^j\ddlmZddlZddlZddlZddlmZddlZddl m Z GddZ y)) annotationsN)Any) DetectionsceZdZdZd d dZddZ ddZddZeddZ ddZ eddZ e d dd Z d dd Z y )JSONSinka A utility class for saving detection data to a JSON file. This class is designed to efficiently serialize detection objects into a JSON format, allowing for the inclusion of bounding box coordinates and additional attributes like `confidence`, `class_id`, and `tracker_id`. !!! tip JSONSink allows passing custom data alongside detection fields, providing flexibility for logging various types of information. When a list or tuple value in custom_data (or detections.data) has the same length as the detection count, each element is written to the corresponding detection row; any other value is broadcast to all rows. NumPy scalars (e.g. ``np.int64``, ``np.float32``) are serialized as JSON numbers; NumPy arrays are serialized as JSON arrays. Args: file_name: The name of the JSON file where the detections will be stored. Defaults to 'output.json'. Example: ```python import supervision as sv from ultralytics import YOLO model = YOLO("") json_sink = sv.JSONSink() frames_generator = sv.get_video_frames_generator("") with json_sink as sink: for frame in frames_generator: result = model(frame)[0] detections = sv.Detections.from_ultralytics(result) sink.append(detections, custom_data={"":""}) ``` c.||_d|_g|_y)zt Initialize the JSONSink instance. Args: file_name: The name of the JSON file. N) file_namefiledata)selfr s p/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/supervision/detection/tools/json_sink.py__init__zJSONSink.__init__3s#-1 *, c&|j|SN)openr s r __enter__zJSONSink.__enter__>s  rc$|jyr)write_and_close)r exc_typeexc_valexc_tbs r __exit__zJSONSink.__exit__Bs rctjj|j}|r4tjj |stj |t |jd|_y)z1 Open the JSON file for writing. wN)ospathdirnamer existsmakedirsrr )r parent_directorys r rz JSONSink.openJsM77??4>>: BGGNN3C$D KK( )- rct|tjr|jSt|tjr|j St dt|jd)aReturn a JSON-serializable equivalent of a NumPy scalar or array. Called as the ``default`` hook by :func:`json.dump`. Converts :class:`numpy.generic` scalars via ``.item()`` and :class:`numpy.ndarray` instances via ``.tolist()``. Args: value: Object the standard JSON encoder could not serialize. Returns: A Python scalar or nested list equivalent of ``value``. Raises: TypeError: If ``value`` is neither a NumPy scalar nor an ndarray. zObject of type z is not JSON serializable) isinstancenpgenericitemndarraytolist TypeErrortype__name__)values r _json_defaultzJSONSink._json_defaultTs]" eRZZ (::<  eRZZ (<<> !d5k2233L M  rc|jrW tj|j|jdtj |jj yy#|jj wxYw)z0 Write and close the JSON file. )indentdefaultN)r jsondumpr rr.closers r rzJSONSink.write_and_closemsZ 99 " IItyyH= 1: return value[i] - list or tuple with len equal to n: return value[i] - any other type: return as-is for broadcasting Args: value: Custom-data field value. i: Zero-based detection index. n: Total number of detections. Returns: Element at position i if value is a per-detection sequence, otherwise value unchanged. r)r$r%r(ndimlisttuplelen)r-ins r _slice_valuezJSONSink._slice_valueysQ( eRZZ (!JJ!O5 9q 9 edE] +E a8O rNc g}t|j}t|D]}t|j|dt|j|dt|j|dt|j|d|jdnt |j||j dnt|j ||jdnt |j|d}t|drE|jjD](\}}ttj|||||<*|rY|jD]F\}}tj|||}t|tj r t|n|||<H|j#||S)aK Convert detections and optional custom data into per-detection rows. Builds one dictionary per detection containing bounding box coordinates, detection attributes, and any values from ``detections.data`` or ``custom_data``. List and tuple values in ``custom_data`` with length equal to ``len(detections.xyxy)`` are sliced one element per row; all other values are broadcast to every row. Args: detections: Detection data to serialize into row dictionaries. custom_data: Optional extra fields to include in each row. Returns: A list of dictionaries, one per detection, containing ``xyxy`` coordinates, ``class_id``, ``confidence``, ``tracker_id``, and any values from ``detections.data`` or ``custom_data``. r)x_miny_minx_maxy_maxclass_id confidence tracker_idr )r:xyxyrangefloatrGintrHrIhasattrr itemsstrrr=r$r%r(append) detections custom_data parsed_rowsr<r;rowkeyr-vs r parse_detection_datazJSONSink.parse_detection_datas,   q $Azq1!45zq1!45zq1!45zq1!45&&.,,Q/0((0!:0034((0!..q12C z6*",//"7"7"9GJC"8#8#81#EFCHG"-"3"3"5NJC --eQ:A)3E2::)Fs1vACHN   s #5 $6rcftj||}|jj|y)a Append detection data to the JSON file. Args: detections: The detection data. custom_data: Custom data to include. Scalars, dictionaries, and other non-sequence values are broadcast to every detection in this batch. NumPy arrays, lists, and tuples with length equal to ``len(detections)`` are sliced per detection; other lists and tuples are broadcast unchanged. N)rrXr extend)r rRrSrTs r rQzJSONSink.appends(33J L  %r)z output.json)r rPreturnNone)r[r)rz type | NonerzException | Nonerz Any | Noner[r\)r[r\)r-rr[r)r-rr;rMr<rMr[rr)rRrrSdict[str, Any] | Noner[zlist[dict[str, Any]])rRrrSr]r[r\)r, __module__ __qualname____doc__rrrr staticmethodr.rr=rXrQrr rr s#J -"   .  0 "2EI22-B2 22jLP&$&3H& &rr) __future__rior3rtypingrnumpyr%supervision.detection.corerrrbrr rhs&" 1J&J&r