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Referência para ultralytics/utils/plotting.py

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Este ficheiro está disponível em https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/utils/plotting .py. Se detectares um problema, por favor ajuda a corrigi-lo contribuindo com um Pull Request 🛠️. Obrigado 🙏!



ultralytics.utils.plotting.Colors

Ultralytics paleta de cores predefinida https://ultralytics.com/.

Esta classe fornece métodos para trabalhar com a paleta de cores Ultralytics , incluindo a conversão de códigos de cores hexadecimais para valores RGB.

Atributos:

Nome Tipo Descrição
palette list of tuple

Lista de valores de cor RGB.

n int

O número de cores na paleta.

pose_palette ndarray

Uma matriz de paleta de cores específica com o tipo de dados np.uint8.

Código fonte em ultralytics/utils/plotting.py
class Colors:
    """
    Ultralytics default color palette https://ultralytics.com/.

    This class provides methods to work with the Ultralytics color palette, including converting hex color codes to
    RGB values.

    Attributes:
        palette (list of tuple): List of RGB color values.
        n (int): The number of colors in the palette.
        pose_palette (np.ndarray): A specific color palette array with dtype np.uint8.
    """

    def __init__(self):
        """Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values()."""
        hexs = (
            "042AFF",
            "0BDBEB",
            "F3F3F3",
            "00DFB7",
            "111F68",
            "FF6FDD",
            "FF444F",
            "CCED00",
            "00F344",
            "BD00FF",
            "00B4FF",
            "DD00BA",
            "00FFFF",
            "26C000",
            "01FFB3",
            "7D24FF",
            "7B0068",
            "FF1B6C",
            "FC6D2F",
            "A2FF0B",
        )
        self.palette = [self.hex2rgb(f"#{c}") for c in hexs]
        self.n = len(self.palette)
        self.pose_palette = np.array(
            [
                [255, 128, 0],
                [255, 153, 51],
                [255, 178, 102],
                [230, 230, 0],
                [255, 153, 255],
                [153, 204, 255],
                [255, 102, 255],
                [255, 51, 255],
                [102, 178, 255],
                [51, 153, 255],
                [255, 153, 153],
                [255, 102, 102],
                [255, 51, 51],
                [153, 255, 153],
                [102, 255, 102],
                [51, 255, 51],
                [0, 255, 0],
                [0, 0, 255],
                [255, 0, 0],
                [255, 255, 255],
            ],
            dtype=np.uint8,
        )

    def __call__(self, i, bgr=False):
        """Converts hex color codes to RGB values."""
        c = self.palette[int(i) % self.n]
        return (c[2], c[1], c[0]) if bgr else c

    @staticmethod
    def hex2rgb(h):
        """Converts hex color codes to RGB values (i.e. default PIL order)."""
        return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4))

__call__(i, bgr=False)

Converte os códigos de cor hexadecimais em valores RGB.

Código fonte em ultralytics/utils/plotting.py
def __call__(self, i, bgr=False):
    """Converts hex color codes to RGB values."""
    c = self.palette[int(i) % self.n]
    return (c[2], c[1], c[0]) if bgr else c

__init__()

Inicializa as cores como hex = matplotlib.colors.TABLEAU_COLORS.values().

Código fonte em ultralytics/utils/plotting.py
def __init__(self):
    """Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values()."""
    hexs = (
        "042AFF",
        "0BDBEB",
        "F3F3F3",
        "00DFB7",
        "111F68",
        "FF6FDD",
        "FF444F",
        "CCED00",
        "00F344",
        "BD00FF",
        "00B4FF",
        "DD00BA",
        "00FFFF",
        "26C000",
        "01FFB3",
        "7D24FF",
        "7B0068",
        "FF1B6C",
        "FC6D2F",
        "A2FF0B",
    )
    self.palette = [self.hex2rgb(f"#{c}") for c in hexs]
    self.n = len(self.palette)
    self.pose_palette = np.array(
        [
            [255, 128, 0],
            [255, 153, 51],
            [255, 178, 102],
            [230, 230, 0],
            [255, 153, 255],
            [153, 204, 255],
            [255, 102, 255],
            [255, 51, 255],
            [102, 178, 255],
            [51, 153, 255],
            [255, 153, 153],
            [255, 102, 102],
            [255, 51, 51],
            [153, 255, 153],
            [102, 255, 102],
            [51, 255, 51],
            [0, 255, 0],
            [0, 0, 255],
            [255, 0, 0],
            [255, 255, 255],
        ],
        dtype=np.uint8,
    )

hex2rgb(h) staticmethod

Converte os códigos de cores hexadecimais em valores RGB (ou seja, ordem PIL padrão).

Código fonte em ultralytics/utils/plotting.py
@staticmethod
def hex2rgb(h):
    """Converts hex color codes to RGB values (i.e. default PIL order)."""
    return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4))



ultralytics.utils.plotting.Annotator

Ultralytics Anotador para mosaicos e JPGs de treino/avaliação e anotações de previsões.

Atributos:

Nome Tipo Descrição
im Image.Image or numpy array

A imagem a anotar.

pil bool

Se deves utilizar PIL ou cv2 para desenhar anotações.

font truetype or load_default

Tipo de letra utilizado para as anotações de texto.

lw float

Largura da linha para o desenho.

skeleton List[List[int]]

Estrutura de esqueleto para pontos-chave.

limb_color List[int]

Paleta de cores para os membros.

kpt_color List[int]

Paleta de cores para os pontos-chave.

Código fonte em ultralytics/utils/plotting.py
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class Annotator:
    """
    Ultralytics Annotator for train/val mosaics and JPGs and predictions annotations.

    Attributes:
        im (Image.Image or numpy array): The image to annotate.
        pil (bool): Whether to use PIL or cv2 for drawing annotations.
        font (ImageFont.truetype or ImageFont.load_default): Font used for text annotations.
        lw (float): Line width for drawing.
        skeleton (List[List[int]]): Skeleton structure for keypoints.
        limb_color (List[int]): Color palette for limbs.
        kpt_color (List[int]): Color palette for keypoints.
    """

    def __init__(self, im, line_width=None, font_size=None, font="Arial.ttf", pil=False, example="abc"):
        """Initialize the Annotator class with image and line width along with color palette for keypoints and limbs."""
        non_ascii = not is_ascii(example)  # non-latin labels, i.e. asian, arabic, cyrillic
        input_is_pil = isinstance(im, Image.Image)
        self.pil = pil or non_ascii or input_is_pil
        self.lw = line_width or max(round(sum(im.size if input_is_pil else im.shape) / 2 * 0.003), 2)
        if self.pil:  # use PIL
            self.im = im if input_is_pil else Image.fromarray(im)
            self.draw = ImageDraw.Draw(self.im)
            try:
                font = check_font("Arial.Unicode.ttf" if non_ascii else font)
                size = font_size or max(round(sum(self.im.size) / 2 * 0.035), 12)
                self.font = ImageFont.truetype(str(font), size)
            except Exception:
                self.font = ImageFont.load_default()
            # Deprecation fix for w, h = getsize(string) -> _, _, w, h = getbox(string)
            if check_version(pil_version, "9.2.0"):
                self.font.getsize = lambda x: self.font.getbbox(x)[2:4]  # text width, height
        else:  # use cv2
            assert im.data.contiguous, "Image not contiguous. Apply np.ascontiguousarray(im) to Annotator input images."
            self.im = im if im.flags.writeable else im.copy()
            self.tf = max(self.lw - 1, 1)  # font thickness
            self.sf = self.lw / 3  # font scale
        # Pose
        self.skeleton = [
            [16, 14],
            [14, 12],
            [17, 15],
            [15, 13],
            [12, 13],
            [6, 12],
            [7, 13],
            [6, 7],
            [6, 8],
            [7, 9],
            [8, 10],
            [9, 11],
            [2, 3],
            [1, 2],
            [1, 3],
            [2, 4],
            [3, 5],
            [4, 6],
            [5, 7],
        ]

        self.limb_color = colors.pose_palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
        self.kpt_color = colors.pose_palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
        self.dark_colors = {
            (235, 219, 11),
            (243, 243, 243),
            (183, 223, 0),
            (221, 111, 255),
            (0, 237, 204),
            (68, 243, 0),
            (255, 255, 0),
            (179, 255, 1),
            (11, 255, 162),
        }
        self.light_colors = {
            (255, 42, 4),
            (79, 68, 255),
            (255, 0, 189),
            (255, 180, 0),
            (186, 0, 221),
            (0, 192, 38),
            (255, 36, 125),
            (104, 0, 123),
            (108, 27, 255),
            (47, 109, 252),
            (104, 31, 17),
        }

    def get_txt_color(self, color=(128, 128, 128), txt_color=(255, 255, 255)):
        """Assign text color based on background color."""
        if color in self.dark_colors:
            return 104, 31, 17
        elif color in self.light_colors:
            return 255, 255, 255
        else:
            return txt_color

    def circle_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), margin=2):
        """
        Draws a label with a background rectangle centered within a given bounding box.

        Args:
            box (tuple): The bounding box coordinates (x1, y1, x2, y2).
            label (str): The text label to be displayed.
            color (tuple, optional): The background color of the rectangle (R, G, B).
            txt_color (tuple, optional): The color of the text (R, G, B).
            margin (int, optional): The margin between the text and the rectangle border.
        """

        # If label have more than 3 characters, skip other characters, due to circle size
        if len(label) > 3:
            print(
                f"Length of label is {len(label)}, initial 3 label characters will be considered for circle annotation!"
            )
            label = label[:3]

        # Calculate the center of the box
        x_center, y_center = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
        # Get the text size
        text_size = cv2.getTextSize(str(label), cv2.FONT_HERSHEY_SIMPLEX, self.sf - 0.15, self.tf)[0]
        # Calculate the required radius to fit the text with the margin
        required_radius = int(((text_size[0] ** 2 + text_size[1] ** 2) ** 0.5) / 2) + margin
        # Draw the circle with the required radius
        cv2.circle(self.im, (x_center, y_center), required_radius, color, -1)
        # Calculate the position for the text
        text_x = x_center - text_size[0] // 2
        text_y = y_center + text_size[1] // 2
        # Draw the text
        cv2.putText(
            self.im,
            str(label),
            (text_x, text_y),
            cv2.FONT_HERSHEY_SIMPLEX,
            self.sf - 0.15,
            self.get_txt_color(color, txt_color),
            self.tf,
            lineType=cv2.LINE_AA,
        )

    def text_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), margin=5):
        """
        Draws a label with a background rectangle centered within a given bounding box.

        Args:
            box (tuple): The bounding box coordinates (x1, y1, x2, y2).
            label (str): The text label to be displayed.
            color (tuple, optional): The background color of the rectangle (R, G, B).
            txt_color (tuple, optional): The color of the text (R, G, B).
            margin (int, optional): The margin between the text and the rectangle border.
        """

        # Calculate the center of the bounding box
        x_center, y_center = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
        # Get the size of the text
        text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, self.sf - 0.1, self.tf)[0]
        # Calculate the top-left corner of the text (to center it)
        text_x = x_center - text_size[0] // 2
        text_y = y_center + text_size[1] // 2
        # Calculate the coordinates of the background rectangle
        rect_x1 = text_x - margin
        rect_y1 = text_y - text_size[1] - margin
        rect_x2 = text_x + text_size[0] + margin
        rect_y2 = text_y + margin
        # Draw the background rectangle
        cv2.rectangle(self.im, (rect_x1, rect_y1), (rect_x2, rect_y2), color, -1)
        # Draw the text on top of the rectangle
        cv2.putText(
            self.im,
            label,
            (text_x, text_y),
            cv2.FONT_HERSHEY_SIMPLEX,
            self.sf - 0.1,
            self.get_txt_color(color, txt_color),
            self.tf,
            lineType=cv2.LINE_AA,
        )

    def box_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), rotated=False):
        """
        Draws a bounding box to image with label.

