def get_t(J, type, gammaI=1):
Jadjusted = natural_histogram_matching(J, type=type)**gammaI
# Jadjusted = natural_histogram_matching(J, type=type)
texture = Image.open(texture_file_name)
texture = np.array(texture.convert("L"))
# texture = np.array(texture)
texture = texture[99:texture.shape[0] - 100, 99:texture.shape[1] - 100]
ratio = texture_resize_ratio * min(J.shape[0], J.shape[1]) / float(1024)
texture_resize = interpolation.zoom(texture, (ratio, ratio))
texture = im2double(texture_resize)
htexture = hstitch(texture, J.shape[1])
Jtexture = vstitch(htexture, J.shape[0])
size = J.shape[0] * J.shape[1]
nzmax = 2 * (size - 1)
i = np.zeros((nzmax, 1))
j = np.zeros((nzmax, 1))
s = np.zeros((nzmax, 1))
for m in range(1, nzmax + 1):
i[m - 1] = int(math.ceil((m + 0.1) / 2)) - 1
j[m - 1] = int(math.ceil((m - 0.1) / 2)) - 1
s[m - 1] = -2 * (m % 2) + 1
dx = csr_matrix((s.T[0], (i.T[0], j.T[0])), shape=(size, size))
nzmax = 2 * (size - J.shape[1])
i = np.zeros((nzmax, 1))
j = np.zeros((nzmax, 1))
s = np.zeros((nzmax, 1))
for m in range(1, nzmax + 1):
i[m - 1, :] = int(math.ceil((m - 1 + 0.1) / 2) + J.shape[1] *
(m % 2)) - 1
j[m - 1, :] = math.ceil((m - 0.1) / 2) - 1
s[m - 1, :] = -2 * (m % 2) + 1
dy = csr_matrix((s.T[0], (i.T[0], j.T[0])), shape=(size, size))
Jtexture1d = np.log(
np.reshape(Jtexture.T, (1, Jtexture.size), order="f") + 0.01)
Jtsparse = spdiags(Jtexture1d, 0, size, size)
Jadjusted1d = np.log(
np.reshape(Jadjusted.T, (1, Jadjusted.size), order="f").T + 0.01)
nat = Jtsparse.T.dot(Jadjusted1d) # lnJ(x)
a = np.dot(Jtsparse.T, Jtsparse)
b = dx.T.dot(dx)
c = dy.T.dot(dy)
mat = a + Lambda * (b + c) # lnH(x)
# x = spsolve(a,b) <--> a*x = b
# lnH(x) * beta(x) = lnJ(x) --> beta(x) = spsolve(lnH(x), lnJ(x))
beta1d = spsolve(mat, nat) # eq.8
beta = np.reshape(beta1d, (J.shape[0], J.shape[1]), order="c")
T = Jtexture**beta # eq.9
T = (T - T.min()) / (T.max() - T.min())
img = Image.fromarray(T * 255)
# img.show()
return T
def get_t(J, type, gammaI=1):
'''
色调渲染(tone rendering):
Tone Rendering tone drawing focuses more on shapes, shadow, and shading than on the use of lines
铅笔画的直方图有一定的pattern, 因为只是铅笔和白纸的结合
可以分成三个区域: 1.亮 2.暗 3.居于中间的部分, 于是就有三个用来模拟的模型
亮的部分使用Laplace分布
中间的部分使用平均分布
暗的部分使用高斯分布
随后作者列出了从收集到的简笔画图像中学出来对应的参数
铅笔画的色调 颜色等通过用铅笔重复的涂画来体现
1. 直方图匹配
运用三种分布计算图片的直方图, 然后匹配一个正常图片的直方图
2. 纹理渲染(texture rendering):
计算模拟需要用铅笔重复涂画的次数beta
:param J: 图片转换成灰度后的矩阵
:param type: 图片类型
:param gammaI: 控制参数, 值越大最后的结果颜色越深
:return: 色调渲染后的图片矩阵T
'''
#直方图匹配
Jadjusted = natural_histogram_matching(J, type=type) ** gammaI
# Jadjusted = natural_histogram_matching(J, type=type)
#将铅笔纹理图，转换到和待处理图一样的大小
texture = Image.open(texture_file_name)
texture = np.array(texture.convert("L"))
# texture = np.array(texture)
texture = texture[99: texture.shape[0]-100, 99: texture.shape[1]-100]
ratio = texture_resize_ratio * min(J.shape[0], J.shape[1]) / float(1024)
texture_resize = interpolation.zoom(texture, (ratio, ratio))
texture = im2double(texture_resize)
htexture = hstitch(texture, J.shape[1])
Jtexture = vstitch(htexture, J.shape[0])
size = J.shape[0] * J.shape[1]
nzmax = 2 * (size-1)
i = np.zeros((nzmax, 1))
j = np.zeros((nzmax, 1))
s = np.zeros((nzmax, 1))
for m in range(1, nzmax+1):
i[m-1] = int(math.ceil((m+0.1) / 2)) - 1
j[m-1] = int(math.ceil((m-0.1) / 2)) - 1
s[m-1] = -2 * (m % 2) + 1
dx = csr_matrix((s.T[0], (i.T[0], j.T[0])), shape=(size, size))
nzmax = 2 * (size - J.shape[1])
i = np.zeros((nzmax, 1))
j = np.zeros((nzmax, 1))
s = np.zeros((nzmax, 1))
for m in range(1, nzmax+1):
i[m-1, :] = int(math.ceil((m-1+0.1)/2) + J.shape[1] * (m % 2)) - 1
j[m-1, :] = math.ceil((m-0.1)/2) - 1
s[m-1, :] = -2 * (m % 2) + 1
dy = csr_matrix((s.T[0], (i.T[0], j.T[0])), shape=(size, size))
# +0.01是为了避免出现有0被进行log运算的情况, 但对正常值影响可以被忽略
Jtexture1d = np.log(np.reshape(Jtexture.T, (1, Jtexture.size), order="f") + 0.01)
Jtsparse = spdiags(Jtexture1d, 0, size, size)
Jadjusted1d = np.log(np.reshape(Jadjusted.T, (1, Jadjusted.size), order="f").T + 0.01)
#构建eq 8，使用Ax=b的形式
nat = Jtsparse.T.dot(Jadjusted1d) # lnJ(x)
a = np.dot(Jtsparse.T, Jtsparse)
b = dx.T.dot(dx)
c = dy.T.dot(dy)
mat = a + Lambda * (b + c) # lnH(x)
# x = spsolve(a,b) <--> a*x = b
# lnH(x) * beta(x) = lnJ(x) --> beta(x) = spsolve(lnH(x), lnJ(x))
# 使用sparse matrix的spsolve 而不是linalg.solve()
beta1d = spsolve(mat, nat) # eq.8
beta = np.reshape(beta1d, (J.shape[0], J.shape[1]), order="c")
# 模拟素描时通过重复画线来加深阴影, 用pattern Jtexture重复画beta次
T = Jtexture ** beta # eq.9
T = (T - T.min()) / (T.max() - T.min())
img = Image.fromarray(T * 255)
# img.show()
return T