|h2ZddlmZddlmZmZmZddlZddlZddl m Z m Z Gdde Z y))cycle)AnyDictOptionalN) BaseSolutionSolutionResultsc eZdZdZdeddffd Zdejdede fdZ d ded e e e efd e dejfd ZxZS) 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) kwargsreturnNc >t|di|ddlm}ddlm}ddlm}|jd|_ |jdvrdnd|_ d |_ d |_ d |_ d |_d |_d|_|jd}t#gd|_d|_i|_|j+dd|_d|_|jdvri|_||j||_||j2|_|j2j7d|j|_|jdk(r1|j8j;ggd|j<\|_yy|jdvr|jA||j\|_|_||j2|_|j8jC|ji|_"|jdk(r|j8jGdyyy)zSInitialize Analytics class with various chart types for visual data representation.rN)FigureCanvasAgg)Figureanalytics_type>barpieClasseszFrame#z Total Countsz#F3F3F3z#111E68zUltralytics Solutions-figsizez#DD00BAz#042AFFz#FF4447z#7D24FFz#BD00FF update_every>arealine) facecolorro)rrcyan)color linewidth)rrrequal)$super__init__matplotlib.pyplotpyplotmatplotlib.backends.backend_aggrmatplotlib.figurerCFGtypex_labely_labelbg_colorfg_colortitle max_pointsfontsizer color_cycle total_counts clswise_countgetr last_plot_imlinesfigcanvas add_subplotaxplot line_widthrsubplots set_facecolor color_mappingaxis)selfr pltrrr __class__s ^/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/solutions/analytics.pyr$zAnalytics.__init__0s "6"'C,HH-. $(II$?yX % " ! ,  ((9% !XY"JJ~r:  99( (DJ wGDH)$((3DKhh**3$--*HDGyyF"#ww||B&DOO|\ # YY. ( # W V DHdg)$((3DK GG ! !$-- 0!#D yyE! W%")im0 frame_numbercj|jdk(rujD]}xjdz c_|jzdk(xsj du}|rj |_j }d_njdvrddlm}|fdjD_ |jzdk(xsj du}|r-j |jj _j }ntjd t|tjj S) a 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: ModuleNotFoundError: 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) rrN)rH>rrr)Counterc3NK|]}jt|yw)N)namesint).0clsrBs rE z$Analytics.process..|s(S#CH)=(Ss"%)rH count_dictr<u chart is not supported ❌)plot_im total_tracksclasswise_count)extract_tracksr*boxesr3rr6 update_graph collectionsrKclssr4ModuleNotFoundErrorrlen track_ids)rBrGrH_update_requiredrSrKs` rEprocesszAnalytics.process\sV( C 99 ZZ '!!Q&! 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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") NCountsz#7b0068*rrrrJg?)ralphaoz Data Points)rr marker markersizelabelr)rcenterbottom)havarz upper left )locr1r edgecolorrdZr)labels startangle textpropsautopctz (z.1fz%)rz center left)rJr?rJ)r/rpbbox_to_anchorg?g?)leftrightz#f0f0f0Tz--rx) linestyler re)rr1)@npappendr get_xdatafloat get_ydatar3r\r0set_data set_label set_color set_markerset_markersizer=listkeysvaluesrr;r7arrayzippadclearitemsnext fill_betweenr<r@r2rtextget_x get_width get_heightstrr.legendsumrr8subplots_adjustr?grid set_titler/r1 set_xlabelr+ set_ylabelr,r- get_textsrelimautoscale_viewr9drawrenderer buffer_rgbacv2cvtColorCOLOR_RGBA2BGRdisplay_output)rBrHrRr<x_datay_datartcountsr2key y_data_dictr max_lengthrricolorsbarsrcounttotalsize percentages start_anglewedgesr^ percentage legend_labelsrrrGs rErXzAnalytics.update_graphs*  YYtyy224eL6IJFYYtyy224eD>+; C(<65+>? 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