|hƑddlZddlmZddlmZddlmZmZmZm Z m Z ddl Z ddl Z ddlmZddlmZddlmZmZmZddlmZmZdd lmZGd d ZGd d eZGddZy)N) defaultdict) lru_cache)AnyDictListOptionalTuple)YOLO)SolutionConfig) ASSETS_URLLOGGERops) check_imshowcheck_requirements) Annotatorc eZdZdZddededdfdZddeded e ede e fd Z d e jddfd Zd eddfd ZddZde jddfdZdedefdZdedefdZy) BaseSolutiona A base class for managing Ultralytics Solutions. This class provides core functionality for various Ultralytics Solutions, including model loading, object tracking, and region initialization. It serves as the foundation for implementing specific computer vision solutions such as object counting, pose estimation, and analytics. Attributes: LineString: Class for creating line string geometries from shapely. Polygon: Class for creating polygon geometries from shapely. Point: Class for creating point geometries from shapely. prep: Prepared geometry function from shapely for optimized spatial operations. CFG (Dict[str, Any]): Configuration dictionary loaded from YAML file and updated with kwargs. LOGGER: Logger instance for solution-specific logging. annotator: Annotator instance for drawing on images. tracks: YOLO tracking results from the latest inference. track_data: Extracted tracking data (boxes or OBB) from tracks. boxes (List): Bounding box coordinates from tracking results. clss (List[int]): Class indices from tracking results. track_ids (List[int]): Track IDs from tracking results. confs (List[float]): Confidence scores from tracking results. track_line: Current track line for storing tracking history. masks: Segmentation masks from tracking results. r_s: Region or line geometry object for spatial operations. frame_no (int): Current frame number for logging purposes. region (List[Tuple[int, int]]): List of coordinate tuples defining region of interest. line_width (int): Width of lines used in visualizations. model (YOLO): Loaded YOLO model instance. names (Dict[int, str]): Dictionary mapping class indices to class names. classes (List[int]): List of class indices to track. show_conf (bool): Flag to show confidence scores in annotations. show_labels (bool): Flag to show class labels in annotations. device (str): Device for model inference. track_add_args (Dict[str, Any]): Additional arguments for tracking configuration. env_check (bool): Flag indicating whether environment supports image display. track_history (defaultdict): Dictionary storing tracking history for each object. profilers (Tuple): Profiler instances for performance monitoring. Methods: adjust_box_label: Generate formatted label for bounding box. extract_tracks: Apply object tracking and extract tracks from input image. store_tracking_history: Store object tracking history for given track ID and bounding box. initialize_region: Initialize counting region and line segment based on configuration. display_output: Display processing results including frames or saved results. process: Process method to be implemented by each Solution subclass. Examples: >>> solution = BaseSolution(model="yolo11n.pt", region=[(0, 0), (100, 0), (100, 100), (0, 100)]) >>> solution.initialize_region() >>> image = cv2.imread("image.jpg") >>> solution.extract_tracks(image) >>> solution.display_output(image) is_clikwargsreturnNc Vttjdi||_t|_t dddlm}m}m }ddl m }||_||_ ||_||_ d|_ d|_ d|_g|_g|_g|_d|_d|_d|_d|_|jj-d|j|jd|_|jd |_|jd d |jd <t3|jd |_|j4j6|_|jd |_|jd |_|jd|_|jd|_dDcic]}||j|c}|_ |ro|jd`d|jd vrdnd}|jjCdtDd|ddl#m$} | tDd|||jd<tKd|_&tOtP|_)tUjV|j>tUjV|j>f|_,ycc}w)a Initialize the BaseSolution class with configuration settings and YOLO model. Args: is_cli (bool): Enable CLI mode if set to True. **kwargs (Any): Additional configuration parameters that override defaults. zshapely>=2.0.0r) LineStringPointPolygon)prepNuUltralytics Solutions: ✅ region line_widthmodelz yolo11n.ptclasses show_conf show_labelsdevice>iouconfhalfr#max_dettrackersourcez-posezsolutions_ci_demo.mp4zsolution_ci_pose_demo.mp4z*source not provided. using default source /) safe_downloadT)warn)r#)-varsr updateCFGr rshapely.geometryrrrshapely.preparedr annotatortracks track_databoxesclss track_ids track_linemasksr_sframe_noinforrr rnamesr r!r"r#track_add_argswarningr ultralytics.utils.downloadsr+r env_checkrlist track_historyrProfile profilers) selfrrrrrrkd_sr+s ^/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/solutions/solutions.py__init__zBaseSolution.__init__IsH/(//9&9: +,??)$         6txxjABhhx( ((<0 88G  $ ,DHHW $((7+, ZZ%% xx * +.88M2hhx( %\  Atxx{N  dhhx(0-4DHHWr"r!)rGrLr%rMnames rJadjust_box_labelzBaseSolution.adjust_box_labelsO &xjNdjjoMEIEUEU4&$s$_D_[__im0c |jd5|jjd|d|jdd|jd|_ddd|j j du}|r|j j n|j j|_|jr |jjr|r|jjn|jjj|_|jjjj|_|jj j#jj|_|jj&jj|_y|j*j-dggggf\|_|_|_|_y#1swYxYw)aC Apply object tracking and extract tracks from an input image or frame. Args: im0 (np.ndarray): The input image or frame. Examples: >>> solution = BaseSolution() >>> frame = cv2.imread("path/to/image.jpg") >>> solution.extract_tracks(frame) rTF)r)persistr verboseNzno tracks found!r-)rFrtrackr r?r4obbr6r5is_trackxyxyxyxyxyxycpurLtolistr7idintr8r%confsr r@)rGrUis_obbs rJextract_trackszBaseSolution.extract_tracksst^^A  *$****D$,,QUQdQdDK ,-3$++//9J9J ??t776<$//22$//BVBV[[]DJ++//188:DI!__//33599;BBDDN--113::4:  s >G44G>cz|j||_|jj|jdkDrt |j dn+|dddj |dddj ft |jdkDr|jjdyy)aR Store the tracking history of an object. This method updates the tracking history for a given object by appending the center point of its bounding box to the track line. It maintains a maximum of 30 points in the tracking history. Args: track_id (int): The unique identifier for the tracked object. box (List[float]): The bounding box coordinates of the object in the format [x1, y1, x2, y2]. Examples: >>> solution = BaseSolution() >>> solution.store_tracking_history(1, [100, 200, 300, 400]) r)dimN)rDr9appendnumeltuplemeanlenpop)rGrMboxs rJstore_tracking_historyz#BaseSolution.store_tracking_historys ,,X6 quSXX!X_5sSWTUVWSWy~~O_adefghijejakapaparNst t " $ OO   " %rTc|j gd|_t|jdk\r!|j|j|_y|j|j|_y)zPInitialize the counting region and line segment based on configuration settings.N)) )ru)rv)rtrw)rrorrr;)rGs rJinitialize_regionzBaseSolution.initialize_regionsT ;; HDK),T[[)9Q)>DLL % DHOOTXT_T_D` rTplot_imc|jjdr]|jrPtjd|tj ddzt dk(rtjyyyy)aU Display the results of the processing, which could involve showing frames, printing counts, or saving results. This method is responsible for visualizing the output of the object detection and tracking process. It displays the processed frame with annotations, and allows for user interaction to close the display. Args: plot_im (np.ndarray): The image or frame that has been processed and annotated. Examples: >>> solution = BaseSolution() >>> frame = cv2.imread("path/to/image.jpg") >>> solution.display_output(frame) Notes: - This method will only display output if the 'show' configuration is set to True and the environment supports image display. - The display can be closed by pressing the 'q' key. showzUltralytics SolutionsriqN)r0getrBcv2imshowwaitKeyorddestroyAllWindows)rGrzs rJdisplay_outputzBaseSolution.display_outputs^( 88<< DNN JJ. 8{{1~$C0%%'1%3 rTargscy)z?Process method should be implemented by each Solution subclass.Nr-)rGrrs rJprocesszBaseSolution.processsrTc|jd5|j|i|}dddt|jdk(rdnd}|jdjdz}|jdj|jdjz dz}||d|i_|j d r|xjdz c_tj|jd |jjdd |jjdd |d d|d d|d|d dt|jddd|jjdd|jjdd|S#1swYZxYw)zEAllow instances to be called like a function with flexible arguments.riN ObjectCropperpredictrYrg@@solutionrX: xrPz.1fz ms Speed: zms , z#ms solution per image at shape (1, chrxz) )rFrtype__name__dtspeedr0r<r r=rzshapegetattrr)rGrrresulttrack_or_predicttrack_or_predict_speedsolution_speeds rJ__call__zBaseSolution.__call__s ^^A  3!T\\4262F 3(,T (;(;(N9T[!%!2!5!5!;..+..1B1E1EEL(*@*n] 88I  MMQ M KK==/FNN$8$8$;#R>RST>U=VVWXfgjWkl05S9I8J"!#&'tzz434Bv~~7K7KA7N6OrRXR`R`RfRfghRiQjjmo    3 3s E;;F)F)N)rN)r __module__ __qualname____doc__boolrrKrafloatrstrrSnpndarrayrdrrryrrrr-rTrJrrs4l? t? s? t? B`C`u` `YabeYf`&O"**OO8#s#D#* bjjT4NSNCNcSrTrc$eZdZdZ dd edee e eefde eeefde eeeffdZdej deeefde eeefde eeefdef dZeedde ede ede edefdZ d?de e edee ed ed!edej f d"Z d@d#ed$e eefd e eeefde eeefdef d%Z d@d&ed'ed(ed)e ed e eeefde eeeff d*Z dAd+ed,e e eefd-e eeefd.e eeeffd/Zdej dede eeefde eeefd0ed1edefd2Z dBd3ed4ed eed e eeefde eeeff d5Z dCd6e ed7e eefd e eeefd8e eeeffd9Z dDd6e eeeefd ed e eeefde eeefdef d:Z dEd6e eeeefd ed e eeefde eeefdef d;ZxZS)FSolutionAnnotatora A specialized annotator class for visualizing and analyzing computer vision tasks. This class extends the base Annotator class, providing additional methods for drawing regions, centroids, tracking trails, and visual annotations for Ultralytics Solutions. It offers comprehensive visualization capabilities for various computer vision applications including object detection, tracking, pose estimation, and analytics. Attributes: im (np.ndarray): The image being annotated. line_width (int): Thickness of lines used in annotations. font_size (int): Size of the font used for text annotations. font (str): Path to the font file used for text rendering. pil (bool): Whether to use PIL for text rendering. example (str): An example attribute for demonstration purposes. Methods: draw_region: Draw a region using specified points, colors, and thickness. queue_counts_display: Display queue counts in the specified region. display_analytics: Display overall statistics for parking lot management. estimate_pose_angle: Calculate the angle between three points in an object pose. draw_specific_kpts: Draw specific keypoints on the image. plot_workout_information: Draw a labeled text box on the image. plot_angle_and_count_and_stage: Visualize angle, step count, and stage for workout monitoring. plot_distance_and_line: Display the distance between centroids and connect them with a line. display_objects_labels: Annotate bounding boxes with object class labels. sweep_annotator: Visualize a vertical sweep line and optional label. visioneye: Map and connect