Vai al contenuto

Riferimento per ultralytics/data/loaders.py

Nota

Questo file è disponibile su https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/data/loaders .py. Se riscontri un problema, contribuisci a risolverlo inviando una Pull Request 🛠️. Grazie 🙏!



ultralytics.data.loaders.SourceTypes dataclass

Classe per rappresentare vari tipi di fonti di input per le previsioni.

Codice sorgente in ultralytics/data/loaders.py
@dataclass
class SourceTypes:
    """Class to represent various types of input sources for predictions."""

    stream: bool = False
    screenshot: bool = False
    from_img: bool = False
    tensor: bool = False



ultralytics.data.loaders.LoadStreams

Stream Loader per vari tipi di flussi video, supporta flussi RTSP, RTMP, HTTP e TCP.

Attributi:

Nome Tipo Descrizione
sources str

I percorsi o gli URL di ingresso della sorgente per i flussi video.

vid_stride int

Passo della frequenza dei fotogrammi video, valore predefinito 1.

buffer bool

Se bufferizzare o meno i flussi di input, l'impostazione predefinita è False.

running bool

Flag per indicare se il thread di streaming è in esecuzione.

mode str

Imposta su "stream" per indicare l'acquisizione in tempo reale.

imgs list

Elenco dei fotogrammi dell'immagine per ogni flusso.

fps list

Elenco degli FPS per ogni flusso.

frames list

Elenco dei fotogrammi totali per ogni flusso.

threads list

Elenco dei thread per ogni flusso.

shape list

Elenco di forme per ogni flusso.

caps list

Elenco di oggetti cv2.VideoCapture per ogni flusso.

bs int

Dimensione del lotto per l'elaborazione.

Metodi:

Nome Descrizione
__init__

Inizializza lo stream loader.

update

Leggi i fotogrammi dello stream nel thread del demone.

close

Chiudi lo stream loader e rilascia le risorse.

__iter__

Restituisce un oggetto iteratore per la classe.

__next__

Restituisce i percorsi di origine, le immagini trasformate e quelle originali per l'elaborazione.

__len__

Restituisce la lunghezza dell'oggetto sorgenti.

Esempio
yolo predict source='rtsp://example.com/media.mp4'
Codice sorgente in ultralytics/data/loaders.py
class LoadStreams:
    """
    Stream Loader for various types of video streams, Supports RTSP, RTMP, HTTP, and TCP streams.

    Attributes:
        sources (str): The source input paths or URLs for the video streams.
        vid_stride (int): Video frame-rate stride, defaults to 1.
        buffer (bool): Whether to buffer input streams, defaults to False.
        running (bool): Flag to indicate if the streaming thread is running.
        mode (str): Set to 'stream' indicating real-time capture.
        imgs (list): List of image frames for each stream.
        fps (list): List of FPS for each stream.
        frames (list): List of total frames for each stream.
        threads (list): List of threads for each stream.
        shape (list): List of shapes for each stream.
        caps (list): List of cv2.VideoCapture objects for each stream.
        bs (int): Batch size for processing.

    Methods:
        __init__: Initialize the stream loader.
        update: Read stream frames in daemon thread.
        close: Close stream loader and release resources.
        __iter__: Returns an iterator object for the class.
        __next__: Returns source paths, transformed, and original images for processing.
        __len__: Return the length of the sources object.

    Example:
         ```bash
         yolo predict source='rtsp://example.com/media.mp4'
         ```
    """

    def __init__(self, sources="file.streams", vid_stride=1, buffer=False):
        """Initialize instance variables and check for consistent input stream shapes."""
        torch.backends.cudnn.benchmark = True  # faster for fixed-size inference
        self.buffer = buffer  # buffer input streams
        self.running = True  # running flag for Thread
        self.mode = "stream"
        self.vid_stride = vid_stride  # video frame-rate stride

        sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
        n = len(sources)
        self.bs = n
        self.fps = [0] * n  # frames per second
        self.frames = [0] * n
        self.threads = [None] * n
        self.caps = [None] * n  # video capture objects
        self.imgs = [[] for _ in range(n)]  # images
        self.shape = [[] for _ in range(n)]  # image shapes
        self.sources = [ops.clean_str(x) for x in sources]  # clean source names for later
        for i, s in enumerate(sources):  # index, source
            # Start thread to read frames from video stream
            st = f"{i + 1}/{n}: {s}... "
            if urlparse(s).hostname in {"www.youtube.com", "youtube.com", "youtu.be"}:  # if source is YouTube video
                # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4'
                s = get_best_youtube_url(s)
            s = eval(s) if s.isnumeric() else s  # i.e. s = '0' local webcam
            if s == 0 and (IS_COLAB or IS_KAGGLE):
                raise NotImplementedError(
                    "'source=0' webcam not supported in Colab and Kaggle notebooks. "
                    "Try running 'source=0' in a local environment."
                )
            self.caps[i] = cv2.VideoCapture(s)  # store video capture object
            if not self.caps[i].isOpened():
                raise ConnectionError(f"{st}Failed to open {s}")
            w = int(self.caps[i].get(cv2.CAP_PROP_FRAME_WIDTH))
            h = int(self.caps[i].get(cv2.CAP_PROP_FRAME_HEIGHT))
            fps = self.caps[i].get(cv2.CAP_PROP_FPS)  # warning: may return 0 or nan
            self.frames[i] = max(int(self.caps[i].get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float(
                "inf"
            )  # infinite stream fallback
            self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30  # 30 FPS fallback

