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import sys
import numpy
import PIL
import string
import glob
import os.path
from scipy import optimize
from pylab import *
import random
astroterra_path = 'F:\\EVS\\DEV'
alt_astroterra_path = '\\\\140.94.169.102\\USER\\EVS\\DEV'
trouve = False
trouve = astroterra_path in sys.path
if not trouve:
trouve = alt_astroterra_path in sys.path
sys.path.append(alt_astroterra_path)
if not trouve:
sys.path.append(astroterra_path)
else:
print 'Chemin deje present'
import time
import PyQt4
from PyQt4 import QtCore, QtGui
from equipement.common.image.CGestionImage import CGestionImage as CImage
TailleObjet = 1
def maximum3x3_NOBJETS(img, seuil):
#Recherche du macro pixel max
data = img.donnees
Smax = 0
pixels_selec = []
for l in range(0,img.largeur): #largeur
for h in range(0,img.hauteur): #hauteur
S = SommeArray(data[h-TailleObjet:h+TailleObjet+1,l-TailleObjet:l+TailleObjet+1])
if(S>Smax):
Smax = S
pixels_selec.append([l,h,S])
#recherche de tous les macro pixel superieur a Smax/2
k = 0
pixels_MAX = []
for l in range(0,img.largeur): #largeur
for h in range(0,img.hauteur): #hauteur
S = SommeArray(data[h-TailleObjet:h+TailleObjet+1,l-TailleObjet:l+TailleObjet+1])
if(S>Smax/2):
pixels_MAX.append([l,h,S])
#recherche de tous les objets differents
for px_max in pixels_MAX:
flag = 0
for px_selec in pixels_selec: #tant que le macro pixel n appartient pas a un objet existant
if(numpy.abs(int(px_max[0]) - int(px_selec[0])) < 10 and numpy.abs(int(px_max[1]) - int(px_selec[1])) < 10):
flag = 1 #si le macro pixel appartient a un objet existant a une distance < 10 pixels
if(int(px_max[2])> int(px_selec[2])):
px_selec = px_max
if(flag == 0): #si le macro n appartient a aucun objet existant, alors creer l objet
pixels_selec.append(px_max)
#d?termination de la fenetre de calcul du barycentre
px = []
for px_selec in pixels_selec:
seuil_s = seuil * px_selec[2]
dim_haut = px_selec[0]
i = px_selec[0]
j = px_selec[1]
S = SommeArray(img.donnees[j-2:j+3,i-2:i+3])
while(S > seuil_s):
dim_haut = i
i=i-1
S = SommeArray(img.donnees[j-2:j+3,i-2:i+3])
dim_bas = px_selec[0]
i = px_selec[0]
j = px_selec[1]
S = SommeArray(img.donnees[j-2:j+3,i-2:i+3])
while(S > seuil_s):
dim_bas = i
i=i+1
S = SommeArray(img.donnees[j-2:j+3,i-2:i+3])
dim_gauche = px_selec[2]
i = px_selec[0]
j = px_selec[1]
S = SommeArray(img.donnees[j-2:j+3,i-2:i+3])
while(S > seuil_s):
dim_gauche = j
j=j-1
S = SommeArray(img.donnees[j-2:j+3,i-2:i+3])
dim_droit = px_selec[2]
i = px_selec[0]
j = px_selec[1]
S = SommeArray(img.donnees[j-2:j+3,i-2:i+3])
while(S > seuil_s):
dim_droit = j
j=j+1
S = SommeArray(img.donnees[j-2:j+3,i-2:i+3])
px.append([px_selec[0], px_selec[1], px_selec[2], dim_haut-1, dim_bas+1, dim_gauche-1, dim_droit+1])
#Recherche de la somme et la moyenne des sommes
somme = []
for p in px:
## print p
somme.append(p[2])
return px, numpy.max(somme), numpy.mean(somme)
def barycentre_fenetre(img, px):
#initialisation
px_haut = int(px[3])
px_bas = int(px[4])
px_gauche = int(px[5])
px_droit = int(px[6])
poid_ligne = numpy.zeros(img.largeur)
distance_ligne = numpy.zeros(img.largeur)
Barycentre_ligne = 0
somme_poid_ligne = 0
poid_colonne = numpy.zeros(img.hauteur);
distance_colonne = numpy.zeros(img.hauteur);
Barycentre_colonne = 0;
somme_poid_colonne = 0;
#Balayage ligne
for i in range(px_haut, px_bas+1):
poid_ligne[i] = SommeArray1D(img.donnees[px_gauche:px_droit+1,i])
distance_ligne[i]=i #centre du pixel est i
Barycentre_ligne = poid_ligne[i] * distance_ligne[i] + Barycentre_ligne
somme_poid_ligne = poid_ligne[i] + somme_poid_ligne
Bar_ligne = Barycentre_ligne / somme_poid_ligne
#Balayage colonne
for j in range(px_gauche, px_droit+1):
