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के लिए संदर्भ ultralytics/utils/plotting.py

नोट

यह फ़ाइल यहाँ उपलब्ध है https://github.com/ultralytics/ultralytics/बूँद/मुख्य/ultralytics/utils/plotting.py का उपयोग करें। यदि आप कोई समस्या देखते हैं तो कृपया पुल अनुरोध का योगदान करके इसे ठीक करने में मदद करें 🛠️। 🙏 धन्यवाद !



ultralytics.utils.plotting.Colors

Ultralytics डिफ़ॉल्ट रंग पैलेट https://ultralytics।कॉम/।

यह वर्ग के साथ काम करने के तरीके प्रदान करता है Ultralytics रंग पैलेट, जिसमें हेक्स रंग कोड को RGB मान।

विशेषताएँ:

नाम प्रकार विवरण: __________
palette list of tuple

आरजीबी रंग मूल्यों की सूची।

n int

पैलेट में रंगों की संख्या।

pose_palette ndarray

dtype np.uint8 के साथ एक विशिष्ट रंग पैलेट सरणी।

में स्रोत कोड ultralytics/utils/plotting.py
20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  44 4546474849 5051 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76777879808182838485 868788 899091 9293
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 = (
            "FF3838",
            "FF9D97",
            "FF701F",
            "FFB21D",
            "CFD231",
            "48F90A",
            "92CC17",
            "3DDB86",
            "1A9334",
            "00D4BB",
            "2C99A8",
            "00C2FF",
            "344593",
            "6473FF",
            "0018EC",
            "8438FF",
            "520085",
            "CB38FF",
            "FF95C8",
            "FF37C7",
        )
        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)

हेक्स रंग कोड को RGB मानों में कनवर्ट करता है।

में स्रोत कोड 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__()

रंगों को हेक्स = matplotlib.colors.TABLEAU_COLORS.values() के रूप में प्रारंभ करें।

में स्रोत कोड ultralytics/utils/plotting.py
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 5960 61 626364 6566 6768 69 70 71 72 73 74 7576777879 808182 83
def __init__(self):
    """Initialize colors as hex = matplotlib.colors.TABLEAU_COLORS.values()."""
    hexs = (
        "FF3838",
        "FF9D97",
        "FF701F",
        "FFB21D",
        "CFD231",
        "48F90A",
        "92CC17",
        "3DDB86",
        "1A9334",
        "00D4BB",
        "2C99A8",
        "00C2FF",
        "344593",
        "6473FF",
        "0018EC",
        "8438FF",
        "520085",
        "CB38FF",
        "FF95C8",
        "FF37C7",
    )
    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

हेक्स रंग कोड को RGB मानों (यानी डिफ़ॉल्ट PIL ऑर्डर) में कनवर्ट करता है।

में स्रोत कोड 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 ट्रेन/वैल मोज़ाइक और जेपीजी और भविष्यवाणियों एनोटेशन के लिए एनोटेटर।

विशेषताएँ:

नाम प्रकार विवरण: __________
im Image.Image or numpy array

टिप्पणी करने के लिए छवि।

pil bool

एनोटेशन खींचने के लिए PIL या cv2 का उपयोग करना है या नहीं।

font truetype or load_default

पाठ एनोटेशन के लिए उपयोग किया जाने वाला फ़ॉन्ट.

lw float

ड्राइंग के लिए लाइन चौड़ाई।

skeleton List[List[int]]

कीपॉइंट के लिए कंकाल संरचना।

limb_color List[int]

अंगों के लिए रंग पैलेट।

kpt_color List[int]

कीपॉइंट के लिए रंग पैलेट।

में स्रोत कोड 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]]

    def box_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), rotated=False):
        """Add one xyxy box to image with label."""
        if isinstance(box, torch.Tensor):
            box = box.tolist()
        if self.pil or not is_ascii(label):
            if rotated:
                p1 = box[0]
                # NOTE: PIL-version polygon needs tuple type.
                self.draw.polygon([tuple(b) for b in box], width=self.lw, outline=color)
            else:
                p1 = (box[0], box[1])
                self.draw.rectangle(box, width=self.lw, outline=color)  # box
            if label:
                w, h = self.font.getsize(label)  # text width, height
                outside = p1[1] - h >= 0  # label fits outside box
                self.draw.rectangle(
                    (p1[0], p1[1] - h if outside else p1[1], p1[0] + w + 1, p1[1] + 1 if outside else p1[1] + h + 1),
                    fill=color,
                )
                # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
                self.draw.text((p1[0], p1[1] - h if outside else p1[1]), label, fill=txt_color, font=self.font)
        else:  # cv2
            if rotated:
                p1 = [int(b) for b in box[0]]
                # NOTE: cv2-version polylines needs np.asarray type.
                cv2.polylines(self.im, [np.asarray(box, dtype=int)], True, color, self.lw)
            else:
                p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
                cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
            if label:
                w, h = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.tf)[0]  # text width, height
                outside = p1[1] - h >= 3
                p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
                cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
                cv2.putText(
                    self.im,
                    label,
                    (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
                    0,
                    self.sf,
                    txt_color,
                    thickness=self.tf,
                    lineType=cv2.LINE_AA,
                )

