API Reference

sam_utilities

SamGeo_apb(model_type='vit_h', automatic=True, device=None, checkpoint_dir=None, sam_kwargs=None, **kwargs)

Bases: SamGeo

Source code in apb_spatial_computer_vision/sam_utilities.py

def __init__(self,
    model_type="vit_h",
    automatic=True,
    device=None,
    checkpoint_dir=None,
    sam_kwargs=None,
    **kwargs,):
    super().__init__(model_type,
    automatic,
    device,
    checkpoint_dir,
    sam_kwargs,
    **kwargs,)

predict(point_coords=None, point_labels=None, boxes=None, point_crs=None, mask_input=None, multimask_output=True, return_logits=False, output=None, index=None, mask_multiplier=255, dtype='float32', return_results=False, **kwargs)

Predict masks for the given input prompts, using the currently set image.

Parameters:

Name	Type	Description	Default
point_coords	str | dict | list | ndarray	A Nx2 array of point prompts to the model. Each point is in (X,Y) in pixels. It can be a path to a vector file, a GeoJSON dictionary, a list of coordinates [lon, lat], or a numpy array. Defaults to None.	None
point_labels	list | int | ndarray	A length N array of labels for the point prompts. 1 indicates a foreground point and 0 indicates a background point.	None
point_crs	str	The coordinate reference system (CRS) of the point prompts.	None
boxes	list | ndarray	A length 4 array given a box prompt to the model, in XYXY format.	None
mask_input	ndarray	A low resolution mask input to the model, typically coming from a previous prediction iteration. Has form 1xHxW, where for SAM, H=W=256. multimask_output (bool, optional): If true, the model will return three masks. For ambiguous input prompts (such as a single click), this will often produce better masks than a single prediction. If only a single mask is needed, the model's predicted quality score can be used to select the best mask. For non-ambiguous prompts, such as multiple input prompts, multimask_output=False can give better results.	None
return_logits	bool	If true, returns un-thresholded masks logits instead of a binary mask.	False
output	str	The path to the output image. Defaults to None.	None
index	index	The index of the mask to save. Defaults to None, which will save the mask with the highest score.	None
mask_multiplier	int	The mask multiplier for the output mask, which is usually a binary mask [0, 1].	255
dtype	dtype	The data type of the output image. Defaults to np.float32.	'float32'
return_results	bool	Whether to return the predicted masks, scores, and logits. Defaults to False.	False

Source code in apb_spatial_computer_vision/sam_utilities.py

def predict(
    self,
    point_coords=None,
    point_labels=None,
    boxes=None,
    point_crs=None,
    mask_input=None,
    multimask_output=True,
    return_logits=False,
    output=None,
    index=None,
    mask_multiplier=255,
    dtype="float32",
    return_results=False,
    **kwargs,
):
    """Predict masks for the given input prompts, using the currently set image.

    Args:
        point_coords (str | dict | list | np.ndarray, optional): A Nx2 array of point prompts to the
            model. Each point is in (X,Y) in pixels. It can be a path to a vector file, a GeoJSON
            dictionary, a list of coordinates [lon, lat], or a numpy array. Defaults to None.
        point_labels (list | int | np.ndarray, optional): A length N array of labels for the
            point prompts. 1 indicates a foreground point and 0 indicates a background point.
        point_crs (str, optional): The coordinate reference system (CRS) of the point prompts.
        boxes (list | np.ndarray, optional): A length 4 array given a box prompt to the
            model, in XYXY format.
        mask_input (np.ndarray, optional): A low resolution mask input to the model, typically
            coming from a previous prediction iteration. Has form 1xHxW, where for SAM, H=W=256.
            multimask_output (bool, optional): If true, the model will return three masks.
            For ambiguous input prompts (such as a single click), this will often
            produce better masks than a single prediction. If only a single
            mask is needed, the model's predicted quality score can be used
            to select the best mask. For non-ambiguous prompts, such as multiple
            input prompts, multimask_output=False can give better results.
        return_logits (bool, optional): If true, returns un-thresholded masks logits
            instead of a binary mask.
        output (str, optional): The path to the output image. Defaults to None.
        index (index, optional): The index of the mask to save. Defaults to None,
            which will save the mask with the highest score.
        mask_multiplier (int, optional): The mask multiplier for the output mask, which is usually a binary mask [0, 1].
        dtype (np.dtype, optional): The data type of the output image. Defaults to np.float32.
        return_results (bool, optional): Whether to return the predicted masks, scores, and logits. Defaults to False.

    """
    out_of_bounds = []

    if isinstance(boxes, str):
        gdf = gpd.read_file(boxes)
        if gdf.crs is not None:
            gdf = gdf.to_crs("epsg:4326")
        boxes = gdf.geometry.bounds.values.tolist()
    elif isinstance(boxes, dict):
        import json

        geojson = json.dumps(boxes)
        gdf = gpd.read_file(geojson, driver="GeoJSON")
        boxes = gdf.geometry.bounds.values.tolist()

    if isinstance(point_coords, str):
        point_coords = vector_to_geojson(point_coords)

    if isinstance(point_coords, dict):
        point_coords = geojson_to_coords(point_coords)

    if hasattr(self, "point_coords"):
        point_coords = self.point_coords

    if hasattr(self, "point_labels"):
        point_labels = self.point_labels

    if (point_crs is not None) and (point_coords is not None):
        point_coords, out_of_bounds = coords_to_xy(
            self.source, point_coords, point_crs, return_out_of_bounds=True
        )

