def showAffectation(color_center, centre):
max_color = max_reading(color_center)
fig = plt.figure()
for i in range(0,len(color_center) ):
if i < 6:
plt.scatter(i, 0, s=800, marker='o', c= t255to01(centre[i],max_color) )
for j in range (0 , len(color_center[i])):
plt.scatter(i, 10 + j* 5, s=800, marker='o', c= t255to01(color_center[i][j], max_color) )
plt.show()
def test_rgb_3D(data):
x, y, color, c, max_color = donnees(data)
Fig = plt.figure()
ax = plt.axes(projection='3d')
for s, side in enumerate(data):
for f, (r,g,b) in enumerate(side):
ax.scatter3D(data[s][f][0], data[s][f][1], data[s][f][2], c=t255to01(data[s][f],max_color), cmap=data)
plt.show()
def draw_rgb_debut(data):
x, y, color, c, max_color = donnees(data)
n = 9
m = 6
#color_center = [[] * 54] * 6
color_center = []
for k in range(0,6):
color_center.append([])
cccc = []
compteur = 0
print " JESUIS CENTRE ", c
for s, side in enumerate(data):
for f, (r,g,b) in enumerate(side):
indice = 0
min_color = max_color
size = len(c)
for i in range(0,size):
rr = abs(r - c[i][0])
gg = abs(g - c[i][1])
bb = abs(b - c[i][2])
if (min_color > ((rr +gg+bb)/3) ):
min_color = (rr +gg+bb)/3
indice = i
#print indice
color_center[indice].append( [r,g,b] )
#print ' JESUIS FACE ' , s , ' EN FACET ', f , ' JAJOUTE DANS ' , indice , ' COMPTEUR ' , compteur
#print color_center[indice]
nb_erreur = 0
for i in range(0, len(color_center)):
for j in range (0, len(color_center[i])):
dedans = False
for k in range(0, len(data[i])):
if sameColor( color_center[i][j], data[i][k]):
dedans = True
if dedans == False:
nb_erreur += 1
print float(nb_erreur)/54 *100 , "% d erreur " , nb_erreur , " sur " , 54
for i in range(0,len(color_center) ):
#print ' COULEUR SIDE ', c[i], ' liste facet: ', color_center[i], '\n'
tabColor = []
for j in range (0 , len(color_center[i])):
plt.scatter(i, 10 + j* 5, s=800, marker='o', c= t255to01(color_center[i][j], max_color) )
plt.scatter(i, 0, s=800, marker='o', c= t255to01(c[i],max_color) )
"""
plt.scatter(x, y, s=800, marker='s', c=color)
plt.title('Rubik\'s cube unfolding')
plt.axis('off')
plt.savefig('unfolding.png')
"""
#showData([R1])
tab_tri = repartition_egal(color_center, c, 9)
fig = plt.figure()
for i in range(0,len(tab_tri) ):
#print ' COULEUR SIDE ', c[i], ' liste facet: ', color_center[i], '\n'
tabColor = []
for j in range (0 , len(tab_tri[i])):
plt.scatter(i, 10 + j* 5, s=800, marker='o', c= t255to01(tab_tri[i][j], max_color) )
plt.scatter(i, 0, s=800, marker='o', c= t255to01(c[i],max_color) )
plt.show()
def test_hsv(data, data2 = []):
x, y, color, c, max_color = donnees(data)
centre = couleurCentre(data)
centre_hsv = []
tab_hsv = []
tab_rgb, data_rgb = [], []
#conversion centre rgb to hsv
for i in range(0, len(centre)):
color = t255to01(centre[i],max_color)
#print " couleur des donnees : " , data[1][4] , " couleur en format 0-1 : ", color
centre_hsv.append( colorsys.rgb_to_hsv(color[0], color[1], color[2]) )
#test sur toutes les facets
data_hsv ,color_center, tab_rgb = [], [], []
for i in range(0, len(data)):
data_hsv.append([])
color_center.append([])
tab_hsv.append([])
tab_rgb.append([])
"""
Fig = plt.figure()
ax = plt.axes(projection='3d')
"""
for s, side in enumerate(data):
for f, (r,g,b) in enumerate(side):
data_hsv[s].append( colorsys.rgb_to_hsv( float(r)/max_color, float(g)/max_color, float(b)/max_color) )
