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copied!<p>I detected the peaks using a <strong>local maximum filter</strong>. Here is the result on your first dataset of 4 paws: <img src="https://i.stack.imgur.com/Kgt4H.png" alt="Peaks detection result"></p> <p>I also ran it on the second dataset of 9 paws and <a href="https://i.stack.imgur.com/4CKCh.png" rel="noreferrer">it worked as well</a>.</p> <p>Here is how you do it:</p> <pre><code>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() </code></pre> <p>All you need to do after is use <em>scipy.ndimage.measurements.label</em> on the mask to label all distinct objects. Then you'll be able to play with them individually.</p> <p><strong>Note</strong> 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 <em>neighborhood</em>. You will need to adjust it if the peak size changes (the should remain roughly proportional).</p>

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