def _generate_wavelet_toy_image_data(image_width, num_samples,
wavelet_num_levels):
"""Generates wavelet data for testFittingImageDataIsCorrect().
Constructs a "mean" image in the YUV wavelet domain (parametrized by
`image_width`, and `wavelet_num_levels`) and draws `num_samples` samples
from a normal distribution using that mean, and returns RGB images
corresponding to those samples and to the mean (computed in the
specified latent space) of those samples.
Args:
image_width: The width and height in pixels of the images being produced.
num_samples: The number of samples to generate.
wavelet_num_levels: The number of levels in the wavelet decompositions of
the generated images.
Returns:
A tuple of (samples, reference, color_space, representation), where
samples = A set of sampled images of size
(`num_samples`, `image_width`, `image_width`, 3)
reference = The empirical mean of `samples` (computed in YUV Wavelet space
but returned as an RGB image) of size (`image_width`, `image_width`, 3).
color_space = 'YUV'
representation = 'CDF9/7'
"""
color_space = 'YUV'
representation = 'CDF9/7'
samples = []
reference = []
for level in range(wavelet_num_levels):
samples.append([])
reference.append([])
w = image_width // 2**(level + 1)
scaling = 2**level
for _ in range(3):
# Construct the ground-truth pixel band mean.
mu = scaling * np.random.uniform(size=(3, w, w))
# Draw samples from the ground-truth mean.
band_samples = np.random.normal(
loc=np.tile(mu[np.newaxis], [num_samples, 1, 1, 1]))
# Take the empirical mean of the samples as a reference.
band_reference = np.mean(band_samples, 0)
samples[-1].append(np.reshape(band_samples, [-1, w, w]))
reference[-1].append(band_reference)
# Handle the residual band.
mu = scaling * np.random.uniform(size=(3, w, w))
band_samples = np.random.normal(
loc=np.tile(mu[np.newaxis], [num_samples, 1, 1, 1]))
band_reference = np.mean(band_samples, 0)
samples.append(np.reshape(band_samples, [-1, w, w]))
reference.append(band_reference)
# Collapse and reshape wavelets to be ({_,} width, height, 3).
samples = wavelet.collapse(samples, representation)
reference = wavelet.collapse(reference, representation)
samples = tf.transpose(
tf.reshape(samples, [num_samples, 3, image_width, image_width]),
[0, 2, 3, 1])
reference = tf.transpose(reference, [1, 2, 0])
# Convert into RGB space.
samples = util.syuv_to_rgb(samples)
reference = util.syuv_to_rgb(reference)
with tf.Session() as sess:
samples, reference = sess.run((samples, reference))
return samples, reference, color_space, representation
def testRgbToSyuvRoundTrip(self):
"""Tests that syuv_to_rgb(rgb_to_syuv(x)) == x."""
rgb = np.float32(np.random.uniform(size=(32, 32, 3)))
syuv = util.rgb_to_syuv(rgb)
rgb_recon = util.syuv_to_rgb(syuv)
self.assertAllClose(rgb, rgb_recon)