        Args:
            box (tuple): The bounding box coordinates (x1, y1, x2, y2).
            label (str): The text label to be displayed.
            color (tuple, optional): The background color of the rectangle (R, G, B).
            txt_color (tuple, optional): The color of the text (R, G, B).
            rotated (bool, optional): Variable used to check if task is OBB
        """

        txt_color = self.get_txt_color(color, txt_color)
        if isinstance(box, torch.Tensor):
            box = box.tolist()
        if self.pil or not is_ascii(label):
            if rotated:
                p1 = box[0]
                # NOTE: PIL-version polygon needs tuple type.
                self.draw.polygon([tuple(b) for b in box], width=self.lw, outline=color)
            else:
                p1 = (box[0], box[1])
                self.draw.rectangle(box, width=self.lw, outline=color)  # box
            if label:
                w, h = self.font.getsize(label)  # text width, height
                outside = p1[1] - h >= 0  # label fits outside box
                self.draw.rectangle(
                    (p1[0], p1[1] - h if outside else p1[1], p1[0] + w + 1, p1[1] + 1 if outside else p1[1] + h + 1),
                    fill=color,
                )
                # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
                self.draw.text((p1[0], p1[1] - h if outside else p1[1]), label, fill=txt_color, font=self.font)
        else:  # cv2
            if rotated:
                p1 = [int(b) for b in box[0]]
                # NOTE: cv2-version polylines needs np.asarray type.
                cv2.polylines(self.im, [np.asarray(box, dtype=int)], True, color, self.lw)
            else:
                p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
                cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
            if label:
                w, h = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.tf)[0]  # text width, height
                outside = p1[1] - h >= 3
                p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
                cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
                cv2.putText(
                    self.im,
                    label,
                    (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
                    0,
                    self.sf,
                    txt_color,
                    thickness=self.tf,
                    lineType=cv2.LINE_AA,
                )

    def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
        """
        Plot masks on image.

        Args:
            masks (tensor): Predicted masks on cuda, shape: [n, h, w]
            colors (List[List[Int]]): Colors for predicted masks, [[r, g, b] * n]
            im_gpu (tensor): Image is in cuda, shape: [3, h, w], range: [0, 1]
            alpha (float): Mask transparency: 0.0 fully transparent, 1.0 opaque
            retina_masks (bool): Whether to use high resolution masks or not. Defaults to False.
        """

        if self.pil:
            # Convert to numpy first
            self.im = np.asarray(self.im).copy()
        if len(masks) == 0:
            self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
        if im_gpu.device != masks.device:
            im_gpu = im_gpu.to(masks.device)
        colors = torch.tensor(colors, device=masks.device, dtype=torch.float32) / 255.0  # shape(n,3)
        colors = colors[:, None, None]  # shape(n,1,1,3)
        masks = masks.unsqueeze(3)  # shape(n,h,w,1)
        masks_color = masks * (colors * alpha)  # shape(n,h,w,3)

        inv_alpha_masks = (1 - masks * alpha).cumprod(0)  # shape(n,h,w,1)
        mcs = masks_color.max(dim=0).values  # shape(n,h,w,3)

        im_gpu = im_gpu.flip(dims=[0])  # flip channel
        im_gpu = im_gpu.permute(1, 2, 0).contiguous()  # shape(h,w,3)
        im_gpu = im_gpu * inv_alpha_masks[-1] + mcs
        im_mask = im_gpu * 255
        im_mask_np = im_mask.byte().cpu().numpy()
        self.im[:] = im_mask_np if retina_masks else ops.scale_image(im_mask_np, self.im.shape)
        if self.pil:
            # Convert im back to PIL and update draw
            self.fromarray(self.im)

    def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True, conf_thres=0.25):
        """
        Plot keypoints on the image.

        Args:
            kpts (tensor): Predicted keypoints with shape [17, 3]. Each keypoint has (x, y, confidence).
            shape (tuple): Image shape as a tuple (h, w), where h is the height and w is the width.
            radius (int, optional): Radius of the drawn keypoints. Default is 5.
            kpt_line (bool, optional): If True, the function will draw lines connecting keypoints
                                       for human pose. Default is True.

        Note:
            `kpt_line=True` currently only supports human pose plotting.
        """

        if self.pil:
            # Convert to numpy first
            self.im = np.asarray(self.im).copy()
        nkpt, ndim = kpts.shape
        is_pose = nkpt == 17 and ndim in {2, 3}
        kpt_line &= is_pose  # `kpt_line=True` for now only supports human pose plotting
        for i, k in enumerate(kpts):
            color_k = [int(x) for x in self.kpt_color[i]] if is_pose else colors(i)
            x_coord, y_coord = k[0], k[1]
            if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
                if len(k) == 3:
                    conf = k[2]
                    if conf < conf_thres:
                        continue
                cv2.circle(self.im, (int(x_coord), int(y_coord)), radius, color_k, -1, lineType=cv2.LINE_AA)

        if kpt_line:
            ndim = kpts.shape[-1]
            for i, sk in enumerate(self.skeleton):
                pos1 = (int(kpts[(sk[0] - 1), 0]), int(kpts[(sk[0] - 1), 1]))
                pos2 = (int(kpts[(sk[1] - 1), 0]), int(kpts[(sk[1] - 1), 1]))
                if ndim == 3:
                    conf1 = kpts[(sk[0] - 1), 2]
                    conf2 = kpts[(sk[1] - 1), 2]
                    if conf1 < conf_thres or conf2 < conf_thres:
                        continue
                if pos1[0] % shape[1] == 0 or pos1[1] % shape[0] == 0 or pos1[0] < 0 or pos1[1] < 0:
                    continue
                if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0:
                    continue
                cv2.line(self.im, pos1, pos2, [int(x) for x in self.limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
        if self.pil:
            # Convert im back to PIL and update draw
            self.fromarray(self.im)

    def rectangle(self, xy, fill=None, outline=None, width=1):
        """Add rectangle to image (PIL-only)."""
        self.draw.rectangle(xy, fill, outline, width)

    def text(self, xy, text, txt_color=(255, 255, 255), anchor="top", box_style=False):
        """Adds text to an image using PIL or cv2."""
        if anchor == "bottom":  # start y from font bottom
            w, h = self.font.getsize(text)  # text width, height
            xy[1] += 1 - h
        if self.pil:
            if box_style:
                w, h = self.font.getsize(text)
                self.draw.rectangle((xy[0], xy[1], xy[0] + w + 1, xy[1] + h + 1), fill=txt_color)
                # Using `txt_color` for background and draw fg with white color
                txt_color = (255, 255, 255)
            if "\n" in text:
                lines = text.split("\n")
                _, h = self.font.getsize(text)
                for line in lines:
                    self.draw.text(xy, line, fill=txt_color, font=self.font)
                    xy[1] += h
            else:
                self.draw.text(xy, text, fill=txt_color, font=self.font)
        else:
            if box_style:
                w, h = cv2.getTextSize(text, 0, fontScale=self.sf, thickness=self.tf)[0]  # text width, height
                outside = xy[1] - h >= 3
                p2 = xy[0] + w, xy[1] - h - 3 if outside else xy[1] + h + 3
                cv2.rectangle(self.im, xy, p2, txt_color, -1, cv2.LINE_AA)  # filled
                # Using `txt_color` for background and draw fg with white color
                txt_color = (255, 255, 255)
            cv2.putText(self.im, text, xy, 0, self.sf, txt_color, thickness=self.tf, lineType=cv2.LINE_AA)

    def fromarray(self, im):
        """Update self.im from a numpy array."""
        self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
        self.draw = ImageDraw.Draw(self.im)

    def result(self):
        """Return annotated image as array."""
        return np.asarray(self.im)

    def show(self, title=None):
        """Show the annotated image."""
        Image.fromarray(np.asarray(self.im)[..., ::-1]).show(title)

    def save(self, filename="image.jpg"):
        """Save the annotated image to 'filename'."""
        cv2.imwrite(filename, np.asarray(self.im))

    def get_bbox_dimension(self, bbox=None):
        """
        Calculate the area of a bounding box.

        Args:
            bbox (tuple): Bounding box coordinates in the format (x_min, y_min, x_max, y_max).

        Returns:
            angle (degree): Degree value of angle between three points
        """

        x_min, y_min, x_max, y_max = bbox
        width = x_max - x_min
        height = y_max - y_min
        return width, height, width * height

    def draw_region(self, reg_pts=None, color=(0, 255, 0), thickness=5):
        """
        Draw region line.

        Args:
            reg_pts (list): Region Points (for line 2 points, for region 4 points)
            color (tuple): Region Color value
            thickness (int): Region area thickness value
        """

        cv2.polylines(self.im, [np.array(reg_pts, dtype=np.int32)], isClosed=True, color=color, thickness=thickness)

    def draw_centroid_and_tracks(self, track, color=(255, 0, 255), track_thickness=2):
        """
        Draw centroid point and track trails.

        Args:
            track (list): object tracking points for trails display
            color (tuple): tracks line color
            track_thickness (int): track line thickness value
        """

        points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
        cv2.polylines(self.im, [points], isClosed=False, color=color, thickness=track_thickness)
        cv2.circle(self.im, (int(track[-1][0]), int(track[-1][1])), track_thickness * 2, color, -1)

    def queue_counts_display(self, label, points=None, region_color=(255, 255, 255), txt_color=(0, 0, 0)):
        """
        Displays queue counts on an image centered at the points with customizable font size and colors.

        Args:
            label (str): queue counts label
            points (tuple): region points for center point calculation to display text
            region_color (RGB): queue region color
            txt_color (RGB): text display color
        """

        x_values = [point[0] for point in points]
        y_values = [point[1] for point in points]
        center_x = sum(x_values) // len(points)
        center_y = sum(y_values) // len(points)

        text_size = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.tf)[0]
        text_width = text_size[0]
        text_height = text_size[1]

        rect_width = text_width + 20
        rect_height = text_height + 20
        rect_top_left = (center_x - rect_width // 2, center_y - rect_height // 2)
        rect_bottom_right = (center_x + rect_width // 2, center_y + rect_height // 2)
        cv2.rectangle(self.im, rect_top_left, rect_bottom_right, region_color, -1)

        text_x = center_x - text_width // 2
        text_y = center_y + text_height // 2

        # Draw text
        cv2.putText(
            self.im,
            label,
            (text_x, text_y),
            0,
            fontScale=self.sf,
            color=txt_color,
            thickness=self.tf,
            lineType=cv2.LINE_AA,
        )

    def display_objects_labels(self, im0, text, txt_color, bg_color, x_center, y_center, margin):
        """
        Display the bounding boxes labels in parking management app.

