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Бсылка для ultralytics/data/loaders.py

ΠŸΡ€ΠΈΠΌΠ΅Ρ‡Π°Π½ΠΈΠ΅

Π­Ρ‚ΠΎΡ‚ Ρ„Π°ΠΉΠ» доступСн ΠΏΠΎ адрСсу https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/data/loaders .py. Если Ρ‚Ρ‹ Π·Π°ΠΌΠ΅Ρ‚ΠΈΠ» ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡƒ, поТалуйста, ΠΏΠΎΠΌΠΎΠ³ΠΈ ΠΈΡΠΏΡ€Π°Π²ΠΈΡ‚ΡŒ Π΅Π΅, ΠΎΡ‚ΠΏΡ€Π°Π²ΠΈΠ² Pull Request πŸ› οΈ. Бпасибо πŸ™!



ultralytics.data.loaders.SourceTypes dataclass

Класс для прСдставлСния Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… Ρ‚ΠΈΠΏΠΎΠ² источников Π²Ρ…ΠΎΠ΄Π½Ρ‹Ρ… Π΄Π°Π½Π½Ρ‹Ρ… для прСдсказаний.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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 для Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… Ρ‚ΠΈΠΏΠΎΠ² Π²ΠΈΠ΄Π΅ΠΎΠΏΠΎΡ‚ΠΎΠΊΠΎΠ², ΠŸΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΈΠ²Π°Π΅Ρ‚ ΠΏΠΎΡ‚ΠΎΠΊΠΈ RTSP, RTMP, HTTP ΠΈ TCP.

Атрибуты:

Имя Вип ОписаниС
sources str

ΠŸΡƒΡ‚ΠΈ ΠΈΠ»ΠΈ URL-адрСса исходных Π²Ρ…ΠΎΠ΄Π½Ρ‹Ρ… Π²ΠΈΠ΄Π΅ΠΎΠΏΠΎΡ‚ΠΎΠΊΠΎΠ².

vid_stride int

Частота ΠΊΠ°Π΄Ρ€ΠΎΠ² Π²ΠΈΠ΄Π΅ΠΎ, ΠΏΠΎ ΡƒΠΌΠΎΠ»Ρ‡Π°Π½ΠΈΡŽ Ρ€Π°Π²Π½Π° 1.

buffer bool

НуТно Π»ΠΈ Π±ΡƒΡ„Π΅Ρ€ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ Π²Ρ…ΠΎΠ΄Π½Ρ‹Π΅ ΠΏΠΎΡ‚ΠΎΠΊΠΈ, ΠΏΠΎ ΡƒΠΌΠΎΠ»Ρ‡Π°Π½ΠΈΡŽ False.

running bool

Π€Π»Π°Π³, ΡƒΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‰ΠΈΠΉ, Π·Π°ΠΏΡƒΡ‰Π΅Π½ Π»ΠΈ ΠΏΠΎΡ‚ΠΎΠΊΠΎΠ²Ρ‹ΠΉ ΠΏΠΎΡ‚ΠΎΠΊ.

mode str

Установи Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ "stream", ΠΎΠ·Π½Π°Ρ‡Π°ΡŽΡ‰Π΅Π΅ Π·Π°Ρ…Π²Π°Ρ‚ Π² Ρ€Π΅Π°Π»ΡŒΠ½ΠΎΠΌ Π²Ρ€Π΅ΠΌΠ΅Π½ΠΈ.

imgs list

Бписок ΠΊΠ°Π΄Ρ€ΠΎΠ² изобраТСния для ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠΎΡ‚ΠΎΠΊΠ°.

fps list

Бписок FPS для ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠΎΡ‚ΠΎΠΊΠ°.

frames list

Бписок ΠΎΠ±Ρ‰ΠΈΡ… ΠΊΠ°Π΄Ρ€ΠΎΠ² для ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠΎΡ‚ΠΎΠΊΠ°.

threads list

Бписок ΠΏΠΎΡ‚ΠΎΠΊΠΎΠ² для ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠΎΡ‚ΠΎΠΊΠ°.

shape list

Бписок Ρ„ΠΈΠ³ΡƒΡ€ для ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠΎΡ‚ΠΎΠΊΠ°.

caps list

Бписок ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ² cv2.VideoCapture для ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠΎΡ‚ΠΎΠΊΠ°.

bs int

Π Π°Π·ΠΌΠ΅Ρ€ ΠΏΠ°Ρ€Ρ‚ΠΈΠΈ для ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ.

ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹:

Имя ОписаниС
__init__

Π˜Π½ΠΈΡ†ΠΈΠ°Π»ΠΈΠ·ΠΈΡ€ΡƒΠΉ Π·Π°Π³Ρ€ΡƒΠ·Ρ‡ΠΈΠΊ ΠΏΠΎΡ‚ΠΎΠΊΠΎΠ².

update

Π‘Ρ‡ΠΈΡ‚Ρ‹Π²Π°ΠΉ ΠΊΠ°Π΄Ρ€Ρ‹ ΠΏΠΎΡ‚ΠΎΠΊΠ° Π² ΠΏΠΎΡ‚ΠΎΠΊΠ΅ Π΄Π΅ΠΌΠΎΠ½Π°.

close

Π—Π°ΠΊΡ€ΠΎΠΉ Π·Π°Π³Ρ€ΡƒΠ·Ρ‡ΠΈΠΊ ΠΏΠΎΡ‚ΠΎΠΊΠΎΠ² ΠΈ освободи рСсурсы.

__iter__

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΠΎΡ€Π° для Π΄Π°Π½Π½ΠΎΠ³ΠΎ класса.

__next__

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ исходныС ΠΏΡƒΡ‚ΠΈ, трансформированныС ΠΈ ΠΎΡ€ΠΈΠ³ΠΈΠ½Π°Π»ΡŒΠ½Ρ‹Π΅ изобраТСния для ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ.

__len__

Π’Π΅Ρ€Π½ΠΈ Π΄Π»ΠΈΠ½Ρƒ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚Π° источников.

ΠŸΡ€ΠΈΠΌΠ΅Ρ€
yolo predict source='rtsp://example.com/media.mp4'
Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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 βœ… ({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)

Π˜Π½ΠΈΡ†ΠΈΠ°Π»ΠΈΠ·ΠΈΡ€ΡƒΠΉ ΠΏΠ΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Π΅ экзСмпляра ΠΈ ΠΏΡ€ΠΎΠ²Π΅Ρ€ΡŒ соотвСтствиС Ρ„ΠΎΡ€ΠΌΡ‹ Π²Ρ…ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΏΠΎΡ‚ΠΎΠΊΠ°.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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 βœ… ({self.frames[i]} frames of shape {w}x{h} at {self.fps[i]:.2f} FPS)")
        self.threads[i].start()
    LOGGER.info("")  # newline

__iter__()

Π˜Ρ‚Π΅Ρ€ΠΈΡ€ΡƒΠ΅Ρ‚ ΠΏΠΎΡ‚ΠΎΠΊ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ YOLO ΠΈ Π·Π°Π½ΠΎΠ²ΠΎ ΠΎΡ‚ΠΊΡ€Ρ‹Π²Π°Π΅Ρ‚ Π½Π΅ Ρ€Π΅Π°Π³ΠΈΡ€ΡƒΡŽΡ‰ΠΈΠ΅ ΠΏΠΎΡ‚ΠΎΠΊΠΈ.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² ultralytics/data/loaders.py
def __iter__(self):
    """Iterates through YOLO image feed and re-opens unresponsive streams."""
    self.count = -1
    return self

__len__()

Π’Π΅Ρ€Π½ΠΈ Π΄Π»ΠΈΠ½Ρƒ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚Π° источников.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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__()

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ исходныС ΠΏΡƒΡ‚ΠΈ, трансформированныС ΠΈ ΠΎΡ€ΠΈΠ³ΠΈΠ½Π°Π»ΡŒΠ½Ρ‹Π΅ изобраТСния для ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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()

Π—Π°ΠΊΡ€ΠΎΠΉ Π·Π°Π³Ρ€ΡƒΠ·Ρ‡ΠΈΠΊ ΠΏΠΎΡ‚ΠΎΠΊΠΎΠ² ΠΈ освободи рСсурсы.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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)

Π§ΠΈΡ‚Π°Ρ‚ΡŒ ΠΏΠΎΡ‚ΠΎΠΊ i ΠšΠ°Π΄Ρ€Ρ‹ Π² ΠΏΠΎΡ‚ΠΎΠΊΠ΅ Π΄Π΅ΠΌΠΎΠ½Π°.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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 Π‘ΠΊΡ€ΠΈΠ½ΡˆΠΎΡ‚ dataloader.