            success, im = self.caps[i].read()  # guarantee first frame
            if not success or im is None:
                raise ConnectionError(f"{st}Failed to read images from {s}")
            self.imgs[i].append(im)
            self.shape[i] = im.shape
            self.threads[i] = Thread(target=self.update, args=([i, self.caps[i], s]), daemon=True)
            LOGGER.info(f"{st}Success ✅ ({self.frames[i]} frames of shape {w}x{h} at {self.fps[i]:.2f} FPS)")
            self.threads[i].start()
        LOGGER.info("")  # newline

    def update(self, i, cap, stream):
        """Read stream `i` frames in daemon thread."""
        n, f = 0, self.frames[i]  # frame number, frame array
        while self.running and cap.isOpened() and n < (f - 1):
            if len(self.imgs[i]) < 30:  # keep a <=30-image buffer
                n += 1
                cap.grab()  # .read() = .grab() followed by .retrieve()
                if n % self.vid_stride == 0:
                    success, im = cap.retrieve()
                    if not success:
                        im = np.zeros(self.shape[i], dtype=np.uint8)
                        LOGGER.warning("WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.")
                        cap.open(stream)  # re-open stream if signal was lost
                    if self.buffer:
                        self.imgs[i].append(im)
                    else:
                        self.imgs[i] = [im]
            else:
                time.sleep(0.01)  # wait until the buffer is empty

    def close(self):
        """Close stream loader and release resources."""
        self.running = False  # stop flag for Thread
        for thread in self.threads:
            if thread.is_alive():
                thread.join(timeout=5)  # Add timeout
        for cap in self.caps:  # Iterate through the stored VideoCapture objects
            try:
                cap.release()  # release video capture
            except Exception as e:
                LOGGER.warning(f"WARNING ⚠️ Could not release VideoCapture object: {e}")
        cv2.destroyAllWindows()

    def __iter__(self):
        """Iterates through YOLO image feed and re-opens unresponsive streams."""
        self.count = -1
        return self

    def __next__(self):
        """Returns source paths, transformed and original images for processing."""
        self.count += 1

        images = []
        for i, x in enumerate(self.imgs):
            # Wait until a frame is available in each buffer
            while not x:
                if not self.threads[i].is_alive() or cv2.waitKey(1) == ord("q"):  # q to quit
                    self.close()
                    raise StopIteration
                time.sleep(1 / min(self.fps))
                x = self.imgs[i]
                if not x:
                    LOGGER.warning(f"WARNING ⚠️ Waiting for stream {i}")

            # Get and remove the first frame from imgs buffer
            if self.buffer:
                images.append(x.pop(0))

            # Get the last frame, and clear the rest from the imgs buffer
            else:
                images.append(x.pop(-1) if x else np.zeros(self.shape[i], dtype=np.uint8))
                x.clear()

        return self.sources, images, [""] * self.bs

    def __len__(self):
        """Return the length of the sources object."""
        return self.bs  # 1E12 frames = 32 streams at 30 FPS for 30 years

__init__(sources='file.streams', vid_stride=1, buffer=False)

Inizializza le variabili dell'istanza e controlla che le forme del flusso di input siano coerenti.

Codice sorgente in ultralytics/data/loaders.py
def __init__(self, sources="file.streams", vid_stride=1, buffer=False):
    """Initialize instance variables and check for consistent input stream shapes."""
    torch.backends.cudnn.benchmark = True  # faster for fixed-size inference
    self.buffer = buffer  # buffer input streams
    self.running = True  # running flag for Thread
    self.mode = "stream"
    self.vid_stride = vid_stride  # video frame-rate stride

    sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
    n = len(sources)
    self.bs = n
    self.fps = [0] * n  # frames per second
    self.frames = [0] * n
    self.threads = [None] * n
    self.caps = [None] * n  # video capture objects
    self.imgs = [[] for _ in range(n)]  # images
    self.shape = [[] for _ in range(n)]  # image shapes
    self.sources = [ops.clean_str(x) for x in sources]  # clean source names for later
    for i, s in enumerate(sources):  # index, source
        # Start thread to read frames from video stream
        st = f"{i + 1}/{n}: {s}... "
        if urlparse(s).hostname in {"www.youtube.com", "youtube.com", "youtu.be"}:  # if source is YouTube video
            # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4'
            s = get_best_youtube_url(s)
        s = eval(s) if s.isnumeric() else s  # i.e. s = '0' local webcam
        if s == 0 and (IS_COLAB or IS_KAGGLE):
            raise NotImplementedError(
                "'source=0' webcam not supported in Colab and Kaggle notebooks. "
                "Try running 'source=0' in a local environment."
            )
        self.caps[i] = cv2.VideoCapture(s)  # store video capture object
        if not self.caps[i].isOpened():
            raise ConnectionError(f"{st}Failed to open {s}")
        w = int(self.caps[i].get(cv2.CAP_PROP_FRAME_WIDTH))
        h = int(self.caps[i].get(cv2.CAP_PROP_FRAME_HEIGHT))
        fps = self.caps[i].get(cv2.CAP_PROP_FPS)  # warning: may return 0 or nan
        self.frames[i] = max(int(self.caps[i].get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float(
            "inf"
        )  # infinite stream fallback
        self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30  # 30 FPS fallback

        success, im = self.caps[i].read()  # guarantee first frame
        if not success or im is None:
            raise ConnectionError(f"{st}Failed to read images from {s}")
        self.imgs[i].append(im)
        self.shape[i] = im.shape
        self.threads[i] = Thread(target=self.update, args=([i, self.caps[i], s]), daemon=True)
        LOGGER.info(f"{st}Success ✅ ({self.frames[i]} frames of shape {w}x{h} at {self.fps[i]:.2f} FPS)")
        self.threads[i].start()
    LOGGER.info("")  # newline

