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ultralytics.models.fastsam.prompt.FastSAMPrompt

Classe de modèle de segment rapide pour l'annotation et la visualisation d'images.

Attributs :

Nom Type Description
device str

Dispositif informatique ("cuda" ou "cpu").

results

Résultats de la détection ou de la segmentation d'objets.

source

Image source ou chemin d'accès à l'image.

clip

Modèle CLIP pour l'affectation linéaire.

Code source dans ultralytics/models/fastsam/prompt.py
class FastSAMPrompt:
    """
    Fast Segment Anything Model class for image annotation and visualization.

    Attributes:
        device (str): Computing device ('cuda' or 'cpu').
        results: Object detection or segmentation results.
        source: Source image or image path.
        clip: CLIP model for linear assignment.
    """

    def __init__(self, source, results, device="cuda") -> None:
        """Initializes FastSAMPrompt with given source, results and device, and assigns clip for linear assignment."""
        if isinstance(source, (str, Path)) and os.path.isdir(source):
            raise ValueError("FastSAM only accepts image paths and PIL Image sources, not directories.")
        self.device = device
        self.results = results
        self.source = source

        # Import and assign clip
        try:
            import clip
        except ImportError:
            checks.check_requirements("git+https://github.com/ultralytics/CLIP.git")
            import clip
        self.clip = clip

    @staticmethod
    def _segment_image(image, bbox):
        """Segments the given image according to the provided bounding box coordinates."""
        image_array = np.array(image)
        segmented_image_array = np.zeros_like(image_array)
        x1, y1, x2, y2 = bbox
        segmented_image_array[y1:y2, x1:x2] = image_array[y1:y2, x1:x2]
        segmented_image = Image.fromarray(segmented_image_array)
        black_image = Image.new("RGB", image.size, (255, 255, 255))
        # transparency_mask = np.zeros_like((), dtype=np.uint8)
        transparency_mask = np.zeros((image_array.shape[0], image_array.shape[1]), dtype=np.uint8)
        transparency_mask[y1:y2, x1:x2] = 255
        transparency_mask_image = Image.fromarray(transparency_mask, mode="L")
        black_image.paste(segmented_image, mask=transparency_mask_image)
        return black_image

    @staticmethod
    def _format_results(result, filter=0):
        """Formats detection results into list of annotations each containing ID, segmentation, bounding box, score and
        area.
        """
        annotations = []
        n = len(result.masks.data) if result.masks is not None else 0
        for i in range(n):
            mask = result.masks.data[i] == 1.0
            if torch.sum(mask) >= filter:
                annotation = {
                    "id": i,
                    "segmentation": mask.cpu().numpy(),
                    "bbox": result.boxes.data[i],
                    "score": result.boxes.conf[i],
                }
                annotation["area"] = annotation["segmentation"].sum()
                annotations.append(annotation)
        return annotations

    @staticmethod
    def _get_bbox_from_mask(mask):
        """Applies morphological transformations to the mask, displays it, and if with_contours is True, draws
        contours.
        """
        mask = mask.astype(np.uint8)
        contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        x1, y1, w, h = cv2.boundingRect(contours[0])
        x2, y2 = x1 + w, y1 + h
        if len(contours) > 1:
            for b in contours:
                x_t, y_t, w_t, h_t = cv2.boundingRect(b)
                x1 = min(x1, x_t)
                y1 = min(y1, y_t)
                x2 = max(x2, x_t + w_t)
                y2 = max(y2, y_t + h_t)
        return [x1, y1, x2, y2]

    def plot(
        self,
        annotations,
        output,
        bbox=None,
        points=None,
        point_label=None,
        mask_random_color=True,
        better_quality=True,
        retina=False,
        with_contours=True,
    ):
        """
        Plots annotations, bounding boxes, and points on images and saves the output.