object centroids to a visual "eye" point. circle_label: Draw a circular label within a bounding box. text_label: Draw a rectangular label within a bounding box. Examples: >>> annotator = SolutionAnnotator(image) >>> annotator.draw_region([(0, 0), (100, 100)], color=(0, 255, 0), thickness=5) >>> annotator.display_analytics( ... image, text={"Available Spots": 5}, txt_color=(0, 0, 0), bg_color=(255, 255, 255), margin=10 ... ) imr font_sizefontpilexamplec.t|||||||y)a Initialize the SolutionAnnotator class with an image for annotation. Args: im (np.ndarray): The image to be annotated. line_width (int, optional): Line thickness for drawing on the image. font_size (int, optional): Font size for text annotations. font (str): Path to the font file. pil (bool): Indicates whether to use PIL for rendering text. example (str): An example parameter for demonstration purposes. N)superrK)rGrrrrrr __class__s rJrKzSolutionAnnotator.__init__-s( ZD#wGrTreg_ptscolor thicknessctj|jtj|tj gd|||D]0}tj |j|d|df|dz|d2y) aB Draw a region or line on the image. Args: reg_pts (List[Tuple[int, int]], optional): Region points (for line 2 points, for region 4+ points). color (Tuple[int, int, int]): RGB color value for the region. thickness (int): Line thickness for drawing the region. )dtypeT)isClosedrrrrirhrN)r polylinesrrarrayint32circle)rGrrrpoints rJ draw_regionzSolutionAnnotator.draw_regionCsl dgg ABTY^jst PE JJtwwq58 4i!mUB O PrTlabelpoints region_color txt_colorc |Dcgc]}|d }}|Dcgc]}|d }}t|t|z}t|t|z} tj|d|j|j d} | d} | d} | dz} | dz}|| dzz | |dzz f}|| dzz| |dzzf}tj |j|||d|| dzz }| | dzz}tj|j|||fd|j||j tjycc}wcc}w) a Display queue counts on an image centered at the points with customizable font size and colors. Args: label (str): Queue counts label. points (List[Tuple[int, int]], optional): Region points for center point calculation to display text. region_color (Tuple[int, int, int]): RGB queue region color. txt_color (Tuple[int, int, int]): RGB text display color. rri fontScalerrhr)rrrlineTypeN) sumror getTextSizesftf rectanglerputTextLINE_AA)rGrrrrrx_valuesy_valuescenter_xcenter_y text_size text_width text_height rect_width rect_height rect_top_leftrect_bottom_righttext_xtext_ys rJqueue_counts_displayz&SolutionAnnotator.queue_counts_displayWsR +11E!H11*01E!H11x=CK/x=CK/OOE1477STUV q\ l "_ !B& !J!O3X q@P5PQ % a7KSTDT9TU dgg}.?rRJ!O+K1,, GG  V  gggg[[ '21s D6 D;rUtextbg_colormarginc t|jddz}t|jddz}d}|jD] \} } | d| } tj| d|j |j d} | ddks| ddkrd} |jd| dz |dzz |z } || dz|dzz|z}| |dzz }|| dz |dzz }| | dz|dzz}||dzz}tj|||f||f|d tj|| | |fd|j ||j tj |} y ) a Display the overall statistics for parking lots, object counter etc. Args: im0 (np.ndarray): Inference image. text (Dict[str, Any]): Labels dictionary. txt_color (Tuple[int, int, int]): Display color for text foreground. bg_color (Tuple[int, int, int]): Display color for text background. margin (int): Gap between text and rectangle for better display. rig{Gz?rg{Gz?r)rrrhrrN) raritemsrrrrrrr)rGrUrrrrhorizontal_gap vertical_gap text_y_offsetrvaluetxtrrrrect_x1rect_y1rect_x2rect_y2s rJdisplay_analyticsz#SolutionAnnotator.display_analyticssy$SYYq\D01399Q<$./   JJL $LE5G2eW%CQA!DI|a9QMFvz)Gy|+fqj8Gy|+fqj8Gvz)G MM#1GW3ExQS T KKS66"2Atww 477]`]h]h i#M $rT)maxsizeabcrctj|d|dz |d|dz tj|d|dz |d|dz z }t|dztjz }|dkr|Sd|z S)a Calculate the angle between