def load_image(fname):
"""Load one of the samples images and show different color channels
Args:
fname: filename (or path) of the image. Will load a default image from
skimage immunohistochemistry if empty.
Returns:
img: loaded image
"""
if fname is not None:
assert os.path.exists(fname), '{} not found'.format(fname)
img = io.imread(fname)
else:
img = data.immunohistochemistry()
return img
def demo_data():
im = data.immunohistochemistry()
l_c = 128
l_r = 180
n_rows = 1
n_cols = im.shape[1] // l_c
init_i, init_j = 0, 0
overlap_h = [5, 25]
big_im = np.empty((n_rows * l_r, n_cols * l_c, 3), dtype=im.dtype)
i = 0
for j in range(n_cols):
sub_im = im[init_i:init_i + l_r, init_j:init_j + l_c]
big_im[i * l_r:(i + 1) * l_r, j * l_c:(j + 1) * l_c] = sub_im
init_j += l_c - overlap_h[1]
init_i += overlap_h[0]
return big_im
def intense(image, in_range='image', out_range='dtype'):
cmap_hema = LinearSegmentedColormap.from_list('mycmap', ['white', 'navy'])
cmap_dab = LinearSegmentedColormap.from_list('mycmap', ['white',
'saddlebrown'])
cmap_eosin = LinearSegmentedColormap.from_list('mycmap', ['darkviolet',
'white'])
ihc_rgb = data.immunohistochemistry()
ihc_hed = rgb2hed(ihc_rgb)
fig, axes = plt.subplots(2, 2, figsize=(7, 6), sharex=True, sharey=True)
ax = axes.ravel()
ax[0].imshow(ihc_rgb)
ax[0].set_title("Original image")
ax[1].imshow(ihc_hed[:, :, 0], cmap=cmap_hema)
ax[1].set_title("Hematoxylin")
ax[2].imshow(ihc_hed[:, :, 1], cmap=cmap_eosin)
ax[2].set_title("Eosin")
ax[3].imshow(ihc_hed[:, :, 2], cmap=cmap_dab)
ax[3].set_title("DAB")
for a in ax.ravel():
a.axis('off')
h = rescale_intensity(ihc_hed[:, :, 0], out_range=(0, 1))
d = rescale_intensity(ihc_hed[:, :, 2], out_range=(0, 1))
zdh = np.dstack((np.zeros_like(h), d, h))
fig = plt.figure()
axis = plt.subplot(1, 1, 1, sharex=ax[0], sharey=ax[0])
axis.imshow(zdh)
axis.set_title("Stain separated image (rescaled)")
axis.axis('off')
plt.show()
def test_immunohistochemistry():
""" Test that "immunohistochemistry" image can be loaded. """
data.immunohistochemistry()
.. [2] Irving, Benjamin. "maskSLIC: regional superpixel generation
with application to local pathology characterisation in medical
images.", 2016, , :arXiv:`1606.09518`
"""
import matplotlib.pyplot as plt
from skimage import data
from skimage import color
from skimage import morphology
from skimage import segmentation
# Input data
img = data.immunohistochemistry()
# Compute a mask
lum = color.rgb2gray(img)
mask = morphology.remove_small_holes(
morphology.remove_small_objects(lum < 0.7, 500), 500)
mask = morphology.opening(mask, morphology.disk(3))
# SLIC result
slic = segmentation.slic(img, n_segments=200, start_label=1)
# maskSLIC result
m_slic = segmentation.slic(img, n_segments=100, mask=mask)
# Display result
The IHC staining expression of the FHL2 protein is here revealed with
Diaminobenzidine (DAB) which gives a brown color.
.. [1] A. C. Ruifrok and D. A. Johnston, "Quantification of histochemical
staining by color deconvolution.," Analytical and quantitative
cytology and histology / the International Academy of Cytology [and]
American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001.