__iter__()

Itera il feed di immagini di YOLO e riapre i flussi non rispondenti.

Codice sorgente in ultralytics/data/loaders.py
def __iter__(self):
    """Iterates through YOLO image feed and re-opens unresponsive streams."""
    self.count = -1
    return self

__len__()

Restituisce la lunghezza dell'oggetto sorgenti.

Codice sorgente in ultralytics/data/loaders.py
def __len__(self):
    """Return the length of the sources object."""
    return self.bs  # 1E12 frames = 32 streams at 30 FPS for 30 years

__next__()

Restituisce i percorsi di origine, le immagini trasformate e quelle originali per l'elaborazione.

Codice sorgente in ultralytics/data/loaders.py
def __next__(self):
    """Returns source paths, transformed and original images for processing."""
    self.count += 1

    images = []
    for i, x in enumerate(self.imgs):
        # Wait until a frame is available in each buffer
        while not x:
            if not self.threads[i].is_alive() or cv2.waitKey(1) == ord("q"):  # q to quit
                self.close()
                raise StopIteration
            time.sleep(1 / min(self.fps))
            x = self.imgs[i]
            if not x:
                LOGGER.warning(f"WARNING ⚠️ Waiting for stream {i}")

        # Get and remove the first frame from imgs buffer
        if self.buffer:
            images.append(x.pop(0))

        # Get the last frame, and clear the rest from the imgs buffer
        else:
            images.append(x.pop(-1) if x else np.zeros(self.shape[i], dtype=np.uint8))
            x.clear()

    return self.sources, images, [""] * self.bs

close()

Chiudi lo stream loader e rilascia le risorse.

Codice sorgente in ultralytics/data/loaders.py
def close(self):
    """Close stream loader and release resources."""
    self.running = False  # stop flag for Thread
    for thread in self.threads:
        if thread.is_alive():
            thread.join(timeout=5)  # Add timeout
    for cap in self.caps:  # Iterate through the stored VideoCapture objects
        try:
            cap.release()  # release video capture
        except Exception as e:
            LOGGER.warning(f"WARNING ⚠️ Could not release VideoCapture object: {e}")
    cv2.destroyAllWindows()

update(i, cap, stream)

Leggi il flusso i frame nel thread del demone.

Codice sorgente in ultralytics/data/loaders.py
def update(self, i, cap, stream):
    """Read stream `i` frames in daemon thread."""
    n, f = 0, self.frames[i]  # frame number, frame array
    while self.running and cap.isOpened() and n < (f - 1):
        if len(self.imgs[i]) < 30:  # keep a <=30-image buffer
            n += 1
            cap.grab()  # .read() = .grab() followed by .retrieve()
            if n % self.vid_stride == 0:
                success, im = cap.retrieve()
                if not success:
                    im = np.zeros(self.shape[i], dtype=np.uint8)
                    LOGGER.warning("WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.")
                    cap.open(stream)  # re-open stream if signal was lost
                if self.buffer:
                    self.imgs[i].append(im)
                else:
                    self.imgs[i] = [im]
        else:
            time.sleep(0.01)  # wait until the buffer is empty



ultralytics.data.loaders.LoadScreenshots

YOLOv8 screenshot dataloader.

Questa classe gestisce il caricamento di immagini di screenshot per l'elaborazione con YOLOv8. Può essere utilizzata con yolo predict source=screen.

Attributi:

Nome Tipo Descrizione
source str

L'ingresso sorgente che indica la schermata da catturare.

screen int

Il numero della schermata da catturare.

left int

La coordinata sinistra dell'area di cattura dello schermo.

top int

La coordinata superiore dell'area di cattura dello schermo.

width int

La larghezza dell'area di cattura dello schermo.

height int

L'altezza dell'area di cattura dello schermo.

mode str

Imposta su "stream" per indicare l'acquisizione in tempo reale.

frame int

Contatore dei fotogrammi catturati.

sct mss

Oggetto di cattura dello schermo da mss biblioteca.

bs int

Dimensione del lotto, impostata su 1.

monitor dict

Controlla i dettagli della configurazione.

Metodi:

Nome Descrizione
__iter__

Restituisce un oggetto iteratore.

__next__

Cattura il prossimo screenshot e lo restituisce.

Codice sorgente in ultralytics/data/loaders.py
class LoadScreenshots:
    """
    YOLOv8 screenshot dataloader.

    This class manages the loading of screenshot images for processing with YOLOv8.
    Suitable for use with `yolo predict source=screen`.

