OpenCv Python：在識別出面部之后，如何從面部識別程序中保存識別出的面部的名稱？

Question

大家好，我正在人臉識別程序上使用OpenCV（Python）。 我有兩個文件，一個捕獲一個新用戶的臉，並通過用戶提供的名稱存儲它。 第二個文件使用網絡攝像頭識別用戶。 現在，我擔心的是該用戶已被正確識別，但僅顯示該名稱而不保存該名稱。 如何保存已識別人員的姓名，以便可以對其進行轉移或進行某些操作？

#__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

用於從數據集中識別人臉的代碼

__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

這是我的代碼。 我不使用任何SQL Server的地方。 我正在對文件夾中的圖像進行編碼，它將顯示已識別的面部以及所保存圖像的名稱。 如果圖片另存為.. abc.jpg。 然后它將在實時流式傳輸期間檢測到人臉並顯示abc.jpg

這是我的代碼：

 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() 

我還使用face_recognition庫來編碼和檢測面部。

謝謝。