This example shows how to extract the bounding box of the largest object
Python source code: plot_find_object.py
import numpy as np
from scipy import ndimage
import matplotlib.pyplot as plt
np.random.seed(1)
n = 10
l = 256
im = np.zeros((l, l))
points = l*np.random.random((2, n**2))
im[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
im = ndimage.gaussian_filter(im, sigma=l/(4.*n))
mask = im > im.mean()
label_im, nb_labels = ndimage.label(mask)
# Find the largest connect component
sizes = ndimage.sum(mask, label_im, range(nb_labels + 1))
mask_size = sizes < 1000
remove_pixel = mask_size[label_im]
label_im[remove_pixel] = 0
labels = np.unique(label_im)
label_im = np.searchsorted(labels, label_im)
# Now that we have only one connect component, extract it's bounding box
slice_x, slice_y = ndimage.find_objects(label_im==4)[0]
roi = im[slice_x, slice_y]
plt.figure(figsize=(4, 2))
plt.axes([0, 0, 1, 1])
plt.imshow(roi)
plt.axis('off')
plt.show()
Total running time of the example: 0.05 seconds ( 0 minutes 0.05 seconds)