def test_for_images(show_hist=False):
IMG_PATH = abspath(join(dirname(__file__), pardir, 'images'))
print "3. TEST KODOWANIA OBRAZÓW\n"
images = listdir(IMG_PATH)
for idx, image in enumerate(images, start=1):
print " 3.%d. %s\n" % (idx, image)
data = read_image(join(IMG_PATH, image))
diffed = differential_encoding(data)
if show_hist:
show_histogram(diffed, image)
scaled = scale_to_positive(diffed)
encode_and_print_stats(scaled, 8)
print "\n=======================================================================\n"
def test_for_images(show_hist=False):
IMG_PATH = abspath(join(dirname(__file__), pardir, 'images'))
print "3. TEST KODOWANIA OBRAZÓW\n"
images = listdir(IMG_PATH)
for idx, image in enumerate(images, start=1):
print " 3.%d. %s\n" % (idx, image)
data = read_image(join(IMG_PATH, image))
diffed = differential_encoding(data)
if show_hist:
show_histogram(diffed, image)
scaled = scale_to_positive(diffed)
encode_and_print_stats(scaled, 8)
print "\n=======================================================================\n"
def update_histogram(figure):
# Retrieve the image stored inside the figure
enc_str = figure["layout"]["images"][0]["source"].split(";base64,")[-1]
# Creates the PIL Image object from the b64 png encoding
im_pil = drc.b64_to_pil(string=enc_str)
return utils.show_histogram(im_pil)
def update_histogram(figure):
# Retrieve the image stored inside the figure
enc_str = figure['layout']['images'][0]['source'].split(';base64,')[-1]
# Creates the PIL Image object from the b64 png encoding
im_pil = drc.b64_to_pil(string=enc_str)
return show_histogram(im_pil)
def test_for_sample_data(show_hist=False):
print "2. TEST KODOWANIA SZTUCZNYCH CIĄGÓW DANYCH\n"
numpy.random.seed(37)
print " 2.1. Rozkład jednostajny\n"
for i, size_kb in enumerate([1, 128, 1024]):
print " %s. %dKB" % (chr(ord('a') + i), size_kb)
uniform_samples = normalize_to_byte(
numpy.random.uniform(0., 256., size_kb * 1024))
if show_hist:
show_histogram(uniform_samples)
encode_and_print_stats(uniform_samples, 8)
print " 2.2. Rozkład normalny\n"
for i, size_kb in enumerate([1, 128, 1024]):
print " %s. %dKB" % (chr(ord('a') + i), size_kb)
normal_samples = normalize_to_byte(numpy.random.normal(
128., 16., 1024))
if show_hist:
show_histogram(normal_samples)
encode_and_print_stats(normal_samples, 8)
print " 2.3. Rozkład Laplace'a\n"
for i, size_kb in enumerate([1, 128, 1024]):
print " %s. %dKB" % (chr(ord('a') + i), size_kb)
laplace_samples = normalize_to_byte(
numpy.random.laplace(128., 16., 1024))
if show_hist:
show_histogram(laplace_samples)
encode_and_print_stats(laplace_samples, 8)
print "\n=======================================================================\n"
def test_for_sample_data(show_hist=False):
print "2. TEST KODOWANIA SZTUCZNYCH CIĄGÓW DANYCH\n"
numpy.random.seed(37)
print " 2.1. Rozkład jednostajny\n"
for i, size_kb in enumerate([1, 128, 1024]):
print " %s. %dKB" % (chr(ord('a') + i), size_kb)
uniform_samples = normalize_to_byte(numpy.random.uniform(0., 256., size_kb*1024))
if show_hist:
show_histogram(uniform_samples)
encode_and_print_stats(uniform_samples, 8)
print " 2.2. Rozkład normalny\n"
for i, size_kb in enumerate([1, 128, 1024]):
print " %s. %dKB" % (chr(ord('a') + i), size_kb)
normal_samples = normalize_to_byte(numpy.random.normal(128., 16., 1024))
if show_hist:
show_histogram(normal_samples)
encode_and_print_stats(normal_samples, 8)
print " 2.3. Rozkład Laplace'a\n"
for i, size_kb in enumerate([1, 128, 1024]):
print " %s. %dKB" % (chr(ord('a') + i), size_kb)
laplace_samples = normalize_to_byte(numpy.random.laplace(128., 16., 1024))
if show_hist:
show_histogram(laplace_samples)
encode_and_print_stats(laplace_samples, 8)
print "\n=======================================================================\n"
from matplotlib import pyplot as plt
from skimage import exposure
from utils import show_image, show_histogram
chest_xray_img = plt.imread('images/chest_xray.png')
show_image(chest_xray_img, "Chest x-ray")
show_histogram(chest_xray_img.ravel())
chest_xray_img_eq = exposure.equalize_hist(chest_xray_img)
show_image(chest_xray_img_eq, 'Resulting image')