from imutils import paths #imutils includes opencv functions import face_recognition import pickle import cv2 import os
#get paths of each file in folder named Images #Images here that contains data(folders of various people) imagePath = list(paths.list_images('Images')) kEncodings = [] kNames = []
# loop over the image paths for (i, ip) in enumerate(imagePath): # extract the person name from the image path name = ip.split(os.path.sep)[-2] # load the input image and convert it from BGR image = cv2.imread(ip) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
boxes = face_recognition.face_locations(rgb,model='hog') # compute the facial embedding for the any face encodings = face_recognition.face_encodings(rgb, boxes) # loop over the encodings for encoding in encodings: kEncodings.append(encoding) kNames.append(name)
#save emcodings along with their names in dictionary data data = {"encodings": kEncodings, "names": kNames} #use pickle to save data into a file for later use f = open("face_enc", "wb") f.write(pickle.dumps(data))#to open file in write mode f.close()#to close file
下一部分是我们将看到如何从图像中识别人脸。
如何识别图像中的人脸
以下脚本用于检测和识别图像中的人脸。我在需要的地方给出了注释。因此，你将能够有效地理解代码。
import face_recognition import imutils #imutils includes opencv functions import pickle import time import cv2 import os
#to find path of xml file containing haarCascade file cfp = os.path.dirname(cv2.__file__) +
"/data/haarcascade_frontalface_alt2.xml" # load the harcaascade in the cascade classifier fc = cv2.CascadeClassifier(cfp) # load the known faces and embeddings saved in last file data = pickle.loads(open('face_enc', "rb").read())
#Find path to the image you want to detect face and pass it here image = cv2.imread(Path-to-img) rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) #convert image to Greyscale for HaarCascade gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) faces = fc.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=6, minSize=(60, 60), flags=cv2.CASCADE_SCALE_IMAGE)
# the facial embeddings for face in input encodings = face_recognition.face_encodings(rgb) names = [] # loop over the facial embeddings incase # we have multiple embeddings for multiple fcaes for encoding in encodings: #Compare encodings with encodings in data["encodings"] #Matches contain array with boolean values True and False matches = face_recognition.compare_faces(data["encodings"], encoding) #set name =unknown if no encoding matches name = "Unknown" # check to see if we have found a match ifTruein matches: #Find positions at which we get True and store them matchedIdxs = [i for (i, b) in enumerate(matches) if b] count = {} # loop over the matched indexes and maintain a count for # each recognized face face for i in matchedIdxs: #Check the names at respective indexes we stored in matchedIdxs name = data["names"][i] #increase count for the name we got count[name] = count.get(name, 0) + 1 #set name which has highest count name = max(count, key=count.get) # will update the list of names names.append(name)
# do loop over the recognized faces for ((x, y, w, h), name) in zip(faces, names): # rescale the face coordinates # draw the predicted face name on the image cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.putText(image, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2) cv2.imshow("Frame", image) cv2.waitKey(0)
