^jddlmZddlZddlZddlZddlmZddlmZm Z ddl Z ddl m Z ddlmZeeZgdZGdd e ZGd d Zy) ) annotationsN)Iterable)AnyProtocol) Detections) _get_loggerx_miny_minx_maxy_maxclass_id confidence tracker_idceZdZddZy)WriterProtocolcyN)selfrows o/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/supervision/detection/tools/csv_sink.pywriterowzWriterProtocol.writerowsN)rz Iterable[Any]returnr)__name__ __module__ __qualname__rrrrrrs6rrceZdZdZd d dZddZ ddZddZddZe ddZ e d dd Z d dd Z e d dd Z y)CSVSinkaP A utility class for saving detection data to a CSV file. This class is designed to efficiently serialize detection objects into a CSV format, allowing for the inclusion of bounding box coordinates and additional attributes like `confidence`, `class_id`, and `tracker_id`. !!! tip CSVSink 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. Args: file_name: The name of the CSV file where the detections will be stored. Defaults to 'output.csv'. Example: ```pycon >>> import supervision as sv >>> import numpy as np >>> import tempfile >>> import os >>> # Create synthetic detections >>> detections = sv.Detections( ... xyxy=np.array([[10, 20, 30, 40], [50, 60, 70, 80]]), ... confidence=np.array([0.9, 0.8]), ... class_id=np.array([0, 1]) ... ) >>> # Use temporary file >>> temp_file = tempfile.NamedTemporaryFile( ... mode='w', suffix='.csv', delete=False ... ) >>> temp_file.close() >>> csv_sink = sv.CSVSink(temp_file.name) >>> with csv_sink as sink: ... sink.append(detections, custom_data={'frame': 0}) >>> os.unlink(temp_file.name) # Clean up ``` cJ||_d|_d|_d|_g|_y)zr Initialize the CSVSink instance. Args: file_name: The name of the CSV file. NF) file_namefilewriterheader_written field_names)rr"s r__init__zCSVSink.__init__Ks)#-1 -1 #&(rc&|j|Sr)openrs r __enter__zCSVSink.__enter__Xs  rc$|jyr)close)rexc_typeexc_valexc_tbs r__exit__zCSVSink.__exit__\s rcDtjj|j}|r4tjj |stj |t |jdd|_tj|j|_ y)z0 Open the CSV file for writing. w)newlineN) ospathdirnamer"existsmakedirsr)r#csvr$)rparent_directorys rr)z CSVSink.opendsc77??4>>: BGGNN3C$D KK( )b9 jj+ rcR|jr|jjyy)z% Close the CSV file. N)r#r-r*s rr-z CSVSink.closeos 99 IIOO  rct|tjr|jdk(r|S||St|tt frt ||k(r||S|S)a{ Return the i-th element when the value stores per-detection data. Dispatch rules: - np.ndarray with ndim == 0: return as-is for broadcasting - np.ndarray with ndim >= 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) isinstancenpndarrayndimlisttuplelen)valueins r _slice_valuezCSVSink._slice_valuevsQ( eRZZ (!JJ!O5 9q 9 edE] +E a8O rNc g}t|j}t|D]F}|j|d|j|d|j|d|j|d|jdnt |j||j dnt |j ||j dnt |j |d}t|dr<|jjD]\}}tj|||||<!|r2|jD]\}}tj|||||<!|j|I|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``. rr4r data) rExyxyrangerstrrrhasattrrNitemsr rIappend) detections custom_data parsed_rowsrHrGrkeyrFs rparse_detection_datazCSVSink.parse_detection_datasz,   q $A#+A.#+A.#+A.#+A.&&.,,Q/0((0!..q12((0!..q12C z6*",//"7"7"9AJC&33E1a@CHA"-"3"3"5AJC&33E1a@CHA   s #3 $4rc |jstd|jdtj ||}|j s)||_|jj|d|_||j k7r!tjd|j |tj||}|D]E}|jj|j Dcgc]}|j|dc}Gycc}w)a Append detection data to the CSV 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. z Cannot append to CSV: The file 'z' is not open.Tz>2B.Q // KH ""*D  KK  -"&D  $** * NNN    22:{K  C KK ;?;K;KLZR(L  LsD c |rt|jn t}t|tt|dijz}t|zS)NrN)setkeyssortedgetattr BASE_HEADER)rUrV custom_keysdynamic_headers rr\zCSVSink.parse_field_namessS2=c+**,-#%  #gj&"=BBDE E ^++r)z output.csv)r"rQrNone)rr )r.z type | Noner/zException | Noner0z Any | Nonerri)rri)rFrrGintrHrjrrr)rUrrVdict[str, Any] | Nonerzlist[dict[str, Any]])rUrrVrkrri)rUrrVrkrz list[str])rrr__doc__r'r+r1r)r- staticmethodrIrYrTr\rrrr r s)V )"    ,2EI11-B1 11hLP#$#3H# #JEI,,-B, ,,rr ) __future__rr;ior6collections.abcrtypingrrnumpyr@supervision.detection.corersupervision.utils.loggerrrr]rfrr rrrrusI" $ 10 X  7X7R,R,r