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Image_Analysing-Star1.md 9.85 KiB 
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

zapatocaImage = plt.imread('zapatocaImage.jpeg')

plt.imshow(zapatocaImage[:,:,0], cmap='gray')
<matplotlib.image.AxesImage at 0x7f9cc66ceac8>

png

zapatocaImage.shape
(789, 1184, 3)
bs1 = 0.2989 * zapatocaImage[:,:,0]
bs1 = bs1.astype(int)
bs2 = 0.5870 * zapatocaImage[:,:,1]
bs2 = bs2.astype(int)
bs3 = 0.1140 * zapatocaImage[:,:,2]
bs3 = bs3.astype(int)
blckndwht_star1 = bs1 +bs2 + bs3
#plt.imshow(bs3, cmap='gray', vmin=0, vmax=255)
#plt.imshow(bs1 +bs2 + bs3)
plt.imshow(blckndwht_star1)

#plt.imshow(blckndwht_star1, cmap='gray')
#plt.imshow(blckndwht_star1, cmap='gray', vmin=0, vmax=255)

plt.colorbar()
<matplotlib.colorbar.Colorbar at 0x7f9c653d83c8>

png

#plt.imshow(blckndwht_star1[543:564,648:677])
plt.imshow(blckndwht_star1[543:564,652:673])

#plt.imshow(blckndwht_star1[543:564,648:677], vmin=0, vmax=255)
plt.colorbar()
<matplotlib.colorbar.Colorbar at 0x7f9c65328940>

png

#x_axis = np.linspace(-14,14,29)
x_axis = np.linspace(-10,10,21)
y_axis = np.linspace(-10,10,21)

xx_axis, yy_axis = np.meshgrid(x_axis, y_axis)
xx_axis.shape
(21, 21)
#star1 = blckndwht_star1[543:564,648:677].copy()

star1 = blckndwht_star1[543:564,652:673].copy()
star1_nrmlzd = star1/255
#star1_nrmlzd_int = star1_nrmlzd.astype(int)
from scipy.optimize import leastsq
from scipy.optimize import curve_fit
def gauss2D_str1(X, a=255, b=0, c=1, x0=0, y0=0):
    x,y = X
    exponente = -((x-x0)**2 + (y-y0)**2) / (2*c**2)
    z = a * np.exp(exponente) + b
    #print(z)
    return z
#x_axis = np.linspace(-14,14,29)
#y_axis = np.linspace(-10,10,21)
#p0_star1 = 0., 5.
#print(curve_fit(gauss2D_str1, (x_axis,y_axis), z_exp_star1, p0_star1))
#gauss2D_str1((x_axis,y_axis),a,b,c).shape
#gauss2D_str1((x_axis,y_axis),255., 0., 5., 0.0, 0.0).shape
#gauss2D_str1((x_axis,y_axis), a=1, b=0, c=3, x0=0, y0=0) 
gauss2D_str1((x_axis,y_axis), 1, 0, 3, 0, 0).shape
(21,)
gauss2D_str1((x_axis,y_axis), 1, 0, 3, 0, 0)[0]
1.4945338524781451e-05
gauss2D_str1((x_axis,y_axis), 1, 0, 3, 0, 0)[10]
1.0
gauss2D_str1((x_axis,y_axis), 1, 0, 3, 0, 0)[20]
1.4945338524781451e-05
yprueba = gauss2D_str1((x_axis,y_axis), 1, 0, 3, 0, 0)
plt.plot(x_axis,yprueba,"r-")
[<matplotlib.lines.Line2D at 0x7f9c3c016dd8>]

png

plt.plot(y_axis,yprueba,"r-")
[<matplotlib.lines.Line2D at 0x7f9c3bf846a0>]

png

x_exp = np.arange(0,21,1)

y_exp = np.arange(0,21,1)
x_exp
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20])
temppx_xpp ,temppy_ypp =  (x_exp,y_exp)
star1_nrmlzd[temppx_xpp,temppy_ypp] == star1_nrmlzd[temppy_ypp,temppx_xpp]
array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True])
def star1_exp_func(X_exp, x_whch):
    tempx_exp ,tempy_exp = X_exp
    #star1_nrmlzd 
    tempz_exp = np.array([])
    if x_whch == 'Through_x-axis':
        tempz_exp = np.append(tempz_exp, star1_nrmlzd[tempx_exp ,tempy_exp] )
        #star1_nrmlzd[,]
        #z_exp
        return tempz_exp
    elif x_whch == 'Through_y-axis':
        tempz_exp = np.append(tempz_exp, star1_nrmlzd[tempy_exp ,tempx_exp] )
        return tempz_exp
z_expstar1_thrghx = star1_exp_func((x_exp ,y_exp), 'Through_x-axis')  #.shape
z_expstar1_thrghy = star1_exp_func((x_exp,y_exp), 'Through_y-axis')  #.shape
plt.plot(x_axis,z_expstar1_thrghx,"r-")
[<matplotlib.lines.Line2D at 0x7f9c3bf560f0>]

png

plt.plot(x_axis,z_expstar1_thrghy,"r-")
[<matplotlib.lines.Line2D at 0x7f9c3bedf080>]

png

#p0_star1= np.array([255, 0, 5, 0, 0])
#     [a=255, b=0, c=5, x0=0, y0=0]
#    exponente = -((x-x0)**2 + (y-y0)**2) / (2*c**2)
#    z = a * np.exp(exponente) + b
#



p0_star1= np.array([1, 0, 3.5, 0, 0])
print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghx,p0_star1)[0][2])

