Detection

Detection module for object detection using YOLO.

Detector

Detector(
    model: DetectorModel = DetectorModel.YOLO26x,
    weights: str | None = None,
    conf: float = 0.25,
    nms: float = 0.7,
    max_det: int = 300,
    device: str = "auto",
    half: bool = False,
)

A wrapper around Ultralytics detection models for running object detection on videos and selected frames.

This class loads a YOLO (v8, v11, 26) or RT-DETR model from a local models/ directory (or from a user-supplied .pt file) and provides convenience methods to:

• detect objects frame by frame in a video and return results as a pandas DataFrame,
• run detection only on specified frame indices,
• process a batch of videos and save per-video detection text files, and
• query basic video properties (FPS, frame count).

The detector automatically chooses an inference device (cuda, xpu, mps, or cpu) when device="auto", and it can optionally enable half-precision inference on GPU.

Parameters:

Name	Type	Description	Default
model	DetectorModel	Built-in model weights to use (for example DetectorModel.YOLO26x). Default is DetectorModel.YOLO26x.	YOLO26x
weights	str	Optional custom model weights to load. If relative, path is resolved under <module_dir>/models/. Default is None.	None
conf	float	Confidence threshold for detections. Default is 0.25.	0.25
nms	float	IoU / non-maximum suppression threshold. Default is 0.7.	0.7
max_det	int	Maximum number of detections per frame. Default is 300.	300
device	(auto, cuda, xpu, cpu, mps)	Inference device to use. If "auto", the detector will pick an available accelerator first (cuda → xpu → mps) and fall back to CPU. Default is "auto".	"auto"
half	bool	Whether to enable half-precision inference. This is only effective on GPU (CUDA). Default is False.	False

Notes

• The class expects model weight files to be located under <module_dir>/models/ when using the built-in weight names.
• Returned detection tables typically contain the columns: frame, res, x, y, w, h, conf, class.

Initialize a Detector for Ultralytics YOLO/RT-DETR models.

Parameters:

Name	Type	Description	Default
model	DetectorModel	Built-in model to use. Default is "yolo26x".	YOLO26x
weights	str	Customized model weights to load. Default is None, which means using the built-in weights in model choice.	None
conf	float	Confidence threshold. Default is 0.25.	0.25
nms	float	IoU/NMS threshold. Default is 0.7.	0.7
max_det	int	Maximum detections per frame. Default is 300. In crowded scenes, you may want to increase this.	300
device	(auto, cuda, xpu, cpu, mps)	Inference device. Default is "auto".	"auto"
half	bool	Whether to use half precision (GPU only). Default is False.	False

Source code in src/dnt/detect/yolo/detector.py

def __init__(
    self,
    model: DetectorModel = DetectorModel.YOLO26x,
    weights: str | None = None,
    conf: float = 0.25,
    nms: float = 0.7,
    max_det: int = 300,
    device: str = "auto",
    half: bool = False,
):
    """Initialize a Detector for Ultralytics YOLO/RT-DETR models.

    Parameters
    ----------
    model : DetectorModel, optional
        Built-in model to use. Default is "yolo26x".
    weights : str, optional
        Customized model weights to load.
        Default is None, which means using the built-in weights in `model` choice.
    conf : float, optional
        Confidence threshold. Default is 0.25.
    nms : float, optional
        IoU/NMS threshold. Default is 0.7.
    max_det : int, optional
        Maximum detections per frame.
        Default is 300. In crowded scenes, you may want to increase this.
    device : {"auto", "cuda", "xpu", "cpu", "mps"}, optional
        Inference device. Default is "auto".
    half : bool, optional
        Whether to use half precision (GPU only). Default is False.

