async def recognize():
if len([line.strip() for line in open("queue.txt")]) > 0:
filename = await rofl.async_run_from_queue(ioloop,
fps_factor=60,
emotions=True)
api.upload_video(filename,
upload_name=filename.split('/')[-1],
folder_id=rofl_folder)
api.upload_video(filename, upload_name=filename, room_num=room)
return filename
else:
await asyncio.sleep(5)
return 0
def send_file(filename, link=''):
r = api.upload_video("video_output/" + filename,
filename.split('/')[-1],
folder_id=rofl_folder)
_id = r['id']
"""Show result endpoint."""
return "https://drive.google.com/file/d/" + _id + "/" + link
async def message_handler(self, text, message_id):
if not api.validate(text):
logging.debug('invalid url received: {0}'.format(text))
await self.reply(replies.INVALID_URL, message_id)
return
await self.reply(replies.UPLOAD_STARTED, message_id)
video_data = api.download(text, config['tmp_path'])
video_vk_url = api.upload_video(vk_api, video_data.path,
video_data.name)
os.remove(video_data.path)
logging.info("uploaded: {0} -> {1}".format(text, video_vk_url))
await self.reply(replies.UPLOADED_VIDEO.format(video_vk_url),
message_id)
from rofl import ROFL
from recognizer import Recognizer
from encode import encode_cluster_sf
import api
model = "trained_knn_model.clf"
# recog = Recognizer()
# recog.train(n_neighbors=2, model_save_path=model)
# rofl = ROFL(recognizer_path=model, retina=True, on_gpu=True, emotions=True)
filename = "jackle.mp4"
filename1 = "twice.mp4"
api.upload_video("video_output/2020-08-12_08-00_305_54.mp4",
upload_name="2020-08-12_08:00_305.mp4",
folder_id="1iR8EpBv0jGPwVQ7YepeFPccj4a0jhzPJ")
# rofl.basic_run(".", filename1, fps_factor=30, recognize=True, emotions=True)
# rofl.run_emotions(".", filename1, fps_factor=30)
# rofl.basic_run(".", filename, fps_factor=30)
# rofl.run_and_remember_strangers(filename, fps_factor=30)
# encode_cluster_sf("./strangers", "./enc_cluster.pickle")
# rofl.clust.remember_strangers("enc_cluster.pickle", save_path="known_faces")