o W h.@sdddlmZddlZddlmZddlZddlm Z ddl m Z ddl mZmZGdddeZdS))cycleN)FigureCanvasAgg)Figure) BaseSolutionSolutionResultscs2eZdZdZfddZddZd dd ZZS) 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 (FigureCanvas): 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 sDtjdi||jd|_|jdvrdnd|_d|_d|_d|_d|_d |_ d |_ d }t gd |_ d |_ i|_|jdvrpi|_t|j|d|_t|j|_|jjd|jd|_|jdkrn|jjggd|jd\|_dSdS|jdvrtj||jd\|_|_t|j|_|j|ji|_|jdkr|jddSdSdS)zSInitialize Analytics class with various chart types for visual data representation.analytics_type>barpieClasseszFrame#z Total Countsz#F3F3F3z#111E68zUltralytics Solutions-)g)@g@z#DD00BAz#042AFFz#FF4447z#7D24FFz#BD00FFr>arealine) facecolorfigsizeo)rrcyan)color linewidth)rrr equalN)super__init__CFGtypex_labely_labelbg_colorfg_colortitle max_pointsfontsizer color_cycle total_counts clswise_countlinesrfig FigureCanvascanvas add_subplotaxplot line_widthrpltsubplots set_facecolor color_mappingaxis)selfkwargsr __class__rS/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/solutions/analytics.pyr2s<       zAnalytics.__init__cCs|||jdkr!|jD] }|jd7_q |j|d}d|_nD|jdvr]i|_|jD]%}|jt||jvrG|j|jt|d7<q,d|j|jt|<q,|j||j|jd}nt |jdt |t |j |jdS) 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) r) frame_numberr>r r r)r: count_dictr-u chart is not supported ❌)plot_im total_tracksclasswise_count) extract_tracksrboxesr% update_graphr&clssnamesintModuleNotFoundErrorrlen track_ids)r4im0r:_r<clsrrr8processXs      zAnalytics.processNrc s|durWtjt|}tjtj}t|jkr3|j d|j d}}j ||j dj dj dj jdnt|}t|}|dkr*tgd}jjrzjjdntg}d d |D} jjrtjj|D] \} } | | | <qt|t|}t|} |D]*} t| | t|| | | <t| | | krt| | d| t| | f| | <qt|jkr|d d}|D] } | | d d| | <qj| D]'\} }t|} jj||| d d jj||| jdjd| ddq|dkrj|D]}|jvrFtjj|<q6fdd|D}jj|||d}t||D]\}}jj |!|"d|#t$|ddj%dq_t||D] \}}| |qjj&ddj%j%d|dkrt'|fdd|D}d}jjj(|||dj%idd \}}d!dt||D}jj&||d"d#d$d%j)j*d&d'd(j+d)jj,j-j%j.d*jj/j0j%j.d+d*jj1j2j%j.d+d*jj&ddj3j3d}|4D] }| j%qj5j6j78tj7j9:}t;<|dddddd+ft;j=}>||S),a Update the graph with new data for single or multiple classes. Args: frame_number (int): The current frame number. count_dict (Dict[str, int] | None): 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*rrrcSsi|]}|tgqSr)nparray).0keyrrr8 z*Analytics.update_graph..r9gffffff?)ralphaoz Data Points)rrmarker markersizelabelr csg|]}j|qSr)r2)rQrY)r4rr8 sz*Analytics.update_graph..)rcenterbottom)havarz upper left )locr#r edgecolorr csg|]}|dqS)dr)rQsize)totalrr8rZrTZr)labels startangle textpropsautopctcSs"g|] \}}|d|ddqS)z (z.1fz%)r)rQrY percentagerrr8rZs"r z center left)r9rg?r9)r!rabbox_to_anchorg?g?)leftrightz#f0f0f0)rr#)?rOappendr get_xdatafloat get_ydatar%rFr"set_data set_label set_color set_markerset_markersizer.listkeysvaluesrr,r'rPzippadclearitemsnext fill_betweenr-r2r$r textget_x get_width get_heightstrr legendsumr r(subplots_adjustr1 set_titler!r# set_xlabelr set_ylabelrr get_textsrelimautoscale_viewr*drawrenderer buffer_rgbacv2cvtColorCOLOR_RGBA2BGRdisplay_output)r4r:r;r-x_datay_datargcountsr$ y_data_dictrrR max_lengthrrYcolorsbarsr count percentages start_anglewedgesrI legend_labelsrrrHr)r4rer8rAs"       " $               $ zAnalytics.update_graph)Nr)__name__ __module__ __qualname____doc__rrKrA __classcell__rrr6r8rs  #&(r) itertoolsrrmatplotlib.pyplotpyplotr/numpyrOmatplotlib.backends.backend_aggrr)matplotlib.figurerultralytics.solutions.solutionsrrrrrrr8s