title stringclasses 1
value | text stringlengths 30 426k | id stringlengths 27 30 |
|---|---|---|
ultralytics/trackers/bot_sort.py/BOTSORT/get_kalmanfilter
class BOTSORT:
def get_kalmanfilter(self):
"""Returns an instance of KalmanFilterXYWH for predicting and updating object states in the tracking process."""
return KalmanFilterXYWH() | negative_train_query659_01666 | |
ultralytics/trackers/bot_sort.py/BOTSORT/init_track
class BOTSORT:
def init_track(self, dets, scores, cls, img=None):
"""Initialize object tracks using detection bounding boxes, scores, class labels, and optional ReID features."""
if len(dets) == 0:
return []
if self.args.with_reid a... | negative_train_query659_01667 | |
ultralytics/trackers/bot_sort.py/BOTSORT/get_dists
class BOTSORT:
def get_dists(self, tracks, detections):
"""Calculates distances between tracks and detections using IoU and optionally ReID embeddings."""
dists = matching.iou_distance(tracks, detections)
dists_mask = dists > self.proximity_thre... | negative_train_query659_01668 | |
ultralytics/trackers/bot_sort.py/BOTSORT/multi_predict
class BOTSORT:
def multi_predict(self, tracks):
"""Predicts the mean and covariance of multiple object tracks using a shared Kalman filter."""
BOTrack.multi_predict(tracks) | negative_train_query659_01669 | |
ultralytics/trackers/bot_sort.py/BOTSORT/reset
class BOTSORT:
def reset(self):
"""Resets the BOTSORT tracker to its initial state, clearing all tracked objects and internal states."""
super().reset()
self.gmc.reset_params() | negative_train_query659_01670 | |
ultralytics/trackers/basetrack.py/BaseTrack/__init__
class BaseTrack:
def __init__(self):
"""
Initializes a new track with a unique ID and foundational tracking attributes.
Examples:
Initialize a new track
>>> track = BaseTrack()
>>> print(track.track_id)
... | negative_train_query659_01671 | |
ultralytics/trackers/basetrack.py/BaseTrack/end_frame
class BaseTrack:
def end_frame(self):
"""Returns the ID of the most recent frame where the object was tracked."""
return self.frame_id | negative_train_query659_01672 | |
ultralytics/trackers/basetrack.py/BaseTrack/next_id
class BaseTrack:
def next_id():
"""Increment and return the next unique global track ID for object tracking."""
BaseTrack._count += 1
return BaseTrack._count | negative_train_query659_01673 | |
ultralytics/trackers/basetrack.py/BaseTrack/activate
class BaseTrack:
def activate(self, *args):
"""Activates the track with provided arguments, initializing necessary attributes for tracking."""
raise NotImplementedError | negative_train_query659_01674 | |
ultralytics/trackers/basetrack.py/BaseTrack/predict
class BaseTrack:
def predict(self):
"""Predicts the next state of the track based on the current state and tracking model."""
raise NotImplementedError | negative_train_query659_01675 | |
ultralytics/trackers/basetrack.py/BaseTrack/update
class BaseTrack:
def update(self, *args, **kwargs):
"""Updates the track with new observations and data, modifying its state and attributes accordingly."""
raise NotImplementedError | negative_train_query659_01676 | |
ultralytics/trackers/basetrack.py/BaseTrack/mark_lost
class BaseTrack:
def mark_lost(self):
"""Marks the track as lost by updating its state to TrackState.Lost."""
self.state = TrackState.Lost | negative_train_query659_01677 | |
ultralytics/trackers/basetrack.py/BaseTrack/mark_removed
class BaseTrack:
def mark_removed(self):
"""Marks the track as removed by setting its state to TrackState.Removed."""
self.state = TrackState.Removed | negative_train_query659_01678 | |
ultralytics/trackers/basetrack.py/BaseTrack/reset_id
class BaseTrack:
def reset_id():
"""Reset the global track ID counter to its initial value."""