"""
import matplotlib.pyplot as plt
from skimage import data
from skimage.color import rgb2hed
ihc_rgb = data.immunohistochemistry()
ihc_hed = rgb2hed(ihc_rgb)
fig, axes = plt.subplots(2,
2,
figsize=(7, 6),
sharex=True,
sharey=True,
subplot_kw={'adjustable': 'box-forced'})
ax = axes.ravel()
ax[0].imshow(ihc_rgb)
ax[0].set_title("Original image")
ax[1].imshow(ihc_hed[:, :, 0], cmap=plt.cm.gray)
ax[1].set_title("Hematoxylin")
def __init__(self, parent=None):
QMainWindow.__init__(self, parent)
self._main = QWidget()
self.setCentralWidget(self._main)
# Main menu bar
self.menu = self.menuBar()
self.menu_file = self.menu.addMenu("File")
exit = QAction("Exit", self, triggered=qApp.quit)
self.menu_file.addAction(exit)
self.menu_about = self.menu.addMenu("&About")
about = QAction("About Qt", self, shortcut=QKeySequence(QKeySequence.HelpContents),
triggered=qApp.aboutQt)
self.menu_about.addAction(about)
# Create an artificial color close to the original one
self.ihc_rgb = data.immunohistochemistry()
self.ihc_hed = rgb2hed(self.ihc_rgb)
main_layout = QVBoxLayout(self._main)
plot_layout = QHBoxLayout()
button_layout = QHBoxLayout()
label_layout = QHBoxLayout()
self.canvas1 = FigureCanvas(Figure(figsize=(5, 5)))
self.canvas2 = FigureCanvas(Figure(figsize=(5, 5)))
self._ax1 = self.canvas1.figure.subplots()
self._ax2 = self.canvas2.figure.subplots()
self._ax1.imshow(self.ihc_rgb)
plot_layout.addWidget(self.canvas1)
plot_layout.addWidget(self.canvas2)
self.button1 = QPushButton("Hematoxylin")
self.button2 = QPushButton("Eosin")
self.button3 = QPushButton("DAB")
self.button4 = QPushButton("Fluorescence")
self.button1.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Expanding)
self.button2.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Expanding)
self.button3.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Expanding)
self.button4.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Expanding)
self.button1.clicked.connect(self.plot_hematoxylin)
self.button2.clicked.connect(self.plot_eosin)
self.button3.clicked.connect(self.plot_dab)
self.button4.clicked.connect(self.plot_final)
self.label1 = QLabel("Original", alignment=Qt.AlignCenter)
self.label2 = QLabel("", alignment=Qt.AlignCenter)
font = self.label1.font()
font.setPointSize(16)
self.label1.setFont(font)
self.label2.setFont(font)
label_layout.addWidget(self.label1)
label_layout.addWidget(self.label2)
button_layout.addWidget(self.button1)
button_layout.addWidget(self.button2)
button_layout.addWidget(self.button3)
button_layout.addWidget(self.button4)
main_layout.addLayout(label_layout, 2)
main_layout.addLayout(plot_layout, 88)
main_layout.addLayout(button_layout, 10)
# Default image
self.plot_hematoxylin()
'''
Arguments:
color_imgs - Dictionary of Color Images
Returns:
Clusters of colors per image.
Fit KMeans to each class of images (here only 2) to get clusters of colors.
