I detected the peaks using a local maximum filter. Here is the result on your first dataset of 4 paws:
I also ran it on the second dataset of 9 paws and it worked as well.
Here is how you do it:
import numpy as np from scipy.ndimage.filters import maximum_filter from scipy.ndimage.morphology import generate_binary_structure, binary_erosion import matplotlib.pyplot as pp #for some reason I had to reshape. Numpy ignored the shape header. paws_data = np.loadtxt("paws.txt").reshape(4,11,14) #getting a list of images paws = [p.squeeze() for p in np.vsplit(paws_data,4)] def detect_peaks(image): """ Takes an image and detect the peaks usingthe local maximum filter. Returns a boolean mask of the peaks (i.e. 1 when the pixel's value is the neighborhood maximum, 0 otherwise) """ # define an 8-connected neighborhood neighborhood = generate_binary_structure(2,2) #apply the local maximum filter; all pixel of maximal value #in their neighborhood are set to 1 local_max = maximum_filter(image, footprint=neighborhood)==image #local_max is a mask that contains the peaks we are #looking for, but also the background. #In order to isolate the peaks we must remove the background from the mask. #we create the mask of the background background = (image==0) #a little technicality: we must erode the background in order to #successfully subtract it form local_max, otherwise a line will #appear along the background border (artifact of the local maximum filter) eroded_background = binary_erosion(background, structure=neighborhood, border_value=1) #we obtain the final mask, containing only peaks, #by removing the background from the local_max mask (xor operation) detected_peaks = local_max ^ eroded_background return detected_peaks #applying the detection and plotting results for i, paw in enumerate(paws): detected_peaks = detect_peaks(paw) pp.subplot(4,2,(2*i+1)) pp.imshow(paw) pp.subplot(4,2,(2*i+2) ) pp.imshow(detected_peaks) pp.show()
All you need to do after is use
scipy.ndimage.measurements.label on the mask to label all distinct objects. Then you’ll be able to play with them individually.
Note that the method works well because the background is not noisy. If it were, you would detect a bunch of other unwanted peaks in the background. Another important factor is the size of the neighborhood. You will need to adjust it if the peak size changes (the should remain roughly proportional).