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diff --git a/entrega/solucion.md b/entrega/solucion.md
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+++ b/entrega/solucion.md
@@ -0,0 +1,753 @@
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+##import cv2
+import matplotlib.image as mpimg
+from matplotlib.pyplot import figure
+import scipy.optimize as opt
+from scipy.optimize import curve_fit
+```
+
+
+```python
+images_path = 'data/zapatocaImage.jpeg'
+img = mpimg.imread(images_path)
+figure(num=None, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
+plt.imshow(img[300:400,400:600])
+plt.show()
+```
+
+
+
+
+
+
+
+
+```python
+##lena = plt.imread('data/zapatocaImage.jpeg')
+print(type(img))
+print(img.shape)
+size = img.shape
+```
+
+ <class 'numpy.ndarray'>
+ (789, 1184, 3)
+
+
+
+```python
+R = img[:,:,0]
+G = img[:,:,1]
+B = img[:,:,2]
+```
+
+
+```python
+figure(num=None, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
+plt.imshow(R)
+plt.title("Canal Rojo")
+plt.colorbar()
+##plt.figure()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feaf4497ac8>
+
+
+
+
+
+
+
+
+
+
+```python
+figure(num=None, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
+plt.imshow(G)
+plt.title("Canal verde")
+plt.colorbar()
+##plt.figure()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feaf9473fd0>
+
+
+
+
+
+
+
+
+
+
+```python
+figure(num=None, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
+plt.imshow(B)
+##plt.imshow(B,cmap = 'gray')
+plt.title("Canal azul")
+plt.colorbar()
+##plt.figure()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feaf93b3470>
+
+
+
+
+
+
+
+
+
+
+```python
+T = 0.299*R+0.587*G+0.114*B
+notT = R+G+B
+```
+
+
+```python
+figure(num=None, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
+plt.imshow(T, cmap = 'gray')
+plt.title("total blanco y negro")
+plt.colorbar()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feaf92e49b0>
+
+
+
+
+
+
+
+
+
+### En lo que vamos a partir la imagen
+
+
+```python
+particion = 10
+part_elevation = size[0]/particion
+part_wide = size[1]/particion
+```
+
+## Funciones a definir,
+
+### importante, he puesto siempre T, debo cam,biar eso y poner como entrada B de alguna forma (CREO)
+
+
+```python
+CRITT = 8
+##imgtt_copy = T.copy()
+imgtt_copy = np.zeros([size[0],size[1]])
+
+def minn(a):
+ b= int(round(a))
+ if a < b:
+ return int(b-1)
+ else:
+ return int(b)
+
+def maxx(a):
+ b= int(round(a))
+ if a > b:
+ return int(b+1)
+ else:
+ return int(b)
+
+def img_part(a,b,Y): ## a #de matriz en altura, b lo mismo pero con ancho ; 0<a<20
+ X = Y[minn(a*part_elevation):minn((a+1)*part_elevation) , minn(b*part_wide):minn((b+1)*part_wide) ]
+ return X
+
+def histpart_init(img_partt): # creo el histograma y lo inicio en cero
+ hist_range = maxx(np.max(img_partt)) - minn(np.min(img_partt))
+ hist_arrayy = np.zeros( hist_range +1 )
+ return hist_arrayy
+
+def histpart_full(histt,imgtt):
+ sizezz = imgtt.shape
+ for i in range (0,sizezz[0],1):
+ for j in range (0,sizezz[1],1):
+ ##a = imgtt[i,j]
+ v = minn(imgtt[i,j] - np.min( imgtt ) ) ## revizr si poner maximo o minimo, detalles!!
