def on_press(event):
if event.inaxes is None:
print("none")
return
fig = event.inaxes.figure
ax = fig.add_subplot(122)
img_gray = data.clock()
ax.imshow(img_gray, cmap="gray")
print(event)
ax1.scatter(event.xdata, event.ydata)
py.axis("off")
fig.canvas.draw()
def test_clock():
""" Test that "clock" image can be loaded. """
data.clock()
fig = event.inaxes.figure
ax = fig.add_subplot(122)
img_gray = data.clock()
ax.imshow(img_gray, cmap="gray")
print(event)
ax1.scatter(event.xdata, event.ydata)
py.axis("off")
fig.canvas.draw()
def button_press(event):
print('button is pressed!')
def draw_button():
global button # must global
point = py.axes([0.3, 0.03, 0.1, 0.03])
button = Button(point, "click me")
button.on_clicked(button_press)
if __name__ == "__main__":
img = data.clock()
fig = py.figure()
draw_button()
fig.canvas.mpl_connect("button_press_event", on_press)
ax1 = fig.add_subplot(121)
ax1.imshow(img)
py.axis("off")
py.show()
"""
Test adding list of images
"""
import numpy as np
from skimage import data
import napari
list_of_images = [
data.astronaut(),
data.clock(),
data.coins(),
data.binary_blobs(length=50, n_dim=3)
]
with napari.gui_qt():
axis_labels = ['t', 'z', 'y', 'x']
viewer = napari.view_image(np.random.random((10, 6, 30, 40)),
axis_labels=axis_labels)
from skimage.color import rgb2gray, gray2rgb
from skimage.segmentation import mark_boundaries
import time
import matplotlib.image as mpimg
exec(open('/Users/Salim_Andre/Desktop/IMA/PRAT/code/pd_segmentation_0.py').read())
exec(open('/Users/Salim_Andre/Desktop/IMA/PRAT/code/tree.py').read())
### DATASET
PATH_img = '/Users/Salim_Andre/Desktop/IMA/PRAT/' # path to my own images
swans=mpimg.imread(PATH_img+'swans.jpg');
baby=mpimg.imread(PATH_img+'baby.jpg');
img_set = [data.astronaut(), data.camera(), data.coins(), data.checkerboard(), data.chelsea(), \
data.coffee(), data.clock(), data.hubble_deep_field(), data.horse(), data.immunohistochemistry(), \
data.moon(), data.page(), data.rocket(), swans, baby]
### IMAGE
I=img_as_float(img_set[0]);
### PARAMETERS FOR 0-HOMOLOGY GROUPS
mode='customized';
n_superpixels=10000;
RV_epsilon=30;
gauss_sigma=0.5;
list_events=[800];
n_pxl_min_ = 30;
density_excl=0.0;
from skimage import data
from skimage.color import rgb2gray
from skimage import img_as_ubyte,img_as_float
gray_images = {
"cat":rgb2gray(img_as_float(data.chelsea())),
"astro":rgb2gray(img_as_float(data.astronaut())),
"camera":data.camera(),
"coin": data.coins(),
"clock":data.clock(),
"blobs":data.binary_blobs(),
"coffee":rgb2gray(img_as_float(data.coffee()))
}
from numpy.linalg import svd
def compress_svd(image,k):
U,s,V = svd(image,full_matrices=False)
reconst_matrix = np.dot(U[:,:k],np.dot(np.diag(s[:k]),V[:k,:]))
return reconst_matrix,s
def compress_show_gray_images(img_name,k):
image=gray_images[img_name]
original_shape = image.shape
reconst_img,s = compress_svd(image,k)
fig,axes = plt.subplots(1,2,figsize=(8,5))
axes[0].plot(s)
compression_ratio =100.0* (k*(original_shape[0] + original_shape[1])+k)/(original_shape[0]*original_shape[1])
axes[1].set_title("compression ratio={:.2f}".format(compression_ratio)+"%")
axes[1].imshow(reconst_img,cmap='gray')
axes[1].axis('off')
fig.tight_layout()
image = caller()
plt.figure()
plt.title(name)
if image.ndim == 2:
plt.imshow(image, cmap=plt.cm.gray)
else:
plt.imshow(image)
plt.show()
############################################################################
# Thumbnail image for the gallery
# sphinx_gallery_thumbnail_number = -1
fig, axs = plt.subplots(nrows=3, ncols=3)
for ax in axs.flat:
ax.axis("off")
axs[0, 0].imshow(data.astronaut())
axs[0, 1].imshow(data.binary_blobs(), cmap=plt.cm.gray)
axs[0, 2].imshow(data.brick(), cmap=plt.cm.gray)
axs[1, 0].imshow(data.colorwheel())
axs[1, 1].imshow(data.camera(), cmap=plt.cm.gray)
axs[1, 2].imshow(data.cat())
axs[2, 0].imshow(data.checkerboard(), cmap=plt.cm.gray)
axs[2, 1].imshow(data.clock(), cmap=plt.cm.gray)
further_img = np.full((300, 300), 255)
for xpos in [100, 150, 200]:
further_img[150 - 10:150 + 10, xpos - 10:xpos + 10] = 0
axs[2, 2].imshow(further_img, cmap=plt.cm.gray)
plt.subplots_adjust(wspace=0.1, hspace=0.1)
plotComparison(space, removeNoise(space), 'Original',
'Denoised Space Image After Notch Filter')
plotComparison(space, displayNoise(space, removeNoise(space)), 'Original',
'Noise Only')
# In[75]:
#========================Images for testing================================================================
space = io.imread('space_noise_img.png')
denoised_space = removeNoise(space)
checkboard = data.checkerboard()
camerman = data.camera()
grass = data.grass()
gravel = data.gravel()
clock = data.clock()
coins = data.coins()
# In[76]:
#========================resize function================================================================
def resizeToAnyDimension(img, row, col):
newImg = np.array(Image.fromarray(img).resize((row, col), Image.ANTIALIAS))
return newImg
# In[77]:
coins_64 = resizeToAnyDimension(coins, 64, 64)
camerman_64 = resizeToAnyDimension(camerman, 64, 64)
