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fast_sam module

Segmenting remote sensing images with the Fast Segment Anything Model (FastSAM. https://github.com/opengeos/FastSAM

SamGeo

Bases: FastSAM

Segmenting remote sensing images with the Fast Segment Anything Model (FastSAM).

Source code in samgeo/fast_sam.py 
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class SamGeo(FastSAM):
    """Segmenting remote sensing images with the Fast Segment Anything Model (FastSAM)."""

    def __init__(self, model="FastSAM-x.pt", **kwargs):
        """Initialize the FastSAM algorithm."""

        if "checkpoint_dir" in kwargs:
            checkpoint_dir = kwargs["checkpoint_dir"]
            kwargs.pop("checkpoint_dir")
        else:
            checkpoint_dir = os.environ.get(
                "TORCH_HOME", os.path.expanduser("~/.cache/torch/hub/checkpoints")
            )

        models = {
            "FastSAM-x.pt": "https://github.com/opengeos/datasets/releases/download/models/FastSAM-x.pt",
            "FastSAM-s.pt": "https://github.com/opengeos/datasets/releases/download/models/FastSAM-s.pt",
        }

        if model not in models:
            raise ValueError(
                f"Model must be one of {list(models.keys())}, but got {model} instead."
            )

        model_path = os.path.join(checkpoint_dir, model)

        if not os.path.exists(model_path):
            print(f"Downloading {model} to {model_path}...")
            download_file(models[model], model_path)

        super().__init__(model, **kwargs)

    def set_image(self, image, device=None, **kwargs):
        """Set the input image.

        Args:
            image (str): The path to the image file or a HTTP URL.
            device (str, optional): The device to use. Defaults to "cuda" if available, otherwise "cpu".
            kwargs: Additional keyword arguments to pass to the FastSAM model.
        """

        if isinstance(image, str):
            if image.startswith("http"):
                image = download_file(image)

            if not os.path.exists(image):
                raise ValueError(f"Input path {image} does not exist.")

            self.source = image
        else:
            self.source = None

        # Use cuda if available
        if device is None:
            device = "cuda" if torch.cuda.is_available() else "cpu"
            if device == "cuda":
                torch.cuda.empty_cache()

        everything_results = self(image, device=device, **kwargs)

        self.prompt_process = FastSAMPrompt(image, everything_results, device=device)

    def everything_prompt(self, output=None, **kwargs):
        """Segment the image with the everything prompt. Adapted from
        https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L451

        Args:
            output (str, optional): The path to save the output image. Defaults to None.
        """

        prompt_process = self.prompt_process
        ann = prompt_process.everything_prompt()
        self.annotations = ann

        if output is not None:
            self.save_masks(output, **kwargs)
        else:
            return ann

    def point_prompt(self, points, pointlabel, output=None, **kwargs):
        """Segment the image with the point prompt. Adapted from
        https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L414

        Args:
            points (list): A list of points.
            pointlabel (list): A list of labels for each point.
            output (str, optional): The path to save the output image. Defaults to None.
        """

        prompt_process = self.prompt_process
        ann = prompt_process.point_prompt(points, pointlabel)
        self.annotations = ann

        if output is not None:
            self.save_masks(output, **kwargs)
        else:
            return ann

    def box_prompt(self, bbox=None, bboxes=None, output=None, **kwargs):
        """Segment the image with the box prompt. Adapted from
        https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L377

        Args:
            bbox (list, optional): The bounding box. Defaults to None.
            bboxes (list, optional): A list of bounding boxes. Defaults to None.
            output (str, optional): The path to save the output image. Defaults to None.
        """

        prompt_process = self.prompt_process
        ann = prompt_process.box_prompt(bbox, bboxes)
        self.annotations = ann

        if output is not None:
            self.save_masks(output, **kwargs)
        else:
            return ann

    def text_prompt(self, text, output=None, **kwargs):
        """Segment the image with the text prompt. Adapted from
        https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L439