    """
    # Load model
    cwd = Path(__file__).parent.absolute()
    model_dir = cwd / "models"
    if not model_dir.exists():
        os.makedirs(model_dir)

    if weights:
        model_path = Path(weights) if os.path.isabs(weights) else model_dir / weights
    else:
        model_path = model_dir / f"{model.value}"

    # actually load model
    if ("yolo" in str(weights).lower()) or (model in YOLO_MODELS):
        self.model = YOLO(str(model_path))
    elif ("rtdetr" in str(weights).lower()) or (model in RTDETR_MODELS):
        self.model = RTDETR(str(model_path))
    else:
        raise ValueError(
            f"Cannot infer model family from model={model} and weights={weights!r}. "
            "Use a known DetectorModel or provide weights containing 'yolo' or 'rtdetr'."
        )
    self.conf = conf
    self.nms = nms
    self.max_det = max_det

    # device selection
    requested_device = str(device).lower().strip()
    requested_backend = requested_device.split(":", maxsplit=1)[0]
    valid_devices = {"auto", "cuda", "xpu", "mps", "cpu"}
    if requested_backend not in valid_devices:
        raise ValueError(
            f"Invalid device={device!r}. Choose one of {sorted(valid_devices)} or backend:index like 'cuda:0'."
        )

    backend_available = {
        "cuda": torch.cuda.is_available(),
        "xpu": hasattr(torch, "xpu") and hasattr(torch.xpu, "is_available") and torch.xpu.is_available(),
        "mps": hasattr(torch.backends, "mps") and torch.backends.mps.is_available(),
        "cpu": True,
    }

    if requested_backend == "auto":
        auto_priority = ("cuda", "xpu", "mps", "cpu")
        self.device = next(d for d in auto_priority if backend_available[d])
    else:
        self.device = requested_device if backend_available[requested_backend] else "cpu"

    # half precision only makes sense on GPU
    self.half = half and (self.device == "cuda")

detect

detect(
    input_video: str,
    iou_file: str | None = None,
    video_index: int | None = None,
    video_tot: int | None = None,
    start_frame: int | None = None,
    end_frame: int | None = None,
    verbose: bool = True,
    show: bool = False,
    message: str | None = None,
) -> pd.DataFrame

Run object detection on a video and return per-frame detections.

Parameters:

Name	Type	Description	Default
input_video	str	Path to the input video file.	required
iou_file	str	If provided, detection results are written to this file (CSV without header).	None
video_index	int	Index of this video in a batch, used only for progress display.	None
video_tot	int	Total number of videos in the batch, used only for progress display.	None
start_frame	int	Frame index to start detection from. If None or out of range, starts at 0.	None
end_frame	int	Frame index to stop detection at. If None or out of range, uses the last frame.	None
verbose	bool	Whether to show a progress bar. Default is True.	True
show	bool	Whether to display the video frames with detections. Default is False.	False
message	str | None	Optional message shown in the progress bar description. Default is None.	None

Returns:

Type	Description
DataFrame	DataFrame with columns: frame, res, x, y, w, h, conf, class. If the video cannot be opened or no detections are found, an empty DataFrame with those columns is returned.

Source code in src/dnt/detect/yolo/detector.py

def detect(
    self,
    input_video: str,
    iou_file: str | None = None,
    video_index: int | None = None,
    video_tot: int | None = None,
    start_frame: int | None = None,
    end_frame: int | None = None,
    verbose: bool = True,
    show: bool = False,
    message: str | None = None,
) -> pd.DataFrame:
    """Run object detection on a video and return per-frame detections.

    Parameters
    ----------
    input_video : str
        Path to the input video file.
    iou_file : str, optional
        If provided, detection results are written to this file (CSV without header).
    video_index : int, optional
        Index of this video in a batch, used only for progress display.
    video_tot : int, optional
        Total number of videos in the batch, used only for progress display.
    start_frame : int, optional
        Frame index to start detection from. If None or out of range, starts at 0.
    end_frame : int, optional
        Frame index to stop detection at. If None or out of range, uses the last frame.
    verbose : bool, optional
        Whether to show a progress bar. Default is True.
    show : bool, optional
        Whether to display the video frames with detections. Default is False.
    message : str | None, optional
        Optional message shown in the progress bar description.
        Default is None.

    Returns
    -------
    pandas.DataFrame
        DataFrame with columns:
        `frame, res, x, y, w, h, conf, class`.
        If the video cannot be opened or no detections are found, an empty DataFrame
        with those columns is returned.