    Attributes:
        source (str): The source input indicating which screen to capture.
        screen (int): The screen number to capture.
        left (int): The left coordinate for screen capture area.
        top (int): The top coordinate for screen capture area.
        width (int): The width of the screen capture area.
        height (int): The height of the screen capture area.
        mode (str): Set to 'stream' indicating real-time capture.
        frame (int): Counter for captured frames.
        sct (mss.mss): Screen capture object from `mss` library.
        bs (int): Batch size, set to 1.
        monitor (dict): Monitor configuration details.

    Methods:
        __iter__: Returns an iterator object.
        __next__: Captures the next screenshot and returns it.
    """

    def __init__(self, source):
        """Source = [screen_number left top width height] (pixels)."""
        check_requirements("mss")
        import mss  # noqa

        source, *params = source.split()
        self.screen, left, top, width, height = 0, None, None, None, None  # default to full screen 0
        if len(params) == 1:
            self.screen = int(params[0])
        elif len(params) == 4:
            left, top, width, height = (int(x) for x in params)
        elif len(params) == 5:
            self.screen, left, top, width, height = (int(x) for x in params)
        self.mode = "stream"
        self.frame = 0
        self.sct = mss.mss()
        self.bs = 1
        self.fps = 30

        # Parse monitor shape
        monitor = self.sct.monitors[self.screen]
        self.top = monitor["top"] if top is None else (monitor["top"] + top)
        self.left = monitor["left"] if left is None else (monitor["left"] + left)
        self.width = width or monitor["width"]
        self.height = height or monitor["height"]
        self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height}

    def __iter__(self):
        """Returns an iterator of the object."""
        return self

    def __next__(self):
        """mss screen capture: get raw pixels from the screen as np array."""
        im0 = np.asarray(self.sct.grab(self.monitor))[:, :, :3]  # BGRA to BGR
        s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "

        self.frame += 1
        return [str(self.screen)], [im0], [s]  # screen, img, string

__init__(source)

Fonte = [screen_number left top width height] (pixel).

Codice sorgente in ultralytics/data/loaders.py
def __init__(self, source):
    """Source = [screen_number left top width height] (pixels)."""
    check_requirements("mss")
    import mss  # noqa

    source, *params = source.split()
    self.screen, left, top, width, height = 0, None, None, None, None  # default to full screen 0
    if len(params) == 1:
        self.screen = int(params[0])
    elif len(params) == 4:
        left, top, width, height = (int(x) for x in params)
    elif len(params) == 5:
        self.screen, left, top, width, height = (int(x) for x in params)
    self.mode = "stream"
    self.frame = 0
    self.sct = mss.mss()
    self.bs = 1
    self.fps = 30

    # Parse monitor shape
    monitor = self.sct.monitors[self.screen]
    self.top = monitor["top"] if top is None else (monitor["top"] + top)
    self.left = monitor["left"] if left is None else (monitor["left"] + left)
    self.width = width or monitor["width"]
    self.height = height or monitor["height"]
    self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height}

__iter__()

Restituisce un iteratore dell'oggetto.

Codice sorgente in ultralytics/data/loaders.py
def __iter__(self):
    """Returns an iterator of the object."""
    return self

__next__()

cattura schermo mss: ottieni i pixel grezzi dallo schermo come array np.

Codice sorgente in ultralytics/data/loaders.py
def __next__(self):
    """mss screen capture: get raw pixels from the screen as np array."""
    im0 = np.asarray(self.sct.grab(self.monitor))[:, :, :3]  # BGRA to BGR
    s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "

    self.frame += 1
    return [str(self.screen)], [im0], [s]  # screen, img, string



ultralytics.data.loaders.LoadImagesAndVideos

YOLOv8 dataloader immagine/video.

Questa classe gestisce il caricamento e la pre-elaborazione dei dati di immagini e video per YOLOv8. Supporta il caricamento da vari formati, tra cui file di immagini singole, file video ed elenchi di percorsi di immagini e video.

Attributi:

Nome Tipo Descrizione
files list

Elenco dei percorsi dei file immagine e video.

nf int

Numero totale di file (immagini e video).

video_flag list

Flags che indicano se un file è un video (True) o un'immagine (False).

mode str

Modalità corrente, "immagine" o "video".

vid_stride int

Stride per la frequenza dei fotogrammi video, valore predefinito 1.

bs int

Dimensione del lotto, impostata a 1 per questa classe.

cap VideoCapture

Oggetto di acquisizione video per OpenCV.

frame int

Contatore di fotogrammi per il video.

frames int

Numero totale di fotogrammi nel video.

count int

Contatore per l'iterazione, inizializzato a 0 durante la fase di __iter__().

Metodi:

Nome Descrizione
_new_video

Crea un nuovo oggetto cv2.VideoCapture per un determinato percorso video.

Codice sorgente in ultralytics/data/loaders.py
class LoadImagesAndVideos:
    """
    YOLOv8 image/video dataloader.

    This class manages the loading and pre-processing of image and video data for YOLOv8. It supports loading from
    various formats, including single image files, video files, and lists of image and video paths.