        Args:
            text (str): The text to segment.
            output (str, optional): The path to save the output image. Defaults to None.
        """

        prompt_process = self.prompt_process
        ann = prompt_process.text_prompt(text)
        self.annotations = ann

        if output is not None:
            self.save_masks(output, **kwargs)
        else:
            return ann

    def save_masks(
        self,
        output=None,
        better_quality=True,
        dtype=None,
        mask_multiplier=255,
        **kwargs,
    ) -> np.ndarray:
        """Save the mask of the image. Adapted from
        https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L222

        Returns:
            np.ndarray: The mask of the image.
        """
        annotations = self.annotations
        if isinstance(annotations[0], dict):
            annotations = [annotation["segmentation"] for annotation in annotations]
        image = self.prompt_process.img
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        height = image.shape[0]
        width = image.shape[1]

        if better_quality:
            if isinstance(annotations[0], torch.Tensor):
                annotations = np.array(annotations.cpu())
            for i, mask in enumerate(annotations):
                mask = cv2.morphologyEx(
                    mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)
                )
                annotations[i] = cv2.morphologyEx(
                    mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8)
                )
        if self.device == "cpu":
            annotations = np.array(annotations)

        else:
            if isinstance(annotations[0], np.ndarray):
                annotations = torch.from_numpy(annotations)

        if isinstance(annotations, torch.Tensor):
            annotations = annotations.cpu().numpy()

        if dtype is None:
            # Set output image data type based on the number of objects
            if len(annotations) < 255:
                dtype = np.uint8
            elif len(annotations) < 65535:
                dtype = np.uint16
            else:
                dtype = np.uint32

        masks = np.sum(annotations, axis=0)

        masks = cv2.resize(masks, (width, height), interpolation=cv2.INTER_NEAREST)
        masks[masks > 0] = 1
        masks = masks.astype(dtype) * mask_multiplier
        self.objects = masks

        if output is not None:  # Save the output image
            array_to_image(self.objects, output, self.source, **kwargs)
        else:
            return masks

    def fast_show_mask(
        self,
        random_color=False,
    ):
        """Show the mask of the image. Adapted from
        https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L222

        Args:
            random_color (bool, optional): Whether to use random colors for each object. Defaults to False.

        Returns:
            np.ndarray: The mask of the image.
        """
        image = self.prompt_process.img
        target_height = image.shape[0]
        target_width = image.shape[1]
        annotations = self.annotations
        annotation = np.array(annotations.cpu())

        mask_sum = annotation.shape[0]
        height = annotation.shape[1]
        weight = annotation.shape[2]
        # Sort annotations based on area.
        areas = np.sum(annotation, axis=(1, 2))
        sorted_indices = np.argsort(areas)
        annotation = annotation[sorted_indices]

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

        show = np.zeros((height, weight, 4))
        h_indices, w_indices = np.meshgrid(
            np.arange(height), np.arange(weight), indexing="ij"
        )
        indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
        # Use vectorized indexing to update the values of 'show'.
        show[h_indices, w_indices, :] = mask_image[indices]

        show = cv2.resize(
            show, (target_width, target_height), interpolation=cv2.INTER_NEAREST
        )

        return show

    def raster_to_vector(
        self, image, output, simplify_tolerance=None, dst_crs="EPSG:4326", **kwargs
    ):
        """Save the result to a vector file.

        Args:
            image (str): The path to the image file.
            output (str): The path to the vector file.
            simplify_tolerance (float, optional): The maximum allowed geometry displacement.
                The higher this value, the smaller the number of vertices in the resulting geometry.
        """

        raster_to_vector(
            image,
            output,
            simplify_tolerance=simplify_tolerance,
            dst_crs=dst_crs,
            **kwargs,
        )

    def show_anns(
        self,
        output=None,
        **kwargs,
    ):
        """Show the annotations (objects with random color) on the input image.