BaseTrack._count = 0 | negative_train_query659_01679 | |
ultralytics/trackers/utils/gmc.py/GMC/__init__
class GMC:
def __init__(self, method: str = "sparseOptFlow", downscale: int = 2) -> None:
"""
Initialize a Generalized Motion Compensation (GMC) object with tracking method and downscale factor.
Args:
method (str): The method used for t... | negative_train_query659_01680 | |
ultralytics/trackers/utils/gmc.py/GMC/apply
class GMC:
def apply(self, raw_frame: np.array, detections: list = None) -> np.array:
"""
Apply object detection on a raw frame using the specified method.
Args:
raw_frame (np.ndarray): The raw frame to be processed, with shape (H, W, C).
... | negative_train_query659_01681 | |
ultralytics/trackers/utils/gmc.py/GMC/applyEcc
class GMC:
def applyEcc(self, raw_frame: np.array) -> np.array:
"""
Apply the ECC (Enhanced Correlation Coefficient) algorithm to a raw frame for motion compensation.
Args:
raw_frame (np.ndarray): The raw frame to be processed, with sha... | negative_train_query659_01682 | |
ultralytics/trackers/utils/gmc.py/GMC/applyFeatures
class GMC:
def applyFeatures(self, raw_frame: np.array, detections: list = None) -> np.array:
"""
Apply feature-based methods like ORB or SIFT to a raw frame.
Args:
raw_frame (np.ndarray): The raw frame to be processed, with shape ... | negative_train_query659_01683 | |
ultralytics/trackers/utils/gmc.py/GMC/applySparseOptFlow
class GMC:
def applySparseOptFlow(self, raw_frame: np.array) -> np.array:
"""
Apply Sparse Optical Flow method to a raw frame.
Args:
raw_frame (np.ndarray): The raw frame to be processed, with shape (H, W, C).
Returns... | negative_train_query659_01684 | |
ultralytics/trackers/utils/gmc.py/GMC/reset_params
class GMC:
def reset_params(self) -> None:
"""Reset the internal parameters including previous frame, keypoints, and descriptors."""
self.prevFrame = None
self.prevKeyPoints = None
self.prevDescriptors = None
self.initializedFirs... | negative_train_query659_01685 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYAH/__init__
class KalmanFilterXYAH:
def __init__(self):
"""
Initialize Kalman filter model matrices with motion and observation uncertainty weights.
The Kalman filter is initialized with an 8-dimensional state space (x, y, a, h, vx, vy, ... | negative_train_query659_01686 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYAH/initiate
class KalmanFilterXYAH:
def initiate(self, measurement: np.ndarray) -> tuple:
"""
Create a track from an unassociated measurement.
Args:
measurement (ndarray): Bounding box coordinates (x, y, a, h) with center pos... | negative_train_query659_01687 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYAH/predict
class KalmanFilterXYAH:
def predict(self, mean: np.ndarray, covariance: np.ndarray) -> tuple:
"""
Run Kalman filter prediction step.
Args:
mean (ndarray): The 8-dimensional mean vector of the object state at the pr... | negative_train_query659_01688 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYAH/project
class KalmanFilterXYAH:
def project(self, mean: np.ndarray, covariance: np.ndarray) -> tuple:
"""
Project state distribution to measurement space.
Args:
mean (ndarray): The state's mean vector (8 dimensional array)... | negative_train_query659_01689 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYAH/multi_predict
class KalmanFilterXYAH:
def multi_predict(self, mean: np.ndarray, covariance: np.ndarray) -> tuple:
"""
Run Kalman filter prediction step for multiple object states (Vectorized version).
Args:
mean (ndarray):... | negative_train_query659_01690 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYAH/update
class KalmanFilterXYAH:
def update(self, mean: np.ndarray, covariance: np.ndarray, measurement: np.ndarray) -> tuple:
"""
Run Kalman filter correction step.
