def clean_up_print_pics(_print):
pic_dir = f'{_print.printer.id}/{_print.id}'
delete_dir('raw/{}/'.format(pic_dir),
settings.PICS_CONTAINER,
long_term_storage=False)
delete_dir('tagged/{}/'.format(pic_dir),
settings.PICS_CONTAINER,
long_term_storage=False)
delete_dir('p/{}/'.format(pic_dir),
settings.PICS_CONTAINER,
long_term_storage=False)
delete_dir(f'raw/{_print.printer.id}/0/',
settings.PICS_CONTAINER,
long_term_storage=False
) # the pics that may have come in before current_print is set.
def clean_up_print_pics(_print):
pic_dir = f'{_print.printer.id}/{_print.id}'
delete_dir('raw/{}/'.format(pic_dir),
settings.PICS_CONTAINER,
long_term_storage=False)
delete_dir('tagged/{}/'.format(pic_dir),
settings.PICS_CONTAINER,
long_term_storage=False)
delete_dir('p/{}/'.format(pic_dir),
settings.PICS_CONTAINER,
long_term_storage=False)
def detect_timelapse(self, print_id):
MAX_FRAME_NUM = 750
_print = Print.objects.get(pk=print_id)
tmp_dir = os.path.join(tempfile.gettempdir(), str(_print.id))
mp4_filepath = f'private/{_print.id}.mp4'
tl_path = os.path.join(tmp_dir, mp4_filepath)
Path(tl_path).parent.mkdir(parents=True, exist_ok=True)
with open(tl_path, 'wb') as file_obj:
retrieve_to_file_obj(mp4_filepath, file_obj,
settings.TIMELAPSE_CONTAINER)
jpgs_dir = os.path.join(tmp_dir, 'jpgs')
shutil.rmtree(jpgs_dir, ignore_errors=True)
os.makedirs(jpgs_dir)
tagged_jpgs_dir = os.path.join(tmp_dir, 'tagged_jpgs')
shutil.rmtree(tagged_jpgs_dir, ignore_errors=True)
os.makedirs(tagged_jpgs_dir)
ffprobe_cmd = subprocess.run(
f'ffprobe -v error -count_frames -select_streams v:0 -show_entries stream=nb_read_frames -of default=nokey=1:noprint_wrappers=1 {tl_path}'
.split(),
stdout=subprocess.PIPE)
frame_num = int(ffprobe_cmd.stdout.strip())
fps = 30 * MAX_FRAME_NUM / frame_num if frame_num > MAX_FRAME_NUM else 30
subprocess.run(
f'ffmpeg -y -i {tl_path} -vf fps={fps} -qscale:v 2 {jpgs_dir}/%5d.jpg'.
split())
predictions = []
last_prediction = PrinterPrediction()
jpg_filenames = sorted(os.listdir(jpgs_dir))
for jpg_path in jpg_filenames:
jpg_abs_path = os.path.join(jpgs_dir, jpg_path)
with open(jpg_abs_path, 'rb') as pic:
pic_path = f'{_print.user.id}/{_print.id}/{jpg_path}'
internal_url, _ = save_file_obj(f'uploaded/{pic_path}',
pic,
settings.PICS_CONTAINER,
long_term_storage=False)
req = requests.get(settings.ML_API_HOST + '/p/',
params={'img': internal_url},
headers=ml_api_auth_headers(),
verify=False)
req.raise_for_status()
detections = req.json()['detections']
update_prediction_with_detections(last_prediction, detections)
predictions.append(last_prediction)
if is_failing(last_prediction, 1, escalating_factor=1):
_print.alerted_at = timezone.now()
last_prediction = copy.deepcopy(last_prediction)
detections_to_visualize = [
d for d in detections if d[1] > VISUALIZATION_THRESH
]
overlay_detections(Image.open(jpg_abs_path),
detections_to_visualize).save(
os.path.join(tagged_jpgs_dir, jpg_path),
"JPEG")
predictions_json = serializers.serialize("json", predictions)
_, json_url = save_file_obj(f'private/{_print.id}_p.json',
io.BytesIO(str.encode(predictions_json)),
settings.TIMELAPSE_CONTAINER)
mp4_filename = f'{_print.id}_tagged.mp4'
output_mp4 = os.path.join(tmp_dir, mp4_filename)
subprocess.run(
f'ffmpeg -y -r 30 -pattern_type glob -i {tagged_jpgs_dir}/*.jpg -c:v libx264 -pix_fmt yuv420p -vf pad=ceil(iw/2)*2:ceil(ih/2)*2 {output_mp4}'
.split(),
check=True)
with open(output_mp4, 'rb') as mp4_file:
_, mp4_file_url = save_file_obj(f'private/{mp4_filename}', mp4_file,
settings.TIMELAPSE_CONTAINER)
with open(os.path.join(jpgs_dir, jpg_filenames[-1]), 'rb') as poster_file:
_, poster_file_url = save_file_obj(f'private/{_print.id}_poster.jpg',
poster_file,
settings.TIMELAPSE_CONTAINER)
_print.tagged_video_url = mp4_file_url
_print.prediction_json_url = json_url
_print.poster_url = poster_file_url
_print.save()
shutil.rmtree(tmp_dir, ignore_errors=True)
send_timelapse_detection_done_email(_print)
delete_dir(f'uploaded/{_print.user.id}/{_print.id}/',
settings.PICS_CONTAINER,
long_term_storage=False)