+ histt[ int(v) ] += 1
+ return histt
+##sum(hist_full)=minn(part_elevation)*minn(part_wide) ## deberia cumplirse esto
+
+def criteri_histograma(histt): ## criterio: si estamos mas alla de np.max(hist_full)/10 es decir un decimo de la mediana
+ ##CRITT =5
+ maxx = np.max(histt)
+ for i in range (0,histt.size,1):
+ if histt[i] > maxx-1:
+ aa = i
+ for j in range (aa+2,histt.size,1):
+ if histt[j] < maxx/CRITT:
+ ccc = j
+ break
+ return ccc
+
+def clean_part(crit_hist,imgtt):
+ sizezz = imgtt.shape
+ imgttTT = imgtt.copy()
+ for i in range (0,sizezz[0],1):
+ for j in range (0,sizezz[1],1):
+ if imgtt[i,j] < (np.min(imgtt) + crit_hist):
+ imgttTT[i,j] = np.min(imgtt)+crit_hist
+ if imgtt[i,j] < 55: ##solo por jugar
+ imgttTT[i,j] = 55
+ return imgttTT
+
+def New_image(a,b,X,Y): ## parte filtrada, copia de la imagen toral que modificare
+ ii = 0
+ for i in range (minn(x*part_elevation), minn((x+1)*part_elevation) ,1 ):
+ jj=0
+ for j in range (minn(y*part_wide), minn((y+1)*part_wide) , 1 ):
+ X[i,j] = Y[ii,jj]
+ jj += 1
+ ii += 1
+```
+
+
+```python
+T_PART= img_part(6,5,T)
+hist = histpart_init(T_PART)
+hist_full = histpart_full(hist,T_PART)
+plt.title("histograma, minimo es : "+str(np.min(T_PART)) )
+plt.plot(hist_full)
+```
+
+
+
+
+ [<matplotlib.lines.Line2D at 0x7feaf597ee10>]
+
+
+
+
+
+
+
+
+
+
+```python
+criterio_hist = criteri_histograma(hist_full)
+criterio_hist
+```
+
+
+
+
+ 68
+
+
+
+
+```python
+criterio_hist = criteri_histograma(hist_full)
+clean_image = clean_part(criterio_hist,T_PART)
+plt.imshow(clean_image, cmap = 'gray')
+plt.title("parte blanco y negro")
+plt.colorbar()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feaf5837d68>
+
+
+
+
+
+
+
+
+
+
+```python
+plt.imshow(T_PART, cmap = 'gray')
+plt.title("original blanco y negro")
+plt.colorbar()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feaee6479b0>
+
+
+
+
+
+
+
+
+
+
+```python
+for x in range (0,particion,1):
+ for y in range (0,particion,1):
+ T_PART= img_part(x,y,T)
+ hist = histpart_init(T_PART)
+ hist_full = histpart_full(hist,T_PART)
+ criterio_hist = criteri_histograma(hist_full)
+ criterio_hist = criteri_histograma(hist_full)
+ clean_image = clean_part(criterio_hist,T_PART)
+ New_image(x,y,imgtt_copy,clean_image)
+
+figure(num=None, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
+plt.imshow(imgtt_copy, cmap = 'gray')
+plt.title("original blanco y negro")
+plt.colorbar()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feabb6cceb8>
+
+
+
+
+
+
+
+
+
+
+```python
+figure(num=None, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
+plt.imshow(imgtt_copy, cmap = 'gray',vmin=65,vmax=260)
+plt.title("original blanco y negro")
+plt.colorbar()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feabe9b0ef0>
+
+
+
+
+
+
+
+
+
+
+```python
+figure(num=None, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
+plt.imshow(imgtt_copy[300:500,200:400], cmap = 'gray')
+plt.title("blanco y negro")
+plt.colorbar()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feabb940cf8>
+
+
+
+
+
+
+
+
+
+
+```python
+imgttt_copy= imgtt_copy.copy()
+x=4
+y=5
+AA_PART= img_part(x,y,imgttt_copy)
+plt.imshow(AA_PART, cmap = 'gray')
+plt.title("parte blanco y negro")
+plt.colorbar()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feadba63ba8>
+
+
+
+
+
+
+
+
+
+
+```python
+figure(num=None, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
+plt.imshow(imgttt_copy, cmap = 'gray')
+plt.title("parte blanco y negro")
+plt.colorbar()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feadb360da0>
+
+
+
+
+
+
+
+
+
+## nuevo particion para culsterizar cada estrella
+si tomo particion 1, tomo toda la imagen!
