def post(self, request):
printer = request.auth
pic = request.FILES['pic']
pic_id = int(timezone.now().timestamp())
internal_url, external_url = save_file_obj(
'raw/{}/{}.jpg'.format(printer.id, pic_id), pic,
settings.PICS_CONTAINER)
if not printer.is_printing():
redis.printer_pic_set(printer.id, {'img_url': external_url},
ex=STATUS_TTL_SECONDS)
return command_response(printer)
req = requests.get(settings.ML_API_HOST + '/p/',
params={'img': internal_url},
headers=ml_api_auth_headers(),
verify=False)
req.raise_for_status()
resp = req.json()
detections = resp['detections']
prediction, _ = PrinterPrediction.objects.get_or_create(
printer=printer)
update_prediction_with_detections(prediction, detections)
prediction.save()
pic.file.seek(
0) # Reset file object pointer so that we can load it again
tagged_img = io.BytesIO()
detections_to_visualize = [
d for d in detections if d[1] > VISUALIZATION_THRESH
]
overlay_detections(Image.open(pic),
detections_to_visualize).save(tagged_img, "JPEG")
tagged_img.seek(0)
_, external_url = save_file_obj(
'tagged/{}/{}.jpg'.format(printer.id, pic_id), tagged_img,
settings.PICS_CONTAINER)
redis.printer_pic_set(printer.id, {'img_url': external_url},
ex=STATUS_TTL_SECONDS)
prediction_json = serializers.serialize("json", [
prediction,
])
redis.printer_p_json_set(printer.id,
pic_id,
prediction_json,
ex=60 * 60 * 24 * 2)
if is_failing(prediction,
printer.detective_sensitivity,
escalating_factor=settings.ESCALATING_FACTOR):
pause_if_needed(printer)
elif is_failing(prediction,
printer.detective_sensitivity,
escalating_factor=1):
alert_if_needed(printer)
return command_response(printer)
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))
tl_path = download_files([f'private/{_print.id}.mp4'],
tmp_dir,
container=settings.TIMELAPSE_CONTAINER)[0]
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 -i {tl_path} -vf fps={fps} -qscale:v 2 {jpgs_dir}/{print_id}-%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:
internal_url, _ = save_file_obj(f'raw/uploaded_prints/{jpg_path}',
pic, settings.PICS_CONTAINER)
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 {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)
def post(self, request):
printer = request.auth
printer.refresh_from_db(
) # Connection is keep-alive, which means printer object can be stale.
pic = request.FILES['pic']
pic = cap_image_size(pic)
pic_id = str(timezone.now().timestamp())
if not printer.current_print: # Some times pics come in when current_print is not set - maybe because printer status is out of sync between plugin and server?
pic_path = f'{printer.id}/0/{pic_id}.jpg'
else:
pic_path = f'{printer.id}/{printer.current_print.id}/{pic_id}.jpg'
internal_url, external_url = save_file_obj(f'raw/{pic_path}',
pic,
settings.PICS_CONTAINER,
long_term_storage=False)
if not printer.should_watch() or not printer.actively_printing():
cache.printer_pic_set(printer.id, {'img_url': external_url},
ex=IMG_URL_TTL_SECONDS)
send_status_to_web(printer.id)
return Response({'result': 'ok'})
req = requests.get(settings.ML_API_HOST + '/p/',
params={'img': internal_url},
headers=ml_api_auth_headers(),
verify=False)
req.raise_for_status()
resp = req.json()
cache.print_num_predictions_incr(printer.current_print.id)
detections = resp['detections']
prediction, _ = PrinterPrediction.objects.get_or_create(
printer=printer)
update_prediction_with_detections(prediction, detections)
prediction.save()
if prediction.current_p > settings.THRESHOLD_LOW * 0.2: # Select predictions high enough for focused feedback
cache.print_high_prediction_add(printer.current_print.id,
prediction.current_p, pic_id)
pic.file.seek(
0) # Reset file object pointer so that we can load it again
tagged_img = io.BytesIO()
detections_to_visualize = [
d for d in detections if d[1] > VISUALIZATION_THRESH
]
overlay_detections(Image.open(pic),
detections_to_visualize).save(tagged_img, "JPEG")
tagged_img.seek(0)
_, external_url = save_file_obj(f'tagged/{pic_path}',
tagged_img,
settings.PICS_CONTAINER,
long_term_storage=False)
cache.printer_pic_set(printer.id, {'img_url': external_url},
ex=IMG_URL_TTL_SECONDS)
prediction_json = serializers.serialize("json", [
prediction,
])
p_out = io.BytesIO()
p_out.write(prediction_json.encode('UTF-8'))
p_out.seek(0)
save_file_obj(
f'p/{printer.id}/{printer.current_print.id}/{pic_id}.json',
p_out,
settings.PICS_CONTAINER,
long_term_storage=False)
if is_failing(prediction,
printer.detective_sensitivity,
escalating_factor=settings.ESCALATING_FACTOR):
pause_if_needed(printer)
elif is_failing(prediction,
printer.detective_sensitivity,
escalating_factor=1):
alert_if_needed(printer)
send_status_to_web(printer.id)
return Response({'result': 'ok'})
def post(self, request):
printer = request.auth
printer.refresh_from_db() # Connection is keep-alive, which means printer object can be stale.
if not request.path.startswith('/api/v1'):
LOGGER.warn(f'Beta plugin connecting from {printer.id}')
pic = request.FILES['pic']
pic_id = int(timezone.now().timestamp())
internal_url, external_url = save_file_obj('raw/{}/{}.jpg'.format(printer.id, pic_id), pic, settings.PICS_CONTAINER)
if not printer.should_watch() or not printer.actively_printing():
redis.printer_pic_set(printer.id, {'img_url': external_url}, ex=STATUS_TTL_SECONDS)
send_status_to_web(printer.id)
return Response({'result': 'ok'})
req = requests.get(settings.ML_API_HOST + '/p/', params={'img': internal_url}, headers=ml_api_auth_headers(), verify=False)
req.raise_for_status()
resp = req.json()
detections = resp['detections']
prediction, _ = PrinterPrediction.objects.get_or_create(printer=printer)
update_prediction_with_detections(prediction, detections)
prediction.save()
pic.file.seek(0) # Reset file object pointer so that we can load it again
tagged_img = io.BytesIO()
detections_to_visualize = [d for d in detections if d[1] > VISUALIZATION_THRESH]
overlay_detections(Image.open(pic), detections_to_visualize).save(tagged_img, "JPEG")
tagged_img.seek(0)
_, external_url = save_file_obj('tagged/{}/{}.jpg'.format(printer.id, pic_id), tagged_img, settings.PICS_CONTAINER)
redis.printer_pic_set(printer.id, {'img_url': external_url}, ex=STATUS_TTL_SECONDS)
prediction_json = serializers.serialize("json", [prediction, ])
redis.printer_p_json_set(printer.id, pic_id, prediction_json, ex=60*60*24*2)
if is_failing(prediction, printer.detective_sensitivity, escalating_factor=settings.ESCALATING_FACTOR):
pause_if_needed(printer)
elif is_failing(prediction, printer.detective_sensitivity, escalating_factor=1):
alert_if_needed(printer)
redis.print_num_predictions_incr(printer.current_print.id)
send_status_to_web(printer.id)
return Response({'result': 'ok'})