        Args:
            annotations (list): Annotations to be plotted.
            output (str or Path): Output directory for saving the plots.
            bbox (list, optional): Bounding box coordinates [x1, y1, x2, y2]. Defaults to None.
            points (list, optional): Points to be plotted. Defaults to None.
            point_label (list, optional): Labels for the points. Defaults to None.
            mask_random_color (bool, optional): Whether to use random color for masks. Defaults to True.
            better_quality (bool, optional): Whether to apply morphological transformations for better mask quality.
                Defaults to True.
            retina (bool, optional): Whether to use retina mask. Defaults to False.
            with_contours (bool, optional): Whether to plot contours. Defaults to True.
        """
        import matplotlib.pyplot as plt

        pbar = TQDM(annotations, total=len(annotations))
        for ann in pbar:
            result_name = os.path.basename(ann.path)
            image = ann.orig_img[..., ::-1]  # BGR to RGB
            original_h, original_w = ann.orig_shape
            # For macOS only
            # plt.switch_backend('TkAgg')
            plt.figure(figsize=(original_w / 100, original_h / 100))
            # Add subplot with no margin.
            plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
            plt.margins(0, 0)
            plt.gca().xaxis.set_major_locator(plt.NullLocator())
            plt.gca().yaxis.set_major_locator(plt.NullLocator())
            plt.imshow(image)

            if ann.masks is not None:
                masks = ann.masks.data
                if better_quality:
                    if isinstance(masks[0], torch.Tensor):
                        masks = np.array(masks.cpu())
                    for i, mask in enumerate(masks):
                        mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
                        masks[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))

                self.fast_show_mask(
                    masks,
                    plt.gca(),
                    random_color=mask_random_color,
                    bbox=bbox,
                    points=points,
                    pointlabel=point_label,
                    retinamask=retina,
                    target_height=original_h,
                    target_width=original_w,
                )

                if with_contours:
                    contour_all = []
                    temp = np.zeros((original_h, original_w, 1))
                    for i, mask in enumerate(masks):
                        mask = mask.astype(np.uint8)
                        if not retina:
                            mask = cv2.resize(mask, (original_w, original_h), interpolation=cv2.INTER_NEAREST)
                        contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
                        contour_all.extend(iter(contours))
                    cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2)
                    color = np.array([0 / 255, 0 / 255, 1.0, 0.8])
                    contour_mask = temp / 255 * color.reshape(1, 1, -1)
                    plt.imshow(contour_mask)

            # Save the figure
            save_path = Path(output) / result_name
            save_path.parent.mkdir(exist_ok=True, parents=True)
            plt.axis("off")
            plt.savefig(save_path, bbox_inches="tight", pad_inches=0, transparent=True)
            plt.close()
            pbar.set_description(f"Saving {result_name} to {save_path}")

    @staticmethod
    def fast_show_mask(
        annotation,
        ax,
        random_color=False,
        bbox=None,
        points=None,
        pointlabel=None,
        retinamask=True,
        target_height=960,
        target_width=960,
    ):
        """
        Quickly shows the mask annotations on the given matplotlib axis.

        Args:
            annotation (array-like): Mask annotation.
            ax (matplotlib.axes.Axes): Matplotlib axis.
            random_color (bool, optional): Whether to use random color for masks. Defaults to False.
            bbox (list, optional): Bounding box coordinates [x1, y1, x2, y2]. Defaults to None.
            points (list, optional): Points to be plotted. Defaults to None.
            pointlabel (list, optional): Labels for the points. Defaults to None.
            retinamask (bool, optional): Whether to use retina mask. Defaults to True.
            target_height (int, optional): Target height for resizing. Defaults to 960.
            target_width (int, optional): Target width for resizing. Defaults to 960.
        """
        import matplotlib.pyplot as plt

        n, h, w = annotation.shape  # batch, height, width

        areas = np.sum(annotation, axis=(1, 2))
        annotation = annotation[np.argsort(areas)]