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

        Args:
            masks (tensor): Predicted masks on cuda, shape: [n, h, w]
            colors (List[List[Int]]): Colors for predicted masks, [[r, g, b] * n]
            im_gpu (tensor): Image is in cuda, shape: [3, h, w], range: [0, 1]
            alpha (float): Mask transparency: 0.0 fully transparent, 1.0 opaque
            retina_masks (bool): Whether to use high resolution masks or not. Defaults to False.
        """
        if self.pil:
            # Convert to numpy first
            self.im = np.asarray(self.im).copy()
        if len(masks) == 0:
            self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
        if im_gpu.device != masks.device:
            im_gpu = im_gpu.to(masks.device)
        colors = torch.tensor(colors, device=masks.device, dtype=torch.float32) / 255.0  # shape(n,3)
        colors = colors[:, None, None]  # shape(n,1,1,3)
        masks = masks.unsqueeze(3)  # shape(n,h,w,1)
        masks_color = masks * (colors * alpha)  # shape(n,h,w,3)

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

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

    def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True):
        """
        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 < 0.5:
                        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 < 0.5 or conf2 < 0.5:
                        continue
                if pos1[0] % shape[1] == 0 or pos1[1] % shape[0] == 0 or pos1[0] < 0 or pos1[1] < 0:
                    continue
                if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0:
                    continue
                cv2.line(self.im, pos1, pos2, [int(x) for x in self.limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
        if self.pil:
            # Convert im back to PIL and update draw
            self.fromarray(self.im)

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

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

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

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

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

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

    def 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 count_labels(self, counts=0, count_txt_size=2, color=(255, 255, 255), txt_color=(0, 0, 0)):
        """
        Plot counts for object counter.

        Args:
            counts (int): objects counts value
            count_txt_size (int): text size for counts display
            color (tuple): background color of counts display
            txt_color (tuple): text color of counts display
        """
        self.tf = count_txt_size
        tl = self.tf or round(0.002 * (self.im.shape[0] + self.im.shape[1]) / 2) + 1
        tf = max(tl - 1, 1)

        # Get text size for in_count and out_count
        t_size_in = cv2.getTextSize(str(counts), 0, fontScale=tl / 2, thickness=tf)[0]

        # Calculate positions for counts label
        text_width = t_size_in[0]
        text_x = (self.im.shape[1] - text_width) // 2  # Center x-coordinate
        text_y = t_size_in[1]

        # Create a rounded rectangle for in_count
        cv2.rectangle(
            self.im, (text_x - 5, text_y - 5), (text_x + text_width + 7, text_y + t_size_in[1] + 7), color, -1
        )
        cv2.putText(
            self.im, str(counts), (text_x, text_y + t_size_in[1]), 0, tl / 2, txt_color, self.tf, lineType=cv2.LINE_AA
        )

    @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=[2, 5, 7], shape=(640, 640), radius=2):
        """
        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
        """
        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 < 0.5:
                            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, line_thickness=2):
        """
        Plot the pose angle, count value and step stage.

        Args:
            angle_text (str): angle value for workout monitoring
            count_text (str): counts value for workout monitoring
            stage_text (str): stage decision for workout monitoring
            center_kpt (int): centroid pose index for workout monitoring
            line_thickness (int): thickness for text display
        """
        angle_text, count_text, stage_text = (f" {angle_text:.2f}", f"Steps : {count_text}", f" {stage_text}")
        font_scale = 0.6 + (line_thickness / 10.0)

        # Draw angle
        (angle_text_width, angle_text_height), _ = cv2.getTextSize(angle_text, 0, font_scale, line_thickness)
        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 + (line_thickness * 2))
        cv2.rectangle(
            self.im,
            angle_background_position,
            (
                angle_background_position[0] + angle_background_size[0],
                angle_background_position[1] + angle_background_size[1],
            ),
            (255, 255, 255),
            -1,
        )
        cv2.putText(self.im, angle_text, angle_text_position, 0, font_scale, (0, 0, 0), line_thickness)

        # Draw Counts
        (count_text_width, count_text_height), _ = cv2.getTextSize(count_text, 0, font_scale, line_thickness)
        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 + (line_thickness * 2))

        cv2.rectangle(
            self.im,
            count_background_position,
            (
                count_background_position[0] + count_background_size[0],
                count_background_position[1] + count_background_size[1],
            ),
            (255, 255, 255),
            -1,
        )
        cv2.putText(self.im, count_text, count_text_position, 0, font_scale, (0, 0, 0), line_thickness)

