Ë
    Fêñi‹$  ã                  ó^   — d dl mZ d dlmZ d dlmZ d dlmZmZm	Z	 d dl
mZ  G d„ de«      Zy)	é    )Úannotations)Údefaultdict)ÚAny)ÚBaseSolutionÚSolutionAnnotatorÚSolutionResults)Úcolorsc                  óP   ‡ — e Zd ZdZdˆ fd„Z	 	 	 	 	 	 	 	 	 	 dd„Zdd„Zd	d„Zˆ xZS )
ÚObjectCountera‡  A class to manage the counting of objects in a real-time video stream based on their tracks.

    This class extends the BaseSolution class and provides functionality for counting objects moving in and out of a
    specified region in a video stream. It supports both polygonal and linear regions for counting.

    Attributes:
        in_count (int): Counter for objects moving inward.
        out_count (int): Counter for objects moving outward.
        counted_ids (list[int]): List of IDs of objects that have been counted.
        classwise_count (dict[str, dict[str, int]]): Dictionary for counts, categorized by object class.
        region_initialized (bool): Flag indicating whether the counting region has been initialized.
        show_in (bool): Flag to control display of inward count.
        show_out (bool): Flag to control display of outward count.
        margin (int): Margin for background rectangle size to display counts properly.

    Methods:
        count_objects: Count objects within a polygonal or linear region based on their tracks.
        display_counts: Display object counts on the frame.
        process: Process input data and update counts.

    Examples:
        >>> counter = ObjectCounter()
        >>> frame = cv2.imread("frame.jpg")
        >>> results = counter.process(frame)
        >>> print(f"Inward count: {counter.in_count}, Outward count: {counter.out_count}")
    c                óö   •— t        ‰| �  di |¤Ž d| _        d| _        g | _        t        d„ «      | _        d| _        | j                  d   | _	        | j                  d   | _
        | j                  dz  | _        y)	zRInitialize the ObjectCounter class for real-time object counting in video streams.r   c                 ó   — dddœS )Nr   )ÚINÚOUT© r   ó    úf/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/solutions/object_counter.pyú<lambda>z(ObjectCounter.__init__.<locals>.<lambda>/   s   € ¸!ÀAÑ3F€ r   FÚshow_inÚshow_outé   Nr   )ÚsuperÚ__init__Úin_countÚ	out_countÚcounted_idsr   Úclasswise_countÚregion_initializedÚCFGr   r   Ú
line_widthÚmargin)ÚselfÚkwargsÚ	__class__s     €r   r   zObjectCounter.__init__(   so   ø€ ä‰ÑÑ"˜6Ò"àˆŒØˆŒØˆÔÜ*Ñ+FÓGˆÔØ"'ˆÔà—x‘x 	Ñ*ˆŒØŸ™ Ñ,ˆŒØ—o‘o¨Ñ)ˆ�r   c                ó�  — |�|| j                   v ryt        | j                  «      dk(  �r°| j                  j	                  | j                  ||g«      «      �r‚t        | j                  d   d   | j                  d   d   z
  «      t        | j                  d   d   | j                  d   d   z
  «      k  r…|d   |d   kD  r=| xj                  dz  c_        | j                  | j                  |      dxx   dz  cc<   nÁ| xj                  dz  c_
        | j                  | j                  |      dxx   dz  cc<   n„|d   |d   kD  r=| xj                  dz  c_        | j                  | j                  |      dxx   dz  cc<   n<| xj                  dz  c_
        | j                  | j                  |      dxx   dz  cc<   | j                   j                  |«       yyt        | j                  «      dkD  �rS| j                  j                  | j                  |«      «      �r't        d„ | j                  D «       «      t        d„ | j                  D «       «      z
  }t        d	„ | j                  D «       «      t        d
„ | j                  D «       «      z
  }||k  r|d   |d   kD  s||k\  rH|d   |d   kD  r=| xj                  dz  c_        | j                  | j                  |      dxx   dz  cc<   n<| xj                  dz  c_
        | j                  | j                  |      dxx   dz  cc<   | j                   j                  |«       yyy)ap  Count objects within a polygonal or linear region based on their tracks.

        Args:
            current_centroid (tuple[float, float]): Current centroid coordinates (x, y) in the current frame.
            track_id (int): Unique identifier for the tracked object.
            prev_position (tuple[float, float], optional): Last frame position coordinates (x, y) of the track.
            cls (int): Class index for classwise count updates.