        Args:
            im0 (ndarray): inference image
            text (str): object/class name
            txt_color (bgr color): display color for text foreground
            bg_color (bgr color): display color for text background
            x_center (float): x position center point for bounding box
            y_center (float): y position center point for bounding box
            margin (int): gap between text and rectangle for better display
        """

        text_size = cv2.getTextSize(text, 0, fontScale=self.sf, thickness=self.tf)[0]
        text_x = x_center - text_size[0] // 2
        text_y = y_center + text_size[1] // 2

        rect_x1 = text_x - margin
        rect_y1 = text_y - text_size[1] - margin
        rect_x2 = text_x + text_size[0] + margin
        rect_y2 = text_y + margin
        cv2.rectangle(im0, (rect_x1, rect_y1), (rect_x2, rect_y2), bg_color, -1)
        cv2.putText(im0, text, (text_x, text_y), 0, self.sf, txt_color, self.tf, lineType=cv2.LINE_AA)

    def display_analytics(self, im0, text, txt_color, bg_color, margin):
        """
        Display the overall statistics for parking lots
        Args:
            im0 (ndarray): inference image
            text (dict): labels dictionary
            txt_color (bgr color): display color for text foreground
            bg_color (bgr color): display color for text background
            margin (int): gap between text and rectangle for better display
        """

        horizontal_gap = int(im0.shape[1] * 0.02)
        vertical_gap = int(im0.shape[0] * 0.01)
        text_y_offset = 0
        for label, value in text.items():
            txt = f"{label}: {value}"
            text_size = cv2.getTextSize(txt, 0, self.sf, self.tf)[0]
            if text_size[0] < 5 or text_size[1] < 5:
                text_size = (5, 5)
            text_x = im0.shape[1] - text_size[0] - margin * 2 - horizontal_gap
            text_y = text_y_offset + text_size[1] + margin * 2 + vertical_gap
            rect_x1 = text_x - margin * 2
            rect_y1 = text_y - text_size[1] - margin * 2
            rect_x2 = text_x + text_size[0] + margin * 2
            rect_y2 = text_y + margin * 2
            cv2.rectangle(im0, (rect_x1, rect_y1), (rect_x2, rect_y2), bg_color, -1)
            cv2.putText(im0, txt, (text_x, text_y), 0, self.sf, txt_color, self.tf, lineType=cv2.LINE_AA)
            text_y_offset = rect_y2

    @staticmethod
    def estimate_pose_angle(a, b, c):
        """
        Calculate the pose angle for object.

        Args:
            a (float) : The value of pose point a
            b (float): The value of pose point b
            c (float): The value o pose point c

        Returns:
            angle (degree): Degree value of angle between three points
        """

        a, b, c = np.array(a), np.array(b), np.array(c)
        radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0])
        angle = np.abs(radians * 180.0 / np.pi)
        if angle > 180.0:
            angle = 360 - angle
        return angle

    def draw_specific_points(self, keypoints, indices=None, shape=(640, 640), radius=2, conf_thres=0.25):
        """
        Draw specific keypoints for gym steps counting.

        Args:
            keypoints (list): list of keypoints data to be plotted
            indices (list): keypoints ids list to be plotted
            shape (tuple): imgsz for model inference
            radius (int): Keypoint radius value
        """

        if indices is None:
            indices = [2, 5, 7]
        for i, k in enumerate(keypoints):
            if i in indices:
                x_coord, y_coord = k[0], k[1]
                if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
                    if len(k) == 3:
                        conf = k[2]
                        if conf < conf_thres:
                            continue
                    cv2.circle(self.im, (int(x_coord), int(y_coord)), radius, (0, 255, 0), -1, lineType=cv2.LINE_AA)
        return self.im

    def plot_angle_and_count_and_stage(
        self, angle_text, count_text, stage_text, center_kpt, color=(104, 31, 17), txt_color=(255, 255, 255)
    ):
        """
        Plot the pose angle, count value and step stage.

        Args:
            angle_text (str): angle value for workout monitoring
            count_text (str): counts value for workout monitoring
            stage_text (str): stage decision for workout monitoring
            center_kpt (int): centroid pose index for workout monitoring
            color (tuple): text background color for workout monitoring
            txt_color (tuple): text foreground color for workout monitoring
        """

        angle_text, count_text, stage_text = (f" {angle_text:.2f}", f"Steps : {count_text}", f" {stage_text}")

        # Draw angle
        (angle_text_width, angle_text_height), _ = cv2.getTextSize(angle_text, 0, self.sf, self.tf)
        angle_text_position = (int(center_kpt[0]), int(center_kpt[1]))
        angle_background_position = (angle_text_position[0], angle_text_position[1] - angle_text_height - 5)
        angle_background_size = (angle_text_width + 2 * 5, angle_text_height + 2 * 5 + (self.tf * 2))
        cv2.rectangle(
            self.im,
            angle_background_position,
            (
                angle_background_position[0] + angle_background_size[0],
                angle_background_position[1] + angle_background_size[1],
            ),
            color,
            -1,
        )
        cv2.putText(self.im, angle_text, angle_text_position, 0, self.sf, txt_color, self.tf)

        # Draw Counts
        (count_text_width, count_text_height), _ = cv2.getTextSize(count_text, 0, self.sf, self.tf)
        count_text_position = (angle_text_position[0], angle_text_position[1] + angle_text_height + 20)
        count_background_position = (
            angle_background_position[0],
            angle_background_position[1] + angle_background_size[1] + 5,
        )
        count_background_size = (count_text_width + 10, count_text_height + 10 + self.tf)

        cv2.rectangle(
            self.im,
            count_background_position,
            (
                count_background_position[0] + count_background_size[0],
                count_background_position[1] + count_background_size[1],
            ),
            color,
            -1,
        )
        cv2.putText(self.im, count_text, count_text_position, 0, self.sf, txt_color, self.tf)

        # Draw Stage
        (stage_text_width, stage_text_height), _ = cv2.getTextSize(stage_text, 0, self.sf, self.tf)
        stage_text_position = (int(center_kpt[0]), int(center_kpt[1]) + angle_text_height + count_text_height + 40)
        stage_background_position = (stage_text_position[0], stage_text_position[1] - stage_text_height - 5)
        stage_background_size = (stage_text_width + 10, stage_text_height + 10)

        cv2.rectangle(
            self.im,
            stage_background_position,
            (
                stage_background_position[0] + stage_background_size[0],
                stage_background_position[1] + stage_background_size[1],
            ),
            color,
            -1,
        )
        cv2.putText(self.im, stage_text, stage_text_position, 0, self.sf, txt_color, self.tf)

    def seg_bbox(self, mask, mask_color=(255, 0, 255), det_label=None, track_label=None):
        """
        Function for drawing segmented object in bounding box shape.

        Args:
            mask (list): masks data list for instance segmentation area plotting
            mask_color (tuple): mask foreground color
            det_label (str): Detection label text
            track_label (str): Tracking label text
        """

        cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)

        label = f"Track ID: {track_label}" if track_label else det_label
        text_size, _ = cv2.getTextSize(label, 0, 0.7, 1)

        cv2.rectangle(
            self.im,
            (int(mask[0][0]) - text_size[0] // 2 - 10, int(mask[0][1]) - text_size[1] - 10),
            (int(mask[0][0]) + text_size[0] // 2 + 5, int(mask[0][1] + 5)),
            mask_color,
            -1,
        )

        cv2.putText(
            self.im, label, (int(mask[0][0]) - text_size[0] // 2, int(mask[0][1]) - 5), 0, 0.7, (255, 255, 255), 2
        )

    def plot_distance_and_line(self, distance_m, distance_mm, centroids, line_color, centroid_color):
        """
        Plot the distance and line on frame.

        Args:
            distance_m (float): Distance between two bbox centroids in meters.
            distance_mm (float): Distance between two bbox centroids in millimeters.
            centroids (list): Bounding box centroids data.
            line_color (RGB): Distance line color.
            centroid_color (RGB): Bounding box centroid color.
        """

        (text_width_m, text_height_m), _ = cv2.getTextSize(f"Distance M: {distance_m:.2f}m", 0, self.sf, self.tf)
        cv2.rectangle(self.im, (15, 25), (15 + text_width_m + 10, 25 + text_height_m + 20), line_color, -1)
        cv2.putText(
            self.im,
            f"Distance M: {distance_m:.2f}m",
            (20, 50),
            0,
            self.sf,
            centroid_color,
            self.tf,
            cv2.LINE_AA,
        )

        (text_width_mm, text_height_mm), _ = cv2.getTextSize(f"Distance MM: {distance_mm:.2f}mm", 0, self.sf, self.tf)
        cv2.rectangle(self.im, (15, 75), (15 + text_width_mm + 10, 75 + text_height_mm + 20), line_color, -1)
        cv2.putText(
            self.im,
            f"Distance MM: {distance_mm:.2f}mm",
            (20, 100),
            0,
            self.sf,
            centroid_color,
            self.tf,
            cv2.LINE_AA,
        )

        cv2.line(self.im, centroids[0], centroids[1], line_color, 3)
        cv2.circle(self.im, centroids[0], 6, centroid_color, -1)
        cv2.circle(self.im, centroids[1], 6, centroid_color, -1)

    def visioneye(self, box, center_point, color=(235, 219, 11), pin_color=(255, 0, 255)):
        """
        Function for pinpoint human-vision eye mapping and plotting.

        Args:
            box (list): Bounding box coordinates
            center_point (tuple): center point for vision eye view
            color (tuple): object centroid and line color value
            pin_color (tuple): visioneye point color value
        """

        center_bbox = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
        cv2.circle(self.im, center_point, self.tf * 2, pin_color, -1)
        cv2.circle(self.im, center_bbox, self.tf * 2, color, -1)
        cv2.line(self.im, center_point, center_bbox, color, self.tf)

__init__(im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc')

Inicializa a classe Annotator com a imagem e a largura da linha, juntamente com a paleta de cores para os pontos-chave e os membros.

Código fonte em ultralytics/utils/plotting.py
def __init__(self, im, line_width=None, font_size=None, font="Arial.ttf", pil=False, example="abc"):
    """Initialize the Annotator class with image and line width along with color palette for keypoints and limbs."""
    non_ascii = not is_ascii(example)  # non-latin labels, i.e. asian, arabic, cyrillic
    input_is_pil = isinstance(im, Image.Image)
    self.pil = pil or non_ascii or input_is_pil
    self.lw = line_width or max(round(sum(im.size if input_is_pil else im.shape) / 2 * 0.003), 2)
    if self.pil:  # use PIL
        self.im = im if input_is_pil else Image.fromarray(im)
        self.draw = ImageDraw.Draw(self.im)
        try:
            font = check_font("Arial.Unicode.ttf" if non_ascii else font)
            size = font_size or max(round(sum(self.im.size) / 2 * 0.035), 12)
            self.font = ImageFont.truetype(str(font), size)
        except Exception:
            self.font = ImageFont.load_default()
        # Deprecation fix for w, h = getsize(string) -> _, _, w, h = getbox(string)
        if check_version(pil_version, "9.2.0"):
            self.font.getsize = lambda x: self.font.getbbox(x)[2:4]  # text width, height
    else:  # use cv2
        assert im.data.contiguous, "Image not contiguous. Apply np.ascontiguousarray(im) to Annotator input images."
        self.im = im if im.flags.writeable else im.copy()
        self.tf = max(self.lw - 1, 1)  # font thickness
        self.sf = self.lw / 3  # font scale
    # Pose
    self.skeleton = [
        [16, 14],
        [14, 12],
        [17, 15],
        [15, 13],
        [12, 13],
        [6, 12],
        [7, 13],
        [6, 7],
        [6, 8],
        [7, 9],
        [8, 10],
        [9, 11],
        [2, 3],
        [1, 2],
        [1, 3],
        [2, 4],
        [3, 5],
        [4, 6],
        [5, 7],
    ]

    self.limb_color = colors.pose_palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
    self.kpt_color = colors.pose_palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
    self.dark_colors = {
        (235, 219, 11),
        (243, 243, 243),
        (183, 223, 0),
        (221, 111, 255),
        (0, 237, 204),
        (68, 243, 0),
        (255, 255, 0),
        (179, 255, 1),
        (11, 255, 162),
    }
    self.light_colors = {
        (255, 42, 4),
        (79, 68, 255),
        (255, 0, 189),
        (255, 180, 0),
        (186, 0, 221),
        (0, 192, 38),
        (255, 36, 125),
        (104, 0, 123),
        (108, 27, 255),
        (47, 109, 252),
        (104, 31, 17),
    }

box_label(box, label='', color=(128, 128, 128), txt_color=(255, 255, 255), rotated=False)

Draws a bounding box to image with label.