+
+
+
+```python
+particion = 5
+part_elevation = size[0]/particion
+part_wide = size[1]/particion
+```
+
+
+```python
+lum=125 ## tope de luz
+imgttt_copy= imgtt_copy.copy()
+
+def tanteo(x,y,iii,jjj): ## iii y jjj desde donde sigue
+ brek=True
+ ii = iii
+ jj=jjj
+ ##voy a terminar la linea
+ for j in range (minn(y*part_wide)+jjj, minn((y+1)*part_wide),1):
+ if imgttt_copy[ii,j] > lum:
+ u=[ii,jj]
+ brek=False
+ break
+ return u
+ ##ii+=1
+ for i in range (minn(x*part_elevation)+ii, minn((x+1)*part_elevation),1):
+ jj=0
+ for j in range (minn(y*part_wide), minn((y+1)*part_wide),1):
+ if imgttt_copy[i,j] > lum:
+ u=[ii,jj]
+ brek=False
+ break
+ jj+=1
+ if brek==False:
+ break
+ ii+=1
+ if brek==True:
+ u=[-1,-1]
+ return u
+
+def ubicar_estrella(x,y,iii,jjj,COPY):
+ contador_puntos=0
+ ii=iii
+ jj=jjj
+ imin=iii-1
+ imax=0
+ jmin=10000
+ jmax=0
+ line_number=0
+ rig = True
+ lef = True
+ dow = True
+ while dow==True:
+ if ii>=maxx(part_elevation):##revizar ese 1
+ break
+ line_number=0
+ rig = True
+ lef = True
+ jj=jjj
+ if COPY[ii,jj] > lum:
+ COPY[ii,jj]=2
+ imgttt_copy[minn(x*part_elevation)+ii,minn(y*part_wide)+jj]=2
+ contador_puntos+=1
+ line_number+=1
+ while rig==True:
+ if jj+2>=maxx(part_wide):
+ break
+ jj +=1
+ if COPY[ii,jj]>lum:
+ COPY[ii,jj]=2
+ imgttt_copy[minn(x*part_elevation)+ii,minn(y*part_wide)+jj]=2
+ contador_puntos +=1
+ line_number +=1
+ else: ##debo corregir para terminos frontera
+ rig = False
+ if jj>jmax:
+ jmax=jj+2 ##lo realmente aca tiene 1 sumado
+ jj=jjj
+ while lef==True:
+ jj -=1
+ if jj<jmin:
+ jmin=jj-1 ##lo realmente aca tiene 1 restado
+ if COPY[ii,jj]>lum:
+ COPY[ii,jj]=2
+ imgttt_copy[minn(x*part_elevation)+ii,minn(y*part_wide)+jj]=2
+ contador_puntos +=1
+ line_number +=1
+ else: ##debo corregir para terminos frontera
+ lef = False
+ if jj==0:
+ lef = False
+ ii+=1
+ if line_number==0: ##podria dejar puntoas por fuera pero pos nahh
+ dow = False
+ imax=ii
+ p=[contador_puntos , imin , imax, jmin , jmax]
+ return p
+```
+
+
+```python
+imgttt_copy= imgtt_copy.copy()
+x=2
+y=2
+AA_PART= img_part(x,y,imgttt_copy)
+plt.imshow(AA_PART, cmap = 'gray')
+plt.title("parte blanco y negro")
+plt.colorbar()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feab77ccac8>
+
+
+
+
+
+
+
+
+
+### Ejemplo seleccion estrella
+
+
+```python
+a=tanteo(x,y,34,40)
+bb=ubicar_estrella(x,y,a[0],a[1],AA_PART)
+data_array = np.full([bb[2]- bb[1], bb[4]- bb[3] ], None)
+ui=[]
+uj=[]
+ulum=[]
+for i in range (bb[1],bb[2],1):
+ for j in range (bb[3],bb[4],1):
+ if AA_PART[i,j]==2:
+ ui.append(i)
+ uj.append(j)
+ ulum.append( imgtt_copy[minn(x*part_elevation)+i,minn(y*part_wide)+j] )
+ data_array[i-bb[1],j-bb[3]] = imgtt_copy[minn(x*part_elevation)+i,minn(y*part_wide)+j]
+
+plt.imshow(AA_PART[bb[1]:bb[2],bb[3]:bb[4]], cmap = 'gray')
+plt.title("parte blanco y negro")
+plt.colorbar()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feab7504b00>
+
+
+
+
+
+
+
+
+
+
+```python
+plt.axes([0.025,0.025,0.95,0.95])
+plt.xlim(bb[1],bb[2]), plt.xticks([])
+plt.ylim(bb[3],bb[4]), plt.yticks([])
+plt.scatter(ui,uj,s=70, c = ulum, alpha=5)
+plt.colorbar()
+plt.show()
+
+##NOTA, LA IMAGEN ESTA ROTADA
+```
+
+
+
+
+
+
+
+## Se haria el ciclo sobre todas las estrellas
