コード例 #1
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def test_bounding_values():
    image = img_as_float(data.page())
    template = np.zeros((3, 3))
    template[1, 1] = 1
    result = match_template(img_as_float(data.page()), template)
    print(result.max())
    assert result.max() < 1 + 1e-7
    assert result.min() > -1 - 1e-7
コード例 #2
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def test_bounding_values():
    image = img_as_float(data.page())
    template = np.zeros((3, 3))
    template[1, 1] = 1
    result = match_template(img_as_float(data.page()), template)
    print(result.max())
    assert result.max() < 1 + 1e-7
    assert result.min() > -1 - 1e-7
コード例 #3
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def main():

    image_rgb = cv2.imread("1-039 (2).jpg")
    image = cv2.cvtColor(image_rgb, cv2.COLOR_BGR2GRAY)
    '''
    rows = image.shape[0]
    cols = image.shape[1]
    print("length: ", len(image))
    print("Dimensions: ", image.shape)
    print("rows,cols: ", rows, cols)
    '''
    text = data.page()
    print(text.shape)
    image_show(text)
    fig, ax = plt.subplots(1, 1)
    ax.hist(text.ravel(), bins=256, range=[0, 255])
    ax.set_xlim(0, 256)
    ####Basic threshold
    #segmented = text < 60
    #image_show(segmented)
    ####Thresholding methods
    #text_threshold = filters.threshold_sauvola(text)
    #image_show(text < text_threshold)

    plt.show()
コード例 #4
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def show_threshold():
    fig = plt.figure(figsize=(16, 9))
    ax1, ax2 = fig.subplots(1, 2)

    img = data.page()
    ax1.axis('off')
    ax1.imshow(img, cmap='gray')
    ax1.set_title("raw image")

    y = im2bw(img, th=128)
    ax2.axis('off')
    ax2.imshow(y, cmap='gray')
    ax2.set_title("binary image")
    plt.show()
コード例 #5
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def show_image(image, title):
    plt.imshow(image, cmap="gray")
    plt.axis("off")
    plt.title(title)
    plt.show()


def histogram(image):
    plt.hist(image, bins=256)
    plt.show()


#  show  image original
camera = data.page()
# show_image(camera, "Camera Original")
"""
show  thresholding otsu global
"""
thresh_global = threshold_otsu(camera)
camera_binary_global = camera > thresh_global
# show_image(camera_binary_global, "Camera Global")

# show histogram with line in thresh otsu
# plt.hist(camera.ravel(), bins=256)
# plt.axvline(thresh_global, color='r')
# plt.title('Histogram camera')
# plt.show()
"""
show  difference thresholding otsu local and global
コード例 #6
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ax[0].set_title('Original')
ax[0].axis('off')

ax[1].hist(image.ravel(), bins=256)
ax[1].set_title('Histogram')
ax[1].axvline(thresh, color='r')

ax[2].imshow(binary, cmap=plt.cm.gray)
ax[2].set_title('Thresholded')
ax[2].axis('off')

plt.show()

######################################################################
# If you are not familiar with the details of the different algorithms and the
# underlying assumptions, it is often difficult to know which algorithm will give
# the best results. Therefore, Scikit-image includes a function to evaluate
# thresholding algorithms provided by the library. At a glance, you can select
# the best algorithm for you data without a deep understanding of their
# mechanisms.
#

from skimage.filters import try_all_threshold

img = data.page()

# Here, we specify a radius for local thresholding algorithms.
# If it is not specified, only global algorithms are called.
fig, ax = try_all_threshold(img, figsize=(10, 8), verbose=False)
plt.show()
コード例 #7
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the best algorithm for you data without a deep understanding of their
mechanisms.

.. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29

.. seealso::
   Presentation on
   :ref:`sphx_glr_auto_examples_xx_applications_plot_rank_filters.py`.
"""
import matplotlib
import matplotlib.pyplot as plt

from skimage import data
from skimage.filters import try_all_threshold

img = data.page()

fig, ax = try_all_threshold(img, figsize=(10, 8), verbose=False)
plt.show()

######################################################################
# How to apply a threshold?
# =========================
#
# Now, we illustrate how to apply one of these thresholding algorithms.
# This example uses the mean value of pixel intensities. It is a simple
# and naive threshold value, which is sometimes used as a guess value.
#

from skimage.filters import threshold_mean
コード例 #8
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#
# __A pairing between spatial information (position) and some other kind of information (value).__
# $$ \vec{x} \rightarrow \vec{f} $$
#
# We are used to seeing images in a grid format where the position indicates the row and column in the grid and the intensity (absorption, reflection, tip deflection, etc) is shown as a different color. We take an example here of text on a page.

# In[9]:

from skimage.io import imread
import matplotlib.pyplot as plt
import numpy as np
from skimage.data import page
import pandas as pd
from skimage.filters import gaussian, median, threshold_triangle

page_image = page()
just_text = median(page_image, np.ones(
    (2, 2))) - 255 * gaussian(page_image, 20.0)

plt.imshow(page_image, cmap="bone")

# In[10]:

xx, yy = np.meshgrid(np.arange(page_image.shape[1]),
                     np.arange(page_image.shape[0]))
page_table = pd.DataFrame(
    dict(
        x=xx.ravel(),
        y=yy.ravel(),
        intensity=page_image.ravel(),
        is_text=just_text.ravel() > 0,
コード例 #9
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import matplotlib
import matplotlib.pyplot as plt

from skimage import io
from skimage.data import page
from skimage.filters import (threshold_otsu, threshold_niblack,
                             threshold_sauvola)
from skimage.filters import threshold_mean

matplotlib.rcParams['font.size'] = 9

io.use_plugin('matplotlib', 'imread')
image = page()
image_base = "images/crop/v5/"
image_file = "image_3_bin_400x400_crop.jpg"
image = io.imread(image_base + image_file)

binary_global = image > threshold_otsu(image)
#binary_global = image > threshold_mean(image)

window_size = 63
thresh_niblack = threshold_niblack(image, window_size=window_size, k=0.8)
thresh_sauvola = threshold_sauvola(image, window_size=window_size)

binary_niblack = image > thresh_niblack
binary_sauvola = image > thresh_sauvola

plt.figure(figsize=(8, 7))
plt.subplot(2, 2, 1)
plt.imshow(image, cmap=plt.cm.gray)
plt.title('Original')
コード例 #10
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ファイル: test.py プロジェクト: hellodmp/imageProcess
ax[0].axis('off')

ax[1].hist(image.ravel(), bins=256)
ax[1].set_title('Histogram')
ax[1].axvline(thresh, color='r')

ax[2].imshow(binary, cmap=plt.cm.gray)
ax[2].set_title('Thresholded')
ax[2].axis('off')

plt.show()


######################################################################
# If you are not familiar with the details of the different algorithms and the
# underlying assumptions, it is often difficult to know which algorithm will give
# the best results. Therefore, Scikit-image includes a function to evaluate
# thresholding algorithms provided by the library. At a glance, you can select
# the best algorithm for you data without a deep understanding of their
# mechanisms.
#

from skimage.filters import try_all_threshold

img = data.page()

# Here, we specify a radius for local thresholding algorithms.
# If it is not specified, only global algorithms are called.
fig, ax = try_all_threshold(img, radius=20,
                            figsize=(10, 8), verbose=False)
plt.show()
コード例 #11
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import numpy as np
import cv2 as cv2
from skimage import data, io
import matplotlib.pyplot as plt
import matplotlib
from sklearn import preprocessing
from skimage.transform import resize
import random

im = data.page()
if len(im.shape) == 3:  # converting to gray scale if image is RGB
    im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)  # h 400 * w 600

fig = plt.figure(figsize=(30, 30))
rows = 2
columns = 2
fig.add_subplot(rows, columns, 1)
plt.imshow(im, cmap="gray")

out_thresholded = cv2.adaptiveThreshold(
    src=im,
    maxValue=255,
    adaptiveMethod=cv2.ADAPTIVE_THRESH_MEAN_C,
    thresholdType=cv2.THRESH_BINARY,
    blockSize=11,
    C=2,
)
fig.add_subplot(rows, columns, 2)
plt.imshow(out_thresholded, cmap="gray")
plt.show()
コード例 #12
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the best algorithm for you data without a deep understanding of their
mechanisms.

.. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29

.. seealso::
   Presentation on
   :ref:`sphx_glr_auto_examples_xx_applications_plot_rank_filters.py`.
"""
import matplotlib
import matplotlib.pyplot as plt

from skimage import data
from skimage.filters import try_all_threshold

img = data.page()

# Here, we specify a radius for local thresholding algorithms.
# If it is not specified, only global algorithms are called.
fig, ax = try_all_threshold(img, radius=20,
                            figsize=(10, 8), verbose=False)
plt.show()

######################################################################
# How to apply a threshold?
# =========================
#
# Now, we illustrate how to apply one of these thresholding algorithms.
# This example uses the mean value of pixel intensities. It is a simple
# and naive threshold value, which is sometimes used as a guess value.
#
コード例 #13
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import matplotlib.pyplot as plt
from skimage.morphology import diameter_closing
from skimage import data
from skimage.morphology import closing
from skimage.morphology import square

datasets = {
    'retina': {
        'image': data.microaneurysms(),
        'figsize': (15, 9),
        'diameter': 10,
        'vis_factor': 3,
        'title': 'Detection of microaneurysm'
    },
    'page': {
        'image': data.page(),
        'figsize': (15, 7),
        'diameter': 23,
        'vis_factor': 1,
        'title': 'Text detection'
    }
}

for dataset in datasets.values():
    # image with printed letters
    image = dataset['image']
    figsize = dataset['figsize']
    diameter = dataset['diameter']

    fig, ax = plt.subplots(2, 3, figsize=figsize)
    # Original image
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;
entropy_thresh_=1.1;
コード例 #15
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.. [1] http://en.wikipedia.org/wiki/Otsu's_method

"""
import matplotlib
import matplotlib.pyplot as plt

from skimage import data
from skimage.morphology import disk
from skimage.filters import threshold_otsu, rank
from skimage.util import img_as_ubyte
from skimage import io  # Load image file

matplotlib.rcParams['font.size'] = 9

img = img_as_ubyte(data.page())
image = io.imread('dataset/ID_0ed10ec08.dcm')

radius = 15
selem = disk(radius)

local_otsu = rank.otsu(img, selem)
threshold_global_otsu = threshold_otsu(img)
global_otsu = img >= threshold_global_otsu

fig, ax = plt.subplots(2, 2, figsize=(8, 5))
ax1, ax2, ax3, ax4 = ax.ravel()

fig.colorbar(ax1.imshow(img, cmap=plt.cm.gray),
             ax=ax1,
             orientation='horizontal')
コード例 #16
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"""

import numpy as np
import matplotlib.pyplot as plt
from skimage.morphology import diameter_closing
from skimage import data
from skimage.morphology import closing
from skimage.morphology import square

datasets = {
    'retina': {'image': data.microaneurysms(),
               'figsize': (15, 9),
               'diameter': 10,
               'vis_factor': 3,
               'title': 'Detection of microaneurysm'},
    'page': {'image': data.page(),
             'figsize': (15, 7),
             'diameter': 23,
             'vis_factor': 1,
             'title': 'Text detection'}
}

for dataset in datasets.values():
    # image with printed letters
    image = dataset['image']
    figsize = dataset['figsize']
    diameter = dataset['diameter']

    fig, ax = plt.subplots(2, 3, figsize=figsize)
    # Original image
    ax[0, 0].imshow(image, cmap='gray', aspect='equal',
コード例 #17
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# -*- coding: utf-8 -*-
"""
Created on Thu Jun 18 11:22:58 2015