        index = (annotation != 0).argmax(axis=0)
        if random_color:
            color = np.random.random((n, 1, 1, 3))
        else:
            color = np.ones((n, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 1.0])
        transparency = np.ones((n, 1, 1, 1)) * 0.6
        visual = np.concatenate([color, transparency], axis=-1)
        mask_image = np.expand_dims(annotation, -1) * visual

        show = np.zeros((h, w, 4))
        h_indices, w_indices = np.meshgrid(np.arange(h), np.arange(w), indexing="ij")
        indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))

        show[h_indices, w_indices, :] = mask_image[indices]
        if bbox is not None:
            x1, y1, x2, y2 = bbox
            ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1))
        # Draw point
        if points is not None:
            plt.scatter(
                [point[0] for i, point in enumerate(points) if pointlabel[i] == 1],
                [point[1] for i, point in enumerate(points) if pointlabel[i] == 1],
                s=20,
                c="y",
            )
            plt.scatter(
                [point[0] for i, point in enumerate(points) if pointlabel[i] == 0],
                [point[1] for i, point in enumerate(points) if pointlabel[i] == 0],
                s=20,
                c="m",
            )

        if not retinamask:
            show = cv2.resize(show, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
        ax.imshow(show)

    @torch.no_grad()
    def retrieve(self, model, preprocess, elements, search_text: str, device) -> int:
        """Processes images and text with a model, calculates similarity, and returns softmax score."""
        preprocessed_images = [preprocess(image).to(device) for image in elements]
        tokenized_text = self.clip.tokenize([search_text]).to(device)
        stacked_images = torch.stack(preprocessed_images)
        image_features = model.encode_image(stacked_images)
        text_features = model.encode_text(tokenized_text)
        image_features /= image_features.norm(dim=-1, keepdim=True)
        text_features /= text_features.norm(dim=-1, keepdim=True)
        probs = 100.0 * image_features @ text_features.T
        return probs[:, 0].softmax(dim=0)

    def _crop_image(self, format_results):
        """Crops an image based on provided annotation format and returns cropped images and related data."""
        image = Image.fromarray(cv2.cvtColor(self.results[0].orig_img, cv2.COLOR_BGR2RGB))
        ori_w, ori_h = image.size
        annotations = format_results
        mask_h, mask_w = annotations[0]["segmentation"].shape
        if ori_w != mask_w or ori_h != mask_h:
            image = image.resize((mask_w, mask_h))
        cropped_boxes = []
        cropped_images = []
        not_crop = []
        filter_id = []
        for _, mask in enumerate(annotations):
            if np.sum(mask["segmentation"]) <= 100:
                filter_id.append(_)
                continue
            bbox = self._get_bbox_from_mask(mask["segmentation"])  # bbox from mask
            cropped_boxes.append(self._segment_image(image, bbox))  # save cropped image
            cropped_images.append(bbox)  # save cropped image bbox

        return cropped_boxes, cropped_images, not_crop, filter_id, annotations

    def box_prompt(self, bbox):
        """Modifies the bounding box properties and calculates IoU between masks and bounding box."""
        if self.results[0].masks is not None:
            assert bbox[2] != 0 and bbox[3] != 0
            masks = self.results[0].masks.data
            target_height, target_width = self.results[0].orig_shape
            h = masks.shape[1]
            w = masks.shape[2]
            if h != target_height or w != target_width:
                bbox = [
                    int(bbox[0] * w / target_width),
                    int(bbox[1] * h / target_height),
                    int(bbox[2] * w / target_width),
                    int(bbox[3] * h / target_height),
                ]
            bbox[0] = max(round(bbox[0]), 0)
            bbox[1] = max(round(bbox[1]), 0)
            bbox[2] = min(round(bbox[2]), w)
            bbox[3] = min(round(bbox[3]), h)