        Examples:
            >>> counter = ObjectCounter()
            >>> track_line = {1: [100, 200], 2: [110, 210], 3: [120, 220]}
            >>> box = [130, 230, 150, 250]
            >>> track_id_num = 1
            >>> previous_position = (120, 220)
            >>> class_to_count = 0  # In COCO model, class 0 = person
            >>> counter.count_objects((140, 240), track_id_num, previous_position, class_to_count)
        Nr   r   é   r   r   c              3  ó&   K  — | ]	  }|d    –— Œ y­w©r   Nr   ©Ú.0Úps     r   ú	<genexpr>z.ObjectCounter.count_objects.<locals>.<genexpr>h   s   è ø€ Ò"=¨A 1 Q¥4Ñ"=ùó   ‚c              3  ó&   K  — | ]	  }|d    –— Œ y­wr'   r   r(   s     r   r+   z.ObjectCounter.count_objects.<locals>.<genexpr>h   s   è ø€ ÒC^ÈQÀAÀaÅDÑC^ùr,   c              3  ó&   K  — | ]	  }|d    –— Œ y­w©r%   Nr   r(   s     r   r+   z.ObjectCounter.count_objects.<locals>.<genexpr>i   s   è ø€ Ò#>¨Q A a¥DÑ#>ùr,   c              3  ó&   K  — | ]	  }|d    –— Œ y­wr/   r   r(   s     r   r+   z.ObjectCounter.count_objects.<locals>.<genexpr>i   s   è ø€ ÒD_ÈaÀQÀqÅTÑD_ùr,   )r   ÚlenÚregionÚr_sÚ
intersectsÚ
LineStringÚabsr   r   Únamesr   ÚappendÚcontainsÚPointÚmaxÚmin)r!   Úcurrent_centroidÚtrack_idÚprev_positionÚclsÚregion_widthÚregion_heights          r   Úcount_objectszObjectCounter.count_objects6   sá  € ð0 Ð  H°×0@Ñ0@Ñ$@Øäˆt�{‰{Ó˜qÓ Ø�x‰x×"Ñ" 4§?¡?°MÐCSÐ3TÓ#UÕVä�t—{‘{ 1‘~ aÑ(¨4¯;©;°q©>¸!Ñ+<Ñ<Ó=ÄÀDÇKÁKÐPQÁNÐSTÑDUÐX\×XcÑXcÐdeÑXfÐghÑXiÑDiÓ@jÒjà'¨Ñ*¨]¸1Ñ-=Ò=ØŸš¨Ñ*�Ø×,Ñ,¨T¯Z©Z¸©_Ñ=¸dÓCÀqÑHÔCàŸš¨!Ñ+�Ø×,Ñ,¨T¯Z©Z¸©_Ñ=¸eÓDÈÑIÔDà% aÑ(¨=¸Ñ+;Ò;Ø—M’M QÑ&•MØ×(Ñ(¨¯©°C©Ñ9¸$Ó?À1ÑDÔ?à—N’N aÑ'•NØ×(Ñ(¨¯©°C©Ñ9¸%Ó@ÀAÑEÓ@Ø× Ñ ×'Ñ'¨Õ1ð# Wô& �—‘Ó Ó!Ø�x‰x× Ñ  §¡Ð,<Ó!=Õ>ä"Ñ"=°·±Ô"=Ó=ÄÑC^ÐRV×R]ÑR]ÔC^Ó@^Ñ^�Ü #Ñ#>°$·+±+Ô#>Ó >ÄÑD_ÐSW×S^ÑS^ÔD_ÓA_Ñ _�à  =Ò0Ð5EÀaÑ5HÈ=ÐYZÑK[Ò5[Ø  MÒ1Ð6FÀqÑ6IÈMÐZ[ÑL\Ò6\à—M’M QÑ&•MØ×(Ñ(¨¯©°C©Ñ9¸$Ó?À1ÑDÔ?à—N’N aÑ'•NØ×(Ñ(¨¯©°C©Ñ9¸%Ó@ÀAÑEÓ@Ø× Ñ ×'Ñ'¨Õ1ð ?ð "r   c                óÒ  — | j                   j                  «       D ��ci c]�  \  }}|d   dk7  r| j                  s|d   dk7  ro| j                  rct        j                  |«      | j                  rdt	        |d   «      z   nd› d| j                  rdt	        |d   «      z   nd› �j                  «       “Œ’ }}}|r*| j                  j                  ||dd	| j                  «       y
y
c c}}w )a0  Display object counts on the input image or frame.

        Args:
            plot_im (np.ndarray): The image or frame to display counts on.

