = argparse.ArgumentParser("generate aligned face images from an image")
    parser.add_argument("image", help="input image file path (./image.jpg)")
    args = parser.parse_args()

    path = args.image
    directory = os.path.dirname(args.image)
    if not directory:
        directory = os.path.dirname(__file__)
        path = os.path.join(directory, args.image)

    image = cv2.imread(path)
    if image is None:
        exit()

    channels = 1 if len(image.shape) == 2 else image.shape[2]
    if channels == 1:
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
    if channels == 4:
        image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)

    weights = "onnx/yunet_n_640_640.onnx"
    face_detector = cv2.FaceDetectorYN_create(weights, "", (0, 0))
    weights = "onnx/face_recognizer_fast.onnx"
    face_recognizer = cv2.FaceRecognizerSF_create(weights, "")

    height, width, _ = image.shape
    face_detector.setInputSize((width, height))

    _, faces = face_detector.detect(image)

    aligned_faces = []
    if faces is not None:
        for face in faces:
            aligned_face = face_recognizer.alignCrop(image, face)
            aligned_faces.append(aligned_face)

    for i, aligned_face in enumerate(aligned_faces):
        cv2.imshow("aligned_face {:03}".format(i + 1), aligned_face)
        cv2.imwrite(os.path.join(directory, "face{:03}.jpg".format(i + 1)), aligned_face)

    cv2.waitKey(0)
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

执行命令

python generate_aligned_faces.py input.jpg

输入图像（input.jpg）

在这里插入图片描述

执行结果

face001.jpg	face002.jpg

提取特征并保存为字典

从面部图像中提取特征并将其保存为特征字典。

源代码

import os
import sys
import argparse
import numpy as np
import cv2

def main():

    parser = argparse.ArgumentParser("generate face feature dictionary from an face image")
    parser.add_argument("image", help="input face image file path (./<face名>.jpg)")
    args = parser.parse_args()
    print(args.image)

    path = args.image
    directory = os.path.dirname(args.image)
    if not directory:
        directory = os.path.dirname(__file__)
        path = os.path.join(directory, args.image)

    image = cv2.imread(path)
    if image is None:
        exit()

    channels = 1 if len(image.shape) == 2 else image.shape[2]
    if channels == 1:
        image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
    if channels == 4:
        image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)

    weights = "onnx/face_recognizer_fast.onnx"
    face_recognizer = cv2.FaceRecognizerSF_create(weights, "")

    face_feature = face_recognizer.feature(image)
    print(face_feature)
    print(type(face_feature))

    basename = os.path.splitext(os.path.basename(args.image))[0]
    dictionary = os.path.join(directory, basename)
    np.save(dictionary, face_feature)

if __name__ == '__main__':
    main()

执行命令

python generate_feature_dictionary.py face001.jpg

输入图像（为每个图像执行的源代码）

face001.jpg	face002.jpg

执行结果

输出face001.npy和face002.npy。

人脸识别处理

从捕获的图像中检测人脸，提取特征，并将其与特征字典进行比较以进行面部识别。

源代码

import cv2
import os
import glob
import numpy as np
from picamera2 import Picamera2
from libcamera import controls

COSINE_THRESHOLD = 0.363
NORML2_THRESHOLD = 1.128

def match(recognizer, feature1, dictionary):
    for element in dictionary:
        user_id, feature2 = element
        score = recognizer.match(feature1, feature2, cv2.FaceRecognizerSF_FR_COSINE)
        if score > COSINE_THRESHOLD:
            return True, (user_id, score)
    return False, ("", 0.0)

def main():
    camera = Picamera2()
    camera.configure(camera.create_preview_configuration(main={"format": 'XRGB8888', "size": (640, 480)}))
    camera.start()
    camera.set_controls({'AfMode': controls.AfModeEnum.Continuous})

    dictionary = []
    files = glob.glob(os.path.join("sample_data", "*.npy"))
    for file in files:
        feature = np.load(file)
        user_id = os.path.splitext(os.path.basename(file))[0]
        dictionary.append((user_id, feature))
    
    weights = "onnx/yunet_n_640_640.onnx"
    face_detector = cv2.FaceDetectorYN_create(weights, "", (0, 0))
    weights = "onnx/face_recognizer_fast.onnx"
    face_recognizer = cv2.FaceRecognizerSF_create(weights, "")

    while True:
        image = camera.capture_array()