Π­Ρ‚ΠΎΡ‚ класс управляСт Π·Π°Π³Ρ€ΡƒΠ·ΠΊΠΎΠΉ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΡΠΊΡ€ΠΈΠ½ΡˆΠΎΡ‚ΠΎΠ² для ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ YOLOv8. ΠŸΠΎΠ΄Ρ…ΠΎΠ΄ΠΈΡ‚ для использования с yolo predict source=screen.

Атрибуты:

Имя Вип ОписаниС
source str

Π’Ρ…ΠΎΠ΄ источника, ΡƒΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‰ΠΈΠΉ, ΠΊΠ°ΠΊΠΎΠΉ экран Π½ΡƒΠΆΠ½ΠΎ Π·Π°Ρ…Π²Π°Ρ‚ΠΈΡ‚ΡŒ.

screen int

НомСр экрана, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹ΠΉ Π½ΡƒΠΆΠ½ΠΎ Π·Π°Ρ…Π²Π°Ρ‚ΠΈΡ‚ΡŒ.

left int

ЛСвая ΠΊΠΎΠΎΡ€Π΄ΠΈΠ½Π°Ρ‚Π° для области Π·Π°Ρ…Π²Π°Ρ‚Π° экрана.

top int

ВСрхняя ΠΊΠΎΠΎΡ€Π΄ΠΈΠ½Π°Ρ‚Π° для области Π·Π°Ρ…Π²Π°Ρ‚Π° экрана.

width int

Π¨ΠΈΡ€ΠΈΠ½Π° области Π·Π°Ρ…Π²Π°Ρ‚Π° экрана.

height int

Высота области Π·Π°Ρ…Π²Π°Ρ‚Π° экрана.

mode str

Установи Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ "stream", ΠΎΠ·Π½Π°Ρ‡Π°ΡŽΡ‰Π΅Π΅ Π·Π°Ρ…Π²Π°Ρ‚ Π² Ρ€Π΅Π°Π»ΡŒΠ½ΠΎΠΌ Π²Ρ€Π΅ΠΌΠ΅Π½ΠΈ.

frame int

Π‘Ρ‡Π΅Ρ‚Ρ‡ΠΈΠΊ Π·Π°Ρ…Π²Π°Ρ‡Π΅Π½Π½Ρ‹Ρ… ΠΊΠ°Π΄Ρ€ΠΎΠ².

sct mss

ΠžΠ±ΡŠΠ΅ΠΊΡ‚ для Π·Π°Ρ…Π²Π°Ρ‚Π° экрана ΠΈΠ· mss Π±ΠΈΠ±Π»ΠΈΠΎΡ‚Π΅ΠΊΠ°.

bs int

Π Π°Π·ΠΌΠ΅Ρ€ ΠΏΠ°Ρ€Ρ‚ΠΈΠΈ, установлСнный Π½Π° 1.

monitor dict

ΠŸΡ€ΠΎΡΠ»Π΅Π΄ΠΈ Π·Π° дСталями ΠΊΠΎΠ½Ρ„ΠΈΠ³ΡƒΡ€Π°Ρ†ΠΈΠΈ.

ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹:

Имя ОписаниС
__iter__

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΠΎΡ€Π°.

__next__

Π—Π°Ρ…Π²Π°Ρ‚Ρ‹Π²Π°Π΅Ρ‚ ΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΠΉ ΡΠΊΡ€ΠΈΠ½ΡˆΠΎΡ‚ ΠΈ Π²ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ Π΅Π³ΠΎ.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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)

Π˜ΡΡ‚ΠΎΡ‡Π½ΠΈΠΊ = [screen_number left top width height] (пиксСли).

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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__()

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΠΎΡ€ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚Π°.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² ultralytics/data/loaders.py
def __iter__(self):
    """Returns an iterator of the object."""
    return self

__next__()

Π—Π°Ρ…Π²Π°Ρ‚ экрана mss: ΠΏΠΎΠ»ΡƒΡ‡ΠΈ Π½Π΅ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½Ρ‹Π΅ пиксСли с экрана Π² Π²ΠΈΠ΄Π΅ массива np.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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 image/video dataloader.