    Attributes:
        files (list): List of image and video file paths.
        nf (int): Total number of files (images and videos).
        video_flag (list): Flags indicating whether a file is a video (True) or an image (False).
        mode (str): Current mode, 'image' or 'video'.
        vid_stride (int): Stride for video frame-rate, defaults to 1.
        bs (int): Batch size, set to 1 for this class.
        cap (cv2.VideoCapture): Video capture object for OpenCV.
        frame (int): Frame counter for video.
        frames (int): Total number of frames in the video.
        count (int): Counter for iteration, initialized at 0 during `__iter__()`.

    Methods:
        _new_video(path): Create a new cv2.VideoCapture object for a given video path.
    """

    def __init__(self, path, batch=1, vid_stride=1):
        """Initialize the Dataloader and raise FileNotFoundError if file not found."""
        parent = None
        if isinstance(path, str) and Path(path).suffix == ".txt":  # *.txt file with img/vid/dir on each line
            parent = Path(path).parent
            path = Path(path).read_text().splitlines()  # list of sources
        files = []
        for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
            a = str(Path(p).absolute())  # do not use .resolve() https://github.com/ultralytics/ultralytics/issues/2912
            if "*" in a:
                files.extend(sorted(glob.glob(a, recursive=True)))  # glob
            elif os.path.isdir(a):
                files.extend(sorted(glob.glob(os.path.join(a, "*.*"))))  # dir
            elif os.path.isfile(a):
                files.append(a)  # files (absolute or relative to CWD)
            elif parent and (parent / p).is_file():
                files.append(str((parent / p).absolute()))  # files (relative to *.txt file parent)
            else:
                raise FileNotFoundError(f"{p} does not exist")

        # Define files as images or videos
        images, videos = [], []
        for f in files:
            suffix = f.split(".")[-1].lower()  # Get file extension without the dot and lowercase
            if suffix in IMG_FORMATS:
                images.append(f)
            elif suffix in VID_FORMATS:
                videos.append(f)
        ni, nv = len(images), len(videos)

        self.files = images + videos
        self.nf = ni + nv  # number of files
        self.ni = ni  # number of images
        self.video_flag = [False] * ni + [True] * nv
        self.mode = "image"
        self.vid_stride = vid_stride  # video frame-rate stride
        self.bs = batch
        if any(videos):
            self._new_video(videos[0])  # new video
        else:
            self.cap = None
        if self.nf == 0:
            raise FileNotFoundError(f"No images or videos found in {p}. {FORMATS_HELP_MSG}")

    def __iter__(self):
        """Returns an iterator object for VideoStream or ImageFolder."""
        self.count = 0
        return self

    def __next__(self):
        """Returns the next batch of images or video frames along with their paths and metadata."""
        paths, imgs, info = [], [], []
        while len(imgs) < self.bs:
            if self.count >= self.nf:  # end of file list
                if imgs:
                    return paths, imgs, info  # return last partial batch
                else:
                    raise StopIteration

            path = self.files[self.count]
            if self.video_flag[self.count]:
                self.mode = "video"
                if not self.cap or not self.cap.isOpened():
                    self._new_video(path)

                for _ in range(self.vid_stride):
                    success = self.cap.grab()
                    if not success:
                        break  # end of video or failure

                if success:
                    success, im0 = self.cap.retrieve()
                    if success:
                        self.frame += 1
                        paths.append(path)
                        imgs.append(im0)
                        info.append(f"video {self.count + 1}/{self.nf} (frame {self.frame}/{self.frames}) {path}: ")
                        if self.frame == self.frames:  # end of video
                            self.count += 1
                            self.cap.release()
                else:
                    # Move to the next file if the current video ended or failed to open
                    self.count += 1
                    if self.cap:
                        self.cap.release()
                    if self.count < self.nf:
                        self._new_video(self.files[self.count])
            else:
                self.mode = "image"
                im0 = cv2.imread(path)  # BGR
                if im0 is None:
                    raise FileNotFoundError(f"Image Not Found {path}")
                paths.append(path)
                imgs.append(im0)
                info.append(f"image {self.count + 1}/{self.nf} {path}: ")
                self.count += 1  # move to the next file
                if self.count >= self.ni:  # end of image list
                    break

        return paths, imgs, info

    def _new_video(self, path):
        """Creates a new video capture object for the given path."""
        self.frame = 0
        self.cap = cv2.VideoCapture(path)
        self.fps = int(self.cap.get(cv2.CAP_PROP_FPS))
        if not self.cap.isOpened():
            raise FileNotFoundError(f"Failed to open video {path}")
        self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)

    def __len__(self):
        """Returns the number of batches in the object."""
        return math.ceil(self.nf / self.bs)  # number of files

__init__(path, batch=1, vid_stride=1)

Inizializza il Dataloader e solleva FileNotFoundError se il file non viene trovato.