min_distance = 100000
indice = 0
for i in range(0, len(centre_hsv)):
dist = distance_hsv(data_hsv[s][f], centre_hsv[i])
if dist < min_distance:
min_distance = dist
indice = i
color_center[indice].append( [r,g,b] )
tab_hsv[indice].append( data_hsv[s][f] )
"""
ax.scatter3D(data_hsv[s][f][0], data_hsv[s][f][1], data_hsv[s][f][2], c=t255to01(data[s][f],max_color), cmap=data)
"""
if len(data2) > 0:
taux_erreur(data2, color_center)
fig = plt.figure()
for i in range(0,len(color_center) ):
for j in range (0 , len(color_center[i])):
plt.scatter(i, 10 + j* 5, s=800, marker='o', c= rgb255to01(color_center[i][j], max_color) )
plt.scatter(i, 0, s=800, marker='o', c= t255to01(centre[i],max_color) )
tab_repar = repartition_egal(tab_hsv, centre_hsv, 9, False)
fig = plt.figure()
for i in range(0,len(tab_repar) ):
for j in range (0 , len(tab_repar[i])):
color = colorsys.hsv_to_rgb( tab_repar[i][j][0], tab_repar[i][j][1], tab_repar[i][j][2])
plt.scatter(i, 10 + j* 5, s=800, marker='o', c= color)
plt.scatter(i, 0, s=800, marker='o', c= t255to01(centre[i],max_color) )
plt.show()
def draw_diffRGB(data):
x, y, color, c, max_color = donnees(data)
n = 9
m = 6
c = couleurCentre(data)
color_center = []
for k in range(0,6):
color_center.append([])
"""
utilisation du filtre uniforme
"""
for s, side in enumerate(data):
for f, (r,g,b) in enumerate(side):
if f != 4:
indice = 0
min_color = max_color
size = len(c)
#compare avec chaque centre
for i in range(0,size):
#calcul la nouvelle nuance de chaque centre
color = [0,0,0]
for l in range(0,3):
color[l] = c[i][l] + diff[i][l]
rr = abs(r - color[0])
gg = abs(g - color[1])
bb = abs(b - color[2])
if (min_color > ((rr +gg+bb)/3) ):
min_color = (rr +gg+bb)/3
indice = i
color_center[indice].append( [r,g,b] )
nb_erreur = 0
for i in range(0, len(color_center)):
if ( len(color_center[i]) > 8):
nb_erreur += len(color_center[i]) - 8
print float(nb_erreur)/48 *100 , "% d erreur " , nb_erreur , " sur 48"
#coeff couleur uniforme
plt.figure(0)
for i in range(0, len(c)):
plt.scatter(i, 0, s=800, marker='s', c=t255to01(c[i], max_color))
#decalage
decalage = 5
for j in range (0,len(color_center[i])):
plt.scatter(i, decalage + j, s=800, marker='s', c=t255to01(color_center[i][j], max_color) )
c = couleurCentre(data)
"""
utilisation des filtres cote/coin
"""
color_center = []
for k in range(0,6):
color_center.append([])
for s, side in enumerate(data):
for f, (r,g,b) in enumerate(side):
if f != 4:
indice = 0
min_color = max_color
size = len(c)
for i in range(0,size):
color = [0,0,0]
for l in range(0,3):
if f%2 == 0:
color[l] = c[i][l] + diffCoin[i][l]
else:
color[l] = c[i][l] + diffCote[i][l]
rr = abs(r - color[0])
gg = abs(g - color[1])
bb = abs(b - color[2])
if (min_color > ((rr +gg+bb)/3)):
min_color = (rr +gg+bb)/3
indice = i
color_center[indice].append( [r,g,b] )
nb_erreur = 0
for i in range(0, len(color_center)):
print color_center[i]
if ( len(color_center[i]) > 8):
nb_erreur += len(color_center[i]) - 8
print float(nb_erreur)/48 *100 , "% d erreur " , nb_erreur , " sur 48"
#coeff couleur uniforme
plt.figure(1)
for i in range(0, len(c)):
plt.scatter(i, 0, s=800, marker='s', c=t255to01(c[i], max_color))
#decalage
decalage = 5
for j in range (0,len(color_center[i])):
plt.scatter(i, decalage + j, s=800, marker='s', c=t255to01(color_center[i][j], max_color) )
plt.show()