最后，我发现我可以使用Numpy模块完成所有这些：def sineimg(img, color='black', linewidth=1.5, linestyle="-"):

'''

frequency = X / random.uniform(10.0, 20.0)

amplitude = randint(35, 50)

phase = random.uniform(1.0, 16.0)

rotation = random.uniform(-pi / 2, pi / 2)

'''

#Random rotation angle

rot = random.randint(-90, 90)

x_ = img.shape[0] / np.cos(np.pi * rot / 180)

X = np.linspace(-1 * x_, x_, 512)

axes = plt.subplot(111)

np.cos(np.pi * rot / 180)

#Random amplitude

amp1 = random.randint(35, 50)

amp2 = random.randint(35, 50)

#Random frequency

frequency1 = X / random.uniform(10.0, 20.0)

frequency2 = X / random.uniform(10.0, 20.0)

#Random offset phase

phase1 = np.pi / random.uniform(1.0, 16.0) + np.pi / 2

phase2 = np.pi / random.uniform(1.0, 16.0) + np.pi / 3

#random distance between line cluster

distance = random.randint(90, 115)

#I need roughly 8 times of them

for i in range(8):

Y1 = amp1 * np.sin(frequency1 + phase1) - 450 + i * distance

Y2 = amp2 * np.sin(frequency2 + phase2) - 420 + i * distance

x1_trans = X * np.cos(np.pi * rot / 180) - Y1* np.sin(np.pi * rot / 180)

y1_trans = X * np.sin(np.pi * rot / 180) + Y1* np.cos(np.pi * rot / 180)

x2_trans = X * np.cos(np.pi * rot / 180) - Y2* np.sin(np.pi * rot / 180)

y2_trans = X * np.sin(np.pi * rot / 180) + Y2* np.cos(np.pi * rot / 180)

#Remove label

axes.set_xticks([])

axes.set_yticks([])

axes.spines['right'].set_color('none')

axes.spines['top'].set_color('none')

axes.spines['bottom'].set_color('none')

axes.spines['left'].set_color('none')

axes.plot(x1_trans, y1_trans, color = color, linewidth=linewidth, linestyle=linestyle)

axes.plot(x2_trans, y2_trans, color = color, linewidth=linewidth, linestyle=linestyle)

plt.imshow(img, zorder=0, extent=[148, -148, -225, 225])

他们：

^{pr2}$

我得到的图像是：