poid_colonne[j] = SommeArray1D(img.donnees[j,px_haut:px_bas+1])
distance_colonne[j]=j #centre du pixel est j
Barycentre_colonne = poid_colonne[j] * distance_colonne[j] + Barycentre_colonne
somme_poid_colonne = poid_colonne[j] + somme_poid_colonne
Bar_colonne = Barycentre_colonne / somme_poid_colonne
return Bar_ligne, Bar_colonne
def gauss2D(px, data):
params = fitgaussian(data[px[5]:px[6] +1, px[3]:px[4] +1])
params[1] = params[1] + px[5]
params[2] = params[2] + px[3]
## print params
fit = gaussian(*params)
return fit, params
def SommeArray(tableau):
somme = 0
size=tableau.shape
for Y in range(0, size[1]):
for X in range(0, size[0]):
somme = somme+tableau[X,Y]
return somme
def SommeArray1D(tableau):
somme = 0
size=tableau.shape
for X in range(0, size[0]):
somme = somme+tableau[X]
return somme
def gaussian(height, center_x, center_y, width_x, width_y):
"""Returns a gaussian function with the given parameters"""
width_x = float(width_x)
width_y = float(width_y)
return lambda x,y: height*exp(
-(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)
def moments(data):
"""Returns (height, x, y, width_x, width_y)
the gaussian parameters of a 2D distribution by calculating its
moments """
total = data.sum()
X, Y = indices(data.shape)
x = (X*data).sum()/total
y = (Y*data).sum()/total
col = data[:, int(y)]
width_x = sqrt(abs((arange(col.size)-y)**2*col).sum()/col.sum())
row = data[int(x), :]
width_y = sqrt(abs((arange(row.size)-x)**2*row).sum()/row.sum())
height = data.max()
return height, x, y, width_x, width_y
def fitgaussian(data):
"""Returns (height, x, y, width_x, width_y)
the gaussian parameters of a 2D distribution found by a fit"""
params = moments(data)
errorfunction = lambda p: ravel(gaussian(*p)(*indices(data.shape)) -
data)
p, success = optimize.leastsq(errorfunction, params)
return p
#Ouverture de l'image
PathImg = "F:\\EVS\\DEV\\julien\\AIM-4_PA.tif"
TraitementImage = CImage(None)
Img = TraitementImage.lire(PathImg)
data = Img.donnees
matshow(data, cmap=cm.gist_earth_r)
h = 80
l = 120
##print Img.donnees[h-1:h+2,l-1:l+2]
pixels, max, moy = maximum3x3_NOBJETS(Img, 0)
##print max
##print moy
bar = []
for px in pixels:
lig, col = barycentre_fenetre(Img, px)
bar.append([px[0],px[1],px[2],px[3],px[4],px[5],px[6],lig,col])
t = []
for b in bar:
t.append(gauss2D(b, Img.donnees))
PrecX = -1
PrecY = -1
SommeX = 0
SommeY = 0
Nb = 0
centroide = {}
centre = 0
NameFileOut = 'I:/tmp/centroid.txt'
out = open(NameFileOut, 'w')
out.write('X\tY\tWidth_X\tWidth_Y\n')
for t1 in t:
## print t1[0](*indices(data.shape))
contour(t1[0](*indices(data.shape)), cmap=cm.copper)
ax = gca()
(height, x, y, width_x, width_y) = t1[1]
print "x : " + str(x) + "\t Y : " + str(y) + "\t width_x : " + str(width_x) + "\t width_y : " + str(width_y)
out.write(str(x) + "\t" + str(y) + "\t" + str(width_x) + "\t" + str(width_y) + "\n")
if(numpy.abs(PrecX-x)<=10 and numpy.abs(PrecY-y)<=10):
c = centroide[centre]
centroide[centre][0].append(x)
centroide[centre][1].append(y)
centroide[centre][2].append(width_x)
centroide[centre][3].append(width_y)
PrecX = x
PrecY = y
else:
centre = centre + 1
centroide[centre] = {}
centroide[centre][0] = []
centroide[centre][0].append(x)
centroide[centre][1] = []
centroide[centre][1].append(y)
centroide[centre][2] = []
centroide[centre][2].append(width_x)
centroide[centre][3] = []
centroide[centre][3].append(width_y)
## [x,y,width_x,width_y]
PrecX = x
PrecY = y
out.write("\n\n")
for cen in centroide:
for c1 in centroide[cen]:
out.write(str(numpy.mean(centroide[cen][c1])) + "\t" + str(numpy.std(centroide[cen][c1])) + "\t")
out.write("\n")
out.close()
show() |
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