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Dit bestand is beschikbaar op https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/utils/plotting .py. Als je een probleem ziet, help het dan oplossen door een Pull Request ­čŤá´ŞĆ bij te dragen. Bedankt ­čÖĆ!



ultralytics.utils.plotting.Colors

Ultralytics standaard kleurenpalet https://ultralytics.com/.

Deze klasse biedt methoden om te werken met het Ultralytics kleurenpalet, inclusief het converteren van hex kleurcodes naar RGB waarden.

Kenmerken:

Naam Type Beschrijving
palette list of tuple

Lijst met RGB-kleurwaarden.

n int

Het aantal kleuren in het palet.

pose_palette ndarray

Een specifieke kleurenpalet array met dtype np.uint8.

Broncode in 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)

Converteert hex kleurcodes naar RGB waarden.

Broncode in 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__()

Initialiseer kleuren als hex = matplotlib.colors.TABLEAU_COLORS.values().

Broncode in 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

Converteert hex kleurcodes naar RGB waarden (d.w.z. standaard PIL volgorde).

Broncode in 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 Annotator voor trein/val mozaïeken en JPG's en annotaties voor voorspellingen.

Kenmerken:

Naam Type Beschrijving
im Image.Image or numpy array

De afbeelding om te annoteren.

pil bool

Of PIL of cv2 moet worden gebruikt voor het tekenen van annotaties.

font truetype or load_default

Lettertype gebruikt voor tekstannotaties.

lw float

Lijndikte voor tekening.

skeleton List[List[int]]

Skeletstructuur voor sleutelpunten.

limb_color List[int]

Kleurenpalet voor ledematen.

kpt_color List[int]

Kleurenpalet voor sleutelpunten.

Broncode in 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]
                self.draw.polygon([tuple(b) for b in box], width=self.lw, outline=color)  # PIL requires tuple box
            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  # label fits outside box
                if p1[0] > self.im.size[0] - w:  # size is (w, h), check if label extend beyond right side of image
                    p1 = self.im.size[0] - w, p1[1]
                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]]
                cv2.polylines(self.im, [np.asarray(box, dtype=int)], True, color, self.lw)  # cv2 requires nparray box
            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
                h += 3  # add pixels to pad text
                outside = p1[1] >= h  # label fits outside box
                if p1[0] > self.im.shape[1] - w:  # shape is (h, w), check if label extend beyond right side of image
                    p1 = self.im.shape[1] - w, p1[1]
                p2 = p1[0] + w, p1[1] - h if outside else p1[1] + h
                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 - 1),
                    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
                h += 3  # add pixels to pad text
                outside = xy[1] >= h  # label fits outside box
                p2 = xy[0] + w, xy[1] - h if outside else xy[1] + h
                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 (list): 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), label=None, txt_color=(255, 255, 255)):
        """
        Function for drawing segmented object in bounding box shape.

        Args:
            mask (list): masks data list for instance segmentation area plotting
            mask_color (RGB): mask foreground color
            label (str): Detection label text
            txt_color (RGB): text color
        """

        cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)
        text_size, _ = cv2.getTextSize(label, 0, self.sf, self.tf)

        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 + 10, int(mask[0][1] + 10)),
            mask_color,
            -1,
        )

        if label:
            cv2.putText(
                self.im, label, (int(mask[0][0]) - text_size[0] // 2, int(mask[0][1])), 0, self.sf, txt_color, self.tf
            )

    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')

Initialiseer de Annotator klasse met afbeelding en lijndikte samen met kleurenpalet voor sleutelpunten en ledematen.

Broncode in 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)

Tekent een begrenzingsvak naar de afbeelding met label.

Parameters:

Naam Type Beschrijving Standaard
box tuple

De co├Ârdinaten van het begrenzingsvak (x1, y1, x2, y2).

vereist
label str

Het tekstlabel dat moet worden weergegeven.

''
color tuple

De achtergrondkleur van de rechthoek (R, G, B).

(128, 128, 128)
txt_color tuple

De kleur van de tekst (R, G, B).