        Args:
            figsize (tuple, optional): The figure size. Defaults to (12, 10).
            axis (str, optional): Whether to show the axis. Defaults to "off".
            alpha (float, optional): The alpha value for the annotations. Defaults to 0.35.
            output (str, optional): The path to the output image. Defaults to None.
            blend (bool, optional): Whether to show the input image. Defaults to True.
        """

        annotations = self.annotations
        prompt_process = self.prompt_process

        if output is None:
            output = temp_file_path(".png")

        prompt_process.plot(annotations, output, **kwargs)

        show_image(output)

__init__(model='FastSAM-x.pt', **kwargs)

Initialize the FastSAM algorithm.

Source code in samgeo/fast_sam.py 
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def __init__(self, model="FastSAM-x.pt", **kwargs):
    """Initialize the FastSAM algorithm."""

    if "checkpoint_dir" in kwargs:
        checkpoint_dir = kwargs["checkpoint_dir"]
        kwargs.pop("checkpoint_dir")
    else:
        checkpoint_dir = os.environ.get(
            "TORCH_HOME", os.path.expanduser("~/.cache/torch/hub/checkpoints")
        )

    models = {
        "FastSAM-x.pt": "https://github.com/opengeos/datasets/releases/download/models/FastSAM-x.pt",
        "FastSAM-s.pt": "https://github.com/opengeos/datasets/releases/download/models/FastSAM-s.pt",
    }

    if model not in models:
        raise ValueError(
            f"Model must be one of {list(models.keys())}, but got {model} instead."
        )

    model_path = os.path.join(checkpoint_dir, model)

    if not os.path.exists(model_path):
        print(f"Downloading {model} to {model_path}...")
        download_file(models[model], model_path)

    super().__init__(model, **kwargs)

box_prompt(bbox=None, bboxes=None, output=None, **kwargs)

Segment the image with the box prompt. Adapted from https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L377

Parameters:

Name Type Description Default
bbox list

The bounding box. Defaults to None.

 None
bboxes list

A list of bounding boxes. Defaults to None.

 None
output str

The path to save the output image. Defaults to None.

 None
Source code in samgeo/fast_sam.py 
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def box_prompt(self, bbox=None, bboxes=None, output=None, **kwargs):
    """Segment the image with the box prompt. Adapted from
    https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L377

    Args:
        bbox (list, optional): The bounding box. Defaults to None.
        bboxes (list, optional): A list of bounding boxes. Defaults to None.
        output (str, optional): The path to save the output image. Defaults to None.
    """

    prompt_process = self.prompt_process
    ann = prompt_process.box_prompt(bbox, bboxes)
    self.annotations = ann

    if output is not None:
        self.save_masks(output, **kwargs)
    else:
        return ann

everything_prompt(output=None, **kwargs)

Segment the image with the everything prompt. Adapted from https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L451

Parameters:

Name Type Description Default
output str

The path to save the output image. Defaults to None.

 None
Source code in samgeo/fast_sam.py 
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def everything_prompt(self, output=None, **kwargs):
    """Segment the image with the everything prompt. Adapted from
    https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L451

    Args:
        output (str, optional): The path to save the output image. Defaults to None.
    """

    prompt_process = self.prompt_process
    ann = prompt_process.everything_prompt()
    self.annotations = ann

    if output is not None:
        self.save_masks(output, **kwargs)
    else:
        return ann

fast_show_mask(random_color=False)

Show the mask of the image. Adapted from https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L222

Parameters:

Name Type Description Default
random_color bool

Whether to use random colors for each object. Defaults to False.

 False

Returns:

Type Description

np.ndarray: The mask of the image.
Source code in samgeo/fast_sam.py 
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def fast_show_mask(
    self,
    random_color=False,
):
    """Show the mask of the image. Adapted from
    https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L222

    Args:
        random_color (bool, optional): Whether to use random colors for each object. Defaults to False.