        # Draw Stage
        (stage_text_width, stage_text_height), _ = cv2.getTextSize(stage_text, 0, font_scale, line_thickness)
        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],
            ),
            (255, 255, 255),
            -1,
        )
        cv2.putText(self.im, stage_text, stage_text_position, 0, font_scale, (0, 0, 0), line_thickness)

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

        Args:
            mask (list): masks data list for instance segmentation area plotting
            mask_color (tuple): mask foreground color
            det_label (str): Detection label text
            track_label (str): Tracking label text
        """
        cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)

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

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

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

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

        Args:
            distance_m (float): Distance between two bbox centroids in meters.
            distance_mm (float): Distance between two bbox centroids in millimeters.
            centroids (list): Bounding box centroids data.
            line_color (RGB): Distance line color.
            centroid_color (RGB): Bounding box centroid color.
        """
        (text_width_m, text_height_m), _ = cv2.getTextSize(
            f"Distance M: {distance_m:.2f}m", cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2
        )
        cv2.rectangle(self.im, (15, 25), (15 + text_width_m + 10, 25 + text_height_m + 20), (255, 255, 255), -1)
        cv2.putText(
            self.im,
            f"Distance M: {distance_m:.2f}m",
            (20, 50),
            cv2.FONT_HERSHEY_SIMPLEX,
            0.8,
            (0, 0, 0),
            2,
            cv2.LINE_AA,
        )

        (text_width_mm, text_height_mm), _ = cv2.getTextSize(
            f"Distance MM: {distance_mm:.2f}mm", cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2
        )
        cv2.rectangle(self.im, (15, 75), (15 + text_width_mm + 10, 75 + text_height_mm + 20), (255, 255, 255), -1)
        cv2.putText(
            self.im,
            f"Distance MM: {distance_mm:.2f}mm",
            (20, 100),
            cv2.FONT_HERSHEY_SIMPLEX,
            0.8,
            (0, 0, 0),
            2,
            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), thickness=2, pins_radius=10):
        """
        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
            thickness (int): int value for line thickness
            pins_radius (int): visioneye point radius value
        """
        center_bbox = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
        cv2.circle(self.im, center_point, pins_radius, pin_color, -1)
        cv2.circle(self.im, center_bbox, pins_radius, color, -1)
        cv2.line(self.im, center_point, center_bbox, color, thickness)

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

कीपॉइंट्स और अंगों के लिए रंग पैलेट के साथ छवि और रेखा चौड़ाई के साथ एनोटेटर वर्ग को प्रारंभ करें।

में स्रोत कोड ultralytics/utils/plotting.py
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138139 140141 142143 144 145 146 147 148 149 150 151 152 153 154 155 156 157158159160
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]]

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

लेबल के साथ छवि के लिए एक xyxy बॉक्स जोड़ें.

में स्रोत कोड ultralytics/utils/plotting.py
162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178179 180 181 182 183 184 185 186 187 188 189 190 191 192193194 195 196 197 198199 200 201 202 203 204 205
def box_label(self, box, label="", color=(128, 128, 128), txt_color=(255, 255, 255), rotated=False):
    """Add one xyxy box to image with label."""
    if isinstance(box, torch.Tensor):
        box = box.tolist()
    if self.pil or not is_ascii(label):
        if rotated:
            p1 = box[0]
            # NOTE: PIL-version polygon needs tuple type.
            self.draw.polygon([tuple(b) for b in box], width=self.lw, outline=color)
        else:
            p1 = (box[0], box[1])
            self.draw.rectangle(box, width=self.lw, outline=color)  # box
        if label:
            w, h = self.font.getsize(label)  # text width, height
            outside = p1[1] - h >= 0  # label fits outside box
            self.draw.rectangle(
                (p1[0], p1[1] - h if outside else p1[1], p1[0] + w + 1, p1[1] + 1 if outside else p1[1] + h + 1),
                fill=color,
            )
            # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls')  # for PIL>8.0
            self.draw.text((p1[0], p1[1] - h if outside else p1[1]), label, fill=txt_color, font=self.font)
    else:  # cv2
        if rotated:
            p1 = [int(b) for b in box[0]]
            # NOTE: cv2-version polylines needs np.asarray type.
            cv2.polylines(self.im, [np.asarray(box, dtype=int)], True, color, self.lw)
        else:
            p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
            cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
        if label:
            w, h = cv2.getTextSize(label, 0, fontScale=self.sf, thickness=self.tf)[0]  # text width, height
            outside = p1[1] - h >= 3
            p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
            cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA)  # filled
            cv2.putText(
                self.im,
                label,
                (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
                0,
                self.sf,
                txt_color,
                thickness=self.tf,
                lineType=cv2.LINE_AA,
            )

count_labels(counts=0, count_txt_size=2, color=(255, 255, 255), txt_color=(0, 0, 0))