            # IoUs = torch.zeros(len(masks), dtype=torch.float32)
            bbox_area = (bbox[3] - bbox[1]) * (bbox[2] - bbox[0])

            masks_area = torch.sum(masks[:, bbox[1] : bbox[3], bbox[0] : bbox[2]], dim=(1, 2))
            orig_masks_area = torch.sum(masks, dim=(1, 2))

            union = bbox_area + orig_masks_area - masks_area
            iou = masks_area / union
            max_iou_index = torch.argmax(iou)

            self.results[0].masks.data = torch.tensor(np.array([masks[max_iou_index].cpu().numpy()]))
        return self.results

    def point_prompt(self, points, pointlabel):  # numpy
        """Adjusts points on detected masks based on user input and returns the modified results."""
        if self.results[0].masks is not None:
            masks = self._format_results(self.results[0], 0)
            target_height, target_width = self.results[0].orig_shape
            h = masks[0]["segmentation"].shape[0]
            w = masks[0]["segmentation"].shape[1]
            if h != target_height or w != target_width:
                points = [[int(point[0] * w / target_width), int(point[1] * h / target_height)] for point in points]
            onemask = np.zeros((h, w))
            for annotation in masks:
                mask = annotation["segmentation"] if isinstance(annotation, dict) else annotation
                for i, point in enumerate(points):
                    if mask[point[1], point[0]] == 1 and pointlabel[i] == 1:
                        onemask += mask
                    if mask[point[1], point[0]] == 1 and pointlabel[i] == 0:
                        onemask -= mask
            onemask = onemask >= 1
            self.results[0].masks.data = torch.tensor(np.array([onemask]))
        return self.results

    def text_prompt(self, text):
        """Processes a text prompt, applies it to existing results and returns the updated results."""
        if self.results[0].masks is not None:
            format_results = self._format_results(self.results[0], 0)
            cropped_boxes, cropped_images, not_crop, filter_id, annotations = self._crop_image(format_results)
            clip_model, preprocess = self.clip.load("ViT-B/32", device=self.device)
            scores = self.retrieve(clip_model, preprocess, cropped_boxes, text, device=self.device)
            max_idx = scores.argsort()
            max_idx = max_idx[-1]
            max_idx += sum(np.array(filter_id) <= int(max_idx))
            self.results[0].masks.data = torch.tensor(np.array([annotations[max_idx]["segmentation"]]))
        return self.results

    def everything_prompt(self):
        """Returns the processed results from the previous methods in the class."""
        return self.results

__init__(source, results, device='cuda')

Initialise FastSAMPrompt avec la source, les résultats et l'appareil donnés, et attribue un clip pour l'affectation linéaire.

Code source dans ultralytics/models/fastsam/prompt.py
def __init__(self, source, results, device="cuda") -> None:
    """Initializes FastSAMPrompt with given source, results and device, and assigns clip for linear assignment."""
    if isinstance(source, (str, Path)) and os.path.isdir(source):
        raise ValueError("FastSAM only accepts image paths and PIL Image sources, not directories.")
    self.device = device
    self.results = results
    self.source = source

    # Import and assign clip
    try:
        import clip
    except ImportError:
        checks.check_requirements("git+https://github.com/ultralytics/CLIP.git")
        import clip
    self.clip = clip

box_prompt(bbox)

Modifie les propriétés de la boîte de délimitation et calcule l'indice d'utilité entre les masques et la boîte de délimitation.

Code source dans ultralytics/models/fastsam/prompt.py
def box_prompt(self, bbox):
    """Modifies the bounding box properties and calculates IoU between masks and bounding box."""
    if self.results[0].masks is not None:
        assert bbox[2] != 0 and bbox[3] != 0
        masks = self.results[0].masks.data
        target_height, target_width = self.results[0].orig_shape
        h = masks.shape[1]
        w = masks.shape[2]
        if h != target_height or w != target_width:
            bbox = [
                int(bbox[0] * w / target_width),
                int(bbox[1] * h / target_height),
                int(bbox[2] * w / target_width),
                int(bbox[3] * h / target_height),
            ]
        bbox[0] = max(round(bbox[0]), 0)
        bbox[1] = max(round(bbox[1]), 0)
        bbox[2] = min(round(bbox[2]), w)
        bbox[3] = min(round(bbox[3]), h)