Parâmetros:

Nome Tipo Descrição Predefinição
box tuple

The bounding box coordinates (x1, y1, x2, y2).

necessário
label str

The text label to be displayed.

''
color tuple

The background color of the rectangle (R, G, B).

(128, 128, 128)
txt_color tuple

The color of the text (R, G, B).

(255, 255, 255)
rotated bool

Variable used to check if task is OBB

False
Código fonte em ultralytics/utils/plotting.py
def box_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), rotated=False):
    """
    Draws a bounding box to image with label.

    Args:
        box (tuple): The bounding box coordinates (x1, y1, x2, y2).
        label (str): The text label to be displayed.
        color (tuple, optional): The background color of the rectangle (R, G, B).
        txt_color (tuple, optional): The color of the text (R, G, B).
        rotated (bool, optional): Variable used to check if task is OBB
    """

    txt_color = self.get_txt_color(color, txt_color)
    if isinstance(box, torch.Tensor):
        box = box.tolist()
    if self.pil or not is_ascii(label):
        if rotated:
            p1 = box[0]
            # NOTE: PIL-version polygon needs tuple type.
            self.draw.polygon([tuple(b) for b in box], width=self.lw, outline=color)
        else:
            p1 = (box[0], box[1])
            self.draw.rectangle(box, width=self.lw, outline=color)  # box
        if label:
            w, h = self.font.getsize(label)  # text width, height
            outside = p1[1] - h >= 0  # label fits outside box
            self.draw.rectangle(
                (p1[0], p1[1] - h if outside else p1[1], p1[0] + w + 1, p1[1] + 1 if outside else p1[1] + h + 1),
                fill=color,
            )
            # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
            self.draw.text((p1[0], p1[1] - h if outside else p1[1]), label, fill=txt_color, font=self.font)
    else:  # cv2
        if rotated:
            p1 = [int(b) for b in box[0]]
            # NOTE: cv2-version polylines needs np.asarray type.
            cv2.polylines(self.im, [np.asarray(box, dtype=int)], True, color, self.lw)
        else:
            p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
            cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
        if label:
            w, h = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.tf)[0]  # text width, height
            outside = p1[1] - h >= 3
            p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
            cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
            cv2.putText(
                self.im,
                label,
                (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
                0,
                self.sf,
                txt_color,
                thickness=self.tf,
                lineType=cv2.LINE_AA,
            )

circle_label(box, label='', color=(128, 128, 128), txt_color=(255, 255, 255), margin=2)

Draws a label with a background rectangle centered within a given bounding box.

Parâmetros:

Nome Tipo Descrição Predefinição
box tuple

The bounding box coordinates (x1, y1, x2, y2).

necessário
label str

The text label to be displayed.

''
color tuple

The background color of the rectangle (R, G, B).

(128, 128, 128)
txt_color tuple

The color of the text (R, G, B).

(255, 255, 255)
margin int

The margin between the text and the rectangle border.

2
Código fonte em ultralytics/utils/plotting.py
def circle_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), margin=2):
    """
    Draws a label with a background rectangle centered within a given bounding box.

    Args:
        box (tuple): The bounding box coordinates (x1, y1, x2, y2).
        label (str): The text label to be displayed.
        color (tuple, optional): The background color of the rectangle (R, G, B).
        txt_color (tuple, optional): The color of the text (R, G, B).
        margin (int, optional): The margin between the text and the rectangle border.
    """

    # If label have more than 3 characters, skip other characters, due to circle size
    if len(label) > 3:
        print(
            f"Length of label is {len(label)}, initial 3 label characters will be considered for circle annotation!"
        )
        label = label[:3]

    # Calculate the center of the box
    x_center, y_center = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
    # Get the text size
    text_size = cv2.getTextSize(str(label), cv2.FONT_HERSHEY_SIMPLEX, self.sf - 0.15, self.tf)[0]
    # Calculate the required radius to fit the text with the margin
    required_radius = int(((text_size[0] ** 2 + text_size[1] ** 2) ** 0.5) / 2) + margin
    # Draw the circle with the required radius
    cv2.circle(self.im, (x_center, y_center), required_radius, color, -1)
    # Calculate the position for the text
    text_x = x_center - text_size[0] // 2
    text_y = y_center + text_size[1] // 2
    # Draw the text
    cv2.putText(
        self.im,
        str(label),
        (text_x, text_y),
        cv2.FONT_HERSHEY_SIMPLEX,
        self.sf - 0.15,
        self.get_txt_color(color, txt_color),
        self.tf,
        lineType=cv2.LINE_AA,
    )

display_analytics(im0, text, txt_color, bg_color, margin)

Mostra as estatísticas gerais dos parques de estacionamento Args: im0 (ndarray): imagem de inferência text (dict): dicionário de etiquetas txt_color (cor da grelha): cor de apresentação do texto em primeiro plano bg_color (bgr color): cor de fundo do texto margin (int): espaço entre o texto e o retângulo para uma melhor visualização

Código fonte em ultralytics/utils/plotting.py
def display_analytics(self, im0, text, txt_color, bg_color, margin):
    """
    Display the overall statistics for parking lots
    Args:
        im0 (ndarray): inference image
        text (dict): labels dictionary
        txt_color (bgr color): display color for text foreground
        bg_color (bgr color): display color for text background
        margin (int): gap between text and rectangle for better display
    """

    horizontal_gap = int(im0.shape[1] * 0.02)
    vertical_gap = int(im0.shape[0] * 0.01)
    text_y_offset = 0
    for label, value in text.items():
        txt = f"{label}: {value}"
        text_size = cv2.getTextSize(txt, 0, self.sf, self.tf)[0]
        if text_size[0] < 5 or text_size[1] < 5:
            text_size = (5, 5)
        text_x = im0.shape[1] - text_size[0] - margin * 2 - horizontal_gap
        text_y = text_y_offset + text_size[1] + margin * 2 + vertical_gap
        rect_x1 = text_x - margin * 2
        rect_y1 = text_y - text_size[1] - margin * 2
        rect_x2 = text_x + text_size[0] + margin * 2
        rect_y2 = text_y + margin * 2
        cv2.rectangle(im0, (rect_x1, rect_y1), (rect_x2, rect_y2), bg_color, -1)
        cv2.putText(im0, txt, (text_x, text_y), 0, self.sf, txt_color, self.tf, lineType=cv2.LINE_AA)
        text_y_offset = rect_y2

display_objects_labels(im0, text, txt_color, bg_color, x_center, y_center, margin)

Apresenta as etiquetas das caixas delimitadoras na aplicação de gestão de estacionamento.

Parâmetros:

Nome Tipo Descrição Predefinição
im0 ndarray

imagem de inferência

necessário
text str

nome do objeto/classe

necessário
txt_color bgr color

cor de apresentação do texto em primeiro plano

necessário
bg_color bgr color

cor de apresentação do fundo do texto

necessário
x_center float

ponto central da posição x para a caixa delimitadora

necessário
y_center float

ponto central da posição y para a caixa delimitadora

necessário
margin int

espaço entre o texto e o retângulo para melhor visualização

necessário
Código fonte em ultralytics/utils/plotting.py
def display_objects_labels(self, im0, text, txt_color, bg_color, x_center, y_center, margin):
    """
    Display the bounding boxes labels in parking management app.

    Args:
        im0 (ndarray): inference image
        text (str): object/class name
        txt_color (bgr color): display color for text foreground
        bg_color (bgr color): display color for text background
        x_center (float): x position center point for bounding box
        y_center (float): y position center point for bounding box
        margin (int): gap between text and rectangle for better display
    """

    text_size = cv2.getTextSize(text, 0, fontScale=self.sf, thickness=self.tf)[0]
    text_x = x_center - text_size[0] // 2
    text_y = y_center + text_size[1] // 2

    rect_x1 = text_x - margin
    rect_y1 = text_y - text_size[1] - margin
    rect_x2 = text_x + text_size[0] + margin
    rect_y2 = text_y + margin
    cv2.rectangle(im0, (rect_x1, rect_y1), (rect_x2, rect_y2), bg_color, -1)
    cv2.putText(im0, text, (text_x, text_y), 0, self.sf, txt_color, self.tf, lineType=cv2.LINE_AA)

draw_centroid_and_tracks(track, color=(255, 0, 255), track_thickness=2)

Desenha o ponto centróide e segue os rastos.

Parâmetros:

Nome Tipo Descrição Predefinição
track list

pontos de seguimento de objectos para visualização de trilhos

necessário
color tuple

cor da linha das pistas

(255, 0, 255)
track_thickness int

valor da espessura da linha da pista

2
Código fonte em ultralytics/utils/plotting.py
def draw_centroid_and_tracks(self, track, color=(255, 0, 255), track_thickness=2):
    """
    Draw centroid point and track trails.

    Args:
        track (list): object tracking points for trails display
        color (tuple): tracks line color
        track_thickness (int): track line thickness value
    """

    points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
    cv2.polylines(self.im, [points], isClosed=False, color=color, thickness=track_thickness)
    cv2.circle(self.im, (int(track[-1][0]), int(track[-1][1])), track_thickness * 2, color, -1)

draw_region(reg_pts=None, color=(0, 255, 0), thickness=5)

Desenha a linha da região.

Parâmetros:

Nome Tipo Descrição Predefinição
reg_pts list

Pontos de região (para pontos de linha 2, para pontos de região 4)

None
color tuple

Região Valor da cor

(0, 255, 0)
thickness int

Valor da espessura da área da região

5
Código fonte em ultralytics/utils/plotting.py
def draw_region(self, reg_pts=None, color=(0, 255, 0), thickness=5):
    """
    Draw region line.

    Args:
        reg_pts (list): Region Points (for line 2 points, for region 4 points)
        color (tuple): Region Color value
        thickness (int): Region area thickness value
    """

    cv2.polylines(self.im, [np.array(reg_pts, dtype=np.int32)], isClosed=True, color=color, thickness=thickness)

draw_specific_points(keypoints, indices=None, shape=(640, 640), radius=2, conf_thres=0.25)

Desenha pontos-chave específicos para a contagem dos passos do ginásio.

Parâmetros:

Nome Tipo Descrição Predefinição
keypoints list

lista de dados de pontos-chave a serem traçados

necessário
indices list

lista de ids dos pontos-chave a serem traçados

None
shape tuple

imgsz para inferência de modelos

(640, 640)
radius int

Valor do raio do ponto-chave

2
Código fonte em ultralytics/utils/plotting.py
def draw_specific_points(self, keypoints, indices=None, shape=(640, 640), radius=2, conf_thres=0.25):
    """
    Draw specific keypoints for gym steps counting.

    Args:
        keypoints (list): list of keypoints data to be plotted
        indices (list): keypoints ids list to be plotted
        shape (tuple): imgsz for model inference
        radius (int): Keypoint radius value
    """

    if indices is None:
        indices = [2, 5, 7]
    for i, k in enumerate(keypoints):
        if i in indices:
            x_coord, y_coord = k[0], k[1]
            if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
                if len(k) == 3:
                    conf = k[2]
                    if conf < conf_thres:
                        continue
                cv2.circle(self.im, (int(x_coord), int(y_coord)), radius, (0, 255, 0), -1, lineType=cv2.LINE_AA)
    return self.im

estimate_pose_angle(a, b, c) staticmethod

Calcula o ângulo de pose do objeto.

Parâmetros:

Nome Tipo Descrição Predefinição
a float)

O valor do ponto de pose a

necessário
b float

O valor do ponto de pose b

necessário
c float

O valor do ponto de pose c

necessário

Devolve:

Nome Tipo Descrição
angle degree

Valor do grau do ângulo entre três pontos

Código fonte em ultralytics/utils/plotting.py
@staticmethod
def estimate_pose_angle(a, b, c):
    """
    Calculate the pose angle for object.

