def enroll_face(image,
label,
embeddings_path="face_embeddings.npy",
labels_path="labels.pkl",
down_scale=1.0):
faces = detect_faces(image, down_scale)
if len(faces) == 1:
face = faces[0]
if len(faces) < 1:
print("[!] Skipping. No faces detected.")
return False
if len(faces) > 1:
#TODO: verify with g.t if taking multiple faces increases performance
# face = faces[get_centered_face_index(image, faces)]
print("[!] Skipping. Multiple faces detected.")
return False
face_embeddings = extract_face_embeddings(image, face, shape_predictor,
face_recognizer)
add_embeddings(face_embeddings,
label,
embeddings_path=embeddings_path,
labels_path=labels_path)
return True
def enroll_face(image,
label,
label1,
label2,
label3,
label4,
embeddings_path="face_embeddings.npy",
labels_path="labels.cpickle",
labels_path1="labels1.cpickle",
labels_path2="labels2.cpickle",
labels_path3="labels3.cpickle",
labels_path4="labels4.cpickle",
down_scale=1.0):
faces = detect_faces(image, down_scale)
if len(faces) < 1:
return False
if len(faces) > 1:
raise ValueError("Multiple faces not allowed for enrolling")
face = faces[0]
face_embeddings = extract_face_embeddings(image, face, shape_predictor,
face_recognizer)
add_embeddings(face_embeddings,
label,
label1,
label2,
label3,
label4,
embeddings_path=embeddings_path,
labels_path=labels_path)
return True
ap.add_argument("-i", "--image", help="Path to image", required=True)
ap.add_argument("-e", "--embeddings", help="Path to saved embeddings",
default="tmp/face_embeddings.npy")
ap.add_argument("-l", "--labels", help="Path to saved labels",
default="tmp/labels.pkl")
args = vars(ap.parse_args())
embeddings = np.load(args["embeddings"])
labels = pickle.load(open(args["labels"], 'rb'))
shape_predictor = dlib.shape_predictor("models/"
"shape_predictor_5_face_landmarks.dat")
face_recognizer = dlib.face_recognition_model_v1("models/"
"dlib_face_recognition_resnet_model_v1.dat")
image = cv2.imread(args["image"])
image_original = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
faces = detect_faces(image)
print("Detected {} faces".format(len(faces)))
for face in faces:
embedding = extract_face_embeddings(image, face, shape_predictor, face_recognizer)
label = recognize_face(embedding, embeddings, labels)
(x1, y1, x2, y2) = face.left(), face.top(), face.right(), face.bottom()
cv2.rectangle(image_original, (x1, y1), (x2, y2), (255, 120, 120), 2, cv2.LINE_AA)
cv2.putText(image_original, label[0], (x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 2)
cv2.imshow("Image", image_original)
cv2.waitKey(0)
def run(self):
import numpy as np
import datetime
now = datetime.datetime.now()
def recognize_face(embedding,
embeddings,
labels,
labels1,
labels2,
labels3,
labels4,
threshold=0.5):
distances = np.linalg.norm(embeddings - embedding,
axis=1)
argmin = np.argmin(distances)
minDistance = distances[argmin]
if minDistance > threshold:
label = ""
else:
label = labels[argmin]
return (label, minDistance)
if __name__ == "__main__":
import cv2
import argparse
from face_embeddings import extract_face_embeddings
from face_detector import detect_faces
import cloudpickle as cPickle
import dlib
import urllib.request
import os
global off
off = False
a = os.getcwd()
url = enter1.get()
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default="4.jpg")
ap.add_argument("-e",
"--embeddings",
default="face_embeddings.npy")
ap.add_argument("-l",
"--labels",
default="labels.cpickle")
ap.add_argument("-l1",
"--labels1",
default="labels1.cpickle")
ap.add_argument("-l2",
"--labels2",
default="labels2.cpickle")
ap.add_argument("-l3",
"--labels3",
default="labels3.cpickle")
ap.add_argument("-l4",
"--labels4",
default="labels4.cpickle")
args = vars(ap.parse_args())
embeddings = np.load(args["embeddings"])
labels = cPickle.load(open(args["labels"], "rb"))
labels1 = cPickle.load(open(args["labels1"], "rb"))
labels2 = cPickle.load(open(args["labels2"], "rb"))
labels3 = cPickle.load(open(args["labels3"], "rb"))
labels4 = cPickle.load(open(args["labels4"], "rb"))
shape_predictor = dlib.shape_predictor(
"models/"
"shape_predictor_5_face_landmarks.dat")
face_recognizer = dlib.face_recognition_model_v1(
"models/"
"dlib_face_recognition_resnet_model_v1.dat")
while (not off):
imgResp = urllib.request.urlopen(url)
imgNp = np.array(bytearray(imgResp.read()),
dtype=np.uint8)
image = cv2.imdecode(imgNp, -1)
image_original = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
faces = detect_faces(image)
for face in faces:
embedding = extract_face_embeddings(
image, face, shape_predictor,
face_recognizer)
label = recognize_face(embedding, embeddings,
labels, labels1,
labels2, labels3,
labels4)
label1 = recognize_face(
embedding, embeddings, labels1, labels,
labels2, labels3, labels4)
label2 = recognize_face(
embedding, embeddings, labels2, labels,
labels1, labels3, labels4)
label3 = recognize_face(
embedding, embeddings, labels3, labels,
labels1, labels2, labels4)
label4 = recognize_face(
embedding, embeddings, labels4, labels,
labels1, labels2, labels3)
(x1, y1, x2, y2) = face.left(), face.top(
), face.right(), face.bottom()
cv2.rectangle(image_original, (x1, y1),
(x2, y2), (255, 120, 120), 2)
cv2.putText(image_original, label4[0],
(x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.8,
(255, 255, 0), 2)
file1 = open(a + "\\firebase1.txt", "w")
file2 = open(a + "\\firebase2.txt", "w")
file3 = open(a + "\\firebase3.txt", "w")
file4 = open(a + "\\firebase4.txt", "w")
file5 = open(a + "\\firebase5.txt", "w")
file6 = open(a + "\\firebase6.txt", "w")
file1.write(str(label1[0]))
file2.write(label[0])
file3.write(label4[0])
file4.write(label2[0])
file5.write(label3[0])
file6.write(
now.strftime("%Y-%m-%d " + "%H:%M:%S"))
file1.close()
file2.close()
file3.close()
file4.close()
file5.close()
file6.close()
cv2.imshow("Image", image_original)
cv2.waitKey(1)
if off == True:
break
cv2.destroyAllWindows()