        # IoUs = torch.zeros(len(masks), dtype=torch.float32)
        bbox_area = (bbox[3] - bbox[1]) * (bbox[2] - bbox[0])

        masks_area = torch.sum(masks[:, bbox[1] : bbox[3], bbox[0] : bbox[2]], dim=(1, 2))
        orig_masks_area = torch.sum(masks, dim=(1, 2))

        union = bbox_area + orig_masks_area - masks_area
        iou = masks_area / union
        max_iou_index = torch.argmax(iou)

        self.results[0].masks.data = torch.tensor(np.array([masks[max_iou_index].cpu().numpy()]))
    return self.results

everything_prompt()

Renvoie les résultats traités par les méthodes précédentes de la classe.

Code source dans ultralytics/models/fastsam/prompt.py
def everything_prompt(self):
    """Returns the processed results from the previous methods in the class."""
    return self.results

fast_show_mask(annotation, ax, random_color=False, bbox=None, points=None, pointlabel=None, retinamask=True, target_height=960, target_width=960) staticmethod

Affiche rapidement les annotations du masque sur l'axe matplotlib donné.

Paramètres :

Nom Type Description DĂ©faut
annotation array - like

Annotation du masque.

requis
ax Axes

Axe Matplotlib.

requis
random_color bool

Utiliser ou non une couleur aléatoire pour les masques. La valeur par défaut est False.

False
bbox list

Coordonnées de la boîte englobante [x1, y1, x2, y2]. La valeur par défaut est Aucun.

None
points list

Points à tracer. La valeur par défaut est Aucun.

None
pointlabel list

Étiquettes pour les points. La valeur par défaut est Aucun.

None
retinamask bool

Indique s'il faut utiliser le masque de rétine. La valeur par défaut est True.

True
target_height int

Hauteur cible pour le redimensionnement. La valeur par défaut est 960.

960
target_width int

Largeur cible pour le redimensionnement. La valeur par défaut est 960.

960
Code source dans ultralytics/models/fastsam/prompt.py
@staticmethod
def fast_show_mask(
    annotation,
    ax,
    random_color=False,
    bbox=None,
    points=None,
    pointlabel=None,
    retinamask=True,
    target_height=960,
    target_width=960,
):
    """
    Quickly shows the mask annotations on the given matplotlib axis.

    Args:
        annotation (array-like): Mask annotation.
        ax (matplotlib.axes.Axes): Matplotlib axis.
        random_color (bool, optional): Whether to use random color for masks. Defaults to False.
        bbox (list, optional): Bounding box coordinates [x1, y1, x2, y2]. Defaults to None.
        points (list, optional): Points to be plotted. Defaults to None.
        pointlabel (list, optional): Labels for the points. Defaults to None.
        retinamask (bool, optional): Whether to use retina mask. Defaults to True.
        target_height (int, optional): Target height for resizing. Defaults to 960.
        target_width (int, optional): Target width for resizing. Defaults to 960.
    """
    import matplotlib.pyplot as plt

    n, h, w = annotation.shape  # batch, height, width

    areas = np.sum(annotation, axis=(1, 2))
    annotation = annotation[np.argsort(areas)]

    index = (annotation != 0).argmax(axis=0)
    if random_color:
        color = np.random.random((n, 1, 1, 3))
    else:
        color = np.ones((n, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 1.0])
    transparency = np.ones((n, 1, 1, 1)) * 0.6
    visual = np.concatenate([color, transparency], axis=-1)
    mask_image = np.expand_dims(annotation, -1) * visual

    show = np.zeros((h, w, 4))
    h_indices, w_indices = np.meshgrid(np.arange(h), np.arange(w), indexing="ij")
    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))