Codice sorgente in ultralytics/data/loaders.py
def __init__(self, path, batch=1, vid_stride=1):
    """Initialize the Dataloader and raise FileNotFoundError if file not found."""
    parent = None
    if isinstance(path, str) and Path(path).suffix == ".txt":  # *.txt file with img/vid/dir on each line
        parent = Path(path).parent
        path = Path(path).read_text().splitlines()  # list of sources
    files = []
    for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
        a = str(Path(p).absolute())  # do not use .resolve() https://github.com/ultralytics/ultralytics/issues/2912
        if "*" in a:
            files.extend(sorted(glob.glob(a, recursive=True)))  # glob
        elif os.path.isdir(a):
            files.extend(sorted(glob.glob(os.path.join(a, "*.*"))))  # dir
        elif os.path.isfile(a):
            files.append(a)  # files (absolute or relative to CWD)
        elif parent and (parent / p).is_file():
            files.append(str((parent / p).absolute()))  # files (relative to *.txt file parent)
        else:
            raise FileNotFoundError(f"{p} does not exist")

    # Define files as images or videos
    images, videos = [], []
    for f in files:
        suffix = f.split(".")[-1].lower()  # Get file extension without the dot and lowercase
        if suffix in IMG_FORMATS:
            images.append(f)
        elif suffix in VID_FORMATS:
            videos.append(f)
    ni, nv = len(images), len(videos)

    self.files = images + videos
    self.nf = ni + nv  # number of files
    self.ni = ni  # number of images
    self.video_flag = [False] * ni + [True] * nv
    self.mode = "image"
    self.vid_stride = vid_stride  # video frame-rate stride
    self.bs = batch
    if any(videos):
        self._new_video(videos[0])  # new video
    else:
        self.cap = None
    if self.nf == 0:
        raise FileNotFoundError(f"No images or videos found in {p}. {FORMATS_HELP_MSG}")

__iter__()

Restituisce un oggetto iteratore per VideoStream o ImageFolder.

Codice sorgente in ultralytics/data/loaders.py
def __iter__(self):
    """Returns an iterator object for VideoStream or ImageFolder."""
    self.count = 0
    return self

__len__()

Restituisce il numero di lotti nell'oggetto.

Codice sorgente in ultralytics/data/loaders.py
def __len__(self):
    """Returns the number of batches in the object."""
    return math.ceil(self.nf / self.bs)  # number of files

__next__()

Restituisce il prossimo gruppo di immagini o fotogrammi video con i relativi percorsi e metadati.

Codice sorgente in ultralytics/data/loaders.py
def __next__(self):
    """Returns the next batch of images or video frames along with their paths and metadata."""
    paths, imgs, info = [], [], []
    while len(imgs) < self.bs:
        if self.count >= self.nf:  # end of file list
            if imgs:
                return paths, imgs, info  # return last partial batch
            else:
                raise StopIteration

        path = self.files[self.count]
        if self.video_flag[self.count]:
            self.mode = "video"
            if not self.cap or not self.cap.isOpened():
                self._new_video(path)

            for _ in range(self.vid_stride):
                success = self.cap.grab()
                if not success:
                    break  # end of video or failure

            if success:
                success, im0 = self.cap.retrieve()
                if success:
                    self.frame += 1
                    paths.append(path)
                    imgs.append(im0)
                    info.append(f"video {self.count + 1}/{self.nf} (frame {self.frame}/{self.frames}) {path}: ")
                    if self.frame == self.frames:  # end of video
                        self.count += 1
                        self.cap.release()
            else:
                # Move to the next file if the current video ended or failed to open
                self.count += 1
                if self.cap:
                    self.cap.release()
                if self.count < self.nf:
                    self._new_video(self.files[self.count])
        else:
            self.mode = "image"
            im0 = cv2.imread(path)  # BGR
            if im0 is None:
                raise FileNotFoundError(f"Image Not Found {path}")
            paths.append(path)
            imgs.append(im0)
            info.append(f"image {self.count + 1}/{self.nf} {path}: ")
            self.count += 1  # move to the next file
            if self.count >= self.ni:  # end of image list
                break

    return paths, imgs, info



ultralytics.data.loaders.LoadPilAndNumpy

Carica le immagini da array PIL e Numpy per l'elaborazione in batch.

Questa classe è stata progettata per gestire il caricamento e la pre-elaborazione dei dati delle immagini da entrambi i formati PIL e Numpy. Esegue la convalida di base e la conversione del formato per garantire che le immagini siano nel formato richiesto per l'elaborazione a valle. l'elaborazione a valle.

Attributi:

Nome Tipo Descrizione
paths list

Elenco dei percorsi delle immagini o dei nomi dei file autogenerati.

im0 list

Elenco di immagini memorizzate come array Numpy.

mode str

Tipo di dati da elaborare, per impostazione predefinita "immagine".

bs int

Dimensione del lotto, equivalente alla lunghezza di im0.

Metodi:

Nome Descrizione
_single_check

Convalida e formatta una singola immagine in un array Numpy.

Codice sorgente in ultralytics/data/loaders.py
class LoadPilAndNumpy:
    """
    Load images from PIL and Numpy arrays for batch processing.

    This class is designed to manage loading and pre-processing of image data from both PIL and Numpy formats.
    It performs basic validation and format conversion to ensure that the images are in the required format for
    downstream processing.

    Attributes:
        paths (list): List of image paths or autogenerated filenames.
        im0 (list): List of images stored as Numpy arrays.
        mode (str): Type of data being processed, defaults to 'image'.
        bs (int): Batch size, equivalent to the length of `im0`.

