def setUp(self):
self.configuration = Configuration()
self.recognition = Recognition(self.configuration)
self.tuple = self.recognition.extract()
self.recognition.train()
self.names_array = self.tuple[0]
self.embeddings_array = self.tuple[1]
def setUp(self):
self.configuration = Configuration()
self.recognition = Recognition(self.configuration)
self.valid_payload = {
'name': 'Ragnar',
'detector': 'advertisement board'
}
self.invalid_payload = {'name': '', 'detector': 'advertisement board'}
def recognize_from_video(request, video):
configuration = Configuration()
recognition = Recognition(configuration)
recognition.recognize_from_video(video)
return HttpResponse("SUCCESS")
def recognize_from_picture(request, picture):
configuration = Configuration()
recognition = Recognition(configuration)
recognition.recognize(picture)
return HttpResponse("SUCCESS")
def extraction_training(request):
configuration = Configuration()
recognition = Recognition(configuration)
recognition.extract()
recognition.train()
return HttpResponse("SUCCESS")
def setUp(self):
self.configuration = Configuration()
self.recognition = Recognition(self.configuration)
def setUp(self):
self.configuration = Configuration()
self.recognition = Recognition(self.configuration)
self.response_http_is_ok = status.HTTP_200_OK
self.response_http_is_ko = status.HTTP_404_NOT_FOUND
self.response_exception = "Connection Error"
def run(stream):
configuration = Configuration()
recognition = Recognition(configuration)
recognition.recognize_from_stream(stream)
def run():
configuration = Configuration()
logging.info("OUTPUT PICKLE FILES")
logging.info(configuration.get_embeddings())
logging.info(configuration.get_trained_model())
logging.info(configuration.get_label_encoder())
logging.info("OPENCV MODELS & PROTOTYPES")
logging.info(configuration.get_opencv_deep_learning_prototype())
logging.info(configuration.get_opencv_deep_learning_model())
logging.info(configuration.get_opencv_embedding_model())
logging.info("GENERAL PARAMETERS")
logging.info(configuration.get_dataset())
logging.info(configuration.get_confidence_score())
logging.info("CONSTRUCTION BLOB FROM IMAGE PARAMETERS")
logging.info(configuration.get_blob_from_image_scalefactor())
logging.info(configuration.get_blob_from_image_size())
logging.info(configuration.get_blob_from_image_mean_r())
logging.info(configuration.get_blob_from_image_mean_g())
logging.info(configuration.get_blob_from_image_mean_b())
logging.info(configuration.get_blob_from_image_swapRB())
logging.info(configuration.get_blob_from_image_crop())
logging.info("CONSTRUCTION BLOB FROM FACE PARAMETERS")
logging.info(configuration.get_blob_from_face_scalefactor())
logging.info(configuration.get_blob_from_face_size())
logging.info(configuration.get_blob_from_face_mean_r())
logging.info(configuration.get_blob_from_face_mean_g())
logging.info(configuration.get_blob_from_face_mean_b())
logging.info(configuration.get_blob_from_face_swapRB())
logging.info(configuration.get_blob_from_face_crop())
logging.info("SVC PARAMETERS")
logging.info(configuration.get_svc_c())
logging.info(configuration.get_svc_kernel())
logging.info(configuration.get_svc_probability())
logging.info("EXTRACTION AND TRAINING STARTED ...")
recognition = Recognition(configuration)
if os.path.exists("data/output/embeddings.pickle") is False:
logging.info(" **** EXTRACTION STARTED **** ")
recognition.extract()
logging.info(" **** TRAINING STARTED **** ")
recognition.train()
def ready(self):
configuration = Configuration()
consumer = AMQPConsumer(configuration)
consumer.daemon = True
print("AMQP Daemon started")
consumer.start()
def run(video):
configuration = Configuration()
recognition = Recognition(configuration)
recognition.recognize_from_video(video)
def run(picture):
configuration = Configuration()
recognition = Recognition(configuration)
recognition.recognize(picture)