    show[h_indices, w_indices, :] = mask_image[indices]
    if bbox is not None:
        x1, y1, x2, y2 = bbox
        ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1))
    # Draw point
    if points is not None:
        plt.scatter(
            [point[0] for i, point in enumerate(points) if pointlabel[i] == 1],
            [point[1] for i, point in enumerate(points) if pointlabel[i] == 1],
            s=20,
            c="y",
        )
        plt.scatter(
            [point[0] for i, point in enumerate(points) if pointlabel[i] == 0],
            [point[1] for i, point in enumerate(points) if pointlabel[i] == 0],
            s=20,
            c="m",
        )

    if not retinamask:
        show = cv2.resize(show, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
    ax.imshow(show)

plot(annotations, output, bbox=None, points=None, point_label=None, mask_random_color=True, better_quality=True, retina=False, with_contours=True)

Trace des annotations, des boîtes de délimitation et des points sur des images et enregistre le résultat.

Paramètres :

Nom Type Description DĂ©faut
annotations list

Annotations Ă  tracer.

requis
output str or Path

Répertoire de sortie pour l'enregistrement des tracés.

requis
bbox list

Coordonnées de la boîte englobante [x1, y1, x2, y2]. La valeur par défaut est Aucun.

None
points list

Points à tracer. La valeur par défaut est Aucun.

None
point_label list

Étiquettes pour les points. La valeur par défaut est Aucun.

None
mask_random_color bool

Utiliser ou non une couleur aléatoire pour les masques. La valeur par défaut est True.

True
better_quality bool

Indique s'il faut appliquer des transformations morphologiques pour améliorer la qualité du masque. La valeur par défaut est True.

True
retina bool

Indique s'il faut utiliser le masque de rétine. La valeur par défaut est False.

False
with_contours bool

Indique s'il faut tracer des contours. La valeur par défaut est True.

True
Code source dans ultralytics/models/fastsam/prompt.py
def plot(
    self,
    annotations,
    output,
    bbox=None,
    points=None,
    point_label=None,
    mask_random_color=True,
    better_quality=True,
    retina=False,
    with_contours=True,
):
    """
    Plots annotations, bounding boxes, and points on images and saves the output.

    Args:
        annotations (list): Annotations to be plotted.
        output (str or Path): Output directory for saving the plots.
        bbox (list, optional): Bounding box coordinates [x1, y1, x2, y2]. Defaults to None.
        points (list, optional): Points to be plotted. Defaults to None.
        point_label (list, optional): Labels for the points. Defaults to None.
        mask_random_color (bool, optional): Whether to use random color for masks. Defaults to True.
        better_quality (bool, optional): Whether to apply morphological transformations for better mask quality.
            Defaults to True.
        retina (bool, optional): Whether to use retina mask. Defaults to False.
        with_contours (bool, optional): Whether to plot contours. Defaults to True.
    """
    import matplotlib.pyplot as plt

    pbar = TQDM(annotations, total=len(annotations))
    for ann in pbar:
        result_name = os.path.basename(ann.path)
        image = ann.orig_img[..., ::-1]  # BGR to RGB
        original_h, original_w = ann.orig_shape
        # For macOS only
        # plt.switch_backend('TkAgg')
        plt.figure(figsize=(original_w / 100, original_h / 100))
        # Add subplot with no margin.
        plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
        plt.margins(0, 0)
        plt.gca().xaxis.set_major_locator(plt.NullLocator())
        plt.gca().yaxis.set_major_locator(plt.NullLocator())
        plt.imshow(image)

        if ann.masks is not None:
            masks = ann.masks.data
            if better_quality:
                if isinstance(masks[0], torch.Tensor):
                    masks = np.array(masks.cpu())
                for i, mask in enumerate(masks):
                    mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
                    masks[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))

            self.fast_show_mask(
                masks,
                plt.gca(),
                random_color=mask_random_color,
                bbox=bbox,
                points=points,
                pointlabel=point_label,
                retinamask=retina,
                target_height=original_h,
                target_width=original_w,
            )

            if with_contours:
                contour_all = []
                temp = np.zeros((original_h, original_w, 1))
                for i, mask in enumerate(masks):
                    mask = mask.astype(np.uint8)
                    if not retina:
                        mask = cv2.resize(mask, (original_w, original_h), interpolation=cv2.INTER_NEAREST)
                    contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
                    contour_all.extend(iter(contours))
                cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2)
                color = np.array([0 / 255, 0 / 255, 1.0, 0.8])
                contour_mask = temp / 255 * color.reshape(1, 1, -1)
                plt.imshow(contour_mask)