'''
clusters = []
for img in color_imgs.values():
nrow, ncol, depth = img.shape
lst_of_pixels = [
img[irow][icol] for irow in range(nrow) for icol in range(ncol)
]
X = np.array(lst_of_pixels)
sklearn_km = KMeans(n_clusters=3)
result = sklearn_km.fit_predict(X)
clusters.append(sklearn_km.cluster_centers_)
return clusters
if __name__ == '__main__':
ihc_rgb = data.immunohistochemistry() # sample from library
ihc_hed = dye_color_separation(ihc_rgb)
ax = plot_dye_separation(ihc_rgb, ihc_hed)
rescaled_img = rescale_dye_signals(ihc_hed, ax)
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 25 20:15:14 2018
@author: Xiaopeng
"""
import numpy as np
from skimage import io, data, color
img = data.coffee()
cutp = img[100:200, 300:400, :]
#裁剪图片
io.imsave('C:\\Users\\Xiaopeng\\Desktop\\Picture\\coffeeCut.jpg', cutp)
#对图片特定部分赋值
img[40:70, :] = img[350:380, :]
io.imsave('C:\\Users\\Xiaopeng\\Desktop\\Picture\\coffee=.jpg', img)
#对图片进行二值化
picture = data.immunohistochemistry()
img_grey = color.rgb2grey(picture)
print(img_grey.shape)
rows, cols = img_grey.shape
for i in range(rows):
for j in range(cols):
if img_grey[i, j] <= 0.5:
img_grey[i, j] = 0
else:
img_grey[i, j] = 1
io.imshow(img_grey)
io.imsave('C:\\Users\\Xiaopeng\\Desktop\\Picture\\separate.jpg', img_grey)
"""
import matplotlib.pyplot as plt
from skimage import data
from skimage.color import rgb2hed
from matplotlib.colors import LinearSegmentedColormap
# Create an artificial color close to the original one
cmap_hema = LinearSegmentedColormap.from_list('mycmap', ['white', 'navy'])
cmap_dab = LinearSegmentedColormap.from_list('mycmap', ['white',
'saddlebrown'])
cmap_eosin = LinearSegmentedColormap.from_list('mycmap', ['darkviolet',
'white'])
ihc_rgb = data.immunohistochemistry()
ihc_hed = rgb2hed(ihc_rgb)
fig, axes = plt.subplots(2, 2, figsize=(7, 6), sharex=True, sharey=True)
ax = axes.ravel()
ax[0].imshow(ihc_rgb)
ax[0].set_title("Original image")
ax[1].imshow(ihc_hed[:, :, 0], cmap=cmap_hema)
ax[1].set_title("Hematoxylin")
ax[2].imshow(ihc_hed[:, :, 1], cmap=cmap_eosin)
ax[2].set_title("Eosin")
ax[3].imshow(ihc_hed[:, :, 2], cmap=cmap_dab)
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.hubble_deep_field())
axs[0, 1].imshow(data.immunohistochemistry())
axs[0, 2].imshow(data.lily())
axs[1, 0].imshow(data.microaneurysms())
axs[1, 1].imshow(data.moon(), cmap=plt.cm.gray)
axs[1, 2].imshow(data.retina())
axs[2, 0].imshow(data.shepp_logan_phantom(), cmap=plt.cm.gray)
axs[2, 1].imshow(data.skin())
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=-0.3, hspace=0.1)
#io.imshow(filter.gaussian_filter(e, 21))
#print np.mean(e)
plt.plot(exposure.histogram(e)[1], exposure.histogram(e)[0])
# <codecell>
io.imshow(filter.threshold_adaptive(e, 301, offset=0.025))
# <codecell>
io.imshow(A)
# <codecell>
from skimage import data
ihc = data.immunohistochemistry()
ihc_hdx = color.separate_stains(ihc, color.hdx_from_rgb)
ihc_rgb = color.combine_stains(ihc_hdx, color.rgb_from_hdx)
io.imshow(ihc_hdx[:,:,0])
# <codecell>
print [[0.644211, .835*0.716556, 0.266844], [0.092789, .835*0.954111, 0.283111], [0.00001, 0.00001, 0.00001]]
io.imshow(color.separate_stains(A,
np.linalg.inv([[0.644211, 0.716556, 0.266844], [0.092789, 0.954111, 0.283111], [0.00001, 0.00001, 0.00001]]))[:, :, 2])
# <codecell>
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.segmentation import watershed
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
import numpy as np
#%% example of a local image
image = imread("./images/coast_arnat59.jpg")
plt.imshow(image)
plt.imshow(rgb2gray(image), cmap="gray")
#%% example of loading image from the data module
image = data.immunohistochemistry()
plt.imshow(image)
image = rgb2gray(image)
plt.imshow(image, cmap="gray")
#plt.savefig('original.png')
#%% thresholding example (global thresholds): thresholding is used to segment images
#can threshold only gray images
fig, ax = try_all_threshold(image, verbose=False)
plt.show()
#%% apply minimum thresholding algorithm (performs better than other global thresholds algorithms)
thresh_min = threshold_minimum(image)
binary_min = image > thresh_min