ऑब्जेक्ट काउंटर के लिए प्लॉट मायने रखता है।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
counts int

ऑब्जेक्ट मूल्य की गणना करता है

0
count_txt_size int

गणना प्रदर्शन के लिए पाठ आकार

2
color tuple

गणना प्रदर्शन की पृष्ठभूमि रंग

(255, 255, 255)
txt_color tuple

गणना प्रदर्शन का पाठ रंग

(0, 0, 0)
में स्रोत कोड ultralytics/utils/plotting.py
366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388389 390391 392393 394
def count_labels(self, counts=0, count_txt_size=2, color=(255, 255, 255), txt_color=(0, 0, 0)):
    """
    Plot counts for object counter.

    Args:
        counts (int): objects counts value
        count_txt_size (int): text size for counts display
        color (tuple): background color of counts display
        txt_color (tuple): text color of counts display
    """
    self.tf = count_txt_size
    tl = self.tf or round(0.002 * (self.im.shape[0] + self.im.shape[1]) / 2) + 1
    tf = max(tl - 1, 1)

    # Get text size for in_count and out_count
    t_size_in = cv2.getTextSize(str(counts), 0, fontScale=tl / 2, thickness=tf)[0]

    # Calculate positions for counts label
    text_width = t_size_in[0]
    text_x = (self.im.shape[1] - text_width) // 2  # Center x-coordinate
    text_y = t_size_in[1]

    # Create a rounded rectangle for in_count
    cv2.rectangle(
        self.im, (text_x - 5, text_y - 5), (text_x + text_width + 7, text_y + t_size_in[1] + 7), color, -1
    )
    cv2.putText(
        self.im, str(counts), (text_x, text_y + t_size_in[1]), 0, tl / 2, txt_color, self.tf, lineType=cv2.LINE_AA
    )

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

केन्द्रक बिंदु और ट्रैक ट्रेल्स ड्रा.

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
track list

ट्रेल्स प्रदर्शन के लिए ऑब्जेक्ट ट्रैकिंग पॉइंट

आवश्यक
color tuple

लाइन रंग ट्रैक करता है

(255, 0, 255)
track_thickness int

ट्रैक लाइन मोटाई मूल्य

2
में स्रोत कोड ultralytics/utils/plotting.py
353 354 355 356 357358 359 360 361 362 363364
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)

क्षेत्र रेखा ड्रा.

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
reg_pts list

क्षेत्र बिंदु (पंक्ति 2 अंक के लिए, क्षेत्र 4 अंक के लिए)

None
color tuple

क्षेत्र रंग मान

(0, 255, 0)
thickness int

क्षेत्र क्षेत्र मोटाई मूल्य

5
में स्रोत कोड ultralytics/utils/plotting.py
342 343 344 345 346 347 348349 350 351 
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=[2, 5, 7], shape=(640, 640), radius=2)

जिम कदम गिनती के लिए विशिष्ट keypoints ड्रा.

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
keypoints list

प्लॉट किए जाने वाले कीपॉइंट डेटा की सूची

आवश्यक
indices list

कीपॉइंट्स आईडी लिस्ट प्लॉट की जाएगी

[2, 5, 7]
shape tuple

मॉडल अनुमान के लिए imgsz

(640, 640)
radius int

कीपॉइंट त्रिज्या मान

2
में स्रोत कोड ultralytics/utils/plotting.py
416 417 418 419 420 421 422 423 424 425 426 427 428 429430 431 432 433 434 435
def draw_specific_points(self, keypoints, indices=[2, 5, 7], shape=(640, 640), radius=2):
    """
    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
    """
    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 < 0.5:
                        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

वस्तु के लिए मुद्रा कोण की गणना करें।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
a float)

मुद्रा बिंदु का मान a

आवश्यक
b float

मुद्रा बिंदु b का मान

आवश्यक
c float

मान o मुद्रा बिंदु c

आवश्यक

देता:

नाम प्रकार विवरण: __________
angle degree

तीन बिंदुओं के बीच कोण का डिग्री मान

में स्रोत कोड ultralytics/utils/plotting.py
396 397 398 399400 401 402 403 404 405 406 407 408409 410 411 412413414
@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)

एक numpy सरणी से self.im अपडेट करें।

में स्रोत कोड ultralytics/utils/plotting.py
325 326 327 328
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)

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

छवि पर प्लॉट कीपॉइंट।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
kpts tensor

आकार के साथ अनुमानित कीपॉइंट [17, 3]। प्रत्येक कीपॉइंट में (x, y, आत्मविश्वास) होता है।

आवश्यक
shape tuple

टपल (h, w) के रूप में छवि आकार, जहां h ऊंचाई है और w चौड़ाई है।

(640, 640)
radius int

खींचे गए कीपॉइंट्स की त्रिज्या। डिफ़ॉल्ट 5 है।

5
kpt_line bool

यदि सही है, तो फ़ंक्शन कीपॉइंट को जोड़ने वाली रेखाएँ खींचेगा मानव मुद्रा के लिए। डिफ़ॉल्ट True है.

