def train(data_c, data_p, l, epoch, LR, w_size):
print('lamda = ', 400 + l * 10)
n = data_c.shape[0]
c = data_c.shape[1]
x = data_c
# 以 K/S作为Y_fade
y = data_p[l][1:].reshape(n, c)
y = mf.math_model(y) - mf.math_model(data_p[l][0])
w = np.random.uniform(-1, 1, w_size)
print('total_loss', total_loss(np.zeros(w_size), n, c, y, x))
for i in range(epoch):
dw = np.zeros_like(w)
for t in range(n):
for j in range(c):
for k in range(c - j - 1):
dw += dloss(y[t][j], x[t][j], y[t][k + j + 1],
x[t][k + j + 1], w)
w = w - dw * LR
if i % 1000 == 0:
print('total_loss', total_loss(w, n, c, y, x), w, dw)
return w
def Mix(compose, data_c, data_p):
base_f = mf.math_model(data_p[0])
F = np.zeros_like(base_f)
for i in range(compose.size):
df = (mf.math_model(data_p[i * 3 + 2]) - base_f) / data_c[i][1]
F += df * compose[i]
return mf.i_math_model(F + base_f)
def get_dfs_KM(w, data_c, data_p):
base_f = mf.math_model(data_p[0])
dfs = []
for i in range(data_c.shape[0]):
df = (mf.math_model(data_p[i * 3 + 1]) - base_f -
correct_func(data_c[i][0], w)) / data_c[i][0]
df += (mf.math_model(data_p[i * 3 + 2]) - base_f -
correct_func(data_c[i][1], w)) / data_c[i][1]
df += (mf.math_model(data_p[i * 3 + 3]) - base_f -
correct_func(data_c[i][2], w)) / data_c[i][2]
dfs.append(df / 3)
return np.array(dfs)
def color_diff_by_compose(fil, w_names, scale, data_c, data_p, compose1,
compose2):
# 按照filiter截取C,P
c, p = mf.data_filiter(filiters[fil], data_c, data_p)
p = p.T
base_f = mf.math_model(p[0])
# 获取w,计算单位k/s值
w = np.load(w_names[fil]).T
dfs = get_dfs_KM(w, c, p)
return mf.color_diff(
corrected_Mix(np.array([compose1]), w, dfs, base_f, scale[fil]),
corrected_Mix(np.array([compose2]), w, dfs, base_f, scale[fil]))
def main():
epoch = 10000
LR = 0.001
w_size = 2
fil = 3
save_name_List = [
'data_w_size6', 'data_w_size12', 'data_w_size18', 'data_w_size21_2'
]
save_name = save_name_List[fil]
# data_c 浓度数据，21种色浆 * 3次取点
# data_p 分光反射率数据 size: (1 + 21 * 3) * 31 , 1为基底
data_c = np.load('data_c.npy')
data_p = np.load('data_p.npy') - 0.074
# 6 , 12 , 18
filiters = np.array(
[[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
# 过滤后的数据
t_c, t_p = mf.data_filiter(filiters[fil], data_c, data_p)
data_w = []
for i in range(31):
data_w.append(train(t_c, t_p, i, epoch, LR, w_size))
print(data_w)
np.save(save_name, data_w)
import matplotlib.pyplot as plt
import Math_v2.Functions as mf
import numpy as np
data_c = np.load('data_c.npy')
data_p = np.load('data_p.npy').T
print(np.load('RealCompose_2.npy'))
C = [
mf.reflectance2rgb(data_p[0]),
mf.reflectance2rgb(data_p[0]),
mf.reflectance2rgb(data_p[0])
]
for i in data_p[1:]:
C.append(mf.reflectance2rgb(i) / 100.0)
C = np.array(C).reshape(22, 3, 3)
print(C)
plt.figure()
plt.imshow(C)
plt.figsize = (150, 50 * 64)
# ignore ticks
plt.xticks([])
plt.yticks([])
# plt.legend()
plt.savefig('梯度.pdf')
plt.show()
def main():
R0 = 0.074
fil = 3
scale = [0.515, 0.515, 0.52, 0.8]
extend_size = [3, 9, 15, 18]
compose_size = [20, 220, 816, 1330]
save_names = [
'dataset_Corrected_size6', 'dataset_Corrected_size12',
'dataset_Corrected_size18', 'dataset_Corrected_size21'
]
w_names = [
'data_w_size6.npy',
'data_w_size12.npy',
'data_w_size18.npy',
'data_w_size21.npy',
]