        channels = 1 if len(image.shape) == 2 else image.shape[2]
        if channels == 1:
            image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
        if channels == 4:
            image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)

        height, width, _ = image.shape
        face_detector.setInputSize((width, height))

        result, faces = face_detector.detect(image)
        faces = faces if faces is not None else []

        for face in faces:

            aligned_face = face_recognizer.alignCrop(image, face)
            feature = face_recognizer.feature(aligned_face)

            result, user = match(face_recognizer, feature, dictionary)

            box = list(map(int, face[:4]))
            color = (0, 255, 0) if result else (0, 0, 255)
            thickness = 2
            cv2.rectangle(image, box, color, thickness, cv2.LINE_AA)

            id, score = user if result else ("unknown", 0.0)
            text = "{0} ({1:.2f})".format(id, score)
            position = (box[0], box[1] - 10)
            font = cv2.FONT_HERSHEY_SIMPLEX
            scale = 0.6
            cv2.putText(image, text, position, font, scale, color, thickness, cv2.LINE_AA)

        cv2.imshow("face recognition", image)
        key = cv2.waitKey(1)
        if key == ord('q'):
            break

    camera.close()
    cv2.destroyAllWindows()
    
if __name__ == '__main__':
    main()

执行命令

python camface_recognizer.py

执行结果

在这里插入图片描述

使用 Streamlit 的 Web 应用程序

关于 Streamlit

Streamlit 是一个开源框架，可使用 Python 创建 Web 应用程序。它拥有丰富的UI组件，可以用简短的代码直观地编写，可以轻松地使用WebUI创建应用程序。适合小规模开发，例如演示应用程序。

安装包

安装与 Streamlit 相关的软件包。

pip install　streamlit
pip install　streamlit-webrtc
pip install　streamlit_server_state

在 Web 应用程序屏幕上显示捕获的视频

import cv2
import os
import glob
import numpy as np
from picamera2 import Picamera2
from libcamera import controls
import streamlit as st
from streamlit_server_state import server_state, server_state_lock

def get_camera() -> Picamera2:
    with server_state_lock["camera"]:
        if "camera" not in server_state:
            camera = Picamera2()
            camera.configure(camera.create_preview_configuration(main={
                "format": 'XRGB8888',
                "size": (640, 480)
            }))
            camera.start()
            camera.set_controls({'AfMode': controls.AfModeEnum.Continuous})
            server_state.camera = camera
    return server_state.camera

def main():

    st.markdown("# Face Recognition Application")
    
    camera = get_camera()

    try:
        image_loc = st.empty()
        while True:
            image = camera.capture_array()

            channels = 1 if len(image.shape) == 2 else image.shape[2]
            if channels == 1:
                image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
            if channels == 4:
                image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
            
            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
            image_loc.image(image)
    except KeyboardInterrupt:
        print('app stop!!')
    except Exception as e:
        st.markdown("### 错误。")
        st.markdown(f'{e}')

    camera.close()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

执行命令

python run_streamlit.py

添加人脸识别处理

import cv2
import os
import glob
import numpy as np
from picamera2 import Picamera2
from libcamera import controls
import streamlit as st
from streamlit_server_state import server_state, server_state_lock

COSINE_THRESHOLD = 0.363
NORML2_THRESHOLD = 1.128

GLOB_PATH = '*********/sample_data/*.npy'
FACE_DETECTOR_WEIGHTS = '*********/onnx/yunet_n_640_640.onnx'
FACE_RECOGNIZER_WEIGHTS = '*********/onnx/face_recognizer_fast.onnx'

def match(recognizer, feature1, dictionary) -> tuple[bool,set[str,float]]:
    for element in dictionary:
        user_id, feature2 = element
        score = recognizer.match(feature1, feature2, cv2.FaceRecognizerSF_FR_COSINE)
        if score > COSINE_THRESHOLD:
            return True, (user_id, score)
    return False, ("", 0.0)

def get_camera() -> Picamera2:
    with server_state_lock["camera"]:
        if "camera" not in server_state:
            camera = Picamera2()
            camera.configure(camera.create_preview_configuration(main={
                "format": 'XRGB8888',
                "size": (640, 480)
            }))
            camera.start()
            camera.set_controls({'AfMode': controls.AfModeEnum.Continuous})
            server_state.camera = camera
    return server_state.camera

def recognize_face(face_recognizer, dictionary, image, face) -> np.ndarray:

    aligned_face = face_recognizer.alignCrop(image, face)
    feature = face_recognizer.feature(aligned_face)

