# 2D Peak Detection#

DataLab provides a â2D Peak Detectionâ feature which is based on a minimum-maximum filter algorithm.

- How to use the feature:
Create or open an image in DataLab workspace

Select â2d peak detectionâ in âAnalysisâ menu

Enter parameters âNeighborhoods sizeâ and âRelative threholdâ

Check âCreate regions of interestâ if you want a ROI defined for each detected peak (this may become useful when using another computation afterwards on each area around peaks, e.g. contour detection)

- Results are shown in a table:
Each row is associated to a detected peak

First column shows the ROI index (0 if no ROI is defined on input image)

Second and third columns show peak coordinates

- The 2d peak detection algorithm works in the following way:
First, the minimum and maximum filtered images are computed using a sliding window algorithm with a user-defined size (implementation based on scipy.ndimage.minimum_filter and scipy.ndimage.maximum_filter)

Then, the difference between the maximum and minimum filtered images is clipped at a user-defined threshold

Resulting image features are labeled using scipy.ndimage.label

Peak coordinates are then obtained from labels center

Duplicates are eventually removed

- The 2d peak detection parameters are the following:
âNeighborhoods sizeâ: size of the sliding window (see above)

âRelative thresholdâ: detection threshold

Feature is based on `get_2d_peaks_coords`

function
from `cdl.algorithms`

module:

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)