    Returns:
        np.ndarray: The mask of the image.
    """
    image = self.prompt_process.img
    target_height = image.shape[0]
    target_width = image.shape[1]
    annotations = self.annotations
    annotation = np.array(annotations.cpu())

    mask_sum = annotation.shape[0]
    height = annotation.shape[1]
    weight = annotation.shape[2]
    # Sort annotations based on area.
    areas = np.sum(annotation, axis=(1, 2))
    sorted_indices = np.argsort(areas)
    annotation = annotation[sorted_indices]

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

    show = np.zeros((height, weight, 4))
    h_indices, w_indices = np.meshgrid(
        np.arange(height), np.arange(weight), indexing="ij"
    )
    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
    # Use vectorized indexing to update the values of 'show'.
    show[h_indices, w_indices, :] = mask_image[indices]

    show = cv2.resize(
        show, (target_width, target_height), interpolation=cv2.INTER_NEAREST
    )

    return show

point_prompt(points, pointlabel, output=None, **kwargs)

Segment the image with the point prompt. Adapted from https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L414

Parameters:

Name Type Description Default
points list

A list of points.

 required
pointlabel list

A list of labels for each point.

 required
output str

The path to save the output image. Defaults to None.

 None
Source code in samgeo/fast_sam.py 
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def point_prompt(self, points, pointlabel, output=None, **kwargs):
    """Segment the image with the point prompt. Adapted from
    https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L414

    Args:
        points (list): A list of points.
        pointlabel (list): A list of labels for each point.
        output (str, optional): The path to save the output image. Defaults to None.
    """

    prompt_process = self.prompt_process
    ann = prompt_process.point_prompt(points, pointlabel)
    self.annotations = ann

    if output is not None:
        self.save_masks(output, **kwargs)
    else:
        return ann

raster_to_vector(image, output, simplify_tolerance=None, dst_crs='EPSG:4326', **kwargs)

Save the result to a vector file.

Parameters:

Name Type Description Default
image str

The path to the image file.

 required
output str

The path to the vector file.

 required
simplify_tolerance float

The maximum allowed geometry displacement. The higher this value, the smaller the number of vertices in the resulting geometry.

 None
Source code in samgeo/fast_sam.py 
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def raster_to_vector(
    self, image, output, simplify_tolerance=None, dst_crs="EPSG:4326", **kwargs
):
    """Save the result to a vector file.

    Args:
        image (str): The path to the image file.
        output (str): The path to the vector file.
        simplify_tolerance (float, optional): The maximum allowed geometry displacement.
            The higher this value, the smaller the number of vertices in the resulting geometry.
    """

    raster_to_vector(
        image,
        output,
        simplify_tolerance=simplify_tolerance,
        dst_crs=dst_crs,
        **kwargs,
    )

save_masks(output=None, better_quality=True, dtype=None, mask_multiplier=255, **kwargs)

Save the mask of the image. Adapted from https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L222

Returns:

Type Description
ndarray

np.ndarray: The mask of the image.
Source code in samgeo/fast_sam.py 
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def save_masks(
    self,
    output=None,
    better_quality=True,
    dtype=None,
    mask_multiplier=255,
    **kwargs,
) -> np.ndarray:
    """Save the mask of the image. Adapted from
    https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L222

    Returns:
        np.ndarray: The mask of the image.
    """
    annotations = self.annotations
    if isinstance(annotations[0], dict):
        annotations = [annotation["segmentation"] for annotation in annotations]
    image = self.prompt_process.img
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    height = image.shape[0]
    width = image.shape[1]

    if better_quality:
        if isinstance(annotations[0], torch.Tensor):
            annotations = np.array(annotations.cpu())
        for i, mask in enumerate(annotations):
            mask = cv2.morphologyEx(
                mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)
            )
            annotations[i] = cv2.morphologyEx(
                mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8)
            )
    if self.device == "cpu":
        annotations = np.array(annotations)