Args:
mean (ndarray): The predicted state's mean vector (8... | negative_train_query659_01691 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYAH/gating_distance
class KalmanFilterXYAH:
def gating_distance(
self,
mean: np.ndarray,
covariance: np.ndarray,
measurements: np.ndarray,
only_position: bool = False,
metric: str = "maha",
) -> np.ndarray:
... | negative_train_query659_01692 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYWH/initiate
class KalmanFilterXYWH:
def initiate(self, measurement: np.ndarray) -> tuple:
"""
Create track from unassociated measurement.
Args:
measurement (ndarray): Bounding box coordinates (x, y, w, h) with center position... | negative_train_query659_01693 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYWH/predict
class KalmanFilterXYWH:
def predict(self, mean, covariance) -> tuple:
"""
Run Kalman filter prediction step.
Args:
mean (ndarray): The 8-dimensional mean vector of the object state at the previous time step.
... | negative_train_query659_01694 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYWH/project
class KalmanFilterXYWH:
def project(self, mean, covariance) -> tuple:
"""
Project state distribution to measurement space.
Args:
mean (ndarray): The state's mean vector (8 dimensional array).
covariance... | negative_train_query659_01695 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYWH/multi_predict
class KalmanFilterXYWH:
def multi_predict(self, mean, covariance) -> tuple:
"""
Run Kalman filter prediction step (Vectorized version).
Args:
mean (ndarray): The Nx8 dimensional mean matrix of the object stat... | negative_train_query659_01696 | |
ultralytics/trackers/utils/kalman_filter.py/KalmanFilterXYWH/update
class KalmanFilterXYWH:
def update(self, mean, covariance, measurement) -> tuple:
"""
Run Kalman filter correction step.
Args:
mean (ndarray): The predicted state's mean vector (8 dimensional).
covarianc... | negative_train_query659_01697 | |
ultralytics/trackers/utils/matching.py/linear_assignment
def linear_assignment(cost_matrix: np.ndarray, thresh: float, use_lap: bool = True) -> tuple:
"""
Perform linear assignment using either the scipy or lap.lapjv method.
Args:
cost_matrix (np.ndarray): The matrix containing cost values for assi... | negative_train_query659_01698 | |
ultralytics/trackers/utils/matching.py/iou_distance
def iou_distance(atracks: list, btracks: list) -> np.ndarray:
"""
Compute cost based on Intersection over Union (IoU) between tracks.
Args:
atracks (list[STrack] | list[np.ndarray]): List of tracks 'a' or bounding boxes.
btracks (list[STra... | negative_train_query659_01699 | |
ultralytics/trackers/utils/matching.py/embedding_distance
def embedding_distance(tracks: list, detections: list, metric: str = "cosine") -> np.ndarray:
"""
Compute distance between tracks and detections based on embeddings.
Args:
tracks (list[STrack]): List of tracks, where each track contains embe... | negative_train_query659_01700 | |
ultralytics/trackers/utils/matching.py/fuse_score
def fuse_score(cost_matrix: np.ndarray, detections: list) -> np.ndarray:
"""
Fuses cost matrix with detection scores to produce a single similarity matrix.
Args:
cost_matrix (np.ndarray): The matrix containing cost values for assignments, with shape... | negative_train_query659_01701 | |
ultralytics/cfg/__init__.py/cfg2dict
def cfg2dict(cfg):
"""
Converts a configuration object to a dictionary.
Args:
cfg (str | Path | Dict | SimpleNamespace): Configuration object to be converted. Can be a file path,
a string, a dictionary, or a SimpleNamespace object.
Returns:
... | negative_train_query659_01702 | |
ultralytics/cfg/__init__.py/get_cfg
def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG_DICT, overrides: Dict = None):
"""
Load and merge configuration data from a file or dictionary, with optional overrides.
Args:
cfg (str | Path | Dict | SimpleNamespace): Configuration data sou... | negative_train_query659_01703 | |
ultralytics/cfg/__init__.py/check_cfg
def check_cfg(cfg, hard=True):
"""
Checks configuration argument types and values for the Ultralytics library.
