Search for all templates using scikit-image

Question

I am trying to follow the tutorial from scikit-image regarding Template Matching (check it here).

Using just this example, I would like to find all matching coins (maxima) in the image, not only this one which gave the highest score. I was thinking about using:

maxima = argrelextrema(result, np.greater)

but the problem is that it finds also very small local maxima, which are just a noise. Is there any way to screen numpy array and find the strongest maxima? Thanks!

Answer 1

I have been digging and found some solution but unfortunately I am not sure if I know what exactly is done in the script. I have slightly modified script found here:

neighborhood_size = 20   #how many pixels
threshold = 0.01  #threshold of maxima?

data_max = filters.maximum_filter(result, neighborhood_size)
maxima = (result == data_max)
data_min = filters.minimum_filter(result, neighborhood_size)
diff = ((data_max - data_min) > threshold)
maxima[diff == 0] = 0

x_image,y_image = [], []
temp_size = coin.shape[0]

labeled, num_objects = ndimage.label(maxima)
slices = ndimage.find_objects(labeled)
x, y = [], []
for dy,dx in slices:
    x_center = (dx.start + dx.stop - 1)/2
    x.append(x_center)
    y_center = (dy.start + dy.stop - 1)/2
    y.append(y_center)

fig, (raw,found) = plt.subplots(1,2)
raw.imshow(image,cmap=plt.cm.gray)
raw.set_axis_off()
found.imshow(result)
found.autoscale(False)
found.set_axis_off()
plt.plot(x,y, 'ro')
plt.show()

and does this:

I also realized, that coordinates of found peaks are shifted in comparison to raw image. I think the difference comes from the template size. I will update when I will find out more.

EDIT: with slight code modification I was able also to find places on the input image:

x_image_center = (dx.start + dx.stop - 1 + temp_size) / 2
x_image.append(x_image_center)
y_image_center = (dy.start + dy.stop - 1 + temp_size) / 2
y_image.append(y_image_center)