@author: George
"""

import matplotlib.pyplot as plt

from skimage import data
from skimage.filters import threshold_otsu, threshold_adaptive


image = data.page()

global_thresh = threshold_otsu(image)
binary_global = image > global_thresh

block_size = 40
binary_adaptive = threshold_adaptive(image, block_size, offset=10)

fig, axes = plt.subplots(nrows=3, figsize=(7, 8))
ax0, ax1, ax2 = axes
plt.gray()

ax0.imshow(image)
ax0.set_title('Image')

ax1.imshow(binary_global)
ax1.set_title('Global thresholding')
コード例 #18
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from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from pylab import *
from skimage import data

# Binarisation
started = data.page()
plt.imshow(started, cmap=cm.gray)
plt.show()
imageArray = np.zeros(shape(started)).astype('uint8')
threshold = 90

imageArray[started < threshold] = 0
imageArray[started >= threshold] = 255
plt.imshow(imageArray, cmap=cm.gray)
plt.show()
コード例 #19
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from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from pylab import *

from skimage import data

scanned = data.page()

plt.imshow(scanned, cmap=cm.gray)

thres = np.zeros(shape(scanned)).astype('uint8')
threshold = 90

thres[scanned < threshold] = 0
thres[scanned >= threshold] = 255

plt.imshow(thres, cmap=cm.gray)

plt.show()
コード例 #20
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       binarization," Pattern Recognition 33(2),
       pp. 225-236, 2000.
       :DOI:`10.1016/S0031-3203(99)00055-2`
"""
import matplotlib
import matplotlib.pyplot as plt

from skimage.data import page
from skimage.filters import (threshold_otsu, threshold_niblack,
                             threshold_sauvola)


matplotlib.rcParams['font.size'] = 9


image = page()
binary_global = image > threshold_otsu(image)

window_size = 25
thresh_niblack = threshold_niblack(image, window_size=window_size, k=0.8)
thresh_sauvola = threshold_sauvola(image, window_size=window_size)

binary_niblack = image > thresh_niblack
binary_sauvola = image > thresh_sauvola

plt.figure(figsize=(8, 7))
plt.subplot(2, 2, 1)
plt.imshow(image, cmap=plt.cm.gray)
plt.title('Original')
plt.axis('off')
コード例 #21
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import numpy as np
import matplotlib.pyplot as plt
import skimage.data as data
import skimage.segmentation as seg
import skimage.filters as filters
import skimage.draw as draw
import skimage.color as color


def image_show(image, nrows=1, ncols=1, cmap='gray'):
    fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(14, 14))
    ax.imshow(image, cmap='gray')
    plt.show()
    ax.axis('off')
    return fig, ax


text = data.page()
image_show(text)

fig, ax = plt.subplots(1, 1)
ax.hist(text.ravel(), bins=32, range=[0, 256])
ax.set_xlim(0, 256)
plt.show()

image_slic = seg.slic(text, n_segments=2)
image_show(color.label2rgb(image_slic, text, kind='avg'))

# image = data.binary_blobs()
# plt.imshow(image)
# plt.show()
コード例 #22
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.. [1] http://en.wikipedia.org/wiki/Otsu's_method

"""
import matplotlib
import matplotlib.pyplot as plt

from skimage import data
from skimage.morphology import disk
from skimage.filters import threshold_otsu, rank
from skimage.util import img_as_ubyte


matplotlib.rcParams['font.size'] = 9


img = img_as_ubyte(data.page())

radius = 15
selem = disk(radius)

local_otsu = rank.otsu(img, selem)
threshold_global_otsu = threshold_otsu(img)
global_otsu = img >= threshold_global_otsu


fig, ax = plt.subplots(2, 2, figsize=(8, 5))
ax1, ax2, ax3, ax4 = ax.ravel()

fig.colorbar(ax1.imshow(img, cmap=plt.cm.gray),
           ax=ax1, orientation='horizontal')
ax1.set_title('Original')
コード例 #23
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# pixels of the local neighborhood defined by a structuring element.
#
# The example compares the local threshold with the global threshold
# `skimage.filters.threshold_otsu`.
#
# .. note::
#
#     Local is much slower than global thresholding. A function for global
#     Otsu thresholding can be found in : `skimage.filters.threshold_otsu`.
#
# .. [4] http://en.wikipedia.org/wiki/Otsu's_method

from skimage.filters.rank import otsu
from skimage.filters import threshold_otsu

p8 = data.page()

radius = 10
selem = disk(radius)