(255, 255, 255)
rotated bool

Variabele die wordt gebruikt om te controleren of de taak OBB is

False
Broncode in 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]
            self.draw.polygon([tuple(b) for b in box], width=self.lw, outline=color)  # PIL requires tuple box
        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  # label fits outside box
            if p1[0] > self.im.size[0] - w:  # size is (w, h), check if label extend beyond right side of image
                p1 = self.im.size[0] - w, p1[1]
            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]]
            cv2.polylines(self.im, [np.asarray(box, dtype=int)], True, color, self.lw)  # cv2 requires nparray box
        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
            h += 3  # add pixels to pad text
            outside = p1[1] >= h  # label fits outside box
            if p1[0] > self.im.shape[1] - w:  # shape is (h, w), check if label extend beyond right side of image
                p1 = self.im.shape[1] - w, p1[1]
            p2 = p1[0] + w, p1[1] - h if outside else p1[1] + h
            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 - 1),
                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)

Hiermee tekent u een label met een rechthoek op de achtergrond in het midden van een bepaald begrenzingsvak.

Parameters:

Naam Type Beschrijving Standaard
box tuple

De co├Ârdinaten van het begrenzingsvak (x1, y1, x2, y2).

vereist
label str

Het tekstlabel dat moet worden weergegeven.

''
color tuple

De achtergrondkleur van de rechthoek (R, G, B).

(128, 128, 128)
txt_color tuple

De kleur van de tekst (R, G, B).

(255, 255, 255)
margin int

De marge tussen de tekst en de rechthoekige rand.

2
Broncode in 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)

Geef de algemene statistieken voor parkeerplaatsen weer.

Parameters:

Naam Type Beschrijving Standaard
im0 ndarray

inferentiebeeld

vereist
text dict

Etiketten woordenboek

vereist
txt_color bgr color

weergavekleur voor tekst voorgrond

vereist
bg_color bgr color

weergavekleur voor tekstachtergrond

vereist
margin int

Spleet tussen tekst en rechthoek voor betere weergave

vereist
Broncode in 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)

Geef de bounding boxes labels weer in de parkeerbeheer app.

Parameters:

Naam Type Beschrijving Standaard
im0 ndarray

inferentiebeeld

vereist
text str

object-/klasse-naam

vereist
txt_color bgr color

weergavekleur voor tekst voorgrond

vereist
bg_color bgr color

weergavekleur voor tekstachtergrond

vereist
x_center float

x positie middelpunt voor bounding box

vereist
y_center float

y positie middelpunt voor bounding box

vereist
margin int

Spleet tussen tekst en rechthoek voor betere weergave

vereist
Broncode in 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)

Teken een middelpunt en sporen.

Parameters:

Naam Type Beschrijving Standaard
track list

objectvolgpunten voor sporenweergave

vereist
color tuple

kleur spoorlijn

(255, 0, 255)
track_thickness int

waarde spoorlijndikte

2
Broncode in 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)

Trek een regiolijn.

Parameters:

Naam Type Beschrijving Standaard
reg_pts list

Regiopunten (voor lijn 2 punten, voor regio 4 punten)

None
color tuple

Regio Kleurwaarde

(0, 255, 0)
thickness int

Regio gebied dikte waarde

5
Broncode in 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)

Teken specifieke sleutelpunten voor het tellen van gymstappen.

Parameters:

Naam Type Beschrijving Standaard
keypoints list

lijst met te plotten sleutelpuntengegevens

vereist
indices list

lijst met sleutelpunten ids die moeten worden geplot

None
shape tuple

imgsz voor modelinferentie

(640, 640)
radius int

Radiuswaarde sleutelpunt

2
Broncode in 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

Bereken de poseerhoek voor het object.

Parameters:

Naam Type Beschrijving Standaard
a float)

De waarde van poseerpunt a

vereist
b float

De waarde van poseerpunt b

vereist
c float

De waarde van poseerpunt c

vereist

Retourneert:

Naam Type Beschrijving
angle degree

Graadwaarde van hoek tussen drie punten

Broncode in 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)

Werk self.im bij vanuit een numpy array.

Broncode in 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)

Bereken de oppervlakte van een bounding box.

Parameters:

Naam Type Beschrijving Standaard
bbox tuple

Bounding box co├Ârdinaten in het formaat (x_min, y_min, x_max, y_max).

