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src/n/a/nansat-HEAD/sample_nansatBasemap.py   nansat(Download)
        if mode!="gaussian":
            print "apply Gaussian filter in image_process()"
        return ndimage.gaussian_filter(data, sigma=sigma, order=order)
 
def add_legend(m, orientation='horisontal', pad=0.01,

src/s/c/scipy-lecture-notes-HEAD/advanced/image_processing/examples/image_source_canny.py   scipy-lecture-notes(Download)
import numpy as np
import scipy.ndimage as ndi
from scipy.ndimage import (gaussian_filter, convolve,
                           generate_binary_structure, binary_erosion, label)
 
    if mask is None:
        mask = np.ones(image.shape, dtype=bool)
    fsmooth = lambda x: gaussian_filter(x, sigma, mode='constant')
    smoothed = smooth_with_function_and_mask(image, fsmooth, mask)
    jsobel = ndi.sobel(smoothed, axis=1)

src/s/c/scipy-lecture-notes-HEAD/advanced/image_processing/examples/plot_find_object.py   scipy-lecture-notes(Download)
points = l*np.random.random((2, n**2))
im[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
im = ndimage.gaussian_filter(im, sigma=l/(4.*n))
 
mask = im > im.mean()

src/s/c/scipy-lecture-notes-HEAD/advanced/image_processing/examples/plot_measure_data.py   scipy-lecture-notes(Download)
points = l*np.random.random((2, n**2))
im[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
im = ndimage.gaussian_filter(im, sigma=l/(4.*n))
 
mask = im > im.mean()

src/s/c/scipy-lecture-notes-HEAD/advanced/image_processing/examples/plot_clean_morpho.py   scipy-lecture-notes(Download)
points = l*np.random.random((2, n**2))
im[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
im = ndimage.gaussian_filter(im, sigma=l/(4.*n))
 
mask = (im > im.mean()).astype(np.float)

src/s/c/scipy-lecture-notes-HEAD/advanced/image_processing/examples/plot_blur.py   scipy-lecture-notes(Download)
 
lena = scipy.misc.lena()
blurred_lena = ndimage.gaussian_filter(lena, sigma=3)
very_blurred = ndimage.gaussian_filter(lena, sigma=5)
local_mean = ndimage.uniform_filter(lena, size=11)

src/s/c/scipy-lecture-notes-HEAD/advanced/image_processing/examples/plot_granulo.py   scipy-lecture-notes(Download)
points = l*np.random.random((2, n**2))
im[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
im = ndimage.gaussian_filter(im, sigma=l/(4.*n))
 
mask = im > im.mean()

src/s/c/scipy-lecture-notes-HEAD/advanced/image_processing/examples/plot_synthetic_data.py   scipy-lecture-notes(Download)
points = l*np.random.random((2, n**2))
im[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
im = ndimage.gaussian_filter(im, sigma=l/(4.*n))
 
mask = im > im.mean()

src/c/e/CellProfiler-HEAD/tutorial/example1f_upgradesettings.py   CellProfiler(Download)
import cellprofiler.cpimage as cpi
 
from scipy.ndimage import gaussian_filter
from cellprofiler.cpmath.filter import sobel
 
        pixel_data = image.pixel_data
        if self.filter_choice == S_GAUSSIAN:
            pixel_data = gaussian_filter(pixel_data, sigma=self.sigma.value)
        else:
            pixel_data = sobel(pixel_data)

src/c/e/CellProfiler-HEAD/tutorial/example1f.py   CellProfiler(Download)
import cellprofiler.cpmodule as cpm
import cellprofiler.settings as cps
import cellprofiler.cpimage as cpi
 
from scipy.ndimage import gaussian_filter
        pixel_data = image.pixel_data
        if self.filter_choice == S_GAUSSIAN:
            pixel_data = gaussian_filter(pixel_data, sigma=1)
        else:
            pixel_data = sobel(pixel_data)

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