# t_loc_otsu is an image
t_loc_otsu = otsu(p8, selem)
loc_otsu = p8 >= t_loc_otsu

# t_glob_otsu is a scalar
t_glob_otsu = threshold_otsu(p8)
glob_otsu = p8 >= t_glob_otsu

fig, ax = plt.subplots(2, 2, sharex=True, sharey=True)
ax1, ax2, ax3, ax4 = ax.ravel()
コード例 #24
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import numpy as np
import matplotlib.pyplot as plt
from skimage import data

#chargement de l'image et l'affichage de son histogramme
image = data.page()
plt.hist(image.flatten(), 256)
plt.show()
plt.imshow(image, plt.cm.gray)
plt.axis('off')
plt.show()


#on défini la fonction qui prend l'image et la fenêtre de pixel et retourne une image binarisée
def seuil_sauvola(I, a, k):

    #les constantes
    L = a // 2
    a2 = a * a
    R = 128
    image = np.copy(I)

    #Pour éviter les problèmes de bords on ajoute des bordures de largeur L
    image = np.pad(image, L, mode='edge')

    #les dimensions de l'image
    mx, my = image.shape

    #initialisation des élèments pour le calcul des sommes
    som1 = [0 for i in range(my)]
    som2 = 0
コード例 #25
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ファイル: test_data.py プロジェクト: yukoba/scikit-image
def test_page():
    """ Test that "page" image can be loaded. """
    data.page()
コード例 #26
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ファイル: main.py プロジェクト: LiuXiaolong19920720/planer
    53: 'y',
    54: 'z'
}


def greedy_search(raw, blank=0):
    max_id = raw.argmax(2).ravel()
    msk = max_id[1:] != max_id[:-1]
    max_id = max_id[1:][msk]
    return max_id[max_id != blank]


# net = planer.onnx2planer('./crnn.onnx')
net = planer.read_net('./crnn-ocr')

x = page()[:40, :150].astype('float32')

w = 48 * x.shape[1] // x.shape[0]
x = planer.resize(x, (48, w))
x = (x - 0.5) / (90 / 255)
x = x[None, None, :, :]

net.timer = {}
start = time()
y = net(x)
print(time() - start)

for k in net.timer:
    print(k, net.timer[k])

pred = greedy_search(y)
コード例 #27
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The example compares the local threshold with the global threshold
`skimage.filter.threshold_otsu`.

.. note::

    Local thresholding is much slower than global one. There exists a function
    for global Otsu thresholding: `skimage.filter.threshold_otsu`.

.. [4] http://en.wikipedia.org/wiki/Otsu's_method

"""

from skimage.filter.rank import otsu
from skimage.filter import threshold_otsu

p8 = data.page()

radius = 10
selem = disk(radius)

# t_loc_otsu is an image
t_loc_otsu = otsu(p8, selem)
loc_otsu = p8 >= t_loc_otsu

# t_glob_otsu is a scalar
t_glob_otsu = threshold_otsu(p8)
glob_otsu = p8 >= t_glob_otsu

plt.figure()
plt.subplot(2, 2, 1)
plt.imshow(p8, cmap=plt.cm.gray)
コード例 #28
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 def test_binarize(self):
     b = Binarizer()
     binary_sauvola = b.binarize([page()], Method.SAUVOLA)
     plt.imshow(binary_sauvola[0], cmap=plt.cm.gray)
     plt.show()