Ë
    Fêñiÿ1  ã                  ój   — d dl mZ d dlmZ d dlmZ d dlZd dlZd dl	m
Z
mZ d dlmZ  G d„ de
«      Zy)	é    )Úannotations)Úcycle)ÚAnyN)ÚBaseSolutionÚSolutionResults)Úplt_settingsc                  óZ   ‡ — e Zd ZdZ e«       dˆ fd„«       Zdd„Z	 d	 	 	 	 	 	 	 dd„Zˆ xZS )	Ú	Analyticsa­  A class for creating and updating various types of charts for visual analytics.

    This class extends BaseSolution to provide functionality for generating line, bar, pie, and area charts based on
    object detection and tracking data.

    Attributes:
        type (str): The type of analytics chart to generate ('line', 'bar', 'pie', or 'area').
        x_label (str): Label for the x-axis.
        y_label (str): Label for the y-axis.
        bg_color (str): Background color of the chart frame.
        fg_color (str): Foreground color of the chart frame.
        title (str): Title of the chart window.
        max_points (int): Maximum number of data points to display on the chart.
        fontsize (int): Font size for text display.
        color_cycle (cycle): Cyclic iterator for chart colors.
        total_counts (int): Total count of detected objects (used for line charts).
        clswise_count (dict[str, int]): Dictionary for class-wise object counts.
        fig (Figure): Matplotlib figure object for the chart.
        ax (Axes): Matplotlib axes object for the chart.
        canvas (FigureCanvasAgg): Canvas for rendering the chart.
        lines (dict): Dictionary to store line objects for area charts.
        color_mapping (dict[str, str]): Dictionary mapping class labels to colors for consistent visualization.

    Methods:
        process: Process image data and update the chart.
        update_graph: Update the chart with new data points.

    Examples:
        >>> analytics = Analytics(analytics_type="line")
        >>> frame = cv2.imread("image.jpg")
        >>> results = analytics.process(frame, frame_number=1)
        >>> cv2.imshow("Analytics", results.plot_im)
    c                ó>  •— t        ‰| �  di |¤Ž ddlm} ddlm} ddlm} | j                  d   | _	        | j                  dv rdnd| _
        d	| _        d