#z_expstar1_thrghY

print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghy,p0_star1)[0][2])
4.126063711744642
4.126063711744642


/home/bullam/.local/lib/python3.7/site-packages/scipy/optimize/minpack.py:829: OptimizeWarning: Covariance of the parameters could not be estimated
  category=OptimizeWarning)
p0_star1= np.array([1, 0, 5, 0, 0])
print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghx,p0_star1)[0][2])

print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghy,p0_star1)[0][2])
4.928611745994732
4.928611745994732
#z_expstar1_thrghX

p0_star1= np.array([1, 0, 7, 0, 0])
print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghx,p0_star1)[0][2])

#p0_star1= np.array([1, 0, 7, 0, 0])
print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghy,p0_star1)[0][2])
4.874353706666568
4.874353706666568
p0_star1= np.array([1, 0, curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghy,p0_star1)[0][2], 0, 0])
print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghx,p0_star1)[0][2])

print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghy,p0_star1)[0][2])
4.875021346933124
4.875021346933124
p0_star1= np.array([1, 0, 17, 0, 0])
print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghx,p0_star1)[0][2])

print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghy,p0_star1)[0][2])
4.87435369702605
4.87435369702605

c es aproximadamente igual a 4.8743

p0_star1= np.array([1, 0, 6, 0, 0])
print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghx,p0_star1)[0][0])

print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghx,p0_star1)[0][1])

print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghx,p0_star1)[0][2])

print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghx,p0_star1)[0][3])

print(curve_fit(gauss2D_str1, (x_axis,y_axis),
                z_expstar1_thrghx,p0_star1)[0][4])

#p0_star1= np.array([1, 0, 7, 0, 0])
#print(curve_fit(gauss2D_str1, (x_axis,y_axis),
#                z_expstar1_thrghy,p0_star1)[0][1])
0.5729727621590178
0.46791300710503303
4.874353709858581
0.9316969381425967
-0.7084043204480968
yprueba1 = gauss2D_str1((x_axis,y_axis), 0.5729727621590178, 0.46791300710503303, 4.874353709858581, 0.9316969381425967, -0.7084043204480968)
plt.plot(x_axis,yprueba,"r-")
[<matplotlib.lines.Line2D at 0x7f9c39e692b0>]

png

import matplotlib.cm as cm
from mpl_toolkits.mplot3d import Axes3D

z_model_star1 = gauss2D_str1((xx_axis,yy_axis), a=1, b=0, c=3, x0=0, y0=0)
#z_model_star1 = gauss2D_str1((x_axis,y_axis), a=1, b=0, c=3, x0=0, y0=0)
ax_star1 = Axes3D(plt.figure())
ax_star1.plot_surface(xx_axis,yy_axis,z_model_star1,vmax=abs(z_model_star1).max(), vmin=-abs(z_model_star1).max(), cmap=cm.RdYlGn, rstride=1,cstride=1)
plt.show()


#x = y = np.arange(-3.0, 3.0, 0.25) 
#X, Y = np.meshgrid(x, y)

#def f(x,y):
#    return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)

#Z1 = f(X,Y)

#ax = Axes3D(plt.figure())    # creando el cubo
#ax.plot_surface(X,Y,Z1,vmax=abs(Z1).max(), vmin=-abs(Z1).max(), cmap=cm.RdYlGn, rstride=1,cstride=1)
#plt.show()

png

z_model_star1.shape
(21, 21)
#z_model_star2 = gauss2D_str1((xx_axis,yy_axis), a=1, b=0, c=3, x0=0, y0=0)
z_model_star2 = gauss2D_str1((xx_axis,yy_axis),
            0.5729727621590178, 0.46791300710503303, 4.874353709858581, 0.9316969381425967, -0.7084043204480968)
#plt.plot(x_axis,yprueba,"r-")
#z_model_star1 = gauss2D_str1((x_axis,y_axis), a=1, b=0, c=3, x0=0, y0=0)
ax_star1_patentado = Axes3D(plt.figure())
ax_star1_patentado.plot_surface(xx_axis,yy_axis,z_model_star2,vmax=abs(z_model_star2).max(), vmin=-abs(z_model_star2).max(), cmap=cm.RdYlGn, rstride=1,cstride=1)
plt.show()

png

xx_exp, yy_exp = np.meshgrid(x_exp, y_exp)


#z_model_star2 = gauss2D_str1((xx_axis,yy_axis), a=1, b=0, c=3, x0=0, y0=0)

#star1_exp_func((x_exp ,y_exp), 'Through_x-axis')

#z_model_star1 = gauss2D_str1((x_axis,y_axis), a=1, b=0, c=3, x0=0, y0=0)
ax_star1_patentado = Axes3D(plt.figure())
ax_star1_patentado.plot_surface(xx_exp,yy_exp,star1_nrmlzd ,vmax=abs(z_model_star2).max(), vmin=-abs(z_model_star2).max(), cmap=cm.RdYlGn, rstride=1,cstride=1)
#ax_star1_patentado.plot_surface(xx_axis,yy_axis,z_exp_star3,vmax=abs(z_model_star2).max(), vmin=-abs(z_model_star2).max(), cmap=cm.RdYlGn, rstride=1,cstride=1)
plt.show()

png