None

Retourneert:

Naam Type Beschrijving
angle degree

Graadwaarde van hoek tussen drie punten

Broncode in 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))

Wijs tekstkleur toe op basis van achtergrondkleur.

Broncode in 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)

Teken sleutelpunten uit op de afbeelding.

Parameters:

Naam Type Beschrijving Standaard
kpts tensor

Voorspelde sleutelpunten met vorm [17, 3]. Elk sleutelpunt heeft (x, y, vertrouwen).

vereist
shape tuple

Beeldvorm als een tupel (h, w), waarbij h de hoogte en w de breedte is.

(640, 640)
radius int

Straal van de getekende toetspunten. De standaardinstelling is 5.

5
kpt_line bool

Als dit waar is, tekent de functie lijnen die de sleutelpunten verbinden voor de menselijke houding. De standaardwaarde is Waar.

True
Opmerking

kpt_line=True ondersteunt momenteel alleen het plotten van menselijke houdingen.

Broncode in 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)

Maskers op afbeelding uitzetten.

Parameters:

Naam Type Beschrijving Standaard
masks tensor

Voorspelde maskers op cuda, vorm: [n, h, w]

vereist
colors List[List[Int]]

Kleuren voor voorspelde maskers, [[r, g, b] * n]

vereist
im_gpu tensor

Afbeelding is in cuda, vorm: [3, h, w], bereik: [0, 1]

vereist
alpha float

Transparantie masker: 0,0 volledig transparant, 1,0 ondoorzichtig

0.5
retina_masks bool

Of maskers met een hoge resolutie moeten worden gebruikt of niet. Standaard op Fout.

False
Broncode in 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))

Plot de standhoek, de telwaarde en de stapfase.

Parameters:

Naam Type Beschrijving Standaard
angle_text str

hoekwaarde voor trainingscontrole

vereist
count_text str

telt waarde voor trainingscontrole

vereist
stage_text str

fasebeslissing voor workout monitoring

vereist
center_kpt list

centroid pose index voor trainingsmonitoring

vereist
color tuple

tekst achtergrondkleur voor trainingscontrole

(104, 31, 17)
txt_color tuple

tekst voorgrondkleur voor trainingscontrole

(255, 255, 255)
Broncode in 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 (list): 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)

Plot de afstand en lijn op het frame.

Parameters:

Naam Type Beschrijving Standaard
distance_m float

Afstand tussen twee bbox-centroïden in meters.

vereist
distance_mm float

Afstand tussen twee bbox-centroïden in millimeters.

vereist
centroids list

Bounding box centroids gegevens.

vereist
line_color RGB

Kleur afstandslijn.

vereist
centroid_color RGB

Kleur van het middelpunt van het begrenzingsvak.

vereist
Broncode in 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))

Toont wachtrijtellingen op een afbeelding gecentreerd op de punten met aanpasbare lettergrootte en kleuren.

Parameters:

Naam Type Beschrijving Standaard
label str

wachtrij telt label

vereist
points tuple

regiopunten voor berekening middelpunt om tekst weer te geven

None
region_color RGB

kleur wachtrijgebied

(255, 255, 255)
txt_color RGB

kleur tekstweergave

(0, 0, 0)
Broncode in 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)

Rechthoek toevoegen aan afbeelding (alleen PIL).

Broncode in 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()

Geeft geannoteerde afbeelding als array.

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

save(filename='image.jpg')

Sla de geannoteerde afbeelding op in 'bestandsnaam'.

Broncode in 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), label=None, txt_color=(255, 255, 255))

Functie voor het tekenen van gesegmenteerd object in bounding box vorm.

Parameters:

Naam Type Beschrijving Standaard
mask list

maskeert gegevenslijst voor bijvoorbeeld segmentatiegebied plotten

vereist
mask_color RGB

masker voorgrondkleur

(255, 0, 255)
label str

Detectie label tekst

None
txt_color RGB

text color

(255, 255, 255)
Broncode in ultralytics/utils/plotting.py
def seg_bbox(self, mask, mask_color=(255, 0, 255), label=None, txt_color=(255, 255, 255)):
    """
    Function for drawing segmented object in bounding box shape.

