人脸采集与训练

文件结构

face_train.py     人脸识别和训练

import os
import cv2
import numpy as np
import pickle

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

BASE_DIR = os.path.dirname(os.path.abspath(__file__))
image_dir = os.path.join(BASE_DIR, "images")
cap = cv2.VideoCapture(0)
recognizer = cv2.face_LBPHFaceRecognizer.create()
classifier = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

count = 0

while True:
    ret, frame = cap.read()
    if ret:
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = classifier.detectMultiScale(gray, 1.3, 5)
        # 画一个脸部矩形
        for (x, y, w, h) in faces:
            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 255), 2)
            count += 1
            cv2.imwrite("./images/XXX/face" + str(count) + ".jpg", gray[y - 25:y + h + 25, x - 50:x + w + 50])
        cv2.imshow('frame', frame)
        cv2.imshow('freame_with_gray', gray)
    if cv2.waitKey(1) & 0xFF == ord('q') or count == 100:
        break
cap.release()
cv2.destroyAllWindows()
# 开始训练
for root, dirs, files in os.walk(image_dir):
    for file in files:
        path = os.path.join(root, file)
        # print(path)
        image = cv2.imread(path)
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        image_arr = np.array(gray, "uint8")

        label = os.path.basename(root)

        if label not in label_ids:
            label_ids[label] = current_id
            current_id += 1
        id_ = label_ids[label]
        faces = classifier.detectMultiScale(image_arr,1.5,5)
        for x,y,w,h in faces:
            rol = image_arr[y:y+h, x:x+w]
            x_train.append(rol)
            y_labels.append(id_)
with open("label.pickle", "wb") as file:
    pickle.dump(label_ids, file)
print(label_ids)
recognizer.train(x_train, np.array(y_labels))
recognizer.save("trainner.xml")

face_recognition.py 人脸识别程序

import cv2
import numpy as np
import pickle

cap = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt2.xml')
recognizer = cv2.face_LBPHFaceRecognizer.create()
recognizer.read("trainner.xml")
labels = {}
with open('label.pickle', 'rb') as file:
    origin_labels = pickle.load(file)
    labels = {v: k for k, v in origin_labels.items()}

while True:
    ret, frame = cap.read()
    if ret:
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(gray, 1.3, 5)
        # 画一个脸部矩形
        for (x, y, w, h) in faces:
            gray_roi = gray[y:y + h, x:x + w]
            id_,conf = recognizer.predict(gray_roi)
            if conf>=50:
                print(labels[id_])
                cv2.putText(frame, labels[id_], (x, y), cv2.FONT_HERSHEY_PLAIN,1.0, (0, 255, 0), 2)
            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 255), 2)
        cv2.namedWindow('result', cv2.WINDOW_FULLSCREEN)
        cv2.imshow('result', frame)
        # cv2.imshow('freame_with_gray', gray)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

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