# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Detection algorithms module --------------------------- This module provides various object detection algorithms for image analysis. Features include: - Blob detection using multiple algorithms (DoG, DoH, LoG, OpenCV) - Peak detection with configurable thresholds and neighborhood sizes - Hough transform-based circle detection - Contour shape fitting (circles, ellipses, polygons) - Utility functions for coordinate processing These tools support automated feature extraction and object identification in images for scientific and technical applications. """ from __future__ import annotations import numpy as np import scipy.ndimage as spi from numpy import ma from skimage import exposure, feature, measure, transform from sigima.enums import ContourShape from sigima.tools.checks import check_2d_array from sigima.tools.image.preprocessing import distance_matrix, get_absolute_level @check_2d_array(non_constant=True) def get_2d_peaks_coords( data: np.ndarray, size: int | None = None, level: float = 0.5 ) -> np.ndarray: """Detect peaks in image data, return coordinates. If neighborhoods size is None, default value is the highest value between 50 pixels and the 1/40th of the smallest image dimension. Detection threshold level is relative to difference between data maximum and minimum values. Args: data: Input data size: Neighborhood size (default: None) level: Relative level (default: 0.5) Returns: Coordinates of peaks """ if size is None: size = max(min(data.shape) // 40, 50) data_max = spi.maximum_filter(data, size) data_min = spi.minimum_filter(data, size) data_diff = data_max - data_min diff = (data_max - data_min) > get_absolute_level(data_diff, level) maxima = data == data_max maxima[diff == 0] = 0 labeled, _num_objects = spi.label(maxima) slices = spi.find_objects(labeled) coords = [] for dy, dx in slices: x_center = int(0.5 * (dx.start + dx.stop - 1)) y_center = int(0.5 * (dy.start + dy.stop - 1)) coords.append((x_center, y_center)) if len(coords) > 1: # Eventually removing duplicates dist = distance_matrix(coords) for index in reversed(np.unique(np.where((dist < size) & (dist > 0))[1])): coords.pop(index) return np.array(coords) def get_contour_shapes( data: np.ndarray | ma.MaskedArray, shape: ContourShape = ContourShape.ELLIPSE, level: float = 0.5, ) -> np.ndarray: """Find iso-valued contours in a 2D array, above relative level (.5 means FWHM). Args: data: Input data shape: Shape to fit. Default is ELLIPSE level: Relative level (default: 0.5) Returns: Coordinates of shapes fitted to contours """ # pylint: disable=too-many-locals contours = measure.find_contours(data, level=get_absolute_level(data, level)) coords = [] for contour in contours: # `contour` is a (N, 2) array (rows, cols): we need to check if all those # coordinates are masked: if so, we skip this contour if isinstance(data, ma.MaskedArray) and np.all( data.mask[contour[:, 0].astype(int), contour[:, 1].astype(int)] ): continue if shape == ContourShape.CIRCLE: model = measure.CircleModel() if model.estimate(contour): yc, xc, r = model.params if r <= 1.0: continue coords.append([xc, yc, r]) elif shape == ContourShape.ELLIPSE: model = measure.EllipseModel() if model.estimate(contour): yc, xc, b, a, theta = model.params if a <= 1.0 or b <= 1.0: continue coords.append([xc, yc, a, b, theta]) elif shape == ContourShape.POLYGON: # `contour` is a (N, 2) array (rows, cols): we need to convert it # to a list of x, y coordinates flattened in a single list coords.append(contour[:, ::-1].flatten()) else: raise NotImplementedError(f"Invalid contour model {model}") if shape == ContourShape.POLYGON: # `coords` is a list of arrays of shape (N, 2) where N is the number of points # that can vary from one array to another, so we need to padd with NaNs each # array to get a regular array: max_len = max(coord.shape[0] for coord in coords) arr = np.full((len(coords), max_len), np.nan) for i_row, coord in enumerate(coords): arr[i_row, : coord.shape[0]] = coord return arr return np.array(coords) @check_2d_array(non_constant=True) def get_hough_circle_peaks( data: np.ndarray, min_radius: float | None = None, max_radius: float | None = None, nb_radius: int | None = None, min_distance: int = 1, ) -> np.ndarray: """Detect peaks in image from circle Hough transform, return circle coordinates. Args: data: Input data min_radius: Minimum radius (default: None) max_radius: Maximum radius (default: None) nb_radius: Number of radii (default: None) min_distance: Minimum distance between circles (default: 1) Returns: Coordinates of circles """ assert min_radius is not None and max_radius is not None and max_radius > min_radius if nb_radius is None: nb_radius = max_radius - min_radius + 1 hough_radii = np.arange( min_radius, max_radius + 1, (max_radius - min_radius + 1) // nb_radius ) hough_res = transform.hough_circle(data, hough_radii) _accums, cx, cy, radii = transform.hough_circle_peaks( hough_res, hough_radii, min_xdistance=min_distance, min_ydistance=min_distance ) return np.vstack([cx, cy, radii]).T # MARK: Blob detection ----------------------------------------------------------------- def __blobs_to_coords(blobs: np.ndarray) -> np.ndarray: """Convert blobs to coordinates Args: blobs: Blobs Returns: Coordinates """ cy, cx, radii = blobs.T coords = np.vstack([cx, cy, radii]).T return coords @check_2d_array(non_constant=True) def find_blobs_dog( data: np.ndarray, min_sigma: float = 1, max_sigma: float = 30, overlap: float = 0.5, threshold_rel: float = 0.2, exclude_border: bool = True, ) -> np.ndarray: """ Finds blobs in the given grayscale image using the Difference of Gaussians (DoG) method. Args: data: The grayscale input image. min_sigma: The minimum blob radius in pixels. max_sigma: The maximum blob radius in pixels. overlap: The minimum overlap between two blobs in pixels. For instance, if two blobs are detected with radii of 10 and 12 respectively, and the ``overlap`` is set to 0.5, then the area of the smaller blob will be ignored and only the area of the larger blob will be returned. threshold_rel: The absolute lower bound for scale space maxima. Local maxima smaller than ``threshold_rel`` are ignored. Reduce this to detect blobs with less intensities. exclude_border: If ``True``, exclude blobs from detection if they are too close to the border of the image. Border size is ``min_sigma``. Returns: Coordinates of blobs """ # Use scikit-image's Difference of Gaussians (DoG) method blobs = feature.blob_dog( data, min_sigma=min_sigma, max_sigma=max_sigma, overlap=overlap, threshold_rel=threshold_rel, exclude_border=exclude_border, ) return __blobs_to_coords(blobs) @check_2d_array(non_constant=True) def find_blobs_doh( data: np.ndarray, min_sigma: float = 1, max_sigma: float = 30, overlap: float = 0.5, log_scale: bool = False, threshold_rel: float = 0.2, ) -> np.ndarray: """ Finds blobs in the given grayscale image using the Determinant of Hessian (DoH) method. Args: data: The grayscale input image. min_sigma: The minimum blob radius in pixels. max_sigma: The maximum blob radius in pixels. overlap: The minimum overlap between two blobs in pixels. For instance, if two blobs are detected with radii of 10 and 12 respectively, and the ``overlap`` is set to 0.5, then the area of the smaller blob will be ignored and only the area of the larger blob will be returned. log_scale: If ``True``, the radius of each blob is returned as ``sqrt(sigma)`` for each detected blob. threshold_rel: The absolute lower bound for scale space maxima. Local maxima smaller than ``threshold_rel`` are ignored. Reduce this to detect blobs with less intensities. Returns: Coordinates of blobs """ # Use scikit-image's Determinant of Hessian (DoH) method to detect blobs blobs = feature.blob_doh( data, min_sigma=min_sigma, max_sigma=max_sigma, num_sigma=int(max_sigma - min_sigma + 1), threshold=None, threshold_rel=threshold_rel, overlap=overlap, log_scale=log_scale, ) return __blobs_to_coords(blobs) @check_2d_array(non_constant=True) def find_blobs_log( data: np.ndarray, min_sigma: float = 1, max_sigma: float = 30, overlap: float = 0.5, log_scale: bool = False, threshold_rel: float = 0.2, exclude_border: bool = True, ) -> np.ndarray: """Finds blobs in the given grayscale image using the Laplacian of Gaussian (LoG) method. Args: data: The grayscale input image. min_sigma: The minimum blob radius in pixels. max_sigma: The maximum blob radius in pixels. overlap: The minimum overlap between two blobs in pixels. For instance, if two blobs are detected with radii of 10 and 12 respectively, and the ``overlap`` is set to 0.5, then the area of the smaller blob will be ignored and only the area of the larger blob will be returned. log_scale: If ``True``, the radius of each blob is returned as ``sqrt(sigma)`` for each detected blob. threshold_rel: The absolute lower bound for scale space maxima. Local maxima smaller than ``threshold_rel`` are ignored. Reduce this to detect blobs with less intensities. exclude_border: If ``True``, exclude blobs from detection if they are too close to the border of the image. Border size is ``min_sigma``. Returns: Coordinates of blobs """ # Use scikit-image's Laplacian of Gaussian (LoG) method to detect blobs blobs = feature.blob_log( data, min_sigma=min_sigma, max_sigma=max_sigma, num_sigma=int(max_sigma - min_sigma + 1), threshold=None, threshold_rel=threshold_rel, overlap=overlap, log_scale=log_scale, exclude_border=exclude_border, ) return __blobs_to_coords(blobs) def remove_overlapping_disks(coords: np.ndarray) -> np.ndarray: """Remove overlapping disks among coordinates Args: coords: The coordinates of the disks Returns: The coordinates of the disks with overlapping disks removed """ # Get the radii of each disk from the coordinates radii = coords[:, 2] # Calculate the distance between the center of each pair of disks dist = np.sqrt(np.sum((coords[:, None, :2] - coords[:, :2]) ** 2, axis=-1)) # Create a boolean mask where the distance between the centers # is less than the sum of the radii mask = dist < (radii[:, None] + radii) # Find the indices of overlapping disks overlapping_indices = np.argwhere(mask) # Remove the smaller disk from each overlapping pair for i, j in overlapping_indices: if i != j: if radii[i] < radii[j]: coords[i] = [np.nan, np.nan, np.nan] else: coords[j] = [np.nan, np.nan, np.nan] # Remove rows with NaN values coords = coords[~np.isnan(coords).any(axis=1)] return coords # pylint: disable=too-many-positional-arguments @check_2d_array(non_constant=True) def find_blobs_opencv( data: np.ndarray, min_threshold: float | None = None, max_threshold: float | None = None, min_repeatability: int | None = None, min_dist_between_blobs: float | None = None, filter_by_color: bool | None = None, blob_color: int | None = None, filter_by_area: bool | None = None, min_area: float | None = None, max_area: float | None = None, filter_by_circularity: bool | None = None, min_circularity: float | None = None, max_circularity: float | None = None, filter_by_inertia: bool | None = None, min_inertia_ratio: float | None = None, max_inertia_ratio: float | None = None, filter_by_convexity: bool | None = None, min_convexity: float | None = None, max_convexity: float | None = None, ) -> np.ndarray: """ Finds blobs in the given grayscale image using OpenCV's SimpleBlobDetector. Args: data: The grayscale input image. min_threshold: The minimum blob intensity. max_threshold: The maximum blob intensity. min_repeatability: The minimum number of times a blob is detected before it is reported. min_dist_between_blobs: The minimum distance between blobs. filter_by_color: If ``True``, blobs are filtered by color. blob_color: The color of the blobs to filter by. filter_by_area: If ``True``, blobs are filtered by area. min_area: The minimum blob area. max_area: The maximum blob area. filter_by_circularity: If ``True``, blobs are filtered by circularity. min_circularity: The minimum blob circularity. max_circularity: The maximum blob circularity. filter_by_inertia: If ``True``, blobs are filtered by inertia. min_inertia_ratio: The minimum blob inertia ratio. max_inertia_ratio: The maximum blob inertia ratio. filter_by_convexity: If ``True``, blobs are filtered by convexity. min_convexity: The minimum blob convexity. max_convexity: The maximum blob convexity. Returns: Coordinates of blobs """ # Note: # Importing OpenCV inside the function in order to eventually raise an ImportError # when the function is called and OpenCV is not installed. This error will be # handled by DataLab and the user will be informed that OpenCV is required to use # this function. import cv2 # pylint: disable=import-outside-toplevel params = cv2.SimpleBlobDetector_Params() if min_threshold is not None: params.minThreshold = min_threshold if max_threshold is not None: params.maxThreshold = max_threshold if min_repeatability is not None: params.minRepeatability = min_repeatability if min_dist_between_blobs is not None: params.minDistBetweenBlobs = min_dist_between_blobs if filter_by_color is not None: params.filterByColor = filter_by_color if blob_color is not None: params.blobColor = blob_color if filter_by_area is not None: params.filterByArea = filter_by_area if min_area is not None: params.minArea = min_area if max_area is not None: params.maxArea = max_area if filter_by_circularity is not None: params.filterByCircularity = filter_by_circularity if min_circularity is not None: params.minCircularity = min_circularity if max_circularity is not None: params.maxCircularity = max_circularity if filter_by_inertia is not None: params.filterByInertia = filter_by_inertia if min_inertia_ratio is not None: params.minInertiaRatio = min_inertia_ratio if max_inertia_ratio is not None: params.maxInertiaRatio = max_inertia_ratio if filter_by_convexity is not None: params.filterByConvexity = filter_by_convexity if min_convexity is not None: params.minConvexity = min_convexity if max_convexity is not None: params.maxConvexity = max_convexity detector = cv2.SimpleBlobDetector_create(params) image = exposure.rescale_intensity(data, out_range=np.uint8) keypoints = detector.detect(image) if keypoints: coords = cv2.KeyPoint_convert(keypoints) radii = 0.5 * np.array([kp.size for kp in keypoints]) blobs = np.vstack([coords[:, 1], coords[:, 0], radii]).T blobs = remove_overlapping_disks(blobs) else: blobs = np.array([]).reshape((0, 3)) return __blobs_to_coords(blobs)