Π­Ρ‚ΠΎΡ‚ класс управляСт Π·Π°Π³Ρ€ΡƒΠ·ΠΊΠΎΠΉ ΠΈ ΠΏΡ€Π΅Π΄Π²Π°Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΎΠΉ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΠΈ Π²ΠΈΠ΄Π΅ΠΎΠ΄Π°Π½Π½Ρ‹Ρ… для YOLOv8. Он ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΈΠ²Π°Π΅Ρ‚ Π·Π°Π³Ρ€ΡƒΠ·ΠΊΡƒ ΠΈΠ· Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΎΠ², Π²ΠΊΠ»ΡŽΡ‡Π°Ρ ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹Π΅ Ρ„Π°ΠΉΠ»Ρ‹ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, Π²ΠΈΠ΄Π΅ΠΎΡ„Π°ΠΉΠ»Ρ‹ ΠΈ списки ΠΏΡƒΡ‚Π΅ΠΉ ΠΊ изобраТСниям ΠΈ Π²ΠΈΠ΄Π΅ΠΎ.

Атрибуты:

Имя Вип ОписаниС
files list

Бписок ΠΏΡƒΡ‚Π΅ΠΉ ΠΊ Ρ„Π°ΠΉΠ»Π°ΠΌ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΠΈ Π²ΠΈΠ΄Π΅ΠΎ.

nf int

ΠžΠ±Ρ‰Π΅Π΅ количСство Ρ„Π°ΠΉΠ»ΠΎΠ² (ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΠΈ Π²ΠΈΠ΄Π΅ΠΎ).

video_flag list

Π€Π»Π°Π³ΠΈ, ΡƒΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‰ΠΈΠ΅ Π½Π° Ρ‚ΠΎ, являСтся Π»ΠΈ Ρ„Π°ΠΉΠ» Π²ΠΈΠ΄Π΅ΠΎ (True) ΠΈΠ»ΠΈ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠ΅ΠΌ (False).

mode str

Π’Π΅ΠΊΡƒΡ‰ΠΈΠΉ Ρ€Π΅ΠΆΠΈΠΌ, "ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠ΅" ΠΈΠ»ΠΈ "Π²ΠΈΠ΄Π΅ΠΎ".

vid_stride int

Π‘Ρ‚Ρ€ΠΎΠΊΠ° для частоты ΠΊΠ°Π΄Ρ€ΠΎΠ² Π²ΠΈΠ΄Π΅ΠΎ, ΠΏΠΎ ΡƒΠΌΠΎΠ»Ρ‡Π°Π½ΠΈΡŽ Ρ€Π°Π²Π½Π° 1.

bs int

Π Π°Π·ΠΌΠ΅Ρ€ ΠΏΠ°Ρ€Ρ‚ΠΈΠΈ, установлСнный Π½Π° 1 для этого класса.

cap VideoCapture

ΠžΠ±ΡŠΠ΅ΠΊΡ‚ Π²ΠΈΠ΄Π΅ΠΎΠ·Π°Ρ…Π²Π°Ρ‚Π° для OpenCV.

frame int

Π‘Ρ‡Π΅Ρ‚Ρ‡ΠΈΠΊ ΠΊΠ°Π΄Ρ€ΠΎΠ² для Π²ΠΈΠ΄Π΅ΠΎ.

frames int

ΠžΠ±Ρ‰Π΅Π΅ количСство ΠΊΠ°Π΄Ρ€ΠΎΠ² Π² Π²ΠΈΠ΄Π΅ΠΎ.

count int

Π‘Ρ‡Π΅Ρ‚Ρ‡ΠΈΠΊ ΠΈΡ‚Π΅Ρ€Π°Ρ†ΠΈΠΉ, ΠΈΠ½ΠΈΡ†ΠΈΠ°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹ΠΉ Π½Π° 0 Π²ΠΎ врСмя __iter__().

ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹:

Имя ОписаниС
_new_video

Π‘ΠΎΠ·Π΄Π°ΠΉ Π½ΠΎΠ²Ρ‹ΠΉ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ cv2.VideoCapture для Π·Π°Π΄Π°Π½Π½ΠΎΠΉ Π²ΠΈΠ΄Π΅ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠΊΠΈ.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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 len(imgs) > 0:
                    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)

Π˜Π½ΠΈΡ†ΠΈΠ°Π»ΠΈΠ·ΠΈΡ€ΡƒΠΉ Dataloader ΠΈ ΠΏΠΎΠ΄Π½ΠΈΠΌΠ°ΠΉ ΠΎΡˆΠΈΠ±ΠΊΡƒ FileNotFoundError, Ссли Ρ„Π°ΠΉΠ» Π½Π΅ Π½Π°ΠΉΠ΄Π΅Π½.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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__()

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΠΎΡ€Π° для VideoStream ΠΈΠ»ΠΈ ImageFolder.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² ultralytics/data/loaders.py
def __iter__(self):
    """Returns an iterator object for VideoStream or ImageFolder."""
    self.count = 0
    return self

__len__()

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ количСство ΠΏΠ°Ρ€Ρ‚ΠΈΠΉ Π² ΠΎΠ±ΡŠΠ΅ΠΊΡ‚Π΅.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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__()

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ ΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΡƒΡŽ ΠΏΠΎΡ€Ρ†ΠΈΡŽ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΠΈΠ»ΠΈ Π²ΠΈΠ΄Π΅ΠΎΠΊΠ°Π΄Ρ€ΠΎΠ² вмСстС с путями ΠΊ Π½ΠΈΠΌ ΠΈ ΠΌΠ΅Ρ‚Π°Π΄Π°Π½Π½Ρ‹ΠΌΠΈ.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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 len(imgs) > 0:
                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

Π—Π°Π³Ρ€ΡƒΠΆΠ°ΠΉ изобраТСния ΠΈΠ· массивов PIL ΠΈ Numpy для ΠΏΠ°ΠΊΠ΅Ρ‚Π½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ.

Π­Ρ‚ΠΎΡ‚ класс ΠΏΡ€Π΅Π΄Π½Π°Π·Π½Π°Ρ‡Π΅Π½ для управлСния Π·Π°Π³Ρ€ΡƒΠ·ΠΊΠΎΠΉ ΠΈ ΠΏΡ€Π΅Π΄Π²Π°Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΎΠΉ Π΄Π°Π½Π½Ρ‹Ρ… ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΠΈΠ· Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΎΠ² PIL ΠΈ Numpy. Он выполняСт Π±Π°Π·ΠΎΠ²ΡƒΡŽ ΠΏΡ€ΠΎΠ²Π΅Ρ€ΠΊΡƒ ΠΈ ΠΏΡ€Π΅ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΎΠ², Ρ‡Ρ‚ΠΎΠ±Ρ‹ ΡƒΠ±Π΅Π΄ΠΈΡ‚ΡŒΡΡ, Ρ‡Ρ‚ΠΎ изобраТСния ΠΈΠΌΠ΅ΡŽΡ‚ Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΡ‹ΠΉ Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ для ΠΏΠΎΡΠ»Π΅Π΄ΡƒΡŽΡ‰Π΅ΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ. ΠΏΠΎΡΠ»Π΅Π΄ΡƒΡŽΡ‰Π΅ΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ.

Атрибуты:

Имя Вип ОписаниС
paths list

Бписок ΠΏΡƒΡ‚Π΅ΠΉ ΠΊ изобраТСниям ΠΈΠ»ΠΈ Π°Π²Ρ‚ΠΎΠ³Π΅Π½Π΅Ρ€ΠΈΡ€ΡƒΠ΅ΠΌΡ‹Ρ… ΠΈΠΌΠ΅Π½ Ρ„Π°ΠΉΠ»ΠΎΠ².

im0 list

Бписок ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, хранящихся Π² Π²ΠΈΠ΄Π΅ массивов Numpy.

mode str

Π’ΠΈΠΏ ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°Π΅ΠΌΡ‹Ρ… Π΄Π°Π½Π½Ρ‹Ρ…, ΠΏΠΎ ΡƒΠΌΠΎΠ»Ρ‡Π°Π½ΠΈΡŽ - "ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠ΅".

bs int

Π Π°Π·ΠΌΠ΅Ρ€ ΠΏΠ°Ρ€Ρ‚ΠΈΠΈ, эквивалСнтный Π΄Π»ΠΈΠ½Π΅ im0.

ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹:

Имя ОписаниС
_single_check

ΠŸΡ€ΠΎΠ²Π΅Ρ€ΡŒ ΠΈ ΠΎΡ‚Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΡ€ΡƒΠΉ ΠΎΠ΄Π½ΠΎ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠ΅ Π² массив Numpy.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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)

Π˜Π½ΠΈΡ†ΠΈΠ°Π»ΠΈΠ·ΠΈΡ€ΡƒΠΉ PIL ΠΈ Numpy Dataloader.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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__()

Π’ΠΊΠ»ΡŽΡ‡Π°Π΅Ρ‚ ΠΈΡ‚Π΅Ρ€Π°Ρ†ΠΈΡŽ для класса LoadPilAndNumpy.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² ultralytics/data/loaders.py
def __iter__(self):
    """Enables iteration for class LoadPilAndNumpy."""
    self.count = 0
    return self

__len__()

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ Π΄Π»ΠΈΠ½Ρƒ Π°Ρ‚Ρ€ΠΈΠ±ΡƒΡ‚Π° 'im0'.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² ultralytics/data/loaders.py
def __len__(self):
    """Returns the length of the 'im0' attribute."""
    return len(self.im0)

__next__()

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ ΠΏΡƒΡ‚ΠΈ ΠΊ партиям, изобраТСния, ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½Ρ‹Π΅ изобраТСния, None, ''.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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

Π—Π°Π³Ρ€ΡƒΠ·ΠΈ изобраТСния ΠΈΠ· Π΄Π°Π½Π½Ρ‹Ρ… torch.Tensor .

Π­Ρ‚ΠΎΡ‚ класс управляСт Π·Π°Π³Ρ€ΡƒΠ·ΠΊΠΎΠΉ ΠΈ ΠΏΡ€Π΅Π΄Π²Π°Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΎΠΉ Π΄Π°Π½Π½Ρ‹Ρ… изобраТСния ΠΈΠ· Ρ‚Π΅Π½Π·ΠΎΡ€ΠΎΠ² PyTorch для дальнСйшСй ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ.

Атрибуты:

Имя Вип ОписаниС
im0 Tensor

Π’Ρ…ΠΎΠ΄Π½ΠΎΠΉ tensor , содСрТащий ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠ΅ (изобраТСния).

bs int

Π Π°Π·ΠΌΠ΅Ρ€ ΠΏΠ°Ρ€Ρ‚ΠΈΠΈ, опрСдСляСмый ΠΏΠΎ Ρ„ΠΎΡ€ΠΌΠ΅ im0.

mode str

Π’Π΅ΠΊΡƒΡ‰ΠΈΠΉ Ρ€Π΅ΠΆΠΈΠΌ, установлСнный Π½Π° 'image'.

paths list

Бписок ΠΏΡƒΡ‚Π΅ΠΉ ΠΊ изобраТСниям ΠΈΠ»ΠΈ ΠΈΠΌΠ΅Π½ Ρ„Π°ΠΉΠ»ΠΎΠ².

count int

Π‘Ρ‡Π΅Ρ‚Ρ‡ΠΈΠΊ ΠΈΡ‚Π΅Ρ€Π°Ρ†ΠΈΠΉ, ΠΈΠ½ΠΈΡ†ΠΈΠ°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹ΠΉ Π½Π° 0 Π²ΠΎ врСмя __iter__().

ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹:

Имя ОписаниС
_single_check

ΠŸΡ€ΠΎΠ²Π΅Ρ€ΡŒ ΠΈ, Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ, ΠΈΠ·ΠΌΠ΅Π½ΠΈ Π²Ρ…ΠΎΠ΄Π½Ρ‹Π΅ Π΄Π°Π½Π½Ρ‹Π΅ tensor.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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)

Π˜Π½ΠΈΡ†ΠΈΠ°Π»ΠΈΠ·ΠΈΡ€ΡƒΠΉ Tensor Dataloader.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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__()

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΠΎΡ€Π°.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² ultralytics/data/loaders.py
def __iter__(self):
    """Returns an iterator object."""
    self.count = 0
    return self

__len__()

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ Ρ€Π°Π·ΠΌΠ΅Ρ€ ΠΏΠ°Ρ€Ρ‚ΠΈΠΈ.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² ultralytics/data/loaders.py
def __len__(self):
    """Returns the batch size."""
    return self.bs

__next__()

Π’ΠΎΠ·Π²Ρ€Π°Ρ‰Π°ΠΉ ΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΠΉ элСмСнт Π² ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΠΎΡ€Π΅.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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)