    Args:
        mask (list): masks data list for instance segmentation area plotting
        mask_color (RGB): mask foreground color
        label (str): Detection label text
        txt_color (RGB): text color
    """

    cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)
    text_size, _ = cv2.getTextSize(label, 0, self.sf, self.tf)

    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 + 10, int(mask[0][1] + 10)),
        mask_color,
        -1,
    )

    if label:
        cv2.putText(
            self.im, label, (int(mask[0][0]) - text_size[0] // 2, int(mask[0][1])), 0, self.sf, txt_color, self.tf
        )

show(title=None)

Toon de geannoteerde afbeelding.

Broncode in 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)

Voegt tekst toe aan een afbeelding met PIL of cv2.

Broncode in 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
            h += 3  # add pixels to pad text
            outside = xy[1] >= h  # label fits outside box
            p2 = xy[0] + w, xy[1] - h if outside else xy[1] + h
            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)

Hiermee tekent u een label met een rechthoek op de achtergrond in het midden van een bepaald begrenzingsvak.

Parameters:

Naam Type Beschrijving Standaard
box tuple

De co├Ârdinaten van het begrenzingsvak (x1, y1, x2, y2).

vereist
label str

Het tekstlabel dat moet worden weergegeven.

''
color tuple

De achtergrondkleur van de rechthoek (R, G, B).

(128, 128, 128)
txt_color tuple

De kleur van de tekst (R, G, B).

(255, 255, 255)
margin int

De marge tussen de tekst en de rechthoekige rand.

5
Broncode in 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))

Functie voor het nauwkeurig in kaart brengen en plotten van menselijke ogen.

Parameters:

Naam Type Beschrijving Standaard
box list

Co├Ârdinaten van de begrenzende box

vereist
center_point tuple

middelpunt voor visie oogaanzicht

vereist
color tuple

objectmiddelpunt en lijnkleurwaarde

(235, 219, 11)
pin_color tuple

Visionye punt kleurwaarde

(255, 0, 255)
Broncode in 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)

Teken trainingslabels inclusief klassehistogrammen en boxstatistieken.

Broncode in 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)

Afbeelding bijsnijden opslaan als {file} met bijsnijdgrootte meervoudige {gain} en {pad} pixels. Bijsnijden opslaan en/of retourneren.

Deze functie neemt een bounding box en een afbeelding, en slaat dan een bijgesneden deel van de afbeelding op volgens volgens de bounding box. Optioneel kan de uitsnede worden gekwadrateerd en de functie maakt het mogelijk om de gain en padding aanpassingen aan de bounding box.

Parameters:

Naam Type Beschrijving Standaard
xyxy Tensor or list

Een tensor of lijst die de bounding box weergeeft in xyxy formaat.

vereist
im ndarray

De invoerafbeelding.

vereist
file Path

Het pad waar de bijgesneden afbeelding wordt opgeslagen. Wordt standaard ingesteld op 'im.jpg'.

Path('im.jpg')
gain float

Een vermenigvuldigingsfactor om de bounding box groter te maken. Standaard ingesteld op 1,02.

1.02
pad int

Het aantal pixels dat moet worden toegevoegd aan de breedte en hoogte van de bounding box. Standaard 10.

10
square bool

Indien Waar, dan wordt de bounding box getransformeerd in een vierkant. Standaard is dit Onwaar.

False
BGR bool

Als dit Waar is, wordt de afbeelding opgeslagen in BGR-indeling, anders in RGB. Standaard is dit Onwaar.

False
save bool

Als dit waar is, wordt de bijgesneden afbeelding opgeslagen op schijf. Standaard ingesteld op Waar.

True

Retourneert:

Type Beschrijving
ndarray

De bijgesneden afbeelding.

Voorbeeld
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)
Broncode in 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_size=1920, max_subplots=16, save=True, conf_thres=0.25)

Teken afbeeldingsraster met labels, begrenzingsvakken, maskers en sleutelpunten.