| _        d| _        d| _        d| _        d| _        | j                  d   }t#        g d¢«      | _        d| _        i | _        |j+                  dd«      | _        d| _        | j                  dv r¤i | _         || j                  |¬«      | _         || j2                  «      | _        | j2                  j7                  d| j                  ¬«      | _        | j                  dk(  r1| j8                  j;                  g g d| j<                  ¬«      \  | _        yy| j                  dv r™|jA                  || j                  ¬«      \  | _        | _         || j2                  «      | _        | j8                  jC                  | j                  «       i | _"        | j                  dk(  r| j8                  jG                  d«       yyy)zSInitialize Analytics class with various chart types for visual data representation.r   N)ÚFigureCanvasAgg)ÚFigureÚanalytics_type>   ÚbarÚpieÚClasseszFrame#zTotal Countsz#F3F3F3z#111E68zUltralytics Solutionsé-   é   Úfigsize©z#DD00BAz#042AFFz#FF4447z#7D24FFz#BD00FFÚupdate_everyé   >   ÚareaÚline)Ú	facecolorr   éo   )r   r   Úcyan)ÚcolorÚ	linewidth)r   r   r   Úequal© )$ÚsuperÚ__init__Úmatplotlib.pyplotÚpyplotÚmatplotlib.backends.backend_aggr   Úmatplotlib.figurer   ÚCFGÚtypeÚx_labelÚy_labelÚbg_colorÚfg_colorÚtitleÚ
max_pointsÚfontsizer   Úcolor_cycleÚtotal_countsÚclswise_countÚgetr   Úlast_plot_imÚlinesÚfigÚcanvasÚadd_subplotÚaxÚplotÚ
line_widthr   ÚsubplotsÚset_facecolorÚcolor_mappingÚaxis)ÚselfÚkwargsÚpltr   r   r   Ú	__class__s         €úa/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/solutions/analytics.pyr"   zAnalytics.__init__2   s¿  ø€ ô 	‰ÑÑ"˜6Ò"å'ÝCÝ,à—H‘HÐ-Ñ.ˆŒ	Ø$(§I¡I°Ñ$?‘yÀXˆŒØ%ˆŒð "ˆŒØ!ˆŒØ,ˆŒ
ØˆŒØˆŒØ—(‘(˜9Ñ%ˆÜ Ò!XÓYˆÔàˆÔØˆÔØ"ŸJ™J ~°rÓ:ˆÔØ ˆÔð �9‰9Ð(Ñ(ØˆDŒJÙ¨¯©¸wÔGˆDŒHÙ)¨$¯(©(Ó3ˆDŒKØ—h‘h×*Ñ*¨3¸$¿-¹-Ð*ÓHˆDŒGØ�y‰y˜FÒ"Ø#Ÿw™wŸ|™|¨B°¸&ÈDÏOÉO˜|Ó\‘�•ð #à�Y‰Y˜.Ñ(à #§¡°WÈÏÉ Ó VÑˆDŒH�d”gÙ)¨$¯(©(Ó3ˆDŒKØ�G‰G×!Ñ! $§-¡-Ô0Ø!#ˆDÔà�y‰y˜EÒ!Ø—‘—‘˜WÕ%ð "ð )ó    c                óÚ  ‡ — ‰ j                  |«       ‰ j                  dk(  ru‰ j                  D ]  }‰ xj                  dz  c_        Œ |‰ j                  z  dk(  xs ‰ j
                  du }|r‰ j                  |¬«      ‰ _        ‰ j
                  }d‰ _        n«‰ j                  dv r„ddlm}  |ˆ fd„‰ j                  D «       «      ‰ _
        |‰ j                  z  dk(  xs ‰ j
                  du }|r-‰ j                  |‰ j                  ‰ j                  ¬	«      ‰ _        ‰ j
                  }nt        d
‰ j                  › d�«      ‚t        |t        ‰ j                  «      ‰ j                  ¬«      S )aÚ  Process image data and run object tracking to update analytics charts.

