Blob detection (interactive)

This example demonstrates a mixed workflow using PyImageJ to convert a Java image into an xarray.DataArray (i.e. a metadata wrapped NumPyArray), run scikit-image’s Lapacian of Gaussian (LoG) blob detection (skimage.feature.blob_log()) to identify puncta and then convert the blob LoG detections into ImageJ regions of interest. The image with the blob LoG detections are displayed in ImageJ’s image viewer, matplotlib.pyplot and napari.

import imagej
import scyjava as sj
import napari
import xarray as xr
import numpy as np

from math import sqrt
from skimage.feature import blob_log
from matplotlib import pyplot as plt

def find_blobs(image: xr.DataArray, min_sigma: float, max_sigma: float, num_sigma: int, threshold=0.1, show=False) -> np.ndarray:
    Find blobs with Laplacian of Gaussian (LoG).
    # detect blobs in image
    blobs = blob_log(image, min_sigma=min_sigma, max_sigma=max_sigma, num_sigma=num_sigma, threshold=threshold)
    blobs[:, 2] = blobs[:, 2] * sqrt(2)

    return blobs

def detections_to_pyplot(image: xr.DataArray, detections: np.ndarray):
    Display image with detections in matplotlib.pyplot.
    fig, ax = plt.subplots(1, 2, figsize=(8,4), sharex=True, sharey=True)
    ax[0].imshow(image, interpolation='nearest')
    ax[1].imshow(image, interpolation='nearest')
    for blob in detections:
        y, x, r = blob
        c = plt.Circle((x, y), r, color='white', linewidth=1, fill=False)


def detections_to_imagej(dataset, detections: np.ndarray, add_to_roi_manager=False):
    Convert blob detections to ImageJ oval ROIs.
    Optionally add the ROIs to the RoiManager.
    # get ImageJ resources
    OvalRoi = sj.jimport('ij.gui.OvalRoi')
    Overlay = sj.jimport('ij.gui.Overlay')
    ov = Overlay()

    # convert Dataset to ImagePlus
    imp =

    if add_to_roi_manager:
        rm = ij.RoiManager.getRoiManager()

    for i in range(len(detections)):
        values = detections[i].tolist()
        y = values[0]
        x = values[1]
        r = values[2]
        d = r * 2
        roi = OvalRoi(x - r, y - r, d, d)
        if add_to_roi_manager:


def detections_to_napari(image_array: xr.DataArray, detections: np.ndarray):
    Convert blob detections to Napari oval ROIs.
    ovals = []
    for i in range(len(detections)):
        values = detections[i].tolist()
        y = values[0]
        x = values[1]
        r = values[2]
        pos_1 = [y - r, x - r] # top left
        pos_2 = [y - r, x + r] # top right
        pos_3 = [y + r, x + r] # bottom right
        pos_4 = [y + r, x - r] # bottom left
        ovals.append([pos_1, pos_2, pos_3, pos_4])

    napari_detections = np.asarray(ovals)
    viewer = napari.Viewer()
    shapes_layer = viewer.add_shapes()

if __name__ == "__main__":
    # initialize imagej
    ij = imagej.init(mode='interactive')
    print(f"ImageJ version: {ij.getVersion()}")

    # load some sample data
    img ='../sample-data/test_image.tif')
    img_xr =
    detected_blobs = find_blobs(img_xr, min_sigma=0.5, max_sigma=3, num_sigma=10, threshold=0.0075)
    detections_to_imagej(img, detected_blobs, True)
    detections_to_napari(img_xr, detected_blobs)
    detections_to_pyplot(img_xr, detected_blobs)