This function validates the types and values of configuration arguments, ensuring correctness and converting
them if necessary. It checks for specific key... | negative_train_query659_01704 | |
ultralytics/cfg/__init__.py/get_save_dir
def get_save_dir(args, name=None):
"""
Returns the directory path for saving outputs, derived from arguments or default settings.
Args:
args (SimpleNamespace): Namespace object containing configurations such as 'project', 'name', 'task',
'mode', ... | negative_train_query659_01705 | |
ultralytics/cfg/__init__.py/_handle_deprecation
def _handle_deprecation(custom):
"""
Handles deprecated configuration keys by mapping them to current equivalents with deprecation warnings.
Args:
custom (Dict): Configuration dictionary potentially containing deprecated keys.
Examples:
>... | negative_train_query659_01706 | |
ultralytics/cfg/__init__.py/check_dict_alignment
def check_dict_alignment(base: Dict, custom: Dict, e=None):
"""
Checks alignment between custom and base configuration dictionaries, handling deprecated keys and providing error
messages for mismatched keys.
Args:
base (Dict): The base configurat... | negative_train_query659_01707 | |
ultralytics/cfg/__init__.py/merge_equals_args
def merge_equals_args(args: List[str]) -> List[str]:
"""
Merges arguments around isolated '=' in a list of strings and joins fragments with brackets.
This function handles the following cases:
1. ['arg', '=', 'val'] becomes ['arg=val']
2. ['arg=', 'val'... | negative_train_query659_01708 | |
ultralytics/cfg/__init__.py/handle_yolo_hub
def handle_yolo_hub(args: List[str]) -> None:
"""
Handles Ultralytics HUB command-line interface (CLI) commands for authentication.
This function processes Ultralytics HUB CLI commands such as login and logout. It should be called when executing a
script with... | negative_train_query659_01709 | |
ultralytics/cfg/__init__.py/handle_yolo_settings
def handle_yolo_settings(args: List[str]) -> None:
"""
Handles YOLO settings command-line interface (CLI) commands.
This function processes YOLO settings CLI commands such as reset and updating individual settings. It should be
called when executing a sc... | negative_train_query659_01710 | |
ultralytics/cfg/__init__.py/handle_yolo_solutions
def handle_yolo_solutions(args: List[str]) -> None:
"""
Processes YOLO solutions arguments and runs the specified computer vision solutions pipeline.
Args:
args (List[str]): Command-line arguments for configuring and running the Ultralytics YOLO
... | negative_train_query659_01711 | |
ultralytics/cfg/__init__.py/handle_streamlit_inference
def handle_streamlit_inference():
"""
Open the Ultralytics Live Inference Streamlit app for real-time object detection.
This function initializes and runs a Streamlit application designed for performing live object detection using
Ultralytics model... | negative_train_query659_01712 | |
ultralytics/cfg/__init__.py/parse_key_value_pair
def parse_key_value_pair(pair: str = "key=value"):
"""
Parses a key-value pair string into separate key and value components.
Args:
pair (str): A string containing a key-value pair in the format "key=value".
Returns:
(tuple): A tuple con... | negative_train_query659_01713 | |
ultralytics/cfg/__init__.py/smart_value
def smart_value(v):
"""
Converts a string representation of a value to its appropriate Python type.
This function attempts to convert a given string into a Python object of the most appropriate type. It handles
conversions to None, bool, int, float, and other typ... | negative_train_query659_01714 | |
ultralytics/cfg/__init__.py/entrypoint
def entrypoint(debug=""):
"""
Ultralytics entrypoint function for parsing and executing command-line arguments.
This function serves as the main entry point for the Ultralytics CLI, parsing command-line arguments and
executing the corresponding tasks such as train... | negative_train_query659_01715 | |
ultralytics/cfg/__init__.py/copy_default_cfg
def copy_default_cfg():
"""
Copies the default configuration file and creates a new one with '_copy' appended to its name.
This function duplicates the existing default configuration file (DEFAULT_CFG_PATH) and saves it
with '_copy' appended to its name in t... | negative_train_query659_01716 |
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