OpenCv Python：识别人脸后如何从人脸识别程序中保存识别出的人脸名称？

Question

大家好，我正在开发人脸识别程序的 OpenCV(Python)。我有两个文件，一个捕获新用户的面部并按用户提供的名称存储。第二个文件使用网络摄像头识别用户。现在，我担心的是用户被正确识别，但名称只显示而不保存。如何保存被识别人的姓名以便转移或做一些操作？

#__author__ = 'ADMIN'
import cv2, sys, numpy, os
size = 4
fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'att_faces'
fn_name = "aditya"
path = os.path.join(fn_dir, fn_name)
if not os.path.isdir(path):
    os.mkdir(path)
(im_width, im_height) = (112, 92)
haar_cascade = cv2.CascadeClassifier(fn_haar)
webcam = cv2.VideoCapture(0)

# The program loops until it has 20 images of the face.
count = 0
while count < 20:
    (rval, im) = webcam.read()
    im = cv2.flip(im, 1, 0)
    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    mini = cv2.resize(gray, (gray.shape[1] / size, gray.shape[0] / size))
    faces = haar_cascade.detectMultiScale(mini)
    faces = sorted(faces, key=lambda x: x[3])
    if faces:
        face_i = faces[0]
        (x, y, w, h) = [v * size for v in face_i]
        face = gray[y:y + h, x:x + w]
        face_resize = cv2.resize(face, (im_width, im_height))
        pin=sorted([int(n[:n.find('.')]) for n in os.listdir(path)
               if n[0]!='.' ]+[0])[-1] + 1
        cv2.imwrite('%s/%s.png' % (path, pin), face_resize)
        cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 3)
        cv2.putText(im, fn_name, (x - 10, y - 10), cv2.FONT_HERSHEY_PLAIN,
            1,(0, 255, 0))
        count += 1
    cv2.imshow('OpenCV', im)
    key = cv2.waitKey(10)
    if key == 27:
        break

数据集中的人脸识别代码

__author__ = 'ADMIN'
import cv2, sys, numpy, os
size = 4
fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'att_faces'

# Part 1: Create fisherRecognizer
print('Training...')
# Create a list of images and a list of corresponding names
(images, lables, names, id) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(fn_dir):
    for subdir in dirs:
        names[id] = subdir
        subjectpath = os.path.join(fn_dir, subdir)
        for filename in os.listdir(subjectpath):
            path = subjectpath + '/' + filename
            lable = id
            images.append(cv2.imread(path, 0))
            lables.append(int(lable))
        id += 1
(im_width, im_height) = (112, 92)

    # Create a Numpy array from the two lists above
(images, lables) = [numpy.array(lis) for lis in [images, lables]]

# OpenCV trains a model from the images    
# NOTE FOR OpenCV2: remove '.face'
model = cv2.createFisherFaceRecognizer()
model.train(images, lables)

# Part 2: Use fisherRecognizer on camera stream
haar_cascade = cv2.CascadeClassifier(fn_haar)
webcam = cv2.VideoCapture(0)
while True:
    (rval, frame) = webcam.read()
    frame=cv2.flip(frame,1,0)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    mini = cv2.resize(gray, (gray.shape[1] / size, gray.shape[0] / size))
    faces = haar_cascade.detectMultiScale(mini)
    for i in range(len(faces)):
        face_i = faces[i]
        (x, y, w, h) = [v * size for v in face_i]
        face = gray[y:y + h, x:x + w]
        face_resize = cv2.resize(face, (im_width, im_height))

    # Try to recognize the face
        prediction = model.predict(face_resize)
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)

    # Write the name of recognized face
    # [1]
        cv2.putText(frame,
            '%s - %.0f' % (names[prediction[0]],prediction[1]),
            (x-10, y-10), cv2.FONT_HERSHEY_PLAIN,1,(0, 255, 0))
    cv2.imshow('OpenCV', frame)
    key = cv2.waitKey(10)
    if key == 27:
        break

Answer 1

这是我的代码。我没有使用任何 sql-server。 我正在对文件夹中的图像进行编码，它将显示已识别的面部以及保存的图像名称。如果图像保存为 ..abc.jpg。然后它会在直播过程中检测到人脸并显示 abc.jpg

这是我的代码：

from PIL import Image
import face_recognition
import cv2
import os

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

known_face_encodings=[]

known_face_names = []

user_appeared = []
root = "/home/erp-next/open cv/dataset/"
for filename in os.listdir(root):
    if filename.endswith('.jpg' or '.png'):
        try: 
            print(filename)
            path = os.path.join(root, filename)
            filter_image = face_recognition.load_image_file(path)
            filter_face_encoding = face_recognition.face_encodings(filter_image)
            known_face_encodings.append(filter_face_encoding[0])
            known_face_names.append(filename)
                        
        except:
            print("An exception occurred : " + filename )


#print(known_face_encodings)
print(known_face_names)

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
# process_this_frame = True

def face():

    while True:

        process_this_frame = True

        # Grab a single frame of video
        ret, frame = video_capture.read()

        # Resize frame of video to 1/4 size for faster face recognition processing
        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

        # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
        rgb_small_frame = small_frame[:, :, ::-1]

        k = cv2.waitKey(1)

        if k%256 == 27:
            # ESC pressed
            print("Escape hit, closing...")
            break

        # Only process every other frame of video to save time
        if process_this_frame:
            # Find all the faces and face encodings in the current frame of video
            face_locations = face_recognition.face_locations(rgb_small_frame)
            face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

            face_names = []
            for face_encoding in face_encodings:
                # See if the face is a match for the known face(s)
                matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
                name = "Unknown"

                # If a match was found in known_face_encodings, just use the first one.
                if True in matches:
                    first_match_index = matches.index(True)
                    name = known_face_names[first_match_index]
                    print(name)
                face_names.append(name)

        process_this_frame = not process_this_frame


        # Display the results
        for (top, right, bottom, left), name in zip(face_locations, face_names):
            # Scale back up face locations since the frame we detected in was scaled to 1/4 size
            top *= 4
            right *= 4
            bottom *= 4
            left *= 4

            # Draw a box around the face
            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

            # Draw a label with a name below the face
            cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

        # Display the resulting image
        cv2.imshow('Video', frame)

        # Hit 'q' on the keyboard to quit!
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # Release handle to the webcam
    video_capture.release()
    cv2.destroyAllWindows()


face()

我也在使用 face_recognition 库来编码和检测人脸。

谢谢。