    Args:
        a (float) : The value of pose point a
        b (float): The value of pose point b
        c (float): The value o pose point c

    Returns:
        angle (degree): Degree value of angle between three points
    """

    a, b, c = np.array(a), np.array(b), np.array(c)
    radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0])
    angle = np.abs(radians * 180.0 / np.pi)
    if angle > 180.0:
        angle = 360 - angle
    return angle

fromarray(im)

Actualiza self.im a partir de um array numpy.

Código fonte em ultralytics/utils/plotting.py
def fromarray(self, im):
    """Update self.im from a numpy array."""
    self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
    self.draw = ImageDraw.Draw(self.im)

get_bbox_dimension(bbox=None)

Calcula a área de uma caixa delimitadora.

Parâmetros:

Nome Tipo Descrição Predefinição
bbox tuple

Coordenadas da caixa delimitadora no formato (x_min, y_min, x_max, y_max).

None

Devolve:

Nome Tipo Descrição
angle degree

Valor do grau do ângulo entre três pontos

Código fonte em ultralytics/utils/plotting.py
def get_bbox_dimension(self, bbox=None):
    """
    Calculate the area of a bounding box.

    Args:
        bbox (tuple): Bounding box coordinates in the format (x_min, y_min, x_max, y_max).

    Returns:
        angle (degree): Degree value of angle between three points
    """

    x_min, y_min, x_max, y_max = bbox
    width = x_max - x_min
    height = y_max - y_min
    return width, height, width * height

get_txt_color(color=(128, 128, 128), txt_color=(255, 255, 255))

Assign text color based on background color.

Código fonte em ultralytics/utils/plotting.py
def get_txt_color(self, color=(128, 128, 128), txt_color=(255, 255, 255)):
    """Assign text color based on background color."""
    if color in self.dark_colors:
        return 104, 31, 17
    elif color in self.light_colors:
        return 255, 255, 255
    else:
        return txt_color

kpts(kpts, shape=(640, 640), radius=5, kpt_line=True, conf_thres=0.25)

Traça pontos-chave na imagem.

Parâmetros:

Nome Tipo Descrição Predefinição
kpts tensor

Pontos-chave previstos com forma [17, 3]. Cada ponto-chave tem (x, y, confiança).

necessário
shape tuple

Forma da imagem como uma tupla (h, w), em que h é a altura e w é a largura.

(640, 640)
radius int

Desenha o raio dos pontos-chave. A predefinição é 5.

5
kpt_line bool

Se for True, a função desenhará linhas que ligam os pontos-chave para a pose humana. A predefinição é Verdadeiro.

True
Nota

kpt_line=True atualmente só suporta a plotagem de pose humana.

Código fonte em ultralytics/utils/plotting.py
def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True, conf_thres=0.25):
    """
    Plot keypoints on the image.

    Args:
        kpts (tensor): Predicted keypoints with shape [17, 3]. Each keypoint has (x, y, confidence).
        shape (tuple): Image shape as a tuple (h, w), where h is the height and w is the width.
        radius (int, optional): Radius of the drawn keypoints. Default is 5.
        kpt_line (bool, optional): If True, the function will draw lines connecting keypoints
                                   for human pose. Default is True.

    Note:
        `kpt_line=True` currently only supports human pose plotting.
    """

    if self.pil:
        # Convert to numpy first
        self.im = np.asarray(self.im).copy()
    nkpt, ndim = kpts.shape
    is_pose = nkpt == 17 and ndim in {2, 3}
    kpt_line &= is_pose  # `kpt_line=True` for now only supports human pose plotting
    for i, k in enumerate(kpts):
        color_k = [int(x) for x in self.kpt_color[i]] if is_pose else colors(i)
        x_coord, y_coord = k[0], k[1]
        if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
            if len(k) == 3:
                conf = k[2]
                if conf < conf_thres:
                    continue
            cv2.circle(self.im, (int(x_coord), int(y_coord)), radius, color_k, -1, lineType=cv2.LINE_AA)

    if kpt_line:
        ndim = kpts.shape[-1]
        for i, sk in enumerate(self.skeleton):
            pos1 = (int(kpts[(sk[0] - 1), 0]), int(kpts[(sk[0] - 1), 1]))
            pos2 = (int(kpts[(sk[1] - 1), 0]), int(kpts[(sk[1] - 1), 1]))
            if ndim == 3:
                conf1 = kpts[(sk[0] - 1), 2]
                conf2 = kpts[(sk[1] - 1), 2]
                if conf1 < conf_thres or conf2 < conf_thres:
                    continue
            if pos1[0] % shape[1] == 0 or pos1[1] % shape[0] == 0 or pos1[0] < 0 or pos1[1] < 0:
                continue
            if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0:
                continue
            cv2.line(self.im, pos1, pos2, [int(x) for x in self.limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
    if self.pil:
        # Convert im back to PIL and update draw
        self.fromarray(self.im)

masks(masks, colors, im_gpu, alpha=0.5, retina_masks=False)

Coloca máscaras na imagem.

Parâmetros:

Nome Tipo Descrição Predefinição
masks tensor

Máscaras previstas em cuda, forma: [n, h, w]

necessário
colors List[List[Int]]

Cores para máscaras previstas, [[r, g, b] * n]

necessário
im_gpu tensor

A imagem está em cuda, forma: [3, h, w], range: [0, 1]

necessário
alpha float

Transparência da máscara: 0,0 totalmente transparente, 1,0 opaco

0.5
retina_masks bool

Utiliza ou não máscaras de alta resolução. A predefinição é Falso.

False
Código fonte em ultralytics/utils/plotting.py
def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
    """
    Plot masks on image.

    Args:
        masks (tensor): Predicted masks on cuda, shape: [n, h, w]
        colors (List[List[Int]]): Colors for predicted masks, [[r, g, b] * n]
        im_gpu (tensor): Image is in cuda, shape: [3, h, w], range: [0, 1]
        alpha (float): Mask transparency: 0.0 fully transparent, 1.0 opaque
        retina_masks (bool): Whether to use high resolution masks or not. Defaults to False.
    """

    if self.pil:
        # Convert to numpy first
        self.im = np.asarray(self.im).copy()
    if len(masks) == 0:
        self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
    if im_gpu.device != masks.device:
        im_gpu = im_gpu.to(masks.device)
    colors = torch.tensor(colors, device=masks.device, dtype=torch.float32) / 255.0  # shape(n,3)
    colors = colors[:, None, None]  # shape(n,1,1,3)
    masks = masks.unsqueeze(3)  # shape(n,h,w,1)
    masks_color = masks * (colors * alpha)  # shape(n,h,w,3)

    inv_alpha_masks = (1 - masks * alpha).cumprod(0)  # shape(n,h,w,1)
    mcs = masks_color.max(dim=0).values  # shape(n,h,w,3)

    im_gpu = im_gpu.flip(dims=[0])  # flip channel
    im_gpu = im_gpu.permute(1, 2, 0).contiguous()  # shape(h,w,3)
    im_gpu = im_gpu * inv_alpha_masks[-1] + mcs
    im_mask = im_gpu * 255
    im_mask_np = im_mask.byte().cpu().numpy()
    self.im[:] = im_mask_np if retina_masks else ops.scale_image(im_mask_np, self.im.shape)
    if self.pil:
        # Convert im back to PIL and update draw
        self.fromarray(self.im)

plot_angle_and_count_and_stage(angle_text, count_text, stage_text, center_kpt, color=(104, 31, 17), txt_color=(255, 255, 255))

Traça o ângulo de pose, o valor de contagem e a fase de passo.

Parâmetros:

Nome Tipo Descrição Predefinição
angle_text str

valor do ângulo para monitorização do exercício

necessário
count_text str

conta o valor para a monitorização do exercício

necessário
stage_text str

decisão de fase para o acompanhamento dos treinos

necessário
center_kpt int

índice de pose do centróide para monitorização do exercício

necessário
color tuple

cor de fundo do texto para monitorização do exercício

(104, 31, 17)
txt_color tuple

cor de primeiro plano do texto para monitorização do exercício

(255, 255, 255)
Código fonte em ultralytics/utils/plotting.py
def plot_angle_and_count_and_stage(
    self, angle_text, count_text, stage_text, center_kpt, color=(104, 31, 17), txt_color=(255, 255, 255)
):
    """
    Plot the pose angle, count value and step stage.

    Args:
        angle_text (str): angle value for workout monitoring
        count_text (str): counts value for workout monitoring
        stage_text (str): stage decision for workout monitoring
        center_kpt (int): centroid pose index for workout monitoring
        color (tuple): text background color for workout monitoring
        txt_color (tuple): text foreground color for workout monitoring
    """

    angle_text, count_text, stage_text = (f" {angle_text:.2f}", f"Steps : {count_text}", f" {stage_text}")

    # Draw angle
    (angle_text_width, angle_text_height), _ = cv2.getTextSize(angle_text, 0, self.sf, self.tf)
    angle_text_position = (int(center_kpt[0]), int(center_kpt[1]))
    angle_background_position = (angle_text_position[0], angle_text_position[1] - angle_text_height - 5)
    angle_background_size = (angle_text_width + 2 * 5, angle_text_height + 2 * 5 + (self.tf * 2))
    cv2.rectangle(
        self.im,
        angle_background_position,
        (
            angle_background_position[0] + angle_background_size[0],
            angle_background_position[1] + angle_background_size[1],
        ),
        color,
        -1,
    )
    cv2.putText(self.im, angle_text, angle_text_position, 0, self.sf, txt_color, self.tf)

    # Draw Counts
    (count_text_width, count_text_height), _ = cv2.getTextSize(count_text, 0, self.sf, self.tf)
    count_text_position = (angle_text_position[0], angle_text_position[1] + angle_text_height + 20)
    count_background_position = (
        angle_background_position[0],
        angle_background_position[1] + angle_background_size[1] + 5,
    )
    count_background_size = (count_text_width + 10, count_text_height + 10 + self.tf)

    cv2.rectangle(
        self.im,
        count_background_position,
        (
            count_background_position[0] + count_background_size[0],
            count_background_position[1] + count_background_size[1],
        ),
        color,
        -1,
    )
    cv2.putText(self.im, count_text, count_text_position, 0, self.sf, txt_color, self.tf)

    # Draw Stage
    (stage_text_width, stage_text_height), _ = cv2.getTextSize(stage_text, 0, self.sf, self.tf)
    stage_text_position = (int(center_kpt[0]), int(center_kpt[1]) + angle_text_height + count_text_height + 40)
    stage_background_position = (stage_text_position[0], stage_text_position[1] - stage_text_height - 5)
    stage_background_size = (stage_text_width + 10, stage_text_height + 10)

    cv2.rectangle(
        self.im,
        stage_background_position,
        (
            stage_background_position[0] + stage_background_size[0],
            stage_background_position[1] + stage_background_size[1],
        ),
        color,
        -1,
    )
    cv2.putText(self.im, stage_text, stage_text_position, 0, self.sf, txt_color, self.tf)

plot_distance_and_line(distance_m, distance_mm, centroids, line_color, centroid_color)

Traça a distância e a linha no quadro.

Parâmetros:

Nome Tipo Descrição Predefinição
distance_m float

Distância entre dois centróides de caixa b em metros.

necessário
distance_mm float

Distância entre dois centróides da caixa b em milímetros.

necessário
centroids list

Dados dos centróides da caixa delimitadora.

necessário
line_color RGB

Cor da linha de distância.

necessário
centroid_color RGB

Cor do centróide da caixa delimitadora.

necessário
Código fonte em ultralytics/utils/plotting.py
def plot_distance_and_line(self, distance_m, distance_mm, centroids, line_color, centroid_color):
    """
    Plot the distance and line on frame.

