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)