#! usr/bin/env python
# -*- coding: utf-8 -*-
import os
import cv2
import sys
import glob
import time
import scipy
import cPickle
import numpy as np
import scipy.io as sio
from skimage.filters import threshold_otsu
from skimage.measure import label
def matlab_style_gauss2D(shape=(3,3),sigma=0.5):
"""
2D gaussian mask - should give the same result as MATLAB's
fspecial('gaussian',[shape],[sigma])
"""
m,n = [(ss-1.)/2. for ss in shape]
y,x = np.ogrid[-m:m+1,-n:n+1]
h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )
h[ h < np.finfo(h.dtype).eps*h.max() ] = 0
sumh = h.sum()
if sumh != 0:
h /= sumh
return h
def getDETCentroid(featfolder, savefolder,dirname):
path = featfolder
for i in range(len(dirname)):
filepath = path + dirname[i] + '/*.mat'
fcn = glob.glob(filepath)
n = len(fcn)
for j in range(n):
compCentroid_detect1(fcn[j],savefolder)
#os.remove(glob.glob(path+dirname[i]+'/*'))
def compCentroid_detect1(fcn, savefolder):
data_dict = sio.loadmat(fcn)
f = matlab_style_gauss2D((10,10),0.25)
A = cv2.filter2D(data_dict['A'], -1, f)
level = threshold_otsu(A) #otsu threshold of image
bw = A > level #binary image
L,num = label(bw,8,return_num=True) #label the segmented blobs
#import pdb;pdb.set_trace()
plot_x = np.zeros((num, 1)) # location of centroid
plot_y = np.zeros((num, 1))
sum_x = np.zeros((num, 1))
sum_y = np.zeros((num, 1))
area = np.zeros((num, 1))
score = np.zeros((num, 1))
height,width = bw.shape[0], bw.shape[1]
for i in range(height):
for j in range(width):
if L[i,j] != 0:
N = L[i,j]
sum_x[N-1] = sum_x[N-1]+i*A[i,j]
sum_y[N-1] = sum_y[N-1]+j*A[i,j]
area[N-1] = area[N-1] + 1
score[N-1] = score[N-1] + A[i,j]
plot_x = np.around(sum_x*1.0/score)
plot_y = np.around(sum_y*1.0/score)
score = score*1.0/area
centroid = np.zeros((num,2))
for row in range(num):
centroid[row,0] = plot_x[row,0]
centroid[row,1] = plot_y[row,0]
#centroid = np.mat(centroid)
savefile = savefolder + fcn[-9:]
sio.savemat(savefile,{'centroid':centroid, 'area':area, 'score':score})