    Args:
        distance_m (float): Distance between two bbox centroids in meters.
        distance_mm (float): Distance between two bbox centroids in millimeters.
        centroids (list): Bounding box centroids data.
        line_color (RGB): Distance line color.
        centroid_color (RGB): Bounding box centroid color.
    """

    (text_width_m, text_height_m), _ = cv2.getTextSize(f"Distance M: {distance_m:.2f}m", 0, self.sf, self.tf)
    cv2.rectangle(self.im, (15, 25), (15 + text_width_m + 10, 25 + text_height_m + 20), line_color, -1)
    cv2.putText(
        self.im,
        f"Distance M: {distance_m:.2f}m",
        (20, 50),
        0,
        self.sf,
        centroid_color,
        self.tf,
        cv2.LINE_AA,
    )

    (text_width_mm, text_height_mm), _ = cv2.getTextSize(f"Distance MM: {distance_mm:.2f}mm", 0, self.sf, self.tf)
    cv2.rectangle(self.im, (15, 75), (15 + text_width_mm + 10, 75 + text_height_mm + 20), line_color, -1)
    cv2.putText(
        self.im,
        f"Distance MM: {distance_mm:.2f}mm",
        (20, 100),
        0,
        self.sf,
        centroid_color,
        self.tf,
        cv2.LINE_AA,
    )

    cv2.line(self.im, centroids[0], centroids[1], line_color, 3)
    cv2.circle(self.im, centroids[0], 6, centroid_color, -1)
    cv2.circle(self.im, centroids[1], 6, centroid_color, -1)

queue_counts_display(label, points=None, region_color=(255, 255, 255), txt_color=(0, 0, 0))

Apresenta as contagens de filas numa imagem centrada nos pontos com tamanho de letra e cores personalizáveis.

Parâmetros:

Nome Tipo Descrição Predefinição
label str

contagem de filas etiqueta

necessário
points tuple

pontos de região para o cálculo do ponto central para exibir o texto

None
region_color RGB

cor da região da fila

(255, 255, 255)
txt_color RGB

cor de apresentação do texto

(0, 0, 0)
Código fonte em ultralytics/utils/plotting.py
def queue_counts_display(self, label, points=None, region_color=(255, 255, 255), txt_color=(0, 0, 0)):
    """
    Displays queue counts on an image centered at the points with customizable font size and colors.

    Args:
        label (str): queue counts label
        points (tuple): region points for center point calculation to display text
        region_color (RGB): queue region color
        txt_color (RGB): text display color
    """

    x_values = [point[0] for point in points]
    y_values = [point[1] for point in points]
    center_x = sum(x_values) // len(points)
    center_y = sum(y_values) // len(points)

    text_size = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.tf)[0]
    text_width = text_size[0]
    text_height = text_size[1]

    rect_width = text_width + 20
    rect_height = text_height + 20
    rect_top_left = (center_x - rect_width // 2, center_y - rect_height // 2)
    rect_bottom_right = (center_x + rect_width // 2, center_y + rect_height // 2)
    cv2.rectangle(self.im, rect_top_left, rect_bottom_right, region_color, -1)

    text_x = center_x - text_width // 2
    text_y = center_y + text_height // 2

    # Draw text
    cv2.putText(
        self.im,
        label,
        (text_x, text_y),
        0,
        fontScale=self.sf,
        color=txt_color,
        thickness=self.tf,
        lineType=cv2.LINE_AA,
    )

rectangle(xy, fill=None, outline=None, width=1)

Adiciona um retângulo à imagem (apenas PIL).

Código fonte em ultralytics/utils/plotting.py
def rectangle(self, xy, fill=None, outline=None, width=1):
    """Add rectangle to image (PIL-only)."""
    self.draw.rectangle(xy, fill, outline, width)

result()

Devolve a imagem anotada como uma matriz.

Código fonte em ultralytics/utils/plotting.py
def result(self):
    """Return annotated image as array."""
    return np.asarray(self.im)

save(filename='image.jpg')

Guarda a imagem anotada em 'nome do ficheiro'.

Código fonte em ultralytics/utils/plotting.py
def save(self, filename="image.jpg"):
    """Save the annotated image to 'filename'."""
    cv2.imwrite(filename, np.asarray(self.im))

seg_bbox(mask, mask_color=(255, 0, 255), det_label=None, track_label=None)

Função para desenhar um objeto segmentado em forma de caixa delimitadora.

Parâmetros:

Nome Tipo Descrição Predefinição
mask list

mascara a lista de dados para a plotagem da área de segmentação de exemplo

necessário
mask_color tuple

cor de primeiro plano da máscara

(255, 0, 255)
det_label str

Texto da etiqueta de deteção

None
track_label str

Texto da etiqueta de rastreio

None
Código fonte em ultralytics/utils/plotting.py
def seg_bbox(self, mask, mask_color=(255, 0, 255), det_label=None, track_label=None):
    """
    Function for drawing segmented object in bounding box shape.

    Args:
        mask (list): masks data list for instance segmentation area plotting
        mask_color (tuple): mask foreground color
        det_label (str): Detection label text
        track_label (str): Tracking label text
    """

    cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)

    label = f"Track ID: {track_label}" if track_label else det_label
    text_size, _ = cv2.getTextSize(label, 0, 0.7, 1)

    cv2.rectangle(
        self.im,
        (int(mask[0][0]) - text_size[0] // 2 - 10, int(mask[0][1]) - text_size[1] - 10),
        (int(mask[0][0]) + text_size[0] // 2 + 5, int(mask[0][1] + 5)),
        mask_color,
        -1,
    )

    cv2.putText(
        self.im, label, (int(mask[0][0]) - text_size[0] // 2, int(mask[0][1]) - 5), 0, 0.7, (255, 255, 255), 2
    )

show(title=None)

Mostra a imagem anotada.

Código fonte em ultralytics/utils/plotting.py
def show(self, title=None):
    """Show the annotated image."""
    Image.fromarray(np.asarray(self.im)[..., ::-1]).show(title)

text(xy, text, txt_color=(255, 255, 255), anchor='top', box_style=False)

Adiciona texto a uma imagem utilizando PIL ou cv2.

Código fonte em ultralytics/utils/plotting.py
def text(self, xy, text, txt_color=(255, 255, 255), anchor="top", box_style=False):
    """Adds text to an image using PIL or cv2."""
    if anchor == "bottom":  # start y from font bottom
        w, h = self.font.getsize(text)  # text width, height
        xy[1] += 1 - h
    if self.pil:
        if box_style:
            w, h = self.font.getsize(text)
            self.draw.rectangle((xy[0], xy[1], xy[0] + w + 1, xy[1] + h + 1), fill=txt_color)
            # Using `txt_color` for background and draw fg with white color
            txt_color = (255, 255, 255)
        if "\n" in text:
            lines = text.split("\n")
            _, h = self.font.getsize(text)
            for line in lines:
                self.draw.text(xy, line, fill=txt_color, font=self.font)
                xy[1] += h
        else:
            self.draw.text(xy, text, fill=txt_color, font=self.font)
    else:
        if box_style:
            w, h = cv2.getTextSize(text, 0, fontScale=self.sf, thickness=self.tf)[0]  # text width, height
            outside = xy[1] - h >= 3
            p2 = xy[0] + w, xy[1] - h - 3 if outside else xy[1] + h + 3
            cv2.rectangle(self.im, xy, p2, txt_color, -1, cv2.LINE_AA)  # filled
            # Using `txt_color` for background and draw fg with white color
            txt_color = (255, 255, 255)
        cv2.putText(self.im, text, xy, 0, self.sf, txt_color, thickness=self.tf, lineType=cv2.LINE_AA)

text_label(box, label='', color=(128, 128, 128), txt_color=(255, 255, 255), margin=5)

Draws a label with a background rectangle centered within a given bounding box.

Parâmetros:

Nome Tipo Descrição Predefinição
box tuple

The bounding box coordinates (x1, y1, x2, y2).

necessário
label str

The text label to be displayed.

''
color tuple

The background color of the rectangle (R, G, B).

(128, 128, 128)
txt_color tuple

The color of the text (R, G, B).

(255, 255, 255)
margin int

The margin between the text and the rectangle border.

5
Código fonte em ultralytics/utils/plotting.py
def text_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), margin=5):
    """
    Draws a label with a background rectangle centered within a given bounding box.

    Args:
        box (tuple): The bounding box coordinates (x1, y1, x2, y2).
        label (str): The text label to be displayed.
        color (tuple, optional): The background color of the rectangle (R, G, B).
        txt_color (tuple, optional): The color of the text (R, G, B).
        margin (int, optional): The margin between the text and the rectangle border.
    """

    # Calculate the center of the bounding box
    x_center, y_center = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
    # Get the size of the text
    text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, self.sf - 0.1, self.tf)[0]
    # Calculate the top-left corner of the text (to center it)
    text_x = x_center - text_size[0] // 2
    text_y = y_center + text_size[1] // 2
    # Calculate the coordinates of the background rectangle
    rect_x1 = text_x - margin
    rect_y1 = text_y - text_size[1] - margin
    rect_x2 = text_x + text_size[0] + margin
    rect_y2 = text_y + margin
    # Draw the background rectangle
    cv2.rectangle(self.im, (rect_x1, rect_y1), (rect_x2, rect_y2), color, -1)
    # Draw the text on top of the rectangle
    cv2.putText(
        self.im,
        label,
        (text_x, text_y),
        cv2.FONT_HERSHEY_SIMPLEX,
        self.sf - 0.1,
        self.get_txt_color(color, txt_color),
        self.tf,
        lineType=cv2.LINE_AA,
    )

visioneye(box, center_point, color=(235, 219, 11), pin_color=(255, 0, 255))

Função para mapear e traçar com precisão a visão humana.

Parâmetros:

Nome Tipo Descrição Predefinição
box list

Coordenadas da caixa delimitadora

necessário
center_point tuple

ponto central da visão visão ocular

necessário
color tuple

centroide do objeto e valor da cor da linha

(235, 219, 11)
pin_color tuple

valor da cor do ponto visioneye

(255, 0, 255)
Código fonte em ultralytics/utils/plotting.py
def visioneye(self, box, center_point, color=(235, 219, 11), pin_color=(255, 0, 255)):
    """
    Function for pinpoint human-vision eye mapping and plotting.

    Args:
        box (list): Bounding box coordinates
        center_point (tuple): center point for vision eye view
        color (tuple): object centroid and line color value
        pin_color (tuple): visioneye point color value
    """

    center_bbox = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
    cv2.circle(self.im, center_point, self.tf * 2, pin_color, -1)
    cv2.circle(self.im, center_bbox, self.tf * 2, color, -1)
    cv2.line(self.im, center_point, center_bbox, color, self.tf)



ultralytics.utils.plotting.plot_labels(boxes, cls, names=(), save_dir=Path(''), on_plot=None)

Traça etiquetas de treino, incluindo histogramas de classe e estatísticas de caixa.