three points for workout monitoring. Args: a (List[float]): The coordinates of the first point. b (List[float]): The coordinates of the second point (vertex). c (List[float]): The coordinates of the third point. Returns: (float): The angle in degrees between the three points. rirgf@ih)mathatan2abspi)rrrradiansangles rJestimate_pose_anglez%SolutionAnnotator.estimate_pose_angles**QqTAaD[!A$1+6AaD1Q4KQRSTQUXYZ[X\Q\9]]GeOdgg-.u9S5[9rT keypointsindicesradius conf_threshc |xsgd}t|Dcgc]/\}}||vs |d|k\st|dt|df1}}}t|dd|ddD]8\}} tj|j || ddtj :|D]5} tj|j | |d dtj 7|j Scc}}w) a1 Draw specific keypoints for gym steps counting. Args: keypoints (List[List[float]]): Keypoints data to be plotted, each in format [x, y, confidence]. indices (List[int], optional): Keypoint indices to be plotted. radius (int): Keypoint radius. conf_thresh (float): Confidence threshold for keypoints. Returns: (np.ndarray): Image with drawn keypoints. Notes: Keypoint format: [x, y] or [x, y, confidence]. Modifies self.im in-place. )rhrrhrriNrrr}rr)rrr}) enumerateraziprlinerrr) rGrrrrirHrstartendpts rJdraw_specific_kptsz$SolutionAnnotator.draw_specific_kptss.&Y5>y5IrTQQRY\^_`a^bfq^q3qt9c!A$i(rrfSbk6!":6 PJE3 HHTWWeS+q3;; O P SB JJtwwFKckk R Swwss C!C!C! display_textpositionc tj|d|j|j\\}}}tj|j |d|d|z dz f|d|zdz|d|z dz |zdz|jzf|dtj |j ||d|j||j|S)a Draw workout text with a background on the image. Args: display_text (str): The text to be displayed. position (Tuple[int, int]): Coordinates (x, y) on the image where the text will be placed. color (Tuple[int, int, int]): Text background color. txt_color (Tuple[int, int, int]): Text foreground color. Returns: (int): The height of the text. rrrirrtr)rrrrrrr)rGrrrrrr_s rJplot_workout_informationz*SolutionAnnotator.plot_workout_informations&(+|QRVRYRYeielel'm$![1 GG a[(1+ 3a7 8 a[: % *HQK+,E,IK,WZ\,\_c_f_f,f g     DGG\8QDGGTrT angle_text count_text stage_text center_kptcVd|dd|d|}}}|j|t|dt|df||}|j|t|dt|d|zdzf||}|j|t|dt|d|z|zdzf||y) a  Plot the pose angle, count value, and step stage for workout monitoring. Args: angle_text (str): Angle value for workout monitoring. count_text (str): Counts value for workout monitoring. stage_text (str): Stage decision for workout monitoring. center_kpt (List[int]): Centroid pose index for workout monitoring. color (Tuple[int, int, int]): Text background color. txt_color (Tuple[int, int, int]): Text foreground color. rPrQzSteps : rrir(N)r ra) rGr r r r rr angle_height count_heights rJplot_angle_and_count_and_stagez0SolutionAnnotator.plot_angle_and_count_and_stages*01C0@-AXj\CZ^_`j_k\l J 44 Z]+SA-?@% 44 Z]+SA-?,-NQS-STV[]f  %% Z]+SA-?,-NQ]-]`b-bcejlu rTpixels_distance centroids line_colorcentroid_colorc dd|d}tj|d|j|j\\}}}tj|j dd|zdzd|zdzf|ddd|zd zf} tj |j || d|jd |jtjtj|j |d|d |d tj|j |dd |dtj|j |d d |dy)a Plot the distance and line between two centroids on the frame. Args: pixels_distance (float): Pixels distance between two bbox centroids. centroids (List[Tuple[int, int]]): Bounding box centroids data. line_color (Tuple[int, int, int]): Distance line color. centroid_color (Tuple[int, int, int]): Bounding box centroid color. zPixels Distance: rQr)rrrrrtr}r}r}rirxN) rrrrrrrrrr) rGrrrrr text_width_m text_height_mr text_positions rJplot_distance_and_linez(SolutionAnnotator.plot_distance_and_line#s"#?3"78+.??4DGGTWW+U(%}q dggx"|*;b*@"}BTWYBY)Z\fhjkR-/"45  GG   GG  GG KK  )A, ! j!D 477IaL!