    result, user = match(face_recognizer, feature, dictionary)

    box = list(map(int, face[:4]))
    color = (0, 255, 0) if result else (0, 0, 255)
    thickness = 2
    cv2.rectangle(image, box, color, thickness, cv2.LINE_AA)

    id, score = user if result else ("unknown", 0.0)
    text = "{0} ({1:.2f})".format(id, score)
    position = (box[0], box[1] - 10)
    font = cv2.FONT_HERSHEY_SIMPLEX
    scale = 0.6
    cv2.putText(image, text, position, font, scale, color, thickness, cv2.LINE_AA)

    return image

def main():

    st.markdown("# Face Recognition Application")

    dictionary = []
    files = glob.glob(GLOB_PATH)
    for file in files:
        feature = np.load(file)
        user_id = os.path.splitext(os.path.basename(file))[0]
        dictionary.append((user_id, feature))

    face_detector = cv2.FaceDetectorYN_create(FACE_DETECTOR_WEIGHTS, "", (0, 0))
    face_recognizer = cv2.FaceRecognizerSF_create(FACE_RECOGNIZER_WEIGHTS, "")
    
    camera = get_camera()

    try:
        image_loc = st.empty()
        while True:
            image = camera.capture_array()

            channels = 1 if len(image.shape) == 2 else image.shape[2]
            if channels == 1:
                image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
            if channels == 4:
                image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
            
            height, width, _ = image.shape
            face_detector.setInputSize((width, height))

            result, faces = face_detector.detect(image)
            faces = faces if faces is not None else []

            for face in faces:
                image = recognize_face(face_recognizer, dictionary,image, face)
            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
            image_loc.image(image)
    except KeyboardInterrupt:
        print('app stop!!')
    except Exception as e:
        st.markdown("### 错误")
        st.markdown(f'{e}')

    camera.close()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

执行命令

python run_streamlit.py

执行结果

在这里插入图片描述

我在 Streamlit 中偶然发现的事情

Streamlit 旨在每次用户访问应用程序时从头开始运行脚本。因此，如果在一个用户查看相机图像时另一个用户访问该应用程序，则相机的初始设置过程将重新执行，服务器将停止。我能够通过使用streamlit_server_state库在服务器内共享相机来解决这个问题，而不管会话如何。

与 AWS Rekognition 的比较视频

Amazon Rekognition 流视频事件处理来自新的或现有 Kinesis Video Streams 的视频。仅当你发送开始视频分析的通知时，Rekognition 才会开始处理你的 Kinesis Video 流，并且每个事件最多可以分析 120 秒的视频。只需为 Amazon Rekognition 处理的视频量付费。注意：Amazon Kinesis Video Streams 服务单独计费。

Amazon Rekognition Image 没那么贵，但是Amazon Rekognition Video 比我想象的要贵。
如果使用Amazon Kinesis Video Streams流式传输视频，将需要支付额外的服务费。
视频中的人脸搜索：0.15USD / 1m

树莓派

如果你使用 OpenCV 等，它基本上是免费的。
Raspberry Pi 等硬件成本是在安装时产生的。
由于它取决于硬件，因此可能会产生故障和运营成本等额外成本。
但是，Amazon Rekognition Video 还需要外部摄像头，因此需要考虑如何使用硬件。
存在一些因素，例如如何创建模型以及你想要多少精度，但是当仅考虑价格时，使用 Raspberry Pi 进行操作似乎要便宜得多。

文章来源:https://blog.csdn.net/weixin_41556756/article/details/134989230
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一天到晚闲的没事干，用Raspberry Pi + OpenCV + Streamlit 做了一个人脸识别

目录

介绍

开发环境

使用 Raspberry Pi 和相机拍摄

使用 OpenCV 对拍摄图像进行人脸识别处理

安装包

安装 picamera2 和 OpenCV。

下载训练好的模型

保存面部图像

源代码

执行命令

输入图像（input.jpg）

执行结果

提取特征并保存为字典

源代码

执行命令

输入图像（为每个图像执行的源代码）

执行结果

人脸识别处理

源代码

执行命令

执行结果

使用 Streamlit 的 Web 应用程序

关于 Streamlit

安装包

在 Web 应用程序屏幕上显示捕获的视频

执行命令

添加人脸识别处理

执行命令

执行结果

我在 Streamlit 中偶然发现的事情

与 AWS Rekognition 的比较视频

树莓派