Código fonte em ultralytics/utils/plotting.py
@TryExcept()  # known issue https://github.com/ultralytics/yolov5/issues/5395
@plt_settings()
def plot_labels(boxes, cls, names=(), save_dir=Path(""), on_plot=None):
    """Plot training labels including class histograms and box statistics."""
    import pandas  # scope for faster 'import ultralytics'
    import seaborn  # scope for faster 'import ultralytics'

    # Filter matplotlib>=3.7.2 warning and Seaborn use_inf and is_categorical FutureWarnings
    warnings.filterwarnings("ignore", category=UserWarning, message="The figure layout has changed to tight")
    warnings.filterwarnings("ignore", category=FutureWarning)

    # Plot dataset labels
    LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
    nc = int(cls.max() + 1)  # number of classes
    boxes = boxes[:1000000]  # limit to 1M boxes
    x = pandas.DataFrame(boxes, columns=["x", "y", "width", "height"])

    # Seaborn correlogram
    seaborn.pairplot(x, corner=True, diag_kind="auto", kind="hist", diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
    plt.savefig(save_dir / "labels_correlogram.jpg", dpi=200)
    plt.close()

    # Matplotlib labels
    ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
    y = ax[0].hist(cls, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
    for i in range(nc):
        y[2].patches[i].set_color([x / 255 for x in colors(i)])
    ax[0].set_ylabel("instances")
    if 0 < len(names) < 30:
        ax[0].set_xticks(range(len(names)))
        ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
    else:
        ax[0].set_xlabel("classes")
    seaborn.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9)
    seaborn.histplot(x, x="width", y="height", ax=ax[3], bins=50, pmax=0.9)

    # Rectangles
    boxes[:, 0:2] = 0.5  # center
    boxes = ops.xywh2xyxy(boxes) * 1000
    img = Image.fromarray(np.ones((1000, 1000, 3), dtype=np.uint8) * 255)
    for cls, box in zip(cls[:500], boxes[:500]):
        ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls))  # plot
    ax[1].imshow(img)
    ax[1].axis("off")

    for a in [0, 1, 2, 3]:
        for s in ["top", "right", "left", "bottom"]:
            ax[a].spines[s].set_visible(False)

    fname = save_dir / "labels.jpg"
    plt.savefig(fname, dpi=200)
    plt.close()
    if on_plot:
        on_plot(fname)



ultralytics.utils.plotting.save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True)

Guarda o recorte de imagem como {arquivo} com tamanho de recorte múltiplo de {ganho} e {pad} pixels. Guarda e/ou devolve a imagem cortada.

Esta função recebe uma caixa delimitadora e uma imagem e, em seguida, guarda uma parte cortada da imagem de acordo de acordo com a caixa delimitadora. Opcionalmente, o corte pode ser quadrado, e a função permite ajustes de ganho e preenchimento e de preenchimento para a caixa delimitadora.

Parâmetros:

Nome Tipo Descrição Predefinição
xyxy Tensor or list

Um tensor ou uma lista que representa a caixa delimitadora no formato xyxy.

necessário
im ndarray

A imagem de entrada.

necessário
file Path

O caminho onde a imagem recortada será guardada. A predefinição é 'im.jpg'.

Path('im.jpg')
gain float

Um fator multiplicativo para aumentar o tamanho da caixa delimitadora. Usa o valor padrão de 1,02.

1.02
pad int

O número de pixels a adicionar à largura e altura da caixa delimitadora. A predefinição é 10.

10
square bool

Se for Verdadeiro, a caixa delimitadora será transformada num quadrado. A predefinição é Falso.

False
BGR bool

Se for Verdadeiro, a imagem será guardada no formato BGR, caso contrário em RGB. A predefinição é Falso.

False
save bool

Se for Verdadeiro, a imagem recortada será guardada no disco. A predefinição é Verdadeiro.

True

Devolve:

Tipo Descrição
ndarray

A imagem cortada.

Exemplo
from ultralytics.utils.plotting import save_one_box

xyxy = [50, 50, 150, 150]
im = cv2.imread('image.jpg')
cropped_im = save_one_box(xyxy, im, file='cropped.jpg', square=True)
Código fonte em ultralytics/utils/plotting.py
def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False, BGR=False, save=True):
    """
    Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop.

    This function takes a bounding box and an image, and then saves a cropped portion of the image according
    to the bounding box. Optionally, the crop can be squared, and the function allows for gain and padding
    adjustments to the bounding box.

    Args:
        xyxy (torch.Tensor or list): A tensor or list representing the bounding box in xyxy format.
        im (numpy.ndarray): The input image.
        file (Path, optional): The path where the cropped image will be saved. Defaults to 'im.jpg'.
        gain (float, optional): A multiplicative factor to increase the size of the bounding box. Defaults to 1.02.
        pad (int, optional): The number of pixels to add to the width and height of the bounding box. Defaults to 10.
        square (bool, optional): If True, the bounding box will be transformed into a square. Defaults to False.
        BGR (bool, optional): If True, the image will be saved in BGR format, otherwise in RGB. Defaults to False.
        save (bool, optional): If True, the cropped image will be saved to disk. Defaults to True.

    Returns:
        (numpy.ndarray): The cropped image.

    Example:
        ```python
        from ultralytics.utils.plotting import save_one_box

        xyxy = [50, 50, 150, 150]
        im = cv2.imread('image.jpg')
        cropped_im = save_one_box(xyxy, im, file='cropped.jpg', square=True)
        ```
    """

    if not isinstance(xyxy, torch.Tensor):  # may be list
        xyxy = torch.stack(xyxy)
    b = ops.xyxy2xywh(xyxy.view(-1, 4))  # boxes
    if square:
        b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # attempt rectangle to square
    b[:, 2:] = b[:, 2:] * gain + pad  # box wh * gain + pad
    xyxy = ops.xywh2xyxy(b).long()
    xyxy = ops.clip_boxes(xyxy, im.shape)
    crop = im[int(xyxy[0, 1]) : int(xyxy[0, 3]), int(xyxy[0, 0]) : int(xyxy[0, 2]), :: (1 if BGR else -1)]
    if save:
        file.parent.mkdir(parents=True, exist_ok=True)  # make directory
        f = str(increment_path(file).with_suffix(".jpg"))
        # cv2.imwrite(f, crop)  # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
        Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0)  # save RGB
    return crop



ultralytics.utils.plotting.plot_images(images, batch_idx, cls, bboxes=np.zeros(0, dtype=np.float32), confs=None, masks=np.zeros(0, dtype=np.uint8), kpts=np.zeros((0, 51), dtype=np.float32), paths=None, fname='images.jpg', names=None, on_plot=None, max_subplots=16, save=True, conf_thres=0.25)

Traça uma grelha de imagens com etiquetas.

Código fonte em ultralytics/utils/plotting.py
@threaded
def plot_images(
    images,
    batch_idx,
    cls,
    bboxes=np.zeros(0, dtype=np.float32),
    confs=None,
    masks=np.zeros(0, dtype=np.uint8),
    kpts=np.zeros((0, 51), dtype=np.float32),
    paths=None,
    fname="images.jpg",
    names=None,
    on_plot=None,
    max_subplots=16,
    save=True,
    conf_thres=0.25,
):
    """Plot image grid with labels."""
    if isinstance(images, torch.Tensor):
        images = images.cpu().float().numpy()
    if isinstance(cls, torch.Tensor):
        cls = cls.cpu().numpy()
    if isinstance(bboxes, torch.Tensor):
        bboxes = bboxes.cpu().numpy()
    if isinstance(masks, torch.Tensor):
        masks = masks.cpu().numpy().astype(int)
    if isinstance(kpts, torch.Tensor):
        kpts = kpts.cpu().numpy()
    if isinstance(batch_idx, torch.Tensor):
        batch_idx = batch_idx.cpu().numpy()

    max_size = 1920  # max image size
    bs, _, h, w = images.shape  # batch size, _, height, width
    bs = min(bs, max_subplots)  # limit plot images
    ns = np.ceil(bs**0.5)  # number of subplots (square)
    if np.max(images[0]) <= 1:
        images *= 255  # de-normalise (optional)

    # Build Image
    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init
    for i in range(bs):
        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
        mosaic[y : y + h, x : x + w, :] = images[i].transpose(1, 2, 0)

    # Resize (optional)
    scale = max_size / ns / max(h, w)
    if scale < 1:
        h = math.ceil(scale * h)
        w = math.ceil(scale * w)
        mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))

    # Annotate
    fs = int((h + w) * ns * 0.01)  # font size
    annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
    for i in range(bs):
        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
        annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders
        if paths:
            annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
        if len(cls) > 0:
            idx = batch_idx == i
            classes = cls[idx].astype("int")
            labels = confs is None

            if len(bboxes):
                boxes = bboxes[idx]
                conf = confs[idx] if confs is not None else None  # check for confidence presence (label vs pred)
                if len(boxes):
                    if boxes[:, :4].max() <= 1.1:  # if normalized with tolerance 0.1
                        boxes[..., [0, 2]] *= w  # scale to pixels
                        boxes[..., [1, 3]] *= h
                    elif scale < 1:  # absolute coords need scale if image scales
                        boxes[..., :4] *= scale
                boxes[..., 0] += x
                boxes[..., 1] += y
                is_obb = boxes.shape[-1] == 5  # xywhr
                boxes = ops.xywhr2xyxyxyxy(boxes) if is_obb else ops.xywh2xyxy(boxes)
                for j, box in enumerate(boxes.astype(np.int64).tolist()):
                    c = classes[j]
                    color = colors(c)
                    c = names.get(c, c) if names else c
                    if labels or conf[j] > conf_thres:
                        label = f"{c}" if labels else f"{c} {conf[j]:.1f}"
                        annotator.box_label(box, label, color=color, rotated=is_obb)

            elif len(classes):
                for c in classes:
                    color = colors(c)
                    c = names.get(c, c) if names else c
                    annotator.text((x, y), f"{c}", txt_color=color, box_style=True)

            # Plot keypoints
            if len(kpts):
                kpts_ = kpts[idx].copy()
                if len(kpts_):
                    if kpts_[..., 0].max() <= 1.01 or kpts_[..., 1].max() <= 1.01:  # if normalized with tolerance .01
                        kpts_[..., 0] *= w  # scale to pixels
                        kpts_[..., 1] *= h
                    elif scale < 1:  # absolute coords need scale if image scales
                        kpts_ *= scale
                kpts_[..., 0] += x
                kpts_[..., 1] += y
                for j in range(len(kpts_)):
                    if labels or conf[j] > conf_thres:
                        annotator.kpts(kpts_[j], conf_thres=conf_thres)

            # Plot masks
            if len(masks):
                if idx.shape[0] == masks.shape[0]:  # overlap_masks=False
                    image_masks = masks[idx]
                else:  # overlap_masks=True
                    image_masks = masks[[i]]  # (1, 640, 640)
                    nl = idx.sum()
                    index = np.arange(nl).reshape((nl, 1, 1)) + 1
                    image_masks = np.repeat(image_masks, nl, axis=0)
                    image_masks = np.where(image_masks == index, 1.0, 0.0)

                im = np.asarray(annotator.im).copy()
                for j in range(len(image_masks)):
                    if labels or conf[j] > conf_thres:
                        color = colors(classes[j])
                        mh, mw = image_masks[j].shape
                        if mh != h or mw != w:
                            mask = image_masks[j].astype(np.uint8)
                            mask = cv2.resize(mask, (w, h))
                            mask = mask.astype(bool)
                        else:
                            mask = image_masks[j].astype(bool)
                        with contextlib.suppress(Exception):
                            im[y : y + h, x : x + w, :][mask] = (
                                im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6
                            )
                annotator.fromarray(im)
    if not save:
        return np.asarray(annotator.im)
    annotator.im.save(fname)  # save
    if on_plot:
        on_plot(fname)



ultralytics.utils.plotting.plot_results(file='path/to/results.csv', dir='', segment=False, pose=False, classify=False, on_plot=None)

Traça resultados de treino a partir de um ficheiro CSV de resultados. A função suporta vários tipos de dados, incluindo segmentação, estimativa de pose e classificação. Os gráficos são guardados como 'results.png' no diretório onde o CSV está localizado.

Parâmetros:

Nome Tipo Descrição Predefinição
file str

Caminho para o ficheiro CSV que contém os resultados do treino. Usa como padrão 'path/to/results.csv'.

'path/to/results.csv'
dir str

Diretório onde o arquivo CSV está localizado se 'file' não for fornecido. Usa por defeito ''.

''
segment bool

Sinalizador para indicar se os dados são para segmentação. O valor predefinido é Falso.

False
pose bool

Sinalizador para indicar se os dados são para estimativa de pose. O valor predefinido é Falso.

