用python写个人脸识别

要写一个人脸识别程序，需要使用 OpenCV 库来处理图像，并使用一个人脸识别算法。以下是一个使用 OpenCV 和基于 LBPH（Local Binary Patterns Histograms）算法的人脸识别程序的示例。

1. 安装必要的库：

pip install opencv-python
pip install opencv-contrib-python

2. 导入必要的库：

import cv2
import os
import numpy as np

3. 加载人脸识别模型：

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer.yml')

4. 定义一个函数来识别人脸：

def recognize_face(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5)

    for (x, y, w, h) in faces:
        roi_gray = gray[y:y+h, x:x+w]
        id_, conf = recognizer.predict(roi_gray)

        if conf >= 45 and conf <= 85:
            font = cv2.FONT_HERSHEY_SIMPLEX
            name = labels[id_]
            color = (255, 255, 255)
            stroke = 2
            cv2.putText(img, name, (x,y), font, 1, color, stroke, cv2.LINE_AA)

        img_item = "my-image.png"
        cv2.imwrite(img_item, roi_gray)

        color = (255, 0, 0)
        stroke = 2
        end_cord_x = x + w
        end_cord_y = y + h
        cv2.rectangle(img, (x, y), (end_cord_x, end_cord_y), color, stroke)

    return img

5. 加载训练数据，并训练模型：

BASE_DIR = os.path.dirname(os.path.abspath(__file__))
image_dir = os.path.join(BASE_DIR, "images")

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()

current_id = 0
label_ids = {}
x_train = []
y_labels = []

for root, dirs, files in os.walk(image_dir):
    for file in files:
        if file.endswith("png") or file.endswith("jpg") or file.endswith("jpeg"):
            path = os.path.join(root, file)
            label = os.path.basename(root).replace(" ", "-").lower()

            if not label in label_ids:
                label_ids[label] = current_id
                current_id += 1

            id_ = label_ids[label]
            pil_image = Image.open(path).convert("L")
            size = (550, 550)
            final_image = pil_image.resize(size, Image.ANTIALIAS)
            image_array = np.array(final_image, "uint8")

            faces = face_cascade.detectMultiScale(image_array, scaleFactor=1.5, minNeighbors=5)

            for (x, y, w, h) in faces:
                roi = image_array[y:y+h, x:x+w]
                x_train.append(roi)
                y_labels.append(id_)

with open("labels.pickle", 'wb') as f:
    pickle.dump(label_ids, f)

recognizer.train(x_train, np.array(y_labels))
recognizer.save("trainer.yml")

完整的代码如下：

import cv2
import os
import numpy as np
import pickle
from PIL import Image


face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer.yml')

labels = {}
with open("labels.pickle", 'rb') as f:
    og_labels = pickle.load(f)
    labels = {v:k for k,v in og_labels.items()}

def recognize_face(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5)

    for (x, y, w, h) in faces:
        roi_gray = gray[y:y+h, x:x+w]
        id_, conf = recognizer.predict(roi_gray)

        if conf >= 45 and conf <= 85:
            font = cv2.FONT_HERSHEY_SIMPLEX
            name = labels[id_]
            color = (255, 255, 255)
            stroke = 2
            cv2.putText(img, name, (x,y), font, 1, color, stroke, cv2.LINE_AA)

        img_item = "my-image.png"
        cv2.imwrite(img_item, roi_gray)

        color = (255, 0, 0)
        stroke = 2
        end_cord_x = x + w
        end_cord_y = y + h
        cv2.rectangle(img, (x, y), (end_cord_x, end_cord_y), color, stroke)

    return img

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    frame = recognize_face(frame)
    cv2.imshow('frame',frame)
    if cv2.waitKey(20) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

运行代码，将会启动你的摄像头，程序能够识别摄像头中的人脸并在屏幕上显示人脸的名字。