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
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
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()
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()
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
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()
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
#
# __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,
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')
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()
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()
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.
#
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;
.. [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')
"""
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',
# -*- 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')
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()
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()
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')
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()
.. [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')
# 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()
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
def test_page():
""" Test that "page" image can be loaded. """
data.page()
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)
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)
def test_binarize(self):
b = Binarizer()
binary_sauvola = b.binarize([page()], Method.SAUVOLA)
plt.imshow(binary_sauvola[0], cmap=plt.cm.gray)
plt.show()