False
classify bool

Sinalizador para indicar se os dados são para classificação. O valor predefinido é Falso.

False
on_plot callable

Função de retorno a ser executada após a plotagem. Recebe o nome do arquivo como um argumento. Usa o valor padrão None.

None
Exemplo
from ultralytics.utils.plotting import plot_results

plot_results('path/to/results.csv', segment=True)
Código fonte em ultralytics/utils/plotting.py
@plt_settings()
def plot_results(file="path/to/results.csv", dir="", segment=False, pose=False, classify=False, on_plot=None):
    """
    Plot training results from a results CSV file. The function supports various types of data including segmentation,
    pose estimation, and classification. Plots are saved as 'results.png' in the directory where the CSV is located.

    Args:
        file (str, optional): Path to the CSV file containing the training results. Defaults to 'path/to/results.csv'.
        dir (str, optional): Directory where the CSV file is located if 'file' is not provided. Defaults to ''.
        segment (bool, optional): Flag to indicate if the data is for segmentation. Defaults to False.
        pose (bool, optional): Flag to indicate if the data is for pose estimation. Defaults to False.
        classify (bool, optional): Flag to indicate if the data is for classification. Defaults to False.
        on_plot (callable, optional): Callback function to be executed after plotting. Takes filename as an argument.
            Defaults to None.

    Example:
        ```python
        from ultralytics.utils.plotting import plot_results

        plot_results('path/to/results.csv', segment=True)
        ```
    """
    import pandas as pd  # scope for faster 'import ultralytics'
    from scipy.ndimage import gaussian_filter1d

    save_dir = Path(file).parent if file else Path(dir)
    if classify:
        fig, ax = plt.subplots(2, 2, figsize=(6, 6), tight_layout=True)
        index = [1, 4, 2, 3]
    elif segment:
        fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
        index = [1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]
    elif pose:
        fig, ax = plt.subplots(2, 9, figsize=(21, 6), tight_layout=True)
        index = [1, 2, 3, 4, 5, 6, 7, 10, 11, 14, 15, 16, 17, 18, 8, 9, 12, 13]
    else:
        fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
        index = [1, 2, 3, 4, 5, 8, 9, 10, 6, 7]
    ax = ax.ravel()
    files = list(save_dir.glob("results*.csv"))
    assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot."
    for f in files:
        try:
            data = pd.read_csv(f)
            s = [x.strip() for x in data.columns]
            x = data.values[:, 0]
            for i, j in enumerate(index):
                y = data.values[:, j].astype("float")
                # y[y == 0] = np.nan  # don't show zero values
                ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=8)  # actual results
                ax[i].plot(x, gaussian_filter1d(y, sigma=3), ":", label="smooth", linewidth=2)  # smoothing line
                ax[i].set_title(s[j], fontsize=12)
                # if j in {8, 9, 10}:  # share train and val loss y axes
                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
        except Exception as e:
            LOGGER.warning(f"WARNING: Plotting error for {f}: {e}")
    ax[1].legend()
    fname = save_dir / "results.png"
    fig.savefig(fname, dpi=200)
    plt.close()
    if on_plot:
        on_plot(fname)



ultralytics.utils.plotting.plt_color_scatter(v, f, bins=20, cmap='viridis', alpha=0.8, edgecolors='none')

Traça um gráfico de dispersão com pontos coloridos com base em um histograma 2D.

Parâmetros:

Nome Tipo Descrição Predefinição
v array - like

Valores para o eixo x.

necessário
f array - like

Valores para o eixo y.

necessário
bins int

Número de compartimentos para o histograma. Usa como padrão 20.

20
cmap str

Mapa de cores para o gráfico de dispersão. Usa a predefinição 'viridis'.

'viridis'
alpha float

Alfa para o gráfico de dispersão. Usa o valor padrão de 0,8.

0.8
edgecolors str

Cores das bordas para o gráfico de dispersão. Usa o valor padrão 'nenhum'.

'none'

Exemplos:

>>> v = np.random.rand(100)
>>> f = np.random.rand(100)
>>> plt_color_scatter(v, f)
Código fonte em ultralytics/utils/plotting.py
def plt_color_scatter(v, f, bins=20, cmap="viridis", alpha=0.8, edgecolors="none"):
    """
    Plots a scatter plot with points colored based on a 2D histogram.

    Args:
        v (array-like): Values for the x-axis.
        f (array-like): Values for the y-axis.
        bins (int, optional): Number of bins for the histogram. Defaults to 20.
        cmap (str, optional): Colormap for the scatter plot. Defaults to 'viridis'.
        alpha (float, optional): Alpha for the scatter plot. Defaults to 0.8.
        edgecolors (str, optional): Edge colors for the scatter plot. Defaults to 'none'.

    Examples:
        >>> v = np.random.rand(100)
        >>> f = np.random.rand(100)
        >>> plt_color_scatter(v, f)
    """

    # Calculate 2D histogram and corresponding colors
    hist, xedges, yedges = np.histogram2d(v, f, bins=bins)
    colors = [
        hist[
            min(np.digitize(v[i], xedges, right=True) - 1, hist.shape[0] - 1),
            min(np.digitize(f[i], yedges, right=True) - 1, hist.shape[1] - 1),
        ]
        for i in range(len(v))
    ]

    # Scatter plot
    plt.scatter(v, f, c=colors, cmap=cmap, alpha=alpha, edgecolors=edgecolors)



ultralytics.utils.plotting.plot_tune_results(csv_file='tune_results.csv')

Traça os resultados da evolução armazenados num ficheiro 'tune_results.csv'. A função gera um gráfico de dispersão para cada chave no CSV, codificado por cores com base nas pontuações de aptidão. As configurações com melhor desempenho são destacadas nos gráficos.

Parâmetros:

Nome Tipo Descrição Predefinição
csv_file str

Caminho para o ficheiro CSV que contém os resultados da afinação. A predefinição é "tune_results.csv".

'tune_results.csv'

Exemplos:

>>> plot_tune_results('path/to/tune_results.csv')
Código fonte em ultralytics/utils/plotting.py
def plot_tune_results(csv_file="tune_results.csv"):
    """
    Plot the evolution results stored in an 'tune_results.csv' file. The function generates a scatter plot for each key
    in the CSV, color-coded based on fitness scores. The best-performing configurations are highlighted on the plots.

    Args:
        csv_file (str, optional): Path to the CSV file containing the tuning results. Defaults to 'tune_results.csv'.

    Examples:
        >>> plot_tune_results('path/to/tune_results.csv')
    """

    import pandas as pd  # scope for faster 'import ultralytics'
    from scipy.ndimage import gaussian_filter1d

    # Scatter plots for each hyperparameter
    csv_file = Path(csv_file)
    data = pd.read_csv(csv_file)
    num_metrics_columns = 1
    keys = [x.strip() for x in data.columns][num_metrics_columns:]
    x = data.values
    fitness = x[:, 0]  # fitness
    j = np.argmax(fitness)  # max fitness index
    n = math.ceil(len(keys) ** 0.5)  # columns and rows in plot
    plt.figure(figsize=(10, 10), tight_layout=True)
    for i, k in enumerate(keys):
        v = x[:, i + num_metrics_columns]
        mu = v[j]  # best single result
        plt.subplot(n, n, i + 1)
        plt_color_scatter(v, fitness, cmap="viridis", alpha=0.8, edgecolors="none")
        plt.plot(mu, fitness.max(), "k+", markersize=15)
        plt.title(f"{k} = {mu:.3g}", fontdict={"size": 9})  # limit to 40 characters
        plt.tick_params(axis="both", labelsize=8)  # Set axis label size to 8
        if i % n != 0:
            plt.yticks([])

    file = csv_file.with_name("tune_scatter_plots.png")  # filename
    plt.savefig(file, dpi=200)
    plt.close()
    LOGGER.info(f"Saved {file}")

    # Fitness vs iteration
    x = range(1, len(fitness) + 1)
    plt.figure(figsize=(10, 6), tight_layout=True)
    plt.plot(x, fitness, marker="o", linestyle="none", label="fitness")
    plt.plot(x, gaussian_filter1d(fitness, sigma=3), ":", label="smoothed", linewidth=2)  # smoothing line
    plt.title("Fitness vs Iteration")
    plt.xlabel("Iteration")
    plt.ylabel("Fitness")
    plt.grid(True)
    plt.legend()

    file = csv_file.with_name("tune_fitness.png")  # filename
    plt.savefig(file, dpi=200)
    plt.close()
    LOGGER.info(f"Saved {file}")



ultralytics.utils.plotting.output_to_target(output, max_det=300)

Converte a saída do modelo para o formato de destino [batch_id, class_id, x, y, w, h, conf] para plotagem.

Código fonte em ultralytics/utils/plotting.py
def output_to_target(output, max_det=300):
    """Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting."""
    targets = []
    for i, o in enumerate(output):
        box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
        j = torch.full((conf.shape[0], 1), i)
        targets.append(torch.cat((j, cls, ops.xyxy2xywh(box), conf), 1))
    targets = torch.cat(targets, 0).numpy()
    return targets[:, 0], targets[:, 1], targets[:, 2:-1], targets[:, -1]



ultralytics.utils.plotting.output_to_rotated_target(output, max_det=300)

Converte a saída do modelo para o formato de destino [batch_id, class_id, x, y, w, h, conf] para plotagem.

Código fonte em ultralytics/utils/plotting.py
def output_to_rotated_target(output, max_det=300):
    """Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting."""
    targets = []
    for i, o in enumerate(output):
        box, conf, cls, angle = o[:max_det].cpu().split((4, 1, 1, 1), 1)
        j = torch.full((conf.shape[0], 1), i)
        targets.append(torch.cat((j, cls, box, angle, conf), 1))
    targets = torch.cat(targets, 0).numpy()
    return targets[:, 0], targets[:, 1], targets[:, 2:-1], targets[:, -1]



ultralytics.utils.plotting.feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp'))

Visualiza mapas de características de um determinado módulo de modelo durante a inferência.

Parâmetros:

Nome Tipo Descrição Predefinição
x Tensor

Características a serem visualizadas.

necessário
module_type str

Tipo de módulo.

necessário
stage int

Fase do módulo no modelo.

necessário
n int

Número máximo de mapas de características a representar. A predefinição é 32.

32
save_dir Path

Diretório para guardar os resultados. A predefinição é Path('runs/detect/exp').

Path('runs/detect/exp')
Código fonte em ultralytics/utils/plotting.py
def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detect/exp")):
    """
    Visualize feature maps of a given model module during inference.

    Args:
        x (torch.Tensor): Features to be visualized.
        module_type (str): Module type.
        stage (int): Module stage within the model.
        n (int, optional): Maximum number of feature maps to plot. Defaults to 32.
        save_dir (Path, optional): Directory to save results. Defaults to Path('runs/detect/exp').
    """
    for m in {"Detect", "Segment", "Pose", "Classify", "OBB", "RTDETRDecoder"}:  # all model heads
        if m in module_type:
            return
    if isinstance(x, torch.Tensor):
        _, channels, height, width = x.shape  # batch, channels, height, width
        if height > 1 and width > 1:
            f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png"  # filename

            blocks = torch.chunk(x[0].cpu(), channels, dim=0)  # select batch index 0, block by channels
            n = min(n, channels)  # number of plots
            _, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True)  # 8 rows x n/8 cols
            ax = ax.ravel()
            plt.subplots_adjust(wspace=0.05, hspace=0.05)
            for i in range(n):
                ax[i].imshow(blocks[i].squeeze())  # cmap='gray'
                ax[i].axis("off")

            LOGGER.info(f"Saving {f}... ({n}/{channels})")
            plt.savefig(f, dpi=300, bbox_inches="tight")
            plt.close()
            np.save(str(f.with_suffix(".npy")), x[0].cpu().numpy())  # npy save





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