        Examples:
            >>> counter = ObjectCounter()
            >>> frame = cv2.imread("image.jpg")
            >>> counter.display_counts(frame)
        r   r   r   zIN Ú ú zOUT )éh   é   é   )éÿ   rJ   rJ   N)
r   Úitemsr   r   ÚstrÚ
capitalizeÚstripÚ	annotatorÚdisplay_analyticsr    )r!   Úplot_imÚkeyÚvalueÚlabels_dicts        r   Údisplay_countszObjectCounter.display_countsu   sá   € ð #×2Ñ2×8Ñ8Ó:÷
ñ ��UØ�d‘˜qÒ  T§\¢\°u¸U±|ÀqÒ7HÈTÏ]Ê]ô �N‰N˜3ÓÀÇÂ E¬C°°d±Ó,<Ò$<ÐRTÐ#UÐUVØ-1¯]ª]ˆvœ˜E %™LÓ)Ò)ÀÐCð"EßEJÁUÃWñMð
ˆñ 
ñ Ø�N‰N×,Ñ,¨W°kÀ=ÐRaÐcg×cnÑcnÕoð ùó
s   žBC#c           	     óÔ  — | j                   s| j                  «        d| _         | j                  |«       t        || j                  ¬«      | _        | j
                  j                  | j                  d| j                  dz  ¬«       t        | j                  | j                  | j                  | j                  «      D ]¥  \  }}}}| j
                  j                  || j                  |||«      t        |d«      ¬«       | j!                  ||«       d}t#        | j$                  |   «      dkD  r| j$                  |   d	   }| j'                  | j$                  |   d
   |||«       Œ§ | j
                  j)                  «       }| j+                  |«       | j-                  |«       t/        || j0                  | j2                  t5        | j6                  «      t#        | j                  «      ¬«      S )aO  Process input data (frames or object tracks) and update object counts.

        This method initializes the counting region, extracts tracks, draws bounding boxes and regions, updates object
        counts, and displays the results on the input image.

        Args:
            im0 (np.ndarray): The input image or frame to be processed.

        Returns:
            (SolutionResults): Contains processed image `im0`, 'in_count' (int, count of objects entering the region),
                'out_count' (int, count of objects exiting the region), 'classwise_count' (dict, per-class object
                count), and 'total_tracks' (int, total number of tracked objects).

        Examples:
            >>> counter = ObjectCounter()
            >>> frame = cv2.imread("path/to/image.jpg")
            >>> results = counter.process(frame)
        T)r   )rG   r   é{   r   )Úreg_ptsÚcolorÚ	thickness)ÚlabelrY   Nr%   éþÿÿÿéÿÿÿÿ)rQ   r   r   r   Útotal_tracks)r   Úinitialize_regionÚextract_tracksr   r   rO   Údraw_regionr2   ÚzipÚboxesÚ	track_idsÚclssÚconfsÚ	box_labelÚadjust_box_labelr	   Ústore_tracking_historyr1   Útrack_historyrC   ÚresultrU   Údisplay_outputr   r   r   Údictr   )r!   Úim0Úboxr>   r@   Úconfr?   rQ   s           r   ÚprocesszObjectCounter.process‰   s­  € ð& ×&Ò&Ø×"Ñ"Ô$Ø&*ˆDÔ#à×Ñ˜CÔ Ü*¨3¸4¿?¹?ÔKˆŒà�‰×"Ñ"Ø—K‘K }ÀÇÁÐRSÑ@Sð 	#ô 	
ô
 ),¨D¯J©J¸¿¹ÈÏ	É	ÐSW×S]ÑS]Ó(^ò 		_Ñ$ˆC�˜3 à�N‰N×$Ñ$ S°×0EÑ0EÀcÈ4ÐQYÓ0ZÔbhÐilÐnrÓbsÐ$ÔtØ×'Ñ'¨°#Ô6ð !ˆMÜ�4×%Ñ% hÑ/Ó0°1Ò4Ø $× 2Ñ 2°8Ñ <¸RÑ @�Ø×Ñ˜t×1Ñ1°(Ñ;¸BÑ?ÀÈ=ÐZ]Õ^ð		_ð —.‘.×'Ñ'Ó)ˆØ×Ñ˜GÔ$Ø×Ñ˜GÔ$ô ØØ—]‘]Ø—n‘nÜ  ×!5Ñ!5Ó6Ü˜TŸ^™^Ó,ô
ð 	
r   )r"   r   ÚreturnÚNone)
r=   ztuple[float, float]r>   Úintr?   ztuple[float, float] | Noner@   rt   rr   rs   )rr   rs   )rr   r   )	Ú__name__Ú
__module__Ú__qualname__Ú__doc__r   rC   rU   rq   Ú__classcell__)r#   s   @r   r   r      sN   ø„ ñõ6*ð=2à-ð=2ð ð=2ð 2ð	=2ð
 ð=2ð 
ó=2ó~p÷(5
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