Parameters:

Naam Type Beschrijving Standaard
images Union[Tensor, ndarray]

Batch afbeeldingen om te plotten. Vorm: (batch_size, kanalen, hoogte, breedte).

vereist
batch_idx Union[Tensor, ndarray]

Batchindexen voor elke detectie. Vorm: (num_detections,).

vereist
cls Union[Tensor, ndarray]

Klasselabels voor elke detectie. Vorm: (num_detections,).

vereist
bboxes Union[Tensor, ndarray]

Begrenzingsvakken voor elke detectie. Vorm: (num_detections, 4) of (num_detections, 5) voor gedraaide dozen.

zeros(0, dtype=float32)
confs Optional[Union[Tensor, ndarray]]

Betrouwbaarheidsscores voor elke detectie. Vorm: (num_detections,).

None
masks Union[Tensor, ndarray]

Segmentatiemaskers voor exemplaren. Vorm: (num_detections, hoogte, breedte) of (1, hoogte, breedte).

zeros(0, dtype=uint8)
kpts Union[Tensor, ndarray]

Kernpunten voor elke detectie. Vorm: (num_detections, 51).

zeros((0, 51), dtype=float32)
paths Optional[List[str]]

Lijst met bestandspaden voor elke afbeelding in de batch.

None
fname str

Uitvoerbestandsnaam voor het geplotte afbeeldingsraster.

'images.jpg'
names Optional[Dict[int, str]]

Woordenboek dat klasse-indices aan klassennamen koppelt.

None
on_plot Optional[Callable]

Optionele callback-functie die moet worden opgeroepen na het opslaan van de plot.

None
max_size int

Maximale grootte van het uitvoerafbeeldingsraster.

1920
max_subplots int

Maximum aantal subplots in het afbeeldingsraster.

16
save bool

Of het geplotte afbeeldingsraster moet worden opgeslagen in een bestand.

True
conf_thres float

Betrouwbaarheidsdrempel voor het weergeven van detecties.

0.25

Retourneert:

Type Beschrijving
Optional[ndarray]

np.ndarray: Uitgezet afbeeldingsraster als een numpy-array als opslaan Onwaar is, Anders geen.

Opmerking

Deze functie ondersteunt zowel tensor als numpy array invoer. Het zal automatisch tensor input converteren naar numpy arrays voor verwerking.

Broncode in ultralytics/utils/plotting.py
@threaded
def plot_images(
    images: Union[torch.Tensor, np.ndarray],
    batch_idx: Union[torch.Tensor, np.ndarray],
    cls: Union[torch.Tensor, np.ndarray],
    bboxes: Union[torch.Tensor, np.ndarray] = np.zeros(0, dtype=np.float32),
    confs: Optional[Union[torch.Tensor, np.ndarray]] = None,
    masks: Union[torch.Tensor, np.ndarray] = np.zeros(0, dtype=np.uint8),
    kpts: Union[torch.Tensor, np.ndarray] = np.zeros((0, 51), dtype=np.float32),
    paths: Optional[List[str]] = None,
    fname: str = "images.jpg",
    names: Optional[Dict[int, str]] = None,
    on_plot: Optional[Callable] = None,
    max_size: int = 1920,
    max_subplots: int = 16,
    save: bool = True,
    conf_thres: float = 0.25,
) -> Optional[np.ndarray]:
    """
    Plot image grid with labels, bounding boxes, masks, and keypoints.

    Args:
        images: Batch of images to plot. Shape: (batch_size, channels, height, width).
        batch_idx: Batch indices for each detection. Shape: (num_detections,).
        cls: Class labels for each detection. Shape: (num_detections,).
        bboxes: Bounding boxes for each detection. Shape: (num_detections, 4) or (num_detections, 5) for rotated boxes.
        confs: Confidence scores for each detection. Shape: (num_detections,).
        masks: Instance segmentation masks. Shape: (num_detections, height, width) or (1, height, width).
        kpts: Keypoints for each detection. Shape: (num_detections, 51).
        paths: List of file paths for each image in the batch.
        fname: Output filename for the plotted image grid.
        names: Dictionary mapping class indices to class names.
        on_plot: Optional callback function to be called after saving the plot.
        max_size: Maximum size of the output image grid.
        max_subplots: Maximum number of subplots in the image grid.
        save: Whether to save the plotted image grid to a file.
        conf_thres: Confidence threshold for displaying detections.

    Returns:
        np.ndarray: Plotted image grid as a numpy array if save is False, None otherwise.