True
नोट

kpt_line=True वर्तमान में केवल मानव मुद्रा साजिश का समर्थन करता है।

में स्रोत कोड ultralytics/utils/plotting.py
243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288289290
def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True):
    """
    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 < 0.5:
                    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 < 0.5 or conf2 < 0.5:
                    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)

छवि पर मास्क प्लॉट करें।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
masks tensor

क्यूडा पर अनुमानित मास्क, आकार: [एन, एच, डब्ल्यू]

आवश्यक
colors List[List[Int]]

अनुमानित मास्क के लिए रंग, [[आर, जी, बी] * एन]

आवश्यक
im_gpu tensor

छवि क्यूडा में है, आकार: [3, एच, डब्ल्यू], रेंज: [0, 1]

आवश्यक
alpha float

मास्क पारदर्शिता: 0.0 पूरी तरह से पारदर्शी, 1.0 अपारदर्शी

0.5
retina_masks bool

हाई रेजोल्यूशन मास्क का इस्तेमाल करना है या नहीं। डिफ़ॉल्ट रूप से गलत है.

False
में स्रोत कोड ultralytics/utils/plotting.py
207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228229 230 231 232 233 234 235 236 237 238239240 241
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, line_thickness=2)

मुद्रा कोण, गणना मूल्य और चरण चरण प्लॉट करें।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
angle_text str

कसरत निगरानी के लिए कोण मूल्य

आवश्यक
count_text str

कसरत की निगरानी के लिए मूल्य की गणना करता है

आवश्यक
stage_text str

कसरत निगरानी के लिए मंच निर्णय

आवश्यक
center_kpt int

कसरत निगरानी के लिए Centroid मुद्रा सूचकांक

आवश्यक
line_thickness int

पाठ प्रदर्शन के लिए मोटाई

2
में स्रोत कोड ultralytics/utils/plotting.py
437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488489 490491 492 493 494 495 496 497 498 499 500 501 502 503 504 505
def plot_angle_and_count_and_stage(self, angle_text, count_text, stage_text, center_kpt, line_thickness=2):
    """
    Plot the pose angle, count value and step stage.

    Args:
        angle_text (str): angle value for workout monitoring
        count_text (str): counts value for workout monitoring
        stage_text (str): stage decision for workout monitoring
        center_kpt (int): centroid pose index for workout monitoring
        line_thickness (int): thickness for text display
    """
    angle_text, count_text, stage_text = (f" {angle_text:.2f}", f"Steps : {count_text}", f" {stage_text}")
    font_scale = 0.6 + (line_thickness / 10.0)

    # Draw angle
    (angle_text_width, angle_text_height), _ = cv2.getTextSize(angle_text, 0, font_scale, line_thickness)
    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 + (line_thickness * 2))
    cv2.rectangle(
        self.im,
        angle_background_position,
        (
            angle_background_position[0] + angle_background_size[0],
            angle_background_position[1] + angle_background_size[1],
        ),
        (255, 255, 255),
        -1,
    )
    cv2.putText(self.im, angle_text, angle_text_position, 0, font_scale, (0, 0, 0), line_thickness)

    # Draw Counts
    (count_text_width, count_text_height), _ = cv2.getTextSize(count_text, 0, font_scale, line_thickness)
    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 + (line_thickness * 2))

    cv2.rectangle(
        self.im,
        count_background_position,
        (
            count_background_position[0] + count_background_size[0],
            count_background_position[1] + count_background_size[1],
        ),
        (255, 255, 255),
        -1,
    )
    cv2.putText(self.im, count_text, count_text_position, 0, font_scale, (0, 0, 0), line_thickness)

    # Draw Stage
    (stage_text_width, stage_text_height), _ = cv2.getTextSize(stage_text, 0, font_scale, line_thickness)
    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],
        ),
        (255, 255, 255),
        -1,
    )
    cv2.putText(self.im, stage_text, stage_text_position, 0, font_scale, (0, 0, 0), line_thickness)

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

दूरी और रेखा को फ्रेम पर प्लॉट करें।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
distance_m float