    else:
        if isinstance(annotations[0], np.ndarray):
            annotations = torch.from_numpy(annotations)

    if isinstance(annotations, torch.Tensor):
        annotations = annotations.cpu().numpy()

    if dtype is None:
        # Set output image data type based on the number of objects
        if len(annotations) < 255:
            dtype = np.uint8
        elif len(annotations) < 65535:
            dtype = np.uint16
        else:
            dtype = np.uint32

    masks = np.sum(annotations, axis=0)

    masks = cv2.resize(masks, (width, height), interpolation=cv2.INTER_NEAREST)
    masks[masks > 0] = 1
    masks = masks.astype(dtype) * mask_multiplier
    self.objects = masks

    if output is not None:  # Save the output image
        array_to_image(self.objects, output, self.source, **kwargs)
    else:
        return masks

set_image(image, device=None, **kwargs)

Set the input image.

Parameters:

Name Type Description Default
image str

The path to the image file or a HTTP URL.

 required
device str

The device to use. Defaults to "cuda" if available, otherwise "cpu".

 None
kwargs

Additional keyword arguments to pass to the FastSAM model.

 {}
Source code in samgeo/fast_sam.py 
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def set_image(self, image, device=None, **kwargs):
    """Set the input image.

    Args:
        image (str): The path to the image file or a HTTP URL.
        device (str, optional): The device to use. Defaults to "cuda" if available, otherwise "cpu".
        kwargs: Additional keyword arguments to pass to the FastSAM model.
    """

    if isinstance(image, str):
        if image.startswith("http"):
            image = download_file(image)

        if not os.path.exists(image):
            raise ValueError(f"Input path {image} does not exist.")

        self.source = image
    else:
        self.source = None

    # Use cuda if available
    if device is None:
        device = "cuda" if torch.cuda.is_available() else "cpu"
        if device == "cuda":
            torch.cuda.empty_cache()

    everything_results = self(image, device=device, **kwargs)

    self.prompt_process = FastSAMPrompt(image, everything_results, device=device)

show_anns(output=None, **kwargs)

Show the annotations (objects with random color) on the input image.

Parameters:

Name Type Description Default
figsize tuple

The figure size. Defaults to (12, 10).

 required
axis str

Whether to show the axis. Defaults to "off".

 required
alpha float

The alpha value for the annotations. Defaults to 0.35.

 required
output str

The path to the output image. Defaults to None.

 None
blend bool

Whether to show the input image. Defaults to True.

 required
Source code in samgeo/fast_sam.py 
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def show_anns(
    self,
    output=None,
    **kwargs,
):
    """Show the annotations (objects with random color) on the input image.

    Args:
        figsize (tuple, optional): The figure size. Defaults to (12, 10).
        axis (str, optional): Whether to show the axis. Defaults to "off".
        alpha (float, optional): The alpha value for the annotations. Defaults to 0.35.
        output (str, optional): The path to the output image. Defaults to None.
        blend (bool, optional): Whether to show the input image. Defaults to True.
    """

    annotations = self.annotations
    prompt_process = self.prompt_process

    if output is None:
        output = temp_file_path(".png")

    prompt_process.plot(annotations, output, **kwargs)

    show_image(output)

text_prompt(text, output=None, **kwargs)

Segment the image with the text prompt. Adapted from https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L439

Parameters:

Name Type Description Default
text str

The text to segment.

 required
output str

The path to save the output image. Defaults to None.

 None
Source code in samgeo/fast_sam.py 
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def text_prompt(self, text, output=None, **kwargs):
    """Segment the image with the text prompt. Adapted from
    https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/fastsam/prompt.py#L439

    Args:
        text (str): The text to segment.
        output (str, optional): The path to save the output image. Defaults to None.
    """

    prompt_process = self.prompt_process
    ann = prompt_process.text_prompt(text)
    self.annotations = ann

    if output is not None:
        self.save_masks(output, **kwargs)
    else:
        return ann