# data_c 浓度数据，21种色浆 * 3次取点
# data_p 分光反射率数据 size: (1 + 21 * 3) * 31 , 1为基底
data_c = np.load('data_c.npy')
data_p = np.load('data_p.npy')
'''
c, p = mf.data_filiter(filiters[fil], data_c, data_p)
p = p.T
base_f = mf.math_model(p[0])
w = np.load(w_names[fil]).T
dfs = get_dfs_KM(w, c, p)
dataset_size = compose_size[fil] * 2**17
sum = np.random.rand(dataset_size)
a = sum * np.random.rand(dataset_size)
sum -= a
b = sum * np.random.rand(dataset_size)
concentrations = np.concatenate((a.reshape(dataset_size,1),
b.reshape(dataset_size,1),
(sum - b).reshape(dataset_size,1),
np.zeros((dataset_size,extend_size[fil]))),1)
for i in tqdm(range(dataset_size)):
np.random.shuffle(concentrations[i])
reflectance = corrected_Mix(concentrations,w,dfs,base_f,scale[fil])
np.savez(save_names[fil], concentrations = concentrations, reflectance = reflectance)
'''
c, p = mf.data_filiter(filiters[fil], data_c, data_p)
p = p.T - R0
# 计算基底K/S
base_f = mf.math_model(p[0])
# 获取w,计算单位k/s值
w = np.load(w_names[fil]).T
dfs = get_dfs_KM(w, c, p)
RealCompose_2 = np.load('RealCompose_2.npy')
RealIngredient_2 = np.load('RealIngredint_2.npy')
Composes = np.append(RealCompose, RealCompose_2, axis=0)
Ingredients = np.append(RealIngredient, RealIngredient_2, axis=0)
print('\n using : ', w_names[fil])
Y1 = []
Y2 = []
Y3 = []
for com in range(Composes.shape[0]):
compose = mf.data_filiter_(filiters[fil], Composes[com])
if Counter(compose)[0] == 18:
y = mf.color_diff(Ingredients[com], Mix(compose, c, p + R0))
if y < 6:
Y1.append(y)
y = mf.color_diff(
Ingredients[com],
corrected_Mix(np.array([compose]), w, dfs, base_f, scale[fil])
+ R0)
if y < 6:
Y2.append(y)
y = mf.color_diff(Ingredients[com], Mix(compose, c, p) + R0)
if y < 6:
Y3.append(y)
# Y1.append(mf.reflectance2rgb(Ingredients[com]))
# Y2.append(mf.reflectance2rgb(Mix(compose,c,p + R0)))
# Y3.append(mf.reflectance2rgb(corrected_Mix(np.array([compose]),w,dfs,base_f,scale[fil])+ R0))
# #
# Y1 = np.array(Y1).reshape((21,1,3)) / 100.0
# Y2 = np.array(Y2).reshape((21,1,3)) / 100.0
# Y3 = np.array(Y3).reshape((21,1,3)) / 100.0
# base = np.array(mf.reflectance2rgb(data_p.T[0]))/100.0
# print(base)
# one = []
# for i in range(21):
# one.append(base)
# one = np.array(one).reshape((21,1,3))
# Y = np.append(Y2,one,axis=1)
# Y = np.append(Y, Y1, axis=1)
# Y = np.append(Y, one, axis=1)
# Y = np.append(Y, Y3, axis=1)
plt.figure()
X = np.arange(len(Y1))
plt.scatter(X, Y1, label='KM')
plt.plot(X, np.repeat(sum(Y1) / len(Y1), len(Y1)))
plt.text(1, sum(Y1) / len(Y1), sum(Y1) / len(Y1))
plt.scatter(X, Y3, marker='*', label='corrected_KM_1.0')
plt.plot(X, np.repeat(sum(Y3) / len(Y3), len(Y3)))
plt.text(1, sum(Y3) / len(Y3), sum(Y3) / len(Y3))
plt.scatter(X, Y2, marker='x', label='corrected_KM_2.0')
plt.plot(X, np.repeat(sum(Y2) / len(Y2), len(Y2)))
plt.text(1, sum(Y2) / len(Y2), sum(Y2) / len(Y2))
plt.xlabel('加料方案', fontproperties=font_set)
plt.ylabel('色差', fontproperties=font_set)
# plt.imshow(Y)
# ignore ticks
plt.xticks([])
plt.yticks([])
plt.legend()
plt.show()
def corrected_Mix(compose, w, dfs, base_f, scale=0.56):
total_c = np.sum(compose, 1)
F = compose.dot(dfs)
correct_F = F + correct_func(total_c * 0.78, w) * scale + base_f
return mf.i_math_model(correct_F)