    if isinstance(point_coords, list):
        point_coords = np.array(point_coords)

    if point_coords is not None:
        if point_labels is None:
            point_labels = [1] * len(point_coords)
        elif isinstance(point_labels, int):
            point_labels = [point_labels] * len(point_coords)

    if isinstance(point_labels, list):
        if len(point_labels) != len(point_coords):
            if len(point_labels) == 1:
                point_labels = point_labels * len(point_coords)
            elif len(out_of_bounds) > 0:
                print(f"Removing {len(out_of_bounds)} out-of-bound points.")
                point_labels_new = []
                for i, p in enumerate(point_labels):
                    if i not in out_of_bounds:
                        point_labels_new.append(p)
                point_labels = point_labels_new
            else:
                raise ValueError(
                    "The length of point_labels must be equal to the length of point_coords."
                )
        point_labels = np.array(point_labels)

    predictor = self.predictor

    input_boxes = None
    if isinstance(boxes, list) and (point_crs is not None):
        coords = bbox_to_xy(self.source, boxes, point_crs)
        input_boxes = np.array(coords)
        if isinstance(coords[0], int):
            input_boxes = input_boxes[None, :]
        else:
            input_boxes = torch.tensor(input_boxes, device=self.device)

            input_boxes = predictor.transform.apply_boxes_torch(
                input_boxes, self.image.shape[:2]
            )
    elif isinstance(boxes, list) and (point_crs is None):
        input_boxes = np.array(boxes)
        if isinstance(boxes[0], int):
            input_boxes = input_boxes[None, :]

    self.boxes = input_boxes

    if (
        boxes is None
        or (len(boxes) == 1)
        or (len(boxes) == 4 and isinstance(boxes[0], float))
    ):
        if isinstance(boxes, list) and isinstance(boxes[0], list):
            boxes = boxes[0]
        if isinstance(input_boxes,torch.Tensor):
            masks, scores, logits = predictor.predict(
                point_coords,
                point_labels,
                np.array(input_boxes.cpu()),
                mask_input,
                multimask_output,
                return_logits,
            )
        else:
            masks, scores, logits = predictor.predict(
                point_coords,
                point_labels,
                input_boxes,
                mask_input,
                multimask_output,
                return_logits,
            )
    else:
        masks, scores, logits = predictor.predict_torch(
            point_coords=point_coords,
            point_labels=point_coords,
            boxes=input_boxes,
            multimask_output=True,
        )

    self.masks = masks
    self.scores = scores
    self.logits = logits

    if output is not None:
        if boxes is None or (not isinstance(boxes[0], list)):
            self.save_prediction(output, index, mask_multiplier, dtype, **kwargs)
        else:
            self.tensor_to_numpy(
                index, output, mask_multiplier, dtype, save_args=kwargs
            )

    if return_results:
            return masks, scores, logits

raster_to_vector(source, output=None, simplify_tolerance=None, dst_crs=None, **kwargs)

Vectorize a raster dataset.

Parameters:

Name	Type	Description	Default
source	str	The path to the tiff file.	required
output	str	The path to the vector file.	None
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 apb_spatial_computer_vision/sam_utilities.py

def raster_to_vector(source, output=None, simplify_tolerance=None, dst_crs=None, **kwargs):
    """Vectorize a raster dataset.

    Args:
        source (str): The path to the tiff 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.
    """
    from rasterio import features

    with rasterio.open(source) as src:
        band = src.read()

        mask = band != 0
        shapes = features.shapes(band, mask=mask, transform=src.transform)
        src.close()
    fc = [
        {"geometry": shapely.geometry.shape(shape), "properties": {"value": value}}
        for shape, value in shapes
    ]
    if simplify_tolerance is not None:
        for i in fc:
            i["geometry"] = i["geometry"].simplify(tolerance=simplify_tolerance)

    gdf = gpd.GeoDataFrame.from_features(fc)
    if src.crs is not None:
        gdf.set_crs(crs=src.crs, inplace=True)

    if dst_crs is not None:
        gdf = gdf.to_crs(dst_crs)        

    if output is not None:
        gdf.to_file(output)     

    n_gdf=gpd.tools.collect(gdf.geometry)
    return n_gdf.wkt

set_image(image, image_format='RGB')

Set the input image as a numpy array.

Parameters:

Name	Type	Description	Default
image	ndarray	The input image as a numpy array.	required
image_format	str	The image format, can be RGB or BGR. Defaults to "RGB".	'RGB'

Source code in apb_spatial_computer_vision/sam_utilities.py

def set_image(self, image, image_format="RGB"):
    """Set the input image as a numpy array.

    Args:
        image (np.ndarray): The input image as a numpy array.
        image_format (str, optional): The image format, can be RGB or BGR. Defaults to "RGB".
    """
    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
        image=Ortophoto(image).raster.ReadAsArray()
        ar=image[:3,:,:]
        arr=np.transpose(ar,(1,2,0))
        rgb_image=cv2.cvtColor(arr,cv2.COLOR_BGR2RGB)
        self.image=rgb_image
    elif isinstance(image, np.ndarray):
        pass
    else:
        raise ValueError("Input image must be either a path or a numpy array.")
    try:
        self.predictor.set_image(self.image, image_format=image_format)
    except torch.OutOfMemoryError:
        pass