    Methods:
        _single_check(im): Validate and format a single image to a Numpy array.
    """

    def __init__(self, im0):
        """Initialize PIL and Numpy Dataloader."""
        if not isinstance(im0, list):
            im0 = [im0]
        self.paths = [getattr(im, "filename", f"image{i}.jpg") for i, im in enumerate(im0)]
        self.im0 = [self._single_check(im) for im in im0]
        self.mode = "image"
        self.bs = len(self.im0)

    @staticmethod
    def _single_check(im):
        """Validate and format an image to numpy array."""
        assert isinstance(im, (Image.Image, np.ndarray)), f"Expected PIL/np.ndarray image type, but got {type(im)}"
        if isinstance(im, Image.Image):
            if im.mode != "RGB":
                im = im.convert("RGB")
            im = np.asarray(im)[:, :, ::-1]
            im = np.ascontiguousarray(im)  # contiguous
        return im

    def __len__(self):
        """Returns the length of the 'im0' attribute."""
        return len(self.im0)

    def __next__(self):
        """Returns batch paths, images, processed images, None, ''."""
        if self.count == 1:  # loop only once as it's batch inference
            raise StopIteration
        self.count += 1
        return self.paths, self.im0, [""] * self.bs

    def __iter__(self):
        """Enables iteration for class LoadPilAndNumpy."""
        self.count = 0
        return self

__init__(im0)

Inizializza PIL e Numpy Dataloader.

Codice sorgente in ultralytics/data/loaders.py
def __init__(self, im0):
    """Initialize PIL and Numpy Dataloader."""
    if not isinstance(im0, list):
        im0 = [im0]
    self.paths = [getattr(im, "filename", f"image{i}.jpg") for i, im in enumerate(im0)]
    self.im0 = [self._single_check(im) for im in im0]
    self.mode = "image"
    self.bs = len(self.im0)

__iter__()

Abilita l'iterazione per la classe LoadPilAndNumpy.

Codice sorgente in ultralytics/data/loaders.py
def __iter__(self):
    """Enables iteration for class LoadPilAndNumpy."""
    self.count = 0
    return self

__len__()

Restituisce la lunghezza dell'attributo 'im0'.

Codice sorgente in ultralytics/data/loaders.py
def __len__(self):
    """Returns the length of the 'im0' attribute."""
    return len(self.im0)

__next__()

Restituisce i percorsi dei batch, le immagini, le immagini elaborate, Nessuno, ''.

Codice sorgente in ultralytics/data/loaders.py
def __next__(self):
    """Returns batch paths, images, processed images, None, ''."""
    if self.count == 1:  # loop only once as it's batch inference
        raise StopIteration
    self.count += 1
    return self.paths, self.im0, [""] * self.bs



ultralytics.data.loaders.LoadTensor

Carica le immagini da torch.Tensor dati.

Questa classe gestisce il caricamento e la pre-elaborazione dei dati delle immagini dai tensori di PyTorch per la successiva elaborazione.

Attributi:

Nome Tipo Descrizione
im0 Tensor

L'ingresso tensor contenente le immagini.

bs int

Dimensione del lotto, dedotta dalla forma di im0.

mode str

Modalità corrente, impostata su "immagine".

paths list

Elenco dei percorsi o dei nomi dei file delle immagini.

count int

Contatore per l'iterazione, inizializzato a 0 durante la fase di __iter__().

Metodi:

Nome Descrizione
_single_check

Convalida ed eventualmente modifica l'input tensor.

Codice sorgente in ultralytics/data/loaders.py
class LoadTensor:
    """
    Load images from torch.Tensor data.

    This class manages the loading and pre-processing of image data from PyTorch tensors for further processing.

    Attributes:
        im0 (torch.Tensor): The input tensor containing the image(s).
        bs (int): Batch size, inferred from the shape of `im0`.
        mode (str): Current mode, set to 'image'.
        paths (list): List of image paths or filenames.
        count (int): Counter for iteration, initialized at 0 during `__iter__()`.

    Methods:
        _single_check(im, stride): Validate and possibly modify the input tensor.
    """

    def __init__(self, im0) -> None:
        """Initialize Tensor Dataloader."""
        self.im0 = self._single_check(im0)
        self.bs = self.im0.shape[0]
        self.mode = "image"
        self.paths = [getattr(im, "filename", f"image{i}.jpg") for i, im in enumerate(im0)]

    @staticmethod
    def _single_check(im, stride=32):
        """Validate and format an image to torch.Tensor."""
        s = (
            f"WARNING ⚠️ torch.Tensor inputs should be BCHW i.e. shape(1, 3, 640, 640) "
            f"divisible by stride {stride}. Input shape{tuple(im.shape)} is incompatible."
        )
        if len(im.shape) != 4:
            if len(im.shape) != 3:
                raise ValueError(s)
            LOGGER.warning(s)
            im = im.unsqueeze(0)
        if im.shape[2] % stride or im.shape[3] % stride:
            raise ValueError(s)
        if im.max() > 1.0 + torch.finfo(im.dtype).eps:  # torch.float32 eps is 1.2e-07
            LOGGER.warning(
                f"WARNING ⚠️ torch.Tensor inputs should be normalized 0.0-1.0 but max value is {im.max()}. "
                f"Dividing input by 255."
            )
            im = im.float() / 255.0

        return im

    def __iter__(self):
        """Returns an iterator object."""
        self.count = 0
        return self

    def __next__(self):
        """Return next item in the iterator."""
        if self.count == 1:
            raise StopIteration
        self.count += 1
        return self.paths, self.im0, [""] * self.bs

    def __len__(self):
        """Returns the batch size."""
        return self.bs

__init__(im0)

Inizializza Tensor Dataloader.