    """
    # validate path
    cap = cv2.VideoCapture(input_video)
    if not cap.isOpened():
        if verbose:
            print(f"Cannot open video: {input_video}")
        return pd.DataFrame(columns=self.DET_FIELDS)

    tot_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

    # normalize start_frame
    if start_frame is None or start_frame < 0 or start_frame >= tot_frames:
        start_frame = 0
    # normalize end_frame
    if end_frame is None or end_frame < 0 or end_frame >= tot_frames:
        end_frame = tot_frames - 1
    if start_frame > end_frame:
        cap.release()
        raise ValueError("start_frame must be less than or equal to end_frame.")

    frame_total = end_frame - start_frame + 1

    # Some codecs return 0 or -1 for frame count
    if verbose:
        if (video_index is not None) and (video_tot is not None):
            desc = f"Detecting {video_index} of {video_tot}"
        else:
            desc = "Detecting"

        if message is not None:
            desc += f" {message}"

        if tot_frames <= 0:
            pbar = tqdm(desc=desc, unit="frame")
        else:
            pbar = tqdm(total=frame_total, desc=desc, unit="frame")

    results: list[dict] = []
    frame_idx = start_frame
    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)

    win_name = "Detection (press q/ESC to quit)"
    if show:
        cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)

    # optional FPS calc
    t0 = time()
    n_show = 0

    while cap.isOpened():
        pos_frame = int(cap.get(cv2.CAP_PROP_POS_FRAMES))
        ret, frame = cap.read()
        if not ret:
            break

        if end_frame is not None and frame_idx > end_frame:
            break

        preds = self.model.predict(
            source=frame,
            conf=self.conf,
            iou=self.nms,
            max_det=self.max_det,
            device=self.device,
            half=self.half,
            verbose=False,
        )

        det = preds[0]
        boxes = det.boxes
        if boxes is not None and len(boxes) > 0:
            xyxy = boxes.xyxy.cpu().numpy()  # (N,4)
            confs = boxes.conf.cpu().numpy()  # (N,)
            clss = boxes.cls.cpu().numpy().astype(int)  # (N,)

            for (x1, y1, x2, y2), cf, c in zip(xyxy, confs, clss, strict=True):
                results.append({
                    "frame": pos_frame,
                    "res": -1,
                    "x": float(x1),
                    "y": float(y1),
                    "x2": float(x2),
                    "y2": float(y2),
                    "conf": float(cf),
                    "class": int(c),
                })

        if show:
            # Ultralytics built-in drawing (fast & clean)
            vis = det.plot()  # returns BGR image with boxes/labels

            # add simple overlay: frame index + FPS
            n_show += 1
            dt = time() - t0
            fps = n_show / dt if dt > 0 else 0.0
            cv2.putText(
                vis,
                f"frame={pos_frame}/{frame_total}  fps={fps:.1f}",
                (10, 25),
                cv2.FONT_HERSHEY_SIMPLEX,
                0.7,
                (0, 255, 255),
                2,
            )

            cv2.imshow(win_name, vis)
            key = cv2.waitKey(1) & 0xFF
            if key == ord("q") or key == 27:  # q or ESC
                break

        if verbose and pbar is not None:
            pbar.update(1)

        frame_idx += 1

    cap.release()
    if verbose and pbar is not None:
        pbar.close()

    if not results:
        empty_df = pd.DataFrame(columns=self.DET_FIELDS)
        if iou_file:
            empty_df.to_csv(iou_file, index=False, header=False)
        return empty_df

    else:
        results_df = pd.DataFrame(results, columns=["frame", "res", "x", "y", "x2", "y2", "conf", "class"])
        results_df["w"] = (results_df["x2"] - results_df["x"]).astype(int)
        results_df["h"] = (results_df["y2"] - results_df["y"]).astype(int)
        results_df["x"] = results_df["x"].astype(int)
        results_df["y"] = results_df["y"].astype(int)
        results_df["conf"] = results_df["conf"].round(2)
        results_df = results_df[self.DET_FIELDS].reset_index(drop=True)

    if iou_file:
        folder = Path(iou_file).parent
        if not folder.exists():
            Path(folder).mkdir(parents=True, exist_ok=True)

        results_df.to_csv(iou_file, index=False, header=False)
        if verbose:
            print(f"Wrote detections to {iou_file}")

    return results_df

detect_frames

detect_frames(
    input_video: str,
    frames: list[int],
    verbose: bool = True,
) -> pd.DataFrame

Run object detection on specific frames of a video.

