# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license import math from collections import defaultdict from functools import lru_cache from typing import Any, Dict, List, Optional, Tuple import cv2 import numpy as np from ultralytics import YOLO from ultralytics.solutions.config import SolutionConfig from ultralytics.utils import ASSETS_URL, LOGGER, ops from ultralytics.utils.checks import check_imshow, check_requirements from ultralytics.utils.plotting import Annotator class BaseSolution: """ 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) """ def __init__(self, is_cli: bool = False, **kwargs: Any) -> None: """ 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. """ self.CFG = vars(SolutionConfig().update(**kwargs)) self.LOGGER = LOGGER # Store logger object to be used in multiple solution classes check_requirements("shapely>=2.0.0") from shapely.geometry import LineString, Point, Polygon from shapely.prepared import prep self.LineString = LineString self.Polygon = Polygon self.Point = Point self.prep = prep self.annotator = None # Initialize annotator self.tracks = None self.track_data = None self.boxes = [] self.clss = [] self.track_ids = [] self.track_line = None self.masks = None self.r_s = None self.frame_no = -1 # Only for logging self.LOGGER.info(f"Ultralytics Solutions: ✅ {self.CFG}") self.region = self.CFG["region"] # Store region data for other classes usage self.line_width = self.CFG["line_width"] # Load Model and store additional information (classes, show_conf, show_label) if self.CFG["model"] is None: self.CFG["model"] = "yolo11n.pt" self.model = YOLO(self.CFG["model"]) self.names = self.model.names self.classes = self.CFG["classes"] self.show_conf = self.CFG["show_conf"] self.show_labels = self.CFG["show_labels"] self.device = self.CFG["device"] self.track_add_args = { # Tracker additional arguments for advance configuration k: self.CFG[k] for k in {"iou", "conf", "device", "max_det", "half", "tracker"} } # verbose must be passed to track method; setting it False in YOLO still logs the track information. if is_cli and self.CFG["source"] is None: d_s = "solutions_ci_demo.mp4" if "-pose" not in self.CFG["model"] else "solution_ci_pose_demo.mp4" self.LOGGER.warning(f"source not provided. using default source {ASSETS_URL}/{d_s}") from ultralytics.utils.downloads import safe_download safe_download(f"{ASSETS_URL}/{d_s}") # download source from ultralytics assets self.CFG["source"] = d_s # set default source # Initialize environment and region setup self.env_check = check_imshow(warn=True) self.track_history = defaultdict(list) self.profilers = ( ops.Profile(device=self.device), # track ops.Profile(device=self.device), # solution ) def adjust_box_label(self, cls: int, conf: float, track_id: Optional[int] = None) -> Optional[str]: """ Generate a formatted label for a bounding box. This method constructs a label string for a bounding box using the class index and confidence score. Optionally includes the track ID if provided. The label format adapts based on the display settings defined in `self.show_conf` and `self.show_labels`. Args: cls (int): The class index of the detected object. conf (float): The confidence score of the detection. track_id (int, optional): The unique identifier for the tracked object. Returns: (str | None): The formatted label string if `self.show_labels` is True; otherwise, None. """ name = ("" if track_id is None else f"{track_id} ") + self.names[cls] return (f"{name} {conf:.2f}" if self.show_conf else name) if self.show_labels else None def extract_tracks(self, im0: np.ndarray) -> None: """ 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) """ with self.profilers[0]: self.tracks = self.model.track( source=im0, persist=True, classes=self.classes, verbose=False, **self.track_add_args )[0] is_obb = self.tracks.obb is not None self.track_data = self.tracks.obb if is_obb else self.tracks.boxes # Extract tracks for OBB or object detection if self.track_data and self.track_data.is_track: self.boxes = (self.track_data.xyxyxyxy if is_obb else self.track_data.xyxy).cpu() self.clss = self.track_data.cls.cpu().tolist() self.track_ids = self.track_data.id.int().cpu().tolist() self.confs = self.track_data.conf.cpu().tolist() else: self.LOGGER.warning("no tracks found!") self.boxes, self.clss, self.track_ids, self.confs = [], [], [], [] def store_tracking_history(self, track_id: int, box) -> None: """ 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]) """ # Store tracking history self.track_line = self.track_history[track_id] self.track_line.append(tuple(box.mean(dim=0)) if box.numel() > 4 else (box[:4:2].mean(), box[1:4:2].mean())) if len(self.track_line) > 30: self.track_line.pop(0) def initialize_region(self) -> None: """Initialize the counting region and line segment based on configuration settings.""" if self.region is None: self.region = [(10, 200), (540, 200), (540, 180), (10, 180)] self.r_s = ( self.Polygon(self.region) if len(self.region) >= 3 else self.LineString(self.region) ) # region or line def display_output(self, plot_im: np.ndarray) -> None: """ 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. """ if self.CFG.get("show") and self.env_check: cv2.imshow("Ultralytics Solutions", plot_im) if cv2.waitKey(1) & 0xFF == ord("q"): cv2.destroyAllWindows() # Closes current frame window return def process(self, *args: Any, **kwargs: Any): """Process method should be implemented by each Solution subclass.""" def __call__(self, *args: Any, **kwargs: Any): """Allow instances to be called like a function with flexible arguments.""" with self.profilers[1]: result = self.process(*args, **kwargs) # Call the subclass-specific process method track_or_predict = "predict" if type(self).__name__ == "ObjectCropper" else "track" track_or_predict_speed = self.profilers[0].dt * 1e3 solution_speed = (self.profilers[1].dt - self.profilers[0].dt) * 1e3 # solution time = process - track result.speed = {track_or_predict: track_or_predict_speed, "solution": solution_speed} if self.CFG["verbose"]: self.frame_no += 1 LOGGER.info( f"{self.frame_no}: {result.plot_im.shape[0]}x{result.plot_im.shape[1]} {solution_speed:.1f}ms\n" f"Speed: {track_or_predict_speed:.1f}ms {track_or_predict}, " f"{solution_speed:.1f}ms solution per image at shape " f"(1, {getattr(self.model, 'ch', 3)}, {result.plot_im.shape[0]}, {result.plot_im.shape[1]})\n" ) return result class SolutionAnnotator(Annotator): """ 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 ... ) """ def __init__( self, im: np.ndarray, line_width: Optional[int] = None, font_size: Optional[int] = None, font: str = "Arial.ttf", pil: bool = False, example: str = "abc", ): """ 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. """ super().__init__(im, line_width, font_size, font, pil, example) def draw_region( self, reg_pts: Optional[List[Tuple[int, int]]] = None, color: Tuple[int, int, int] = (0, 255, 0), thickness: int = 5, ): """ 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. """ cv2.polylines(self.im, [np.array(reg_pts, dtype=np.int32)], isClosed=True, color=color, thickness=thickness) # Draw small circles at the corner points for point in reg_pts: cv2.circle(self.im, (point[0], point[1]), thickness * 2, color, -1) # -1 fills the circle def queue_counts_display( self, label: str, points: Optional[List[Tuple[int, int]]] = None, region_color: Tuple[int, int, int] = (255, 255, 255), txt_color: Tuple[int, int, int] = (0, 0, 0), ): """ 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. """ x_values = [point[0] for point in points] y_values = [point[1] for point in points] center_x = sum(x_values) // len(points) center_y = sum(y_values) // len(points) text_size = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.tf)[0] text_width = text_size[0] text_height = text_size[1] rect_width = text_width + 20 rect_height = text_height + 20 rect_top_left = (center_x - rect_width // 2, center_y - rect_height // 2) rect_bottom_right = (center_x + rect_width // 2, center_y + rect_height // 2) cv2.rectangle(self.im, rect_top_left, rect_bottom_right, region_color, -1) text_x = center_x - text_width // 2 text_y = center_y + text_height // 2 # Draw text cv2.putText( self.im, label, (text_x, text_y), 0, fontScale=self.sf, color=txt_color, thickness=self.tf, lineType=cv2.LINE_AA, ) def display_analytics( self, im0: np.ndarray, text: Dict[str, Any], txt_color: Tuple[int, int, int], bg_color: Tuple[int, int, int], margin: int, ): """ 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. """ horizontal_gap = int(im0.shape[1] * 0.02) vertical_gap = int(im0.shape[0] * 0.01) text_y_offset = 0 for label, value in text.items(): txt = f"{label}: {value}" text_size = cv2.getTextSize(txt, 0, self.sf, self.tf)[0] if text_size[0] < 5 or text_size[1] < 5: text_size = (5, 5) text_x = im0.shape[1] - text_size[0] - margin * 2 - horizontal_gap text_y = text_y_offset + text_size[1] + margin * 2 + vertical_gap rect_x1 = text_x - margin * 2 rect_y1 = text_y - text_size[1] - margin * 2 rect_x2 = text_x + text_size[0] + margin * 2 rect_y2 = text_y + margin * 2 cv2.rectangle(im0, (rect_x1, rect_y1), (rect_x2, rect_y2), bg_color, -1) cv2.putText(im0, txt, (text_x, text_y), 0, self.sf, txt_color, self.tf, lineType=cv2.LINE_AA) text_y_offset = rect_y2 @staticmethod @lru_cache(maxsize=256) def estimate_pose_angle(a: List[float], b: List[float], c: List[float]) -> float: """ 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. """ radians = math.atan2(c[1] - b[1], c[0] - b[0]) - math.atan2(a[1] - b[1], a[0] - b[0]) angle = abs(radians * 180.0 / math.pi) return angle if angle <= 180.0 else (360 - angle) def draw_specific_kpts( self, keypoints: List[List[float]], indices: Optional[List[int]] = None, radius: int = 2, conf_thresh: float = 0.25, ) -> np.ndarray: """ 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. """ indices = indices or [2, 5, 7] points = [(int(k[0]), int(k[1])) for i, k in enumerate(keypoints) if i in indices and k[2] >= conf_thresh] # Draw lines between consecutive points for start, end in zip(points[:-1], points[1:]): cv2.line(self.im, start, end, (0, 255, 0), 2, lineType=cv2.LINE_AA) # Draw circles for keypoints for pt in points: cv2.circle(self.im, pt, radius, (0, 0, 255), -1, lineType=cv2.LINE_AA) return self.im def plot_workout_information( self, display_text: str, position: Tuple[int, int], color: Tuple[int, int, int] = (104, 31, 17), txt_color: Tuple[int, int, int] = (255, 255, 255), ) -> int: """ 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. """ (text_width, text_height), _ = cv2.getTextSize(display_text, 0, fontScale=self.sf, thickness=self.tf) # Draw background rectangle cv2.rectangle( self.im, (position[0], position[1] - text_height - 5), (position[0] + text_width + 10, position[1] - text_height - 5 + text_height + 10 + self.tf), color, -1, ) # Draw text cv2.putText(self.im, display_text, position, 0, self.sf, txt_color, self.tf) return text_height def plot_angle_and_count_and_stage( self, angle_text: str, count_text: str, stage_text: str, center_kpt: List[int], color: Tuple[int, int, int] = (104, 31, 17), txt_color: Tuple[int, int, int] = (255, 255, 255), ): """ 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. """ # Format text angle_text, count_text, stage_text = f" {angle_text:.2f}", f"Steps : {count_text}", f" {stage_text}" # Draw angle, count and stage text angle_height = self.plot_workout_information( angle_text, (int(center_kpt[0]), int(center_kpt[1])), color, txt_color ) count_height = self.plot_workout_information( count_text, (int(center_kpt[0]), int(center_kpt[1]) + angle_height + 20), color, txt_color ) self.plot_workout_information( stage_text, (int(center_kpt[0]), int(center_kpt[1]) + angle_height + count_height + 40), color, txt_color ) def plot_distance_and_line( self, pixels_distance: float, centroids: List[Tuple[int, int]], line_color: Tuple[int, int, int] = (104, 31, 17), centroid_color: Tuple[int, int, int] = (255, 0, 255), ): """ 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. """ # Get the text size text = f"Pixels Distance: {pixels_distance:.2f}" (text_width_m, text_height_m), _ = cv2.getTextSize(text, 0, self.sf, self.tf) # Define corners with 10-pixel margin and draw rectangle cv2.rectangle(self.im, (15, 25), (15 + text_width_m + 20, 25 + text_height_m + 20), line_color, -1) # Calculate the position for the text with a 10-pixel margin and draw text text_position = (25, 25 + text_height_m + 10) cv2.putText( self.im, text, text_position, 0, self.sf, (255, 255, 255), self.tf, cv2.LINE_AA, ) cv2.line(self.im, centroids[0], centroids[1], line_color, 3) cv2.circle(self.im, centroids[0], 6, centroid_color, -1) cv2.circle(self.im, centroids[1], 6, centroid_color, -1) def display_objects_labels( self, im0: np.ndarray, text: str, txt_color: Tuple[int, int, int], bg_color: Tuple[int, int, int], x_center: float, y_center: float, margin: int, ): """ 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. """ text_size = cv2.getTextSize(text, 0, fontScale=self.sf, thickness=self.tf)[0] text_x = x_center - text_size[0] // 2 text_y = y_center + text_size[1] // 2 rect_x1 = text_x - margin rect_y1 = text_y - text_size[1] - margin rect_x2 = text_x + text_size[0] + margin rect_y2 = text_y + margin cv2.rectangle( im0, (int(rect_x1), int(rect_y1)), (int(rect_x2), int(rect_y2)), tuple(map(int, bg_color)), # Ensure color values are int -1, ) cv2.putText( im0, text, (int(text_x), int(text_y)), 0, self.sf, tuple(map(int, txt_color)), # Ensure color values are int self.tf, lineType=cv2.LINE_AA, ) def sweep_annotator( self, line_x: int = 0, line_y: int = 0, label: Optional[str] = None, color: Tuple[int, int, int] = (221, 0, 186), txt_color: Tuple[int, int, int] = (255, 255, 255), ): """ 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. """ # Draw the sweep line cv2.line(self.im, (line_x, 0), (line_x, line_y), color, self.tf * 2) # Draw label, if provided if label: (text_width, text_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, self.sf, self.tf) cv2.rectangle( self.im, (line_x - text_width // 2 - 10, line_y // 2 - text_height // 2 - 10), (line_x + text_width // 2 + 10, line_y // 2 + text_height // 2 + 10), color, -1, ) cv2.putText( self.im, label, (line_x - text_width // 2, line_y // 2 + text_height // 2), cv2.FONT_HERSHEY_SIMPLEX, self.sf, txt_color, self.tf, ) def visioneye( self, box: List[float], center_point: Tuple[int, int], color: Tuple[int, int, int] = (235, 219, 11), pin_color: Tuple[int, int, int] = (255, 0, 255), ): """ 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. """ center_bbox = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2) cv2.circle(self.im, center_point, self.tf * 2, pin_color, -1) cv2.circle(self.im, center_bbox, self.tf * 2, color, -1) cv2.line(self.im, center_point, center_bbox, color, self.tf) def circle_label( self, box: Tuple[float, float, float, float], label: str = "", color: Tuple[int, int, int] = (128, 128, 128), txt_color: Tuple[int, int, int] = (255, 255, 255), margin: int = 2, ): """ 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. """ if len(label) > 3: LOGGER.warning(f"Length of label is {len(label)}, only first 3 letters will be used for circle annotation.") label = label[:3] # Calculate the center of the box x_center, y_center = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2) # Get the text size text_size = cv2.getTextSize(str(label), cv2.FONT_HERSHEY_SIMPLEX, self.sf - 0.15, self.tf)[0] # Calculate the required radius to fit the text with the margin required_radius = int(((text_size[0] ** 2 + text_size[1] ** 2) ** 0.5) / 2) + margin # Draw the circle with the required radius cv2.circle(self.im, (x_center, y_center), required_radius, color, -1) # Calculate the position for the text text_x = x_center - text_size[0] // 2 text_y = y_center + text_size[1] // 2 # Draw the text cv2.putText( self.im, str(label), (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, self.sf - 0.15, self.get_txt_color(color, txt_color), self.tf, lineType=cv2.LINE_AA, ) def