Reference for `ultralytics/solutions/config.py`

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/config.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!

ultralytics.solutions.config.SolutionConfig `dataclass`

SolutionConfig(
    source: Optional[str] = None,
    model: Optional[str] = None,
    classes: Optional[List[int]] = None,
    show_conf: bool = True,
    show_labels: bool = True,
    region: Optional[List[Tuple[int, int]]] = None,
    colormap: Optional[int] = cv2.COLORMAP_DEEPGREEN,
    show_in: bool = True,
    show_out: bool = True,
    up_angle: float = 145.0,
    down_angle: int = 90,
    kpts: List[int] = lambda: [6, 8, 10](),
    analytics_type: str = "line",
    figsize: Optional[Tuple[int, int]] = (12.8, 7.2),
    blur_ratio: float = 0.5,
    vision_point: Tuple[int, int] = (20, 20),
    crop_dir: str = "cropped-detections",
    json_file: str = None,
    line_width: int = 2,
    records: int = 5,
    fps: float = 30.0,
    max_hist: int = 5,
    meter_per_pixel: float = 0.05,
    max_speed: int = 120,
    show: bool = False,
    iou: float = 0.7,
    conf: float = 0.25,
    device: Optional[str] = None,
    max_det: int = 300,
    half: bool = False,
    tracker: str = "botsort.yaml",
    verbose: bool = True,
    data: str = "images",
)

Manages configuration parameters for Ultralytics Vision AI solutions.

The SolutionConfig class serves as a centralized configuration container for all the Ultralytics solution modules: https://docs.ultralytics.com/solutions/#solutions. It leverages Python dataclass for clear, type-safe, and maintainable parameter definitions.

Attributes:

Name	Type	Description
`source`	`Optional[str]`	Path to the input source (video, RTSP, etc.). Only usable with Solutions CLI.
`model`	`Optional[str]`	Path to the Ultralytics YOLO model to be used for inference.
`classes`	`Optional[List[int]]`	List of class indices to filter detections.
`show_conf`	`bool`	Whether to show confidence scores on the visual output.
`show_labels`	`bool`	Whether to display class labels on visual output.
`region`	`Optional[List[Tuple[int, int]]]`	Polygonal region or line for object counting.
`colormap`	`Optional[int]`	OpenCV colormap constant for visual overlays (e.g., cv2.COLORMAP_JET).
`show_in`	`bool`	Whether to display count number for objects entering the region.
`show_out`	`bool`	Whether to display count number for objects leaving the region.
`up_angle`	`float`	Upper angle threshold used in pose-based workouts monitoring.
`down_angle`	`int`	Lower angle threshold used in pose-based workouts monitoring.
`kpts`	`List[int]`	Keypoint indices to monitor, e.g., for pose analytics.
`analytics_type`	`str`	Type of analytics to perform ("line", "area", "bar", "pie", etc.).
`figsize`	`Optional[Tuple[int, int]]`	Size of the matplotlib figure used for analytical plots (width, height).
`blur_ratio`	`float`	Ratio used to blur objects in the video frames (0.0 to 1.0).
`vision_point`	`Tuple[int, int]`	Reference point for directional tracking or perspective drawing.
`crop_dir`	`str`	Directory path to save cropped detection images.
`json_file`	`str`	Path to a JSON file containing data for parking areas.
`line_width`	`int`	Width for visual display i.e. bounding boxes, keypoints, counts.
`records`	`int`	Number of detection records to send email alerts.
`fps`	`float`	Frame rate (Frames Per Second) for speed estimation calculation.
`max_hist`	`int`	Maximum number of historical points or states stored per tracked object for speed estimation.
`meter_per_pixel`	`float`	Scale for real-world measurement, used in speed or distance calculations.
`max_speed`	`int`	Maximum speed limit (e.g., km/h or mph) used in visual alerts or constraints.
`show`	`bool`	Whether to display the visual output on screen.
`iou`	`float`	Intersection-over-Union threshold for detection filtering.
`conf`	`float`	Confidence threshold for keeping predictions.
`device`	`Optional[str]`	Device to run inference on (e.g., 'cpu', '0' for CUDA GPU).
`max_det`	`int`	Maximum number of detections allowed per video frame.
`half`	`bool`	Whether to use FP16 precision (requires a supported CUDA device).
`tracker`	`str`	Path to tracking configuration YAML file (e.g., 'botsort.yaml').
`verbose`	`bool`	Enable verbose logging output for debugging or diagnostics.
`data`	`str`	Path to image directory used for similarity search.

Methods:

Name	Description
`update`	Update the configuration with user-defined keyword arguments and raise error on invalid keys.

Examples:

>>> from ultralytics.solutions.config import SolutionConfig
>>> cfg = SolutionConfig(model="yolo11n.pt", region=[(0, 0), (100, 0), (100, 100), (0, 100)])
>>> cfg.update(show=False, conf=0.3)
>>> print(cfg.model)

update

update(**kwargs)

Update configuration parameters with new values provided as keyword arguments.

Source code in ultralytics/solutions/config.py

def update(self, **kwargs):
    """Update configuration parameters with new values provided as keyword arguments."""
    for key, value in kwargs.items():
        if hasattr(self, key):
            setattr(self, key, value)
        else:
            raise ValueError(
                f"❌ {key} is not a valid solution argument, available arguments here: https://docs.ultralytics.com/solutions/#solutions-arguments"
            )
    return self

📅 Created 10 days ago ✏️ Updated 10 days ago

Reference for ultralytics/solutions/config.py

ultralytics.solutions.config.SolutionConfig dataclass

update

Reference for `ultralytics/solutions/config.py`

ultralytics.solutions.config.SolutionConfig `dataclass`