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i want to know how to calculate local minima?

spyderB
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  • just erase the bar. looks like it's always in the same place. -- in general, I'd still recommend morphology operations. -- context: https://stackoverflow.com/questions/70852674/how-do-i-extract-objects-in-image/70853228 – Christoph Rackwitz Jan 26 '22 at 07:59
  • @spyderB what have you tried so far? please see: [MWE](https://stackoverflow.com/help/minimal-reproducible-example) – Bilal Jan 26 '22 at 08:28
  • draw a white rectangle over the bar. done. -- are you asking to to locate the bar? either run an opening/closing operation using an extremely wide kernel, or calculate row sums and threshold in that 1D signal. the bar would be the only object that's _that_ wide. – Christoph Rackwitz Jan 26 '22 at 09:01
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    Count the black pixels along every vertical. – Yves Daoust Jan 26 '22 at 09:29
  • `np.sum(image, axis=1)` – Christoph Rackwitz Jan 26 '22 at 10:35

2 Answers2

2

Let me give it to you in . The code can be easily generalized to python.

% read the image and binarize it
img = imread('https://i.stack.imgur.com/iMKIP.jpg') > 128;
% compute the sum of black pixels, per column
vs = sum(1 - img, 1); 
% compute the minimal number of black pixels (ignoring the completely white boundaries)
% this will tell you at which columns you may "cut" the image
thr = min(vs(vs>0));
% find the indices of the columns that do not contain any "object"
sep = find(vs <= the);
% find where objects starts (col indices)
loc = diff(sep) > 1;
% select the column indices at the middle, between objects
col = round(0.5 * (sep(loc(1:end-1)+1) + sep(loc(2:end))));

% visualize the result
imshow(img);
hold on;  
plot([col;col], [0*col;size(img,1)*ones(1,numel(col))], 'r', 'LineWidth', 2);

This is the output:
enter image description here

with

col =

   130   243   351
Shai
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import urllib.request
from PIL import Image
from scipy import signal
import numpy as np
from matplotlib import pyplot as plt

urllib.request.urlretrieve(
  'https://i.stack.imgur.com/iMKIP.jpg',
   "img.png")
img = Image.open("img.png")
img.show()

enter image description here

arr = (np.array(img)<(255)/2) #I'm considering black parts as filled
plt.imshow(arr)

enter image description here

plt.subplot(211)
plt.imshow(arr, aspect="auto")
plt.subplot(212)
plt.plot(arr.sum(axis=0))
plt.xlim(0,arr.shape[1])

enter image description here

Then, in order to find local min:

x_lines = signal.find_peaks(-arr.sum(axis=0))[0]

plt.imshow(arr, aspect="auto")
plt.vlines(x_lines, 0, arr.shape[0], color="r")

enter image description here

Finally if we consider a threshold (for example 50 filled pixel in columns):

threshold = 50

x_lines = signal.find_peaks(-arr.sum(axis=0))[0]
x_lines = x_lines[arr.sum(axis=0)[x_lines]<threshold]

plt.imshow(arr, aspect="auto")
plt.vlines(x_lines, 0, arr.shape[0], color="r")

enter image description here

Edit:

To split picture you need

for i, x in enumerate(x_lines):
    if i==0:
        plt.imshow(arr[:,:x_lines[i]], cmap="Greys")
        plt.savefig("fig%i"%i)
        plt.show()
    else:
        plt.imshow(arr[:,x_lines[i-1]:x_lines[i]], cmap="Greys")
        plt.savefig("fig%i"%i)
        plt.show()
        
plt.imshow(arr[:,x_lines[i]:], cmap="Greys")
plt.savefig("fig%i"%(i+1))
plt.show()
Salvatore Daniele Bianco
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