This method is useful when you don't need to process the entire video and only want detections for selected frame indices.

Parameters:

Name	Type	Description	Default
input_video	str	Path to the input video file.	required
frames	list of int	List of frame indices to process.	required
verbose	bool	Whether to show a progress bar. Default is True.	True

Returns:

Type	Description
DataFrame	DataFrame with columns frame, res, x, y, w, h, conf, class. If the video cannot be opened or no detections are found, an empty DataFrame with those columns is returned.

Source code in src/dnt/detect/yolo/detector.py

def detect_frames(
    self,
    input_video: str,
    frames: list[int],
    verbose: bool = True,
) -> pd.DataFrame:
    """Run object detection on specific frames of a video.

    This method is useful when you don't need to process the entire video and
    only want detections for selected frame indices.

    Parameters
    ----------
    input_video : str
        Path to the input video file.
    frames : list of int
        List of frame indices to process.
    verbose : bool, optional
        Whether to show a progress bar. Default is True.

    Returns
    -------
    pandas.DataFrame
        DataFrame with columns
        `frame, res, x, y, w, h, conf, class`.
        If the video cannot be opened or no detections are found, an empty
        DataFrame with those columns is returned.

    """
    # validate path
    if not os.path.exists(input_video):
        # return an empty, well-shaped DataFrame instead of None
        if verbose:
            print(f"{input_video} does not exist!")
        return pd.DataFrame(columns=self.DET_FIELDS)

    cap = cv2.VideoCapture(input_video)
    if not cap.isOpened():
        if verbose:
            print(f"Cannot open {input_video}")
        return pd.DataFrame(columns=self.DET_FIELDS)

    results: list[dict] = []

    pbar = tqdm(total=len(frames), unit=" frames") if verbose else None

    for pos_frame in frames:
        # move to target frame
        cap.set(cv2.CAP_PROP_POS_FRAMES, pos_frame)
        ret, frame = cap.read()
        if not ret:
            # e.g. frame index out of range
            continue

        preds = self.model.predict(
            frame,
            verbose=False,
            conf=self.conf,
            iou=self.nms,
            max_det=self.max_det,
            device=self.device,
            half=self.half,
        )
        det = preds[0]
        boxes = det.boxes
        if boxes is not None and len(boxes) > 0:
            xyxy = boxes.xyxy.cpu().numpy()  # (N,4)
            confs = boxes.conf.cpu().numpy()  # (N,)
            clss = boxes.cls.cpu().numpy().astype(int)  # (N,)

            for (x1, y1, x2, y2), cf, c in zip(xyxy, confs, clss, strict=True):
                results.append({
                    "frame": pos_frame,
                    "res": -1,
                    "x": float(x1),
                    "y": float(y1),
                    "x2": float(x2),
                    "y2": float(y2),
                    "conf": float(cf),
                    "class": int(c),
                })

        if pbar is not None:
            pbar.update()

    if pbar is not None:
        pbar.close()
    cap.release()

    # no detections at all

    if not results:
        return pd.DataFrame(columns=self.DET_FIELDS)

    df = pd.DataFrame(results)
    # compute width/height and round
    df["w"] = (df["x2"] - df["x"]).round(0)
    df["h"] = (df["y2"] - df["y"]).round(0)
    df["x"] = df["x"].round(1)
    df["y"] = df["y"].round(1)
    df["conf"] = df["conf"].round(2)
    df["class"] = df["class"].round(0).astype(int)

    df = df[self.DET_FIELDS].reset_index(drop=True)

    return df

detect_batch

detect_batch(
    input_videos: list[str],
    output_path: str | None = None,
    is_overwrite: bool = False,
    is_report: bool = True,
    verbose: bool = True,
    message: str | None = None,
) -> list[str]

Run detection on multiple videos and optionally write per-video output files.

Parameters:

Name	Type	Description	Default
input_videos	list of str	Paths to the input video files to be processed.	required
output_path	str	Directory where per-video detection files will be written. If None, detections are not written to disk and the returned list will be empty.	None
is_overwrite	bool	If False (default), existing detection files with the same name will be skipped. If True, they will be regenerated.	False
is_report	bool	If True (default), existing detection files (that were skipped) are still included in the returned list.	True
verbose	bool	If True, prints progress messages. Default is True.	True
message	str | None	Optional message shown in each progress bar description. Default is None.	None

Returns:

Type	Description
list of str	A list of paths to detection files that were created or already existed. If output_path is None, this will be an empty list.

