Open damey2011 opened 6 years ago
Image.Open? Image is not defined
import io,base64 import face_recognition
def get_face_encoding_from_base64(base64String): image = face_recognition.load_image_file(io.BytesIO(base64.b64decode(base64string))) image_encodeing = face_recognition.face_encodings(image) return image_encodeing
@joastonish i tried the base64 with your way..its working with camera images or internet images but not with webcam images..can u pls help me with this
@damey2011 can you give me the program code you used??
Description
Images taken with camera and internet images are easily and quickly recognized even when still uploaded as base64 images, but images taken with the webcam don't come up with any face with the default 'hog' mode until it is changed to the convolutional neural networks (cnn) which is a lot slower, takes about 7 - 10 secs to process one picture
What I Did
This is my django class view:
class WhoInPhoto(APIView):
def post(self, request):
photo = request.data.get('photo', '')
photo = photo.split(',')[1]
all_user_faces = UserImage.objects.all()
known_faces = [face_recognition.face_encodings(face_recognition.load_image_file(img.facepicture.path))[0] for img in all_user_faces]
unknown_face = get_face_encoding_from_base64(photo)
if len(unknown_face) == 0:
return JsonResponse({'status': False, 'message': 'Face not found in photo uploaded'}, safe=False)
results = face_recognition.compare_faces(known_faces, unknown_face, tolerance=0.7)
if True in results:
user = all_user_faces[results.index(True)].user
return JsonResponse({'status': True, 'message': 'Are you {0} ?'.format(user.get_full_name())}, safe=False)
else:
return JsonResponse({'status': False, 'message': 'Face not found in database'}, safe=False)
Then the get_face_encoding function:
def get_face_encoding_from_base64(base64String):
try:
os.mkdir(os.path.join(MEDIA_ROOT, 'tmp'))
except FileExistsError:
pass
IMAGE = Image.open(BytesIO(b64decode(base64String)))
IMAGE.save(os.path.join(os.path.join(MEDIA_ROOT, 'tmp'), 'temp_encoding.jpg'), IMAGE.format)
image = face_recognition.load_image_file(os.path.join(os.path.join(MEDIA_ROOT, 'tmp'), 'temp_encoding.jpg'))
os.unlink(os.path.join(os.path.join(MEDIA_ROOT, 'tmp'), 'temp_encoding.jpg'))
face_locations = face_recognition.face_locations(image, number_of_times_to_upsample=0, model="cnn")
# face_locations = face_recognition.face_locations(image)
print(face_locations)
face_encodings = face_recognition.face_encodings(image, face_locations, 2)
if len(face_encodings) > 0:
return face_encodings[0]
else:
return []