        Args:
            im0 (np.ndarray): Input image for processing.
            frame_number (int): Video frame number for plotting the data.

        Returns:
            (SolutionResults): Contains processed image `plot_im`, 'total_tracks' (int, total number of tracked objects)
                and 'classwise_count' (dict, per-class object count).

        Raises:
            ValueError: If an unsupported chart type is specified.

        Examples:
            >>> analytics = Analytics(analytics_type="line")
            >>> frame = np.zeros((480, 640, 3), dtype=np.uint8)
            >>> results = analytics.process(frame, frame_number=1)
        r   é   r   N)Úframe_number>   r   r   r   )ÚCounterc              3  óN   •K  — | ]  }‰j                   t        |«         –— Œ y ­w)N)ÚnamesÚint)Ú.0Úclsr@   s     €rD   ú	<genexpr>z$Analytics.process.<locals>.<genexpr>~   s   øè ø€ Ò(SÀ#¨¯©´C¸³HÕ)=Ñ(Sùs   ƒ"%)rH   Ú
count_dictr:   zUnsupported analytics_type='z)'. Supported types: line, bar, pie, area.)Úplot_imÚtotal_tracksÚclasswise_count)Úextract_tracksr(   Úboxesr1   r   r4   Úupdate_graphÚcollectionsrI   Úclssr2   Ú
ValueErrorr   ÚlenÚ	track_ids)r@   Úim0rH   Ú_Úupdate_requiredrQ   rI   s   `      rD   ÚprocesszAnalytics.process_   sY  ø€ ð& 	×Ñ˜CÔ Ø�9‰9˜ÒØ—Z‘Zò '�Ø×!Ò! QÑ&Ö!ð'à*¨T×->Ñ->Ñ>À!ÑCÒ`Àt×GXÑGXÐ\`ÐG`ˆOÙØ$(×$5Ñ$5À<Ð$5Ó$P�Ô!Ø×'Ñ'ˆGØ !ˆDÕØ�Y‰YÐ0Ñ0Ý+á!(Ó(SÈÏÉÔ(SÓ!SˆDÔØ*¨T×->Ñ->Ñ>À!ÑCÒ`Àt×GXÑGXÐ\`ÐG`ˆOÙØ$(×$5Ñ$5Ø!-¸$×:LÑ:LÐSW×S\ÑS\ð %6ó %�Ô!ð ×'Ñ'‰GäÐ;¸D¿I¹I¸;ÐFoÐpÓqÐqô  w¼SÀÇÁÓ=PÐbf×btÑbtÔuÐurE   c                ó\  — |�€Gt        j                  | j                  j                  «       t	        |«      «      }t        j                  | j                  j                  «       t	        | j                  «      «      }t        |«      | j                  kD  r || j                   d || j                   d }}| j                  j                  ||«       | j                  j                  d«       | j                  j                  d«       | j                  j                  d«       | j                  j                  | j                  dz  «       �n¯t        |j!                  «       «      }t        |j#                  «       «      }|dk(  �rBt%        g d¢«      }| j&                  j(                  r'| j&                  j(                  d   j                  «       nt        j*                  g «      }|D �	ci c]  }	|	t        j*                  g «      “Œ }
}	| j&                  j(                  rIt-        | j&                  j(                  |j!                  «       «      D ]  \  }}	|j                  «       |
|	<   Œ t        j                  |t	        |«      «      }t        |«      }|D ]i  }	t        j                  |
|	   t	        ||	   «      «      |
|	<   t        |
|	   «      |k  sŒ=t        j.                  |
|	   d|t        |
|	   «      z
  f«      |
|	<   Œk t        |«      | j                  kD  r|d	d }|D ]  }	|
|	   d	d |
|	<   Œ | j&                  j1                  «        |
j3                  «       D ]k  \  }	}t5        |«      }| j&                  j7                  |||d
¬«       | j&                  j9                  |||| j                  d| j                  dz  |	› d�¬«       Œm �n5|dk(  �r]| j&                  j1                  «        |D ]3  }|| j:                  vsŒt5        | j<                  «      | j:                  |<   Œ5 |D �cg c]  }| j:                  |   ‘Œ }}| j&                  j?                  |||¬«      }t-        ||«      D ]i  \  }}| j&                  jA                  |jC                  «       |jE                  «       dz  z   |jG                  «       tI        |«      dd| jJ                  ¬«       Œk t-        ||«      D ]  \  }}|j                  |«       Œ | j&                  jM                  dd| jJ                  | jJ                  ¬«       nÒ|dk(  rÍtO        |«      }|D �cg c]
  }||z  dz  ‘Œ }}| j&                  j1                  «        d}| j&                  jQ                  |||d| jJ                  id¬«      \  }}t-        ||«      D ��cg c]  \  }}|› d|d›d�‘Œ }}}| j&                  jM                  ||d d!d"¬#«       | jR                  jU                  d$d%¬&«       | j&                  jW                  d'«       | j&                  jY                  d(d)d*d*¬+«       | j&                  j[                  | j\                  | jJ                  | j^                  ¬,«       | j&                  ja                  | jb                  | jJ                  | j^                  d-z
  ¬,«       | j&                  je                  | jf                  | jJ                  | j^                  d-z
  ¬,«       | j&                  jM                  dd| jh                  | jh                  ¬«      }|jk                  «       D ]  }|j                  | jJ                  «       Œ | j&                  jm                  «        | j&                  jo                  «        | jp                  js                  «        t        j*                  | jp                  jt                  jw                  «       «      }ty        jz                  |dd…dd…dd-…f   tx        j|                  «      }| j                  |«       |S c c}	w c c}w c c}w c c}}w ).að  Update the graph with new data for single or multiple classes.

        Args:
            frame_number (int): The current frame number.
            count_dict (dict[str, int], optional): Dictionary with class names as keys and counts as values for multiple
                classes. If None, updates a single line graph.
            plot (str): Type of the plot. Options are 'line', 'bar', 'pie', or 'area'.