text_label( self, box: Tuple[float, float, float, float], label: str = "", color: Tuple[int, int, int] = (128, 128, 128), txt_color: Tuple[int, int, int] = (255, 255, 255), margin: int = 5, ): """ Draw a label with a background rectangle 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 rectangle (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 rectangle border. """ # Calculate the center of the bounding box x_center, y_center = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2) # Get the size of the text text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, self.sf - 0.1, self.tf)[0] # Calculate the top-left corner of the text (to center it) text_x = x_center - text_size[0] // 2 text_y = y_center + text_size[1] // 2 # Calculate the coordinates of the background rectangle rect_x1 = text_x - margin rect_y1 = text_y - text_size[1] - margin rect_x2 = text_x + text_size[0] + margin rect_y2 = text_y + margin # Draw the background rectangle cv2.rectangle(self.im, (rect_x1, rect_y1), (rect_x2, rect_y2), color, -1) # Draw the text on top of the rectangle cv2.putText( self.im, label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, self.sf - 0.1, self.get_txt_color(color, txt_color), self.tf, lineType=cv2.LINE_AA, ) class SolutionResults: """ A class to encapsulate the results of Ultralytics Solutions. This class is designed to store and manage various outputs generated by the solution pipeline, including counts, angles, workout stages, and other analytics data. It provides a structured way to access and manipulate results from different computer vision solutions such as object counting, pose estimation, and tracking analytics. Attributes: plot_im (np.ndarray): Processed image with counts, blurred, or other effects from solutions. in_count (int): The total number of "in" counts in a video stream. out_count (int): The total number of "out" counts in a video stream. classwise_count (Dict[str, int]): A dictionary containing counts of objects categorized by class. queue_count (int): The count of objects in a queue or waiting area. workout_count (int): The count of workout repetitions. workout_angle (float): The angle calculated during a workout exercise. workout_stage (str): The current stage of the workout. pixels_distance (float): The calculated distance in pixels between two points or objects. available_slots (int): The number of available slots in a monitored area. filled_slots (int): The number of filled slots in a monitored area. email_sent (bool): A flag indicating whether an email notification was sent. total_tracks (int): The total number of tracked objects. region_counts (Dict[str, int]): The count of objects within a specific region. speed_dict (Dict[str, float]): A dictionary containing speed information for tracked objects. total_crop_objects (int): Total number of cropped objects using ObjectCropper class. speed (Dict[str, float]): Performance timing information for tracking and solution processing. """ def __init__(self, **kwargs): """ Initialize a SolutionResults object with default or user-specified values. Args: **kwargs (Any): Optional arguments to override default attribute values. """ self.plot_im = None self.in_count = 0 self.out_count = 0 self.classwise_count = {} self.queue_count = 0 self.workout_count = 0 self.workout_angle = 0.0 self.workout_stage = None self.pixels_distance = 0.0 self.available_slots = 0 self.filled_slots = 0 self.email_sent = False self.total_tracks = 0 self.region_counts = {} self.speed_dict = {} # for speed estimation self.total_crop_objects = 0 self.speed = {} # Override with user-defined values self.__dict__.update(kwargs) def __str__(self) -> str: """ Return a formatted string representation of the SolutionResults object. Returns: (str): A string representation listing non-null attributes. """ attrs = { k: v for k, v in self.__dict__.items() if k != "plot_im" and v not in [None, {}, 0, 0.0, False] # Exclude `plot_im` explicitly } return ", ".join(f"{k}={v}" for k, v in attrs.items())