^R@ 477IaL!^R@rTx_centery_centerc Ttj|d|j|jd}||ddzz } ||ddzz} | |z } | |dz |z } | |dz|z} | |z}tj|t | t | ft | t |ft tt |dtj||t | t | fd|jt tt ||jtjy)aA Display the bounding boxes labels in parking management app. Args: im0 (np.ndarray): Inference image. text (str): Object/class name. txt_color (Tuple[int, int, int]): Display color for text foreground. bg_color (Tuple[int, int, int]): Display color for text background. x_center (float): The x position center point for bounding box. y_center (float): The y position center point for bounding box. margin (int): The gap between text and rectangle for better display. rrrhrirrN) rrrrrrarmmaprr)rGrUrrrr r!rrrrrrrrs rJdisplay_objects_labelsz(SolutionAnnotator.display_objects_labelsKs,OOD!tww$''RSTU IaLA--IaLA--6/9Q<'&09Q<'&06/  \3w< ( \3w< ( #c8$ %      [#f+ & GG #c9% & GG[[ rTline_xline_yc Dtj|j|df||f||jdz|rtj|tj |j |j\\}}}tj|j||dzz dz |dz|dzz dz f||dzzdz|dz|dzzdzf|dtj|j|||dzz |dz|dzzftj |j ||jyy)a Draw a sweep annotation line and an optional label. Args: line_x (int): The x-coordinate of the sweep line. line_y (int): The y-coordinate limit of the sweep line. label (str, optional): Text label to be drawn in center of sweep line. If None, no label is drawn. color (Tuple[int, int, int]): RGB color for the line and label background. txt_color (Tuple[int, int, int]): RGB color for the label text. rrhrtrN) rrrrrFONT_HERSHEY_SIMPLEXrrr) rGr%r&rrrrrrs rJsweep_annotatorz!SolutionAnnotator.sweep_annotator|s'& 61+'7! L +.??5#BZBZ\`\c\ceielel+m ( %Zq MM*/)B.! kQ>N0NQS0ST*/)B.! kQ>N0NQS0ST   KK*/)6Q;9I+IJ((  rTrq center_point pin_colorczt|d|dzdz t|d|dzdz f}tj|j||jdz|dtj|j||jdz|dtj |j||||jy)a Perform pinpoint human-vision eye mapping and plotting. Args: box (List[float]): Bounding box coordinates in format [x1, y1, x2, y2]. center_point (Tuple[int, int]): Center point for vision eye view. color (Tuple[int, int, int]): Object centroid and line color. pin_color (Tuple[int, int, int]): Visioneye point color. rrhrirxrN)rarrrrr)rGrqr*rr+ center_bboxs rJ visioneyezSolutionAnnotator.visioneyes 3q6CF?a/0#s1vA!6K2LL  477L$''A+y"E 477K1eR@ , UDGGDrTc t|dkDr'tjdt|d|dd}t|d|dzdz t|d|dzdz }}t j t |tj|jdz |jd}t|ddz|ddzzd zdz |z} t j|j||f| |d ||ddzz } ||ddzz} t j|jt || | ftj|jdz |j|||jtj y) a Draw a label with a background circle centered within a given bounding box. Args: box (Tuple[float, float, float, float]): The bounding box coordinates (x1, y1, x2, y2). label (str): The text label to be displayed. color (Tuple[int, int, int]): The background color of the circle (B, G, R). txt_color (Tuple[int, int, int]): The color of the text (R, G, B). margin (int): The margin between the text and the circle border. rxzLength of label is z:, only first 3 letters will be used for circle annotation.Nrrhrig333333?g?rr)ror r@rarrrr(rrrrr get_txt_colorr) rGrqrrrrr r!rrequired_radiusrrs rJ circle_labelzSolutionAnnotator.circle_labelsj$ u:> NN0U z*SolutionResults.__str__..Xs>1A3as>s)rVrjoin)rGrHr[attrss rJ__str__zSolutionResults.__str__Lsq ++- 1I~!D"ae+D"D qD  yy> >>>  sA"N)rrrrrKrr_r-rTrJrErEs6%8 ? ?rTrE)r collectionsr functoolsrtypingrrrrr rnumpyr ultralyticsr ultralytics.solutions.configr ultralytics.utilsr r rultralytics.utils.checksrrultralytics.utils.plottingrrrrEr-rTrJrisV #33 755E0qqhK K \D?D?rT