        Returns:
            (np.ndarray): Updated image containing the graph.

        Examples:
            >>> analytics = Analytics(analytics_type="bar")
            >>> frame_num = 10
            >>> results_dict = {"person": 5, "car": 3}
            >>> updated_image = analytics.update_graph(frame_num, results_dict, plot="bar")
        NÚCountsz#7b0068Ú*é   r   r   r   rG   gš™™™™™á?)r   ÚalphaÚoz Data Points)r   r   ÚmarkerÚ
markersizeÚlabelr   )r   é   ÚcenterÚbottom)ÚhaÚvar   z
upper lefté   )Úlocr/   r   Ú	edgecolorr   éd   éZ   r   )ÚlabelsÚ
startangleÚ	textpropsÚautopctz (z.1fz%)r   zcenter left)rG   r   ç      à?rG   )r-   ro   Úbbox_to_anchorgš™™™™™¹?g      è?)ÚleftÚrightz#f0f0f0Tz--rw   )Ú	linestyler   rd   )r   r/   é   )@ÚnpÚappendr   Ú	get_xdataÚfloatÚ	get_ydatar1   rZ   r.   Úset_dataÚ	set_labelÚ	set_colorÚ
set_markerÚset_markersizer;   ÚlistÚkeysÚvaluesr   r9   r5   ÚarrayÚzipÚpadÚclearÚitemsÚnextÚfill_betweenr:   r>   r0   r   ÚtextÚget_xÚ	get_widthÚ
get_heightÚstrr,   ÚlegendÚsumr   r6   Úsubplots_adjustr=   ÚgridÚ	set_titler-   r/   Ú
set_xlabelr)   Ú
set_ylabelr*   r+   Ú	get_textsÚrelimÚautoscale_viewr7   ÚdrawÚrendererÚbuffer_rgbaÚcv2ÚcvtColorÚCOLOR_RGBA2BGRÚdisplay_output)r@   rH   rP   r:   Úx_dataÚy_datars   Úcountsr0   ÚkeyÚy_data_dictr   Ú
max_lengthr   rh   ÚcolorsÚbarsr   ÚcountÚtotalÚsizeÚpercentagesÚstart_angleÚwedgesr]   Ú
percentageÚlegend_labelsr–   r‘   r\   s                                 rD   rV   zAnalytics.update_graph‹   sÑ  € ð( Ñä—Y‘Y˜tŸy™y×2Ñ2Ó4´e¸LÓ6IÓJˆFÜ—Y‘Y˜tŸy™y×2Ñ2Ó4´e¸D×<MÑ<MÓ6NÓOˆFä�6‹{˜TŸ_™_Ò,Ø!'¨¯©Ð(8Ð(:Ð!;¸VÀTÇ_Á_ÐDTÐDVÐ=W˜�à�I‰I×Ñ˜v vÔ.Ø�I‰I×Ñ Ô)Ø�I‰I×Ñ 	Ô*Ø�I‰I× Ñ  Ô%Ø�I‰I×$Ñ$ T§_¡_°qÑ%8Ö9ä˜*Ÿ/™/Ó+Ó,ˆFÜ˜*×+Ñ+Ó-Ó.ˆFØ�v‹~Ü#Ò$[Ó\�à9=¿¹¿º˜Ÿ™Ÿ™ qÑ)×3Ñ3Ô5ÌBÏHÉHÐUWËL�Ø<FÖG°S˜s¤B§H¡H¨R£LÑ0ÐG�ÐGØ—7‘7—=’=Ü%(¨¯©¯©¸