+### Se haria en este ciclo cada uno de los analisis imponiendo las condiciones que yo quiera
+
+
+```python
+for x in range (2,3,1): ## cuando termine funcion de fitear hay si pongo x en el rango que es
+ for y in range (2,3,1):
+ AA_PART= img_part(x,y,imgttt_copy)
+ a=[0,0]
+ while a[0]>-1:
+ a=tanteo(x,y,0,0)
+ ubicar_estrella(x,y,a[0],a[1],AA_PART)
+
+plt.imshow(AA_PART, cmap = 'gray')
+plt.title("parte blanco y negro")
+plt.colorbar()
+```
+
+
+
+
+ <matplotlib.colorbar.Colorbar at 0x7feab797a828>
+
+
+
+
+
+
+
+
+
+### Funcion para analizar cada estrella
+
+
+```python
+
+```
+
+
+```python
+ui
+uj
+ulum
+```
+
+## :(
+
+
+```python
+def gauss2D(xdata_tuple,a, b, c, x0, y0):
+ (X,Y)=xdata_tuple
+ X, Y = np.meshgrid(ui, uj)
+ exponente= -((x-x0)**2 + (y-y0)**2) / (2*c**2)
+ z= a * np.exp(exponente) + b
+ return z
+
+```
+
+
+```python
+p0 = [10., 100., 4. ,2. , 2.]
+curve_fit(gauss2D, [X,Y] , ulum ,p0 )
+```
+
+
+```python
+ xData = numpy.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0])
+ yData = numpy.array([11.0, 12.1, 13.0, 14.1, 15.0, 16.1, 17.0, 18.1, 90.0])
+ zData = numpy.array([1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.0, 9.9])
+
+ data = [xData, yData, zData]
+
+ initialParameters = [1.0, 1.0, 1.0] # these are the same as scipy default values in this example
+
+ # here a non-linear surface fit is made with scipy's curve_fit()
+ fittedParameters, pcov = scipy.optimize.curve_fit(func, [xData, yData], zData, p0 = initialParameters)
+
+ ScatterPlot(data)
+ SurfacePlot(func, data, fittedParameters)
+ ContourPlot(func, data, fittedParameters)
+
+ #print('fitted prameters', fittedParameters)
+
+ modelPredictions = func(data, *fittedParameters)
+
+ absError = modelPredictions - zData
+
+ SE = numpy.square(absError) # squared errors
+ MSE = numpy.mean(SE) # mean squared errors
+ RMSE = numpy.sqrt(MSE) # Root Mean Squared Error, RMSE
+ Rsquared = 1.0 - (numpy.var(absError) / numpy.var(zData))
+ #print('RMSE:', RMSE)
+ #print('R-squared:', Rsquared)
+```
+
+
+```python
+def gauss2D( X,Y , a, b, c, x0, y0):
+ exponente= -((X-x0)**2 + (Y-y0)**2) / (2*c**2)
+ z= a * np.exp(exponente) + b
+ return z
+```
+
+
+```python
+import matplotlib.pyplot as plt
+
+x = ui
+y = uj
+X, Y = np.meshgrid(ui, uj) #sparse=True
+z = np.sin(xx + yy)
+h = plt.contourf(x,y,z)
+
+plt.show()
+```
+
+
+
+
+
+
+
+
+```python
+def SurfacePlot(func, data, fittedParameters):
+ f = plt.figure(figsize=(graphWidth/100.0, graphHeight/100.0), dpi=100)
+
+ matplotlib.pyplot.grid(True)
+ axes = Axes3D(f)
+
+ xModel = numpy.linspace(min(ui), max(ui), 20)
+ yModel = numpy.linspace(min(uj), max(uj), 20)
+ X, Y = numpy.meshgrid(xModel, yModel)
+
+ Z = func(numpy.array([X, Y]), *fittedParameters)
+
+ axes.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=1, antialiased=True)
+
+ axes.scatter(x_data, y_data, z_data) # show data along with plotted surface
+
+ axes.set_title('Surface Plot (click-drag with mouse)') # add a title for surface plot
+ axes.set_xlabel('X Data') # X axis data label
+ axes.set_ylabel('Y Data') # Y axis data label
+ axes.set_zlabel('Z Data') # Z axis data label
+
+ plt.show()
+ plt.close('all') # clean up after using pyplot or else there can be memory and process problems
+
+
+```
+
+
+```python
+
+```