मीटर में दो bbox centroids के बीच की दूरी।

आवश्यक
distance_mm float

मिलीमीटर में दो bbox centroids के बीच की दूरी।

आवश्यक
centroids list

बाउंडिंग बॉक्स सेंट्रोइड डेटा।

आवश्यक
line_color RGB

दूरी रेखा का रंग।

आवश्यक
centroid_color RGB

बाउंडिंग बॉक्स केन्द्रक रंग।

आवश्यक
में स्रोत कोड ultralytics/utils/plotting.py
534 535 536 537 538 539 540 541 542 543 544 545 546 547 548549 550 551 552 553 554 555 556 557 558 559 560561 562563564565 566 567 568 569 570 571 572 573 574 575 576577
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", cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2
    )
    cv2.rectangle(self.im, (15, 25), (15 + text_width_m + 10, 25 + text_height_m + 20), (255, 255, 255), -1)
    cv2.putText(
        self.im,
        f"Distance M: {distance_m:.2f}m",
        (20, 50),
        cv2.FONT_HERSHEY_SIMPLEX,
        0.8,
        (0, 0, 0),
        2,
        cv2.LINE_AA,
    )

    (text_width_mm, text_height_mm), _ = cv2.getTextSize(
        f"Distance MM: {distance_mm:.2f}mm", cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2
    )
    cv2.rectangle(self.im, (15, 75), (15 + text_width_mm + 10, 75 + text_height_mm + 20), (255, 255, 255), -1)
    cv2.putText(
        self.im,
        f"Distance MM: {distance_mm:.2f}mm",
        (20, 100),
        cv2.FONT_HERSHEY_SIMPLEX,
        0.8,
        (0, 0, 0),
        2,
        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)

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

छवि में आयत जोड़ें (केवल पीआईएल)।

में स्रोत कोड 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()

एनोटेट की गई छवि को सरणी के रूप में लौटाएं।

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

save(filename='image.jpg')

एनोटेट की गई छवि को 'फ़ाइल नाम' में सहेजें।

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

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

बाउंडिंग बॉक्स आकार में खंडित वस्तु को चित्रित करने के लिए फ़ंक्शन।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
mask list

उदाहरण के लिए मास्क डेटा सूची विभाजन क्षेत्र प्लॉटिंग

आवश्यक
mask_color tuple

मुखौटा अग्रभूमि रंग

(255, 0, 255)
det_label str

डिटेक्शन लेबल टेक्स्ट

None
track_label str

ट्रैकिंग लेबल पाठ

None
में स्रोत कोड ultralytics/utils/plotting.py
507 508 509 510 511 512 513 514 515 516 517 518519 520 521 522 523 524 525 526 527528529 530531532
def seg_bbox(self, mask, mask_color=(255, 0, 255), det_label=None, track_label=None):
    """
    Function for drawing segmented object in bounding box shape.

    Args:
        mask (list): masks data list for instance segmentation area plotting
        mask_color (tuple): mask foreground color
        det_label (str): Detection label text
        track_label (str): Tracking label text
    """
    cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)

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

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

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

show(title=None)

एनोटेट की गई छवि दिखाएं।

में स्रोत कोड 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)

PIL या cv2 का उपयोग करके छवि में पाठ जोड़ता है.

में स्रोत कोड ultralytics/utils/plotting.py
 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311  312 313 314 315 316 317 318319320 321 322323
def text(self, xy, text, txt_color=(255, 255, 255), anchor="top", box_style=False):
    """Adds text to an image using PIL or cv2."""
    if anchor == "bottom":  # start y from font bottom
        w, h = self.font.getsize(text)  # text width, height
        xy[1] += 1 - h
    if self.pil:
        if box_style:
            w, h = self.font.getsize(text)
            self.draw.rectangle((xy[0], xy[1], xy[0] + w + 1, xy[1] + h + 1), fill=txt_color)
            # Using `txt_color` for background and draw fg with white color
            txt_color = (255, 255, 255)
        if "\n" in text:
            lines = text.split("\n")
            _, h = self.font.getsize(text)
            for line in lines:
                self.draw.text(xy, line, fill=txt_color, font=self.font)
                xy[1] += h
        else:
            self.draw.text(xy, text, fill=txt_color, font=self.font)
    else:
        if box_style:
            w, h = cv2.getTextSize(text, 0, fontScale=self.sf, thickness=self.tf)[0]  # text width, height
            outside = xy[1] - h >= 3
            p2 = xy[0] + w, xy[1] - h - 3 if outside else xy[1] + h + 3
            cv2.rectangle(self.im, xy, p2, txt_color, -1, cv2.LINE_AA)  # filled
            # Using `txt_color` for background and draw fg with white color
            txt_color = (255, 255, 255)
        cv2.putText(self.im, text, xy, 0, self.sf, txt_color, thickness=self.tf, lineType=cv2.LINE_AA)

visioneye(box, center_point, color=(235, 219, 11), pin_color=(255, 0, 255), thickness=2, pins_radius=10)

पिनपॉइंट मानव-दृष्टि, नेत्र मानचित्रण और साजिश रचने के लिए कार्य।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
box list