¿¹Ó8IÓ%Jò <™	˜˜cØ+/¯>©>Ó+;˜ CÒ(ð<ô Ÿ™ 6¬5°Ó+>Ó?�Ü  ›[�
Ø%ò m�CÜ')§y¡y°¸SÑ1AÄ5ÈÐTWÉÓCYÓ'Z�K Ñ$Ü˜; sÑ+Ó,¨zÓ9Ü+-¯6©6°+¸cÑ2BÀQÈ
ÔUXÐYdÐehÑYiÓUjÑHjÐDkÓ+l˜ CÒ(ðmô �v“; §¡Ò0Ø# A B˜Z�FØ)ò @˜Ø+6°sÑ+;¸A¸BÐ+?˜ CÒ(ð@ð —‘—‘”Ø#.×#4Ñ#4Ó#6ò ‘K�C˜Ü  Ó-�EØ—G‘G×(Ñ(¨°¸uÈDÐ(ÔQØ—G‘G—L‘LØØØ#Ø"&§/¡/Ø"Ø#'§?¡?°QÑ#6Ø!$  \Ð2ð !õ òð ˜“Ø—‘—‘”Ø#ò K�EØ D×$6Ñ$6Ò6Ü48¸×9IÑ9IÓ4J˜×*Ñ*¨5Ò1ðKð BHÖH¸˜$×,Ñ,¨UÓ3ÐH�ÐHØ—w‘w—{‘{ 6¨6¸�{Ó@�Ü"% d¨FÓ"3ò ‘J�C˜Ø—G‘G—L‘LØŸ	™	› c§m¡m£o¸Ñ&9Ñ9ØŸ™Ó(Ü˜E›
Ø#Ø#Ø"Ÿm™mð !õ ðô #& d¨FÓ"3ò )‘J�C˜Ø—M‘M %Õ(ð)à—‘—‘ <¸"ÈÏÉÐae×anÑan�ÕoØ˜’Ü˜F›�Ø>DÖE°d˜t e™|¨cÓ1ÐE�ÐEØ—‘—‘”à �à ŸG™GŸK™KØ 6°kÈgÐW[×WdÑWdÐMeÐosð (ó ‘	�˜ô Z]Ð]cÐepÓYq× rÑDUÀEÈ: E 7¨"¨Z¸Ð,<¸BÒ!?Ð r�Ñ rð —‘—‘˜v }¸IÈ=Ðiw�ÔxØ—‘×(Ñ(¨c¸Ð(Ô>ð 	�‰×Ñ˜iÔ(Ø�‰�‰�T T°SÀˆÔDØ�‰×Ñ˜$Ÿ*™*¨D¯M©MÀDÇMÁMÐÔRØ�‰×Ñ˜4Ÿ<™<¨t¯}©}ÀtÇ}Á}ÐWXÑGXÐÔYØ�‰×Ñ˜4Ÿ<™<¨t¯}©}ÀtÇ}Á}ÐWXÑGXÐÔYð —‘—‘ L¸2ÈÏÉÐbf×boÑbo�ÓpˆØ×$Ñ$Ó&ò 	*ˆDØ�N‰N˜4Ÿ=™=Õ)ð	*ð 	�‰�‰ŒØ�‰×ÑÔ Ø�‰×ÑÔÜ�h‰h�t—{‘{×+Ñ+×7Ñ7Ó9Ó:ˆÜ�l‰l˜3šq¢! R a R˜x™=¬#×*<Ñ*<Ó=ˆØ×Ñ˜CÔ àˆ
ùòk HùòD Iùò" Fùó !ss   Ç&`Ð!`Ô<`#Ö'`()rA   r   ÚreturnÚNone)r\   ú
np.ndarrayrH   rL   r·   r   )Nr   )rH   rL   rP   zdict[str, int] | Noner:   r•   r·   r¹   )	Ú__name__Ú
__module__Ú__qualname__Ú__doc__r   r"   r_   rV   Ú__classcell__)rC   s   @rD   r
   r
      sX   ø„ ñ ñD ƒ^ô*&ó ð*&óX*vðZ X^ð}Øð}Ø-Bð}ØQTð}à	÷}rE   r
   )Ú
__future__r   Ú	itertoolsr   Útypingr   r£   Únumpyr}   Úultralytics.solutions.solutionsr   r   Úultralytics.utilsr   r
   r    rE   rD   ú<module>rÅ      s*   ðõ #å Ý ã 
Û ç IÝ *ôy�õ yrE   