    Note:
        This function supports both tensor and numpy array inputs. It will automatically
        convert tensor inputs to numpy arrays for processing.
    """
    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()

    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)

Teken trainingsresultaten vanuit een CSV-bestand. De functie ondersteunt verschillende soorten gegevens, waaronder segmentatie, houding schatten en classificatie. Plots worden opgeslagen als 'results.png' in de map waar het CSV-bestand staat.

Parameters:

Naam Type Beschrijving Standaard
file str

Pad naar het CSV-bestand met de trainingsresultaten. Standaard ingesteld op 'path/to/results.csv'.

'path/to/results.csv'
dir str

Directory waar het CSV-bestand zich bevindt als 'bestand' niet is opgegeven. Staat standaard op ''.

''
segment bool

Vlag om aan te geven of de gegevens gesegmenteerd moeten worden. Staat standaard op Onwaar.

False
pose bool

Vlag om aan te geven of de gegevens bedoeld zijn voor het schatten van de houding. Staat standaard op Onwaar.

False
classify bool

Vlag om aan te geven of de gegevens voor classificatie zijn. Staat standaard op Onwaar.

False
on_plot callable

Callback-functie die wordt uitgevoerd na het plotten. Neemt bestandsnaam als argument. Standaard geen.

None
Voorbeeld
from ultralytics.utils.plotting import plot_results

plot_results('path/to/results.csv', segment=True)
Broncode in 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')

Tekent een scatterplot met gekleurde punten op basis van een 2D histogram.

Parameters:

Naam Type Beschrijving Standaard
v array - like

Waarden voor de x-as.

vereist
f array - like

Waarden voor de y-as.

vereist
bins int

Aantal bins voor het histogram. Standaard 20.

20
cmap str

Kleurenkaart voor de scatter plot. Standaard ingesteld op 'viridis'.

'viridis'
alpha float

Alpha voor de scatter plot. Standaard ingesteld op 0,8.

0.8
edgecolors str

Randkleuren voor de scatterplot. Staat standaard op 'geen'.

'none'

Voorbeelden:

>>> v = np.random.rand(100)
>>> f = np.random.rand(100)
>>> plt_color_scatter(v, f)
Broncode in 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')

Plot de evolutieresultaten die zijn opgeslagen in een bestand 'tune_results.csv'. De functie genereert een spreidingsdiagram voor elke sleutel in de CSV, met kleurcodering op basis van fitnessscores. De best presterende configuraties worden gemarkeerd op de plots.

Parameters:

Naam Type Beschrijving Standaard
csv_file str

Pad naar het CSV-bestand met de afstemmingsresultaten. Standaard ingesteld op 'tune_results.csv'.

'tune_results.csv'

Voorbeelden:

>>> plot_tune_results('path/to/tune_results.csv')
Broncode in 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

    def _save_one_file(file):
        """Save one matplotlib plot to 'file'."""
        plt.savefig(file, dpi=200)
        plt.close()
        LOGGER.info(f"Saved {file}")

    # 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([])
    _save_one_file(csv_file.with_name("tune_scatter_plots.png"))

    # 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()
    _save_one_file(csv_file.with_name("tune_fitness.png"))



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

Converteer modeluitvoer naar het doelformaat [batch_id, class_id, x, y, w, h, conf] voor plotten.

Broncode in 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)

Converteer modeluitvoer naar het doelformaat [batch_id, class_id, x, y, w, h, conf] voor plotten.

Broncode in 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'))

Visualiseer kenmerkkaarten van een bepaalde modelmodule tijdens inferentie.

Parameters:

Naam Type Beschrijving Standaard
x Tensor

Te visualiseren functies.

vereist
module_type str

Type module.

vereist
stage int

Modulefase binnen het model.

vereist
n int

Maximum aantal feature maps om uit te zetten. Standaard 32.

32
save_dir Path

Directory om resultaten op te slaan. Standaard Pad('runs/detect/exp').

Path('runs/detect/exp')
Broncode in 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





Aangemaakt 2023-11-12, Bijgewerkt 2024-06-02
Auteurs: glenn-jocher (6), Burhan-Q (1), Laughing-q (1)