Source code in src/dnt/detect/yolo/detector.py

def detect_batch(
    self,
    input_videos: list[str],
    output_path: str | None = None,
    is_overwrite: bool = False,
    is_report: bool = True,
    verbose: bool = True,
    message: str | None = None,
) -> list[str]:
    """Run detection on multiple videos and optionally write per-video output files.

    Parameters
    ----------
    input_videos : list of str
        Paths to the input video files to be processed.
    output_path : str, optional
        Directory where per-video detection files will be written. If None,
        detections are not written to disk and the returned list will be empty.
    is_overwrite : bool, optional
        If False (default), existing detection files with the same name will be
        skipped. If True, they will be regenerated.
    is_report : bool, optional
        If True (default), existing detection files (that were skipped) are still
        included in the returned list.
    verbose : bool, optional
        If True, prints progress messages. Default is True.
    message : str | None, optional
        Optional message shown in each progress bar description.
        Default is None.

    Returns
    -------
    list of str
        A list of paths to detection files that were created or already existed.
        If `output_path` is None, this will be an empty list.

    """
    results: list[str] = []
    total_videos = len(input_videos)

    for idx, input_video in enumerate(input_videos, start=1):
        # default: no output file
        iou_file = None

        # build output path / file name if requested
        if output_path is not None:
            Path(output_path).mkdir(parents=True, exist_ok=True)
            base_filename = os.path.splitext(os.path.basename(input_video))[0]
            iou_file = os.path.join(output_path, f"{base_filename}_iou.txt")

        # if we have an output file name, check overwrite logic
        if (iou_file is not None) and (not is_overwrite) and os.path.exists(iou_file):
            if is_report:
                results.append(iou_file)
            # skip processing this video
            continue

        # run detection (may write to iou_file if not None)
        self.detect(
            input_video=input_video,
            iou_file=iou_file,
            video_index=idx,
            video_tot=total_videos,
            verbose=verbose,
            message=message,
        )

        if iou_file is not None:
            results.append(iou_file)

    return results

get_fps staticmethod

get_fps(video: str) -> float

Return the frames-per-second (FPS) value of a video file.

Parameters:

Name	Type	Description	Default
video	str	Path to the video file.	required

Returns:

Type	Description
float	FPS of the video. Returns 0.0 if the video cannot be opened.

Source code in src/dnt/detect/yolo/detector.py

@staticmethod
def get_fps(video: str) -> float:
    """Return the frames-per-second (FPS) value of a video file.

    Parameters
    ----------
    video : str
        Path to the video file.

    Returns
    -------
    float
        FPS of the video. Returns 0.0 if the video cannot be opened.

    """
    if not Path(video).exists():
        print(f"{video} does not exist!")
        return 0.0
    cap = cv2.VideoCapture(video)
    if not cap.isOpened():
        print(f"Failed to open the video: {video}")
        return 0.0

    fps = float(cap.get(cv2.CAP_PROP_FPS))
    cap.release()
    return fps

get_frames staticmethod

get_frames(video: str) -> int

Return the total number of frames in a video file.

Parameters:

Name	Type	Description	Default
video	str	Path to the video file.	required

Returns:

Type	Description
int	Total frame count. Returns 0 if the video cannot be opened.

Source code in src/dnt/detect/yolo/detector.py

@staticmethod
def get_frames(video: str) -> int:
    """Return the total number of frames in a video file.

    Parameters
    ----------
    video : str
        Path to the video file.

    Returns
    -------
    int
        Total frame count. Returns 0 if the video cannot be opened.

    """
    if not Path(video).exists():
        print(f"{video} does not exist!")
        return 0
    cap = cv2.VideoCapture(video)
    if not cap.isOpened():
        print(f"Failed to open the video: {video}")
        return 0

    frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    cap.release()
    return frames

DetectorModel

Bases: str, Enum

Enum of available YOLO and RT-DETR model weights.

Each member represents a different model variant with its corresponding weight file name.