def testeValidaFace(imagem):
detections = detector.detect_faces(imagem)
for detection in detections:
score = detection["confidence"]
if score >= 0.95:
keypoints = detection["keypoints"]
x, y, w, h = detection['box']
new_image = alinhaFace(normalizaFace(imagem), keypoints)
detected_face = new_image[y:y + h, x - ((x + h) - (x + w)):x + h]
img0 = cv2.resize(detected_face, (100, 100),
interpolation=cv2.INTER_AREA)
img = np.asarray(img0, dtype=np.float32) / 255.0
img = np.expand_dims(img, 0)
feature = nca.transform(modelo.predict(img))
try:
features = np.load("Modelos/features.npy")
except:
features = []
id = len(features) + 1
cadastrarPessoa(img0, feature, id, id, id, id, id, id, id)
else:
print("face nao reconhecida")
def ReconheceFaceVideo(paht, rec, detector):
cap = cv2.VideoCapture(paht)
while (True):
conectado, frame = cap.read()
if not conectado:
break
detections = detector.detect_faces(frame)
for detection in detections:
score = detection["confidence"]
if score >= 0.95:
try:
keypoints = detection["keypoints"]
x, y, w, h = detection['box']
new_image = alinhaFace(normalizaFace(frame), keypoints)
detected_face = new_image[y:y + h,
x - ((x + h) - (w + x)):x + h]
img0 = cv2.resize(detected_face, (100, 100),
interpolation=cv2.INTER_AREA)
except:
print("erro ao normalizar imagens")
else:
img = np.asarray(img0, dtype=np.float32) / 255.0
img = np.expand_dims(img, 0)
pess = rec.validaFace(img)
if (pess != "face nao reconhecida"):
id, path, nome, tel, per, atv, ide2, log, bai, uf, cep = selectFaceDB(
int(pess))[0]
print(
"Id:{0} \nNome:{1} \nTelefone:{2} \nPermissao:{3}\n"
"Logradouro:{4} \nBairro:{5} \nUF:{6} \nCEP:{4}".
format(str(id), str(nome), str(tel), str(per),
str(log), str(bai), str(uf), str(cep)))
text = "Id:{0} Nome:{1}".format(str(id), str(nome))
else:
text = "face nao reconhecida"
frame = cv2.rectangle(frame, (x, y), (x + w, y + h),
(255, 50, 50), 2)
cv2.putText(frame, text, (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 255, 0), 1,
cv2.LINE_AA)
cv2.imshow("oi", frame)
cv2.waitKey(1)
else:
print("face nao detectada")
cap.release()
cv2.destroyAllWindows()
def validaFace(imagem):
detections = detector.detect_faces(imagem)
for detection in detections:
score = detection["confidence"]
if score >= 0.99:
keypoints = detection["keypoints"]
x, y, w, h = detection['box']
new_image = alinhaFace(normalizaFace(imagem), keypoints)
detected_face = new_image[y:y + h, x - ((x + h) - (x + w)):x + h]
img0 = cv2.resize(detected_face, (100, 100),
interpolation=cv2.INTER_AREA)
imgc = np.asarray(img0, dtype=np.float32) / 255.0
imgc = np.expand_dims(imgc, 0)
feature = nca.transform(modelo.predict(imgc))
# extrai os recursos da base de dados
dis, posi = neigh.kneighbors(feature, 2, return_distance=True)
# print(dis, posi + 1)
id1 = selectFaceDB(int(posi[0][0] + 1))[0]
id2 = selectFaceDB(int(posi[0][1] + 1))[0]
# print(id1,id2)
img1 = cv2.imread(id1[1], cv2.IMREAD_COLOR)
imgc = np.asarray(img1, dtype=np.float32) / 255.0
imgc1 = np.expand_dims(imgc, 0)
feature1 = nca.transform(modelo.predict(imgc1))
img2 = cv2.imread(id2[1], cv2.IMREAD_COLOR)
imgc = np.asarray(img2, dtype=np.float32) / 255.0
imgc1 = np.expand_dims(imgc, 0)
feature2 = nca.transform(modelo.predict(imgc1))
cosi1 = cosine_distances(feature, feature1)
simi1 = cosine_similarity(feature, feature1)
cosi2 = cosine_distances(feature, feature2)
simi2 = cosine_similarity(feature, feature2)
if (simi1 >= simi2 and cosi1 <= cosi2):
id = id1[0]
else:
id = id2[0]
return id
else:
return -1
return -1
def ReconheceFaceImagem(paht):
try:
imagem = cv2.imread(paht, cv2.IMREAD_COLOR)
detections = detector.detect_faces(imagem)
except:
print("imagem nao reconhecida")
else:
for detection in detections:
score = detection["confidence"]
if score >= 0.95:
keypoints = detection["keypoints"]
x, y, w, h = detection['box']
new_image = alinhaFace(normalizaFace(imagem), keypoints)
detected_face = new_image[y:y + h, x - ((x + h) - (w + x)):x + h]
img0 = cv2.resize(detected_face, (100, 100), interpolation=cv2.INTER_AREA)
img = np.asarray(img0, dtype=np.float32) / 255.0
img = np.expand_dims(img, 0)
pess = rec.validaFace(img)
if (pess != "face nao reconhecida"):
id, path, nome, tel, per, atv, ide2, log, bai, uf, cep = selectFaceDB(int(pess))[0]
print("Id:{0} \nNome:{1} \nTelefone:{2} \nPermissao:{3}\n"
"Logradouro:{4} \nBairro:{5} \nUF:{6} \nCEP:{4}".format(
str(id), str(nome), str(tel), str(per), str(log), str(bai), str(uf), str(cep)))
text = "Id:{0} Nome:{1}".format(str(id), str(nome))
cv2.rectangle(imagem, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.putText(imagem, text, (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2, cv2.LINE_AA)
cv2.imshow("oi", imagem)
cv2.waitKey(0)
else:
text = "face nao reconhecida"
cv2.rectangle(imagem, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.putText(imagem, text, (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2, cv2.LINE_AA)
cv2.imshow("oi", imagem)
cv2.waitKey(0)
else:
print("face nao detectada")