ΠžΠ±ΡŠΠ΅Π΄ΠΈΠ½ΡΠ΅Ρ‚ список источников Ρ€Π°Π·Π½Ρ‹Ρ… Ρ‚ΠΈΠΏΠΎΠ² Π² список массивов numpy ΠΈΠ»ΠΈ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ PIL.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² 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, use_pafy=True)

ΠŸΠΎΠ»ΡƒΡ‡Π°Π΅Ρ‚ URL-адрСс Π²ΠΈΠ΄Π΅ΠΎΠΏΠΎΡ‚ΠΎΠΊΠ° MP4 Π½Π°ΠΈΠ»ΡƒΡ‡ΡˆΠ΅Π³ΠΎ качСства ΠΈΠ· Π·Π°Π΄Π°Π½Π½ΠΎΠ³ΠΎ Π²ΠΈΠ΄Π΅ΠΎ Π½Π° YouTube.

Π­Ρ‚Π° функция ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅Ρ‚ Π±ΠΈΠ±Π»ΠΈΠΎΡ‚Π΅ΠΊΡƒ pafy ΠΈΠ»ΠΈ yt_dlp для извлСчСния ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΎ Π²ΠΈΠ΄Π΅ΠΎ с YouTube. Π—Π°Ρ‚Π΅ΠΌ ΠΎΠ½Π° Π½Π°Ρ…ΠΎΠ΄ΠΈΡ‚ самый качСствСнный качСство Ρ„ΠΎΡ€ΠΌΠ°Ρ‚Π° MP4, Π² ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠΌ Π΅ΡΡ‚ΡŒ Π²ΠΈΠ΄Π΅ΠΎΠΊΠΎΠ΄Π΅ΠΊ, Π½ΠΎ Π½Π΅Ρ‚ Π°ΡƒΠ΄ΠΈΠΎΠΊΠΎΠ΄Π΅ΠΊΠ°, ΠΈ Π²ΠΎΠ·Π²Ρ€Π°Ρ‰Π°Π΅Ρ‚ URL-адрСс этого Π²ΠΈΠ΄Π΅ΠΎΠΏΠΎΡ‚ΠΎΠΊΠ°.

ΠŸΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ‹:

Имя Π’ΠΈΠΏ ОписаниС По ΡƒΠΌΠΎΠ»Ρ‡Π°Π½ΠΈΡŽ
url str

URL-адрСс Π²ΠΈΠ΄Π΅ΠΎΡ€ΠΎΠ»ΠΈΠΊΠ° Π½Π° YouTube.

трСбуСтся
use_pafy bool

Π˜ΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠΉ ΠΏΠ°ΠΊΠ΅Ρ‚ pafy, ΠΏΠΎ ΡƒΠΌΠΎΠ»Ρ‡Π°Π½ΠΈΡŽ=True, ΠΈΠ½Π°Ρ‡Π΅ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠΉ ΠΏΠ°ΠΊΠ΅Ρ‚ yt_dlp.

True

ВозвращаСтся:

Вип ОписаниС
str

URL-адрСс Π²ΠΈΠ΄Π΅ΠΎΠΏΠΎΡ‚ΠΎΠΊΠ° MP4 Π½Π°ΠΈΠ»ΡƒΡ‡ΡˆΠ΅Π³ΠΎ качСства ΠΈΠ»ΠΈ None, Ссли подходящий ΠΏΠΎΡ‚ΠΎΠΊ Π½Π΅ Π½Π°ΠΉΠ΄Π΅Π½.

Π˜ΡΡ…ΠΎΠ΄Π½Ρ‹ΠΉ ΠΊΠΎΠ΄ Π² ultralytics/data/loaders.py
def get_best_youtube_url(url, use_pafy=True):
    """
    Retrieves the URL of the best quality MP4 video stream from a given YouTube video.

    This function uses the pafy or yt_dlp library to extract the video info from YouTube. It then finds the highest
    quality MP4 format that has video codec but no audio codec, and returns the URL of this video stream.

    Args:
        url (str): The URL of the YouTube video.
        use_pafy (bool): Use the pafy package, default=True, otherwise use yt_dlp package.

    Returns:
        (str): The URL of the best quality MP4 video stream, or None if no suitable stream is found.
    """
    if use_pafy:
        check_requirements(("pafy", "youtube_dl==2020.12.2"))
        import pafy  # noqa

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





Боздано 2023-11-12, ОбновлСно 2024-05-08
Авторы: Burhan-Q (1), Glenn-jocher (4), Laughing-q (1)