Python tile_raster_images 예제들

예제 #1

파일: script_2.py 프로젝트: vikkamath/denoising_autoencoder

def plot_Salah_DAE_samples(samples, image_output_file):

    import yann_dauphin_utils
    import PIL

    #image_output_file = "/u/alaingui/Documents/tmp/Salah_DAE_2013_02_06/metropolis_hastings_langevin_grad_E/1360188911/digits.png"
    #samples_pkl_file = "/u/alaingui/Documents/tmp/Salah_DAE_2013_02_06/metropolis_hastings_langevin_grad_E/1360188911/samples.pkl"
    #samples = cPickle.load(open(samples_pkl_file,"r"))

    assert len(samples.shape) == 2
    N = samples.shape[0]
    n_inputs = samples.shape[1]

    tile_j = int(np.ceil(np.sqrt(N)))
    tile_i = int(np.ceil(float(N) / tile_j))

    img_j = int(np.ceil(np.sqrt(n_inputs)))
    img_i = int(np.ceil(float(n_inputs) / img_j))

    from PIL import Image
    image = Image.fromarray(
        yann_dauphin_utils.tile_raster_images(X=samples,
                                              img_shape=(img_j, img_i),
                                              tile_shape=(tile_j, tile_i),
                                              tile_spacing=(1, 1)))

    image.save(image_output_file)

예제 #2

파일: script_2.py 프로젝트: gyom/denoising_autoencoder

def plot_Salah_DAE_samples(samples, image_output_file):

    import yann_dauphin_utils
    import PIL

    # image_output_file = "/u/alaingui/Documents/tmp/Salah_DAE_2013_02_06/metropolis_hastings_langevin_grad_E/1360188911/digits.png"
    # samples_pkl_file = "/u/alaingui/Documents/tmp/Salah_DAE_2013_02_06/metropolis_hastings_langevin_grad_E/1360188911/samples.pkl"
    # samples = cPickle.load(open(samples_pkl_file,"r"))

    assert len(samples.shape) == 2
    N = samples.shape[0]
    n_inputs = samples.shape[1]

    tile_j = int(np.ceil(np.sqrt(N)))
    tile_i = int(np.ceil(float(N) / tile_j))

    img_j = int(np.ceil(np.sqrt(n_inputs)))
    img_i = int(np.ceil(float(n_inputs) / img_j))

    from PIL import Image

    image = Image.fromarray(
        yann_dauphin_utils.tile_raster_images(
            X=samples, img_shape=(img_j, img_i), tile_shape=(tile_j, tile_i), tile_spacing=(1, 1)
        )
    )

    image.save(image_output_file)

예제 #3

파일: plot_Salah_DAE_samples.py 프로젝트: vikkamath/denoising_autoencoder

import numpy as np
import cPickle
import yann_dauphin_utils
import PIL

image_output_file = "/u/alaingui/Documents/tmp/Salah_DAE_2013_02_06/metropolis_hastings_langevin_grad_E/1360188911/digits.png"

samples_pkl_file = "/u/alaingui/Documents/tmp/Salah_DAE_2013_02_06/metropolis_hastings_langevin_grad_E/1360188911/samples.pkl"
samples = cPickle.load(open(samples_pkl_file, "r"))

assert len(samples.shape) == 2
N = samples.shape[0]
n_inputs = samples.shape[1]

tile_j = int(np.ceil(np.sqrt(N)))
tile_i = int(np.ceil(float(N) / tile_j))

img_j = int(np.ceil(np.sqrt(n_inputs)))
img_i = int(np.ceil(float(n_inputs) / img_j))

from PIL import Image
image = Image.fromarray(
    yann_dauphin_utils.tile_raster_images(X=samples,
                                          img_shape=(img_j, img_i),
                                          tile_shape=(tile_j, tile_i),
                                          tile_spacing=(1, 1)))

image.save(image_output_file)

예제 #4

파일: plot_Salah_DAE_samples.py 프로젝트: gyom/denoising_autoencoder

import numpy as np
import cPickle
import yann_dauphin_utils
import PIL

image_output_file = "/u/alaingui/Documents/tmp/Salah_DAE_2013_02_06/metropolis_hastings_langevin_grad_E/1360188911/digits.png"

samples_pkl_file = "/u/alaingui/Documents/tmp/Salah_DAE_2013_02_06/metropolis_hastings_langevin_grad_E/1360188911/samples.pkl"
samples = cPickle.load(open(samples_pkl_file,"r"))

assert len(samples.shape) == 2
N = samples.shape[0]
n_inputs = samples.shape[1]


tile_j = int(np.ceil(np.sqrt(N)))
tile_i = int(np.ceil(float(N) / tile_j))

img_j = int(np.ceil(np.sqrt(n_inputs)))
img_i = int(np.ceil(float(n_inputs) / img_j))

from PIL import Image
image = Image.fromarray(yann_dauphin_utils.tile_raster_images(
         X = samples,
         img_shape = (img_j,img_i), tile_shape = (tile_j, tile_i),
         tile_spacing=(1,1)))

image.save(image_output_file)