बाउंडिंग बॉक्स निर्देशांक

आवश्यक
center_point tuple

दृष्टि नेत्र दृश्य के लिए केंद्र बिंदु

आवश्यक
color tuple

ऑब्जेक्ट सेंट्रोइड और लाइन रंग मान

(235, 219, 11)
pin_color tuple

VisionEye बिंदु रंग मूल्य

(255, 0, 255)
thickness int

रेखा मोटाई के लिए int मान

2
pins_radius int

VisionEye बिंदु त्रिज्या मान

10
में स्रोत कोड ultralytics/utils/plotting.py
579 580 581 582 583 584 585 586 587 588589590 591 592 593 594
def visioneye(self, box, center_point, color=(235, 219, 11), pin_color=(255, 0, 255), thickness=2, pins_radius=10):
    """
    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
        thickness (int): int value for line thickness
        pins_radius (int): visioneye point radius value
    """
    center_bbox = int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)
    cv2.circle(self.im, center_point, pins_radius, pin_color, -1)
    cv2.circle(self.im, center_bbox, pins_radius, color, -1)
    cv2.line(self.im, center_point, center_bbox, color, thickness)



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

क्लास हिस्टोग्राम और बॉक्स सांख्यिकी सहित प्लॉट प्रशिक्षण लेबल।

में स्रोत कोड ultralytics/utils/plotting.py
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@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 as pd
    import seaborn as sn

    # 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 = pd.DataFrame(boxes, columns=["x", "y", "width", "height"])

    # Seaborn correlogram
    sn.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")
    sn.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9)
    sn.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)

छवि क्रॉप को {फ़ाइल} के रूप में क्रॉप आकार एकाधिक {लाभ} और {पैड} पिक्सेल के साथ सहेजें। फसल बचाओ और/या लौटाओ।

यह फ़ंक्शन एक बाउंडिंग बॉक्स और एक छवि लेता है, और फिर छवि के एक फसली हिस्से को बचाता है बाउंडिंग बॉक्स के लिए। वैकल्पिक रूप से, फसल को चुकता किया जा सकता है, और फ़ंक्शन लाभ और पैडिंग की अनुमति देता है बाउंडिंग बॉक्स में समायोजन।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
xyxy Tensor or list

एक tensor या xyxy प्रारूप में बाउंडिंग बॉक्स का प्रतिनिधित्व करने वाली सूची।

आवश्यक
im ndarray

इनपुट छवि।

आवश्यक
file Path

वह पथ जहाँ क्रॉप की गई छवि सहेजी जाएगी. 'im.jpg' के लिए डिफ़ॉल्ट।

Path('im.jpg')
gain float

बाउंडिंग बॉक्स के आकार को बढ़ाने के लिए एक गुणक कारक। 1.02 के लिए डिफ़ॉल्ट।

1.02
pad int

बाउंडिंग बॉक्स की चौड़ाई और ऊंचाई में जोड़ने के लिए पिक्सेल की संख्या। 10 के लिए डिफ़ॉल्ट।

10
square bool

यदि सही है, तो बाउंडिंग बॉक्स एक वर्ग में बदल जाएगा। डिफ़ॉल्ट रूप से गलत है.

False
BGR bool

यदि सही है, तो छवि को बीजीआर प्रारूप में सहेजा जाएगा, अन्यथा आरजीबी में। डिफ़ॉल्ट रूप से गलत है.

False
save bool

यदि सही है, तो क्रॉप की गई छवि डिस्क पर सहेजी जाएगी। सही करने के लिए डिफ़ॉल्ट।

True

देता:

प्रकार विवरण: __________
ndarray

क्रॉप की गई छवि.

उदाहरण
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)
में स्रोत कोड ultralytics/utils/plotting.py
653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678679 680 681 682 683684 685 686 687 688 689 690 691 692 693 694 695 696 697698
def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False, BGR=False, save=True):
    """
    Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop.

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

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

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

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

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

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



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

लेबल के साथ छवि ग्रिड प्लॉट करें।

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

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

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

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

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

            if len(bboxes):
                boxes = bboxes[idx]
                conf = confs[idx] if confs is not None else None  # check for confidence presence (label vs pred)
                is_obb = boxes.shape[-1] == 5  # xywhr
                boxes = ops.xywhr2xyxyxyxy(boxes) if is_obb else ops.xywh2xyxy(boxes)
                if len(boxes):
                    if boxes[:, :4].max() <= 1.1:  # if normalized with tolerance 0.1
                        boxes[..., 0::2] *= w  # scale to pixels
                        boxes[..., 1::2] *= h
                    elif scale < 1:  # absolute coords need scale if image scales
                        boxes[..., :4] *= scale
                boxes[..., 0::2] += x
                boxes[..., 1::2] += y
                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])

            # 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)

परिणाम CSV फ़ाइल से प्रशिक्षण परिणामों को प्लॉट करें। फ़ंक्शन विभाजन सहित विभिन्न प्रकार के डेटा का समर्थन करता है, अनुमान और वर्गीकरण। प्लॉट को उस निर्देशिका में 'परिणाम.png' के रूप में सहेजा जाता है जहां CSV स्थित है।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
file str

प्रशिक्षण परिणामों वाली CSV फ़ाइल का पथ। डिफ़ॉल्ट रूप से 'path/to/results.csv' पर सेट होता है.

