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plot_utils.py
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plot_utils.py
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import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import imsave
from scipy.misc import imresize
class Plot_Reproduce_Performance():
def __init__(self, DIR, n_img_x=8, n_img_y=8, img_w=28, img_h=28, resize_factor=1.0):
self.DIR = DIR
assert n_img_x > 0 and n_img_y > 0
self.n_img_x = n_img_x
self.n_img_y = n_img_y
self.n_tot_imgs = n_img_x * n_img_y
assert img_w > 0 and img_h > 0
self.img_w = img_w
self.img_h = img_h
assert resize_factor > 0
self.resize_factor = resize_factor
def save_images(self, images, name='result.jpg'):
images = images.reshape(self.n_img_x*self.n_img_y, self.img_h, self.img_w)
imsave(self.DIR + "/"+name, self._merge(images, [self.n_img_y, self.n_img_x]))
def _merge(self, images, size):
h, w = images.shape[1], images.shape[2]
h_ = int(h * self.resize_factor)
w_ = int(w * self.resize_factor)
img = np.zeros((h_ * size[0], w_ * size[1]))
for idx, image in enumerate(images):
i = int(idx % size[1])
j = int(idx / size[1])
image_ = imresize(image, size=(w_,h_), interp='bicubic')
img[j*h_:j*h_+h_, i*w_:i*w_+w_] = image_.T
return img
class Plot_Manifold_Learning_Result():
def __init__(self, DIR, n_img_x=20, n_img_y=20, img_w=28, img_h=28, resize_factor=1.0, z_range=4):
self.DIR = DIR
assert n_img_x > 0 and n_img_y > 0
self.n_img_x = n_img_x
self.n_img_y = n_img_y
self.n_tot_imgs = n_img_x * n_img_y
assert img_w > 0 and img_h > 0
self.img_w = img_w
self.img_h = img_h
assert resize_factor > 0
self.resize_factor = resize_factor
assert z_range > 0
self.z_range = z_range
self._set_latent_vectors()
def _set_latent_vectors(self):
# z1 = np.linspace(-self.z_range, self.z_range, self.n_img_y)
# z2 = np.linspace(-self.z_range, self.z_range, self.n_img_x)
#
# z = np.array(np.meshgrid(z1, z2))
# z = z.reshape([-1, 2])
# borrowed from https://github.com/fastforwardlabs/vae-tf/blob/master/plot.py
z = np.rollaxis(np.mgrid[self.z_range:-self.z_range:self.n_img_y * 1j, self.z_range:-self.z_range:self.n_img_x * 1j], 0, 3)
# z1 = np.rollaxis(np.mgrid[1:-1:self.n_img_y * 1j, 1:-1:self.n_img_x * 1j], 0, 3)
# z = z1**2
# z[z1<0] *= -1
#
# z = z*self.z_range
self.z = z.reshape([-1, 2])
def save_images(self, images, name='result.jpg'):
images = images.reshape(self.n_img_x*self.n_img_y, self.img_h, self.img_w)
imsave(self.DIR + "/"+name, self._merge(images, [self.n_img_y, self.n_img_x]))
def _merge(self, images, size):
h, w = images.shape[1], images.shape[2]
h_ = int(h * self.resize_factor )
w_ = int(w * self.resize_factor)
img = np.zeros((h_ * size[0], w_ * size[1]))
for idx, image in enumerate(images):
i = int(idx % size[1])
j = int(idx / size[1])
image_ = imresize(image, size=(w_, h_), interp='bicubic')
img[j * h_:j * h_ + h_, i * w_:i * w_ + w_] = image_.T
return img
# borrowed from https://github.com/ykwon0407/variational_autoencoder/blob/master/variational_bayes.ipynb
def save_scattered_image(self, z, id, name='scattered_image.jpg', N=10):
plt.figure(figsize=(8, 6))
plt.scatter(z[:, 0], z[:, 1], c=np.argmax(id, 1), marker='o', edgecolor='none', cmap=discrete_cmap(N, 'jet'))
plt.colorbar(ticks=range(N))
axes = plt.gca()
#axes.set_xlim([-4.5, 4.5])
#axes.set_ylim([-4.5, 4.5])
plt.grid(True)
plt.savefig(self.DIR + "/" + name)
# borrowed from https://gist.github.com/jakevdp/91077b0cae40f8f8244a
def discrete_cmap(N, base_cmap=None):
"""Create an N-bin discrete colormap from the specified input map"""
# Note that if base_cmap is a string or None, you can simply do
# return plt.cm.get_cmap(base_cmap, N)
# The following works for string, None, or a colormap instance:
base = plt.cm.get_cmap(base_cmap)
color_list = base(np.linspace(0, 1, N))
cmap_name = base.name + str(N)
return base.from_list(cmap_name, color_list, N)