        # Save the figure
        save_path = Path(output) / result_name
        save_path.parent.mkdir(exist_ok=True, parents=True)
        plt.axis("off")
        plt.savefig(save_path, bbox_inches="tight", pad_inches=0, transparent=True)
        plt.close()
        pbar.set_description(f"Saving {result_name} to {save_path}")

point_prompt(points, pointlabel)

Ajuste les points des masques détectés en fonction des données de l'utilisateur et renvoie les résultats modifiés.

Code source dans ultralytics/models/fastsam/prompt.py
def point_prompt(self, points, pointlabel):  # numpy
    """Adjusts points on detected masks based on user input and returns the modified results."""
    if self.results[0].masks is not None:
        masks = self._format_results(self.results[0], 0)
        target_height, target_width = self.results[0].orig_shape
        h = masks[0]["segmentation"].shape[0]
        w = masks[0]["segmentation"].shape[1]
        if h != target_height or w != target_width:
            points = [[int(point[0] * w / target_width), int(point[1] * h / target_height)] for point in points]
        onemask = np.zeros((h, w))
        for annotation in masks:
            mask = annotation["segmentation"] if isinstance(annotation, dict) else annotation
            for i, point in enumerate(points):
                if mask[point[1], point[0]] == 1 and pointlabel[i] == 1:
                    onemask += mask
                if mask[point[1], point[0]] == 1 and pointlabel[i] == 0:
                    onemask -= mask
        onemask = onemask >= 1
        self.results[0].masks.data = torch.tensor(np.array([onemask]))
    return self.results

retrieve(model, preprocess, elements, search_text, device)

Traite les images et le texte avec un modèle, calcule la similarité et renvoie le score softmax.

Code source dans ultralytics/models/fastsam/prompt.py
@torch.no_grad()
def retrieve(self, model, preprocess, elements, search_text: str, device) -> int:
    """Processes images and text with a model, calculates similarity, and returns softmax score."""
    preprocessed_images = [preprocess(image).to(device) for image in elements]
    tokenized_text = self.clip.tokenize([search_text]).to(device)
    stacked_images = torch.stack(preprocessed_images)
    image_features = model.encode_image(stacked_images)
    text_features = model.encode_text(tokenized_text)
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features /= text_features.norm(dim=-1, keepdim=True)
    probs = 100.0 * image_features @ text_features.T
    return probs[:, 0].softmax(dim=0)

text_prompt(text)

Traite une invite de texte, l'applique aux résultats existants et renvoie les résultats mis à jour.

Code source dans ultralytics/models/fastsam/prompt.py
def text_prompt(self, text):
    """Processes a text prompt, applies it to existing results and returns the updated results."""
    if self.results[0].masks is not None:
        format_results = self._format_results(self.results[0], 0)
        cropped_boxes, cropped_images, not_crop, filter_id, annotations = self._crop_image(format_results)
        clip_model, preprocess = self.clip.load("ViT-B/32", device=self.device)
        scores = self.retrieve(clip_model, preprocess, cropped_boxes, text, device=self.device)
        max_idx = scores.argsort()
        max_idx = max_idx[-1]
        max_idx += sum(np.array(filter_id) <= int(max_idx))
        self.results[0].masks.data = torch.tensor(np.array([annotations[max_idx]["segmentation"]]))
    return self.results





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (5), Burhan-Q (1)