'path/to/results.csv'
dir str

निर्देशिका जहां CSV फ़ाइल स्थित है यदि 'फ़ाइल' प्रदान नहीं की गई है। '' के लिए डिफ़ॉल्ट।

''
segment bool

यह इंगित करने के लिए ध्वजांकित करें कि डेटा सेगमेंटेशन के लिए है या नहीं. डिफ़ॉल्ट रूप से गलत है.

False
pose bool

यह इंगित करने के लिए ध्वजांकित करें कि डेटा मुद्रा अनुमान के लिए है या नहीं. डिफ़ॉल्ट रूप से गलत है.

False
classify bool

यह इंगित करने के लिए ध्वजांकित करें कि डेटा वर्गीकरण के लिए है या नहीं. डिफ़ॉल्ट रूप से गलत है.

False
on_plot callable

प्लॉटिंग के बाद कॉलबैक फ़ंक्शन निष्पादित किया जाना है। फ़ाइल नाम को तर्क के रूप में लेता है. कोई नहीं करने के लिए डिफ़ॉल्ट।

None
उदाहरण
from ultralytics.utils.plotting import plot_results

plot_results('path/to/results.csv', segment=True)
में स्रोत कोड 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
    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')

2D हिस्टोग्राम के आधार पर रंगीन बिंदुओं के साथ एक स्कैटर प्लॉट प्लॉट प्लॉट।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
v array - like

x-अक्ष के लिए मान.

आवश्यक
f array - like

y-अक्ष के लिए मान.

आवश्यक
bins int

हिस्टोग्राम के लिए डिब्बे की संख्या। 20 के लिए डिफ़ॉल्ट।

20
cmap str

स्कैटर प्लॉट के लिए कलरमैप। 'viridis' के लिए चूक.

'viridis'
alpha float

तितर बितर साजिश के लिए अल्फा। 0.8 के लिए डिफ़ॉल्ट।

0.8
edgecolors str

तितर बितर साजिश के लिए किनारे के रंग। डिफ़ॉल्ट रूप से 'कोई नहीं' है।

'none'

उदाहरण:

>>> v = np.random.rand(100)
>>> f = np.random.rand(100)
>>> plt_color_scatter(v, f)
में स्रोत कोड 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')

एक 'tune_results.csv' फ़ाइल में संग्रहीत विकास परिणामों को प्लॉट करें। फ़ंक्शन प्रत्येक कुंजी के लिए एक स्कैटर प्लॉट उत्पन्न करता है सीएसवी में, फिटनेस स्कोर के आधार पर रंग-कोडित। सबसे अच्छा प्रदर्शन करने वाले कॉन्फ़िगरेशन को भूखंडों पर हाइलाइट किया गया है।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
csv_file str

ट्यूनिंग परिणामों वाली CSV फ़ाइल का पथ। 'tune_results.csv' के लिए डिफ़ॉल्ट।

'tune_results.csv'

उदाहरण:

>>> plot_tune_results('path/to/tune_results.csv')
में स्रोत कोड 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
    from scipy.ndimage import gaussian_filter1d

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

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

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

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



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

प्लॉटिंग के लिए मॉडल आउटपुट को लक्ष्य प्रारूप [batch_id, class_id, x, y, w, h, conf] में बदलें।

में स्रोत कोड 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)

प्लॉटिंग के लिए मॉडल आउटपुट को लक्ष्य प्रारूप [batch_id, class_id, x, y, w, h, conf] में बदलें।

में स्रोत कोड 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'))

अनुमान के दौरान किसी दिए गए मॉडल मॉड्यूल के फीचर मैप को विज़ुअलाइज़ करें।

पैरामीटर:

नाम प्रकार विवरण: __________ चूक
x Tensor

कल्पना की जाने वाली विशेषताएं।

आवश्यक
module_type str

मॉड्यूल प्रकार।

आवश्यक
stage int

मॉडल के भीतर मॉड्यूल चरण।

आवश्यक
n int

प्लॉट करने के लिए फीचर मैप्स की अधिकतम संख्या। 32 के लिए डिफ़ॉल्ट।

32
save_dir Path

परिणामों को बचाने के लिए निर्देशिका। पथ के लिए डिफ़ॉल्ट ('रन/डिटेक्ट/एक्सपी')।

Path('runs/detect/exp')
में स्रोत कोड 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", "Pose", "Segment"]:
        if m in module_type:
            return
    batch, 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
        fig, 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





2023-11-12 बनाया गया, अपडेट किया गया 2024-01-05
लेखक: ग्लेन-जोचर (4), लाफिंग-क्यू (1)