Codice sorgente in ultralytics/data/loaders.py
def __init__(self, im0) -> None:
    """Initialize Tensor Dataloader."""
    self.im0 = self._single_check(im0)
    self.bs = self.im0.shape[0]
    self.mode = "image"
    self.paths = [getattr(im, "filename", f"image{i}.jpg") for i, im in enumerate(im0)]

__iter__()

Restituisce un oggetto iteratore.

Codice sorgente in ultralytics/data/loaders.py
def __iter__(self):
    """Returns an iterator object."""
    self.count = 0
    return self

__len__()

Restituisce la dimensione del lotto.

Codice sorgente in ultralytics/data/loaders.py
def __len__(self):
    """Returns the batch size."""
    return self.bs

__next__()

Restituisce il prossimo elemento dell'iteratore.

Codice sorgente in ultralytics/data/loaders.py
def __next__(self):
    """Return next item in the iterator."""
    if self.count == 1:
        raise StopIteration
    self.count += 1
    return self.paths, self.im0, [""] * self.bs



ultralytics.data.loaders.autocast_list(source)

Unisce un elenco di sorgenti di tipo diverso in un elenco di array numpy o immagini PIL.

Codice sorgente in ultralytics/data/loaders.py
def autocast_list(source):
    """Merges a list of source of different types into a list of numpy arrays or PIL images."""
    files = []
    for im in source:
        if isinstance(im, (str, Path)):  # filename or uri
            files.append(Image.open(requests.get(im, stream=True).raw if str(im).startswith("http") else im))
        elif isinstance(im, (Image.Image, np.ndarray)):  # PIL or np Image
            files.append(im)
        else:
            raise TypeError(
                f"type {type(im).__name__} is not a supported Ultralytics prediction source type. \n"
                f"See https://docs.ultralytics.com/modes/predict for supported source types."
            )

    return files



ultralytics.data.loaders.get_best_youtube_url(url, method='pytube')

Recupera l'URL del flusso video MP4 di migliore qualità da un determinato video di YouTube.

Questa funzione utilizza il metodo specificato per estrarre le informazioni sul video da YouTube. Supporta i seguenti metodi: - "pytube": Utilizza la libreria pytube per recuperare i flussi video. - "pafy": Utilizza la libreria pafy per recuperare i flussi video. - "yt-dlp": Utilizza la libreria yt-dlp per recuperare i flussi video.

La funzione trova quindi il formato MP4 di massima qualità che ha un codec video ma non un codec audio e restituisce l'URL di questo flusso video. URL di questo flusso video.

Parametri:

Nome Tipo Descrizione Predefinito
url str

L'URL del video di YouTube.

richiesto
method str

Il metodo da utilizzare per estrarre le informazioni sui video. L'impostazione predefinita è "pytube". Altre opzioni sono "pafy" e "yt-dlp".

'pytube'

Restituzione:

Tipo Descrizione
str

L'URL del flusso video MP4 di migliore qualità, oppure Nessuno se non viene trovato alcun flusso adatto.

Codice sorgente in ultralytics/data/loaders.py
def get_best_youtube_url(url, method="pytube"):
    """
    Retrieves the URL of the best quality MP4 video stream from a given YouTube video.

    This function uses the specified method to extract the video info from YouTube. It supports the following methods:
    - "pytube": Uses the pytube library to fetch the video streams.
    - "pafy": Uses the pafy library to fetch the video streams.
    - "yt-dlp": Uses the yt-dlp library to fetch the video streams.

    The function then finds the highest quality MP4 format that has a video codec but no audio codec, and returns the
    URL of this video stream.

    Args:
        url (str): The URL of the YouTube video.
        method (str): The method to use for extracting video info. Default is "pytube". Other options are "pafy" and
            "yt-dlp".

    Returns:
        (str): The URL of the best quality MP4 video stream, or None if no suitable stream is found.
    """
    if method == "pytube":
        check_requirements("pytube")
        from pytube import YouTube

        streams = YouTube(url).streams.filter(file_extension="mp4", only_video=True)
        streams = sorted(streams, key=lambda s: s.resolution, reverse=True)  # sort streams by resolution
        for stream in streams:
            if stream.resolution and int(stream.resolution[:-1]) >= 1080:  # check if resolution is at least 1080p
                return stream.url

    elif method == "pafy":
        check_requirements(("pafy", "youtube_dl==2020.12.2"))
        import pafy  # noqa

        return pafy.new(url).getbestvideo(preftype="mp4").url

    elif method == "yt-dlp":
        check_requirements("yt-dlp")
        import yt_dlp

        with yt_dlp.YoutubeDL({"quiet": True}) as ydl:
            info_dict = ydl.extract_info(url, download=False)  # extract info
        for f in reversed(info_dict.get("formats", [])):  # reversed because best is usually last
            # Find a format with video codec, no audio, *.mp4 extension at least 1920x1080 size
            good_size = (f.get("width") or 0) >= 1920 or (f.get("height") or 0) >= 1080
            if good_size and f["vcodec"] != "none" and f["acodec"] == "none" and f["ext"] == "mp4":
                return f.get("url")





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (6), Burhan-Q (1), Laughing-q (1)