I'm working on 2D array analysis or an image analysis and get to know the different block of peak values marked "circle" in attached plot [e.g. 2*2 matrix] of the 2D array [as shown in Fig. 01].
The image formation of the plot look like as Fig. 02 
I visited the link https://stackoverflow.com/questions/3684484/peak-detection-in-a-2d-array/3689710#3689710 and use the following script
#NOTE: I get the code from the link above
from scipy import *
from operator import itemgetter
n = 3 # how many fingers are we looking for
#d = loadtxt("paw.txt")
#data = [DATA of a 2d array]
width, height = data.shape
# Create an array where every element is a sum of 2x2 squares.
fourSums = data[:-1,:-1] + data[1:,:-1] + data[1:,1:] + data[:-1,1:]
# Find positions of the fingers.
# Pair each sum with its position number (from 0 to width*height-1),
pairs = zip(arange(width*height), fourSums.flatten())
# Sort by descending sum value, filter overlapping squares
def drop_overlapping(pairs):
no_overlaps = []
def does_not_overlap(p1, p2):
i1, i2 = p1[0], p2[0]
r1, col1 = i1 / (width-1), i1 % (width-1)
r2, col2 = i2 / (width-1), i2 % (width-1)
return (max(abs(r1-r2),abs(col1-col2)) >= 2)
for p in pairs:
if all(map(lambda prev: does_not_overlap(p,prev), no_overlaps)):
no_overlaps.append(p)
return no_overlaps
pairs2 = drop_overlapping(sorted(pairs, key=itemgetter(1), reverse=True))
# Take the first n with the heighest values
positions = pairs2[:n]
# Print results print(data, "\n")
for i, val in positions:
row = i // (width-1)
column = i % (width-1)
print("sum = %f @ %d,%d (%d)" % (val, row, column, i))
print(data[row:row+2,column:column+2])
The above script provides 2 adjacent matrix[2*2] of the same peaks.
## 01: Local matrix of the peak
sum = 0.002482 @ 128,190 (65342)
[[0.0006202 0.00062187]
[0.00061926 0.00062093]]
## 02: Local matrix of the peak
sum = 0.002479 @ 128,191 (65343)
[[0.00062187 0.0006184 ]
[0.00062093 0.00061746]]
Also, I've tried with varying 'n' and no. of rows and columns but it gives me different local matrix for the same peak (probably I missed something) which does not match with my expectation. I want to find local peaks or a number of local maxima as shown in fig. 01 and Fig. 02. How can I get the local maxima efficiently? Can anybody help me in this regard?
