odebeir

from enthought.enable.api import ComponentEditor, BaseTool
from enthought.chaco.api import ArrayPlotData, Plot, jet, gray,  PlotGraphicsContext
from enthought.traits.api import HasTraits, Instance, Enum, Range, Dict, Button, Int, Bool, Str, Property,Event, Array, DelegatesTo, on_trait_change,List, Tuple
from enthought.traits.ui.api import Item, Group, View, InstanceEditor

def draw_plot(filename, container,size=(800,600)):
    """Render a plot to file"""
    container.outer_bounds = list(size)
    container.do_layout(force=True)
    gc = PlotGraphicsContext(size, dpi=150)

odebeir

from enthought.enable.api import ComponentEditor, BaseTool
from enthought.chaco.api import ArrayPlotData, Plot, jet, gray,  PlotGraphicsContext
from enthought.traits.api import HasTraits, Instance, Enum, Range, Dict, Button, Int, Bool, Str, Property,Event, Array, DelegatesTo, on_trait_change,List, Tuple
from enthought.traits.ui.api import Item, Group, View, InstanceEditor

    
def draw_plot(filename, container,size=(800,600)):
    """Render a plot to file"""
    container.outer_bounds = list(size)
    container.do_layout(force=True)

odebeir

import numpy as np


n = 4
data = np.reshape(range(4*4),(n,n))
data = data[:,:,np.newaxis]

print data[:,:,0],data.shape

seq = [0,1,2,3]

odebeir

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm

def create_data(n):
    data = np.reshape(range(n**2),(n,n))
    data = data[:,:,np.newaxis]
    print '-'*40
    print data[:,:,0],data.shape
    print '-'*40

odebeir

#opencv
import cv2.cv as cv
import cv2
import numpy as np

def build_filters():

    filters = []
    ksize = 31
    for theta in np.arange(0, np.pi, np.pi / 32):

odebeir

def convert_to_optical_densities(rgb,r0,g0,b0):
    OD = rgb.astype(float)
    OD[:,:,0] /= r0
    OD[:,:,1] /= g0
    OD[:,:,2] /= b0

    return -np.log(OD)

def color_deconvolution(rgb,r0,g0,b0,verbose=False):
    stain_OD = np.asarray([[0.18,0.20,0.08],[0.01,0.13,0.0166],[0.10,0.21,0.29]]) #hematoxylin, eosyn, DAB

odebeir

# opencv
import cv2.cv as cv
import cv2

def build_filters():
    """ returns a list of kernels in several orientations
    """
    filters = []
    ksize = 31
    for theta in np.arange(0, np.pi, np.pi / 32):

odebeir

label = np.asarray([[0,1],[2,3]])

rgblabel = np.zeros([2,2,3])
rgblabel[label==1,:]=[1,0,0]
rgblabel[label==2,:]=[0,1,0]
rgblabel[label==3,:]=[0,0,1]
plt.imshow(rgblabel,interpolation='nearest')
plt.show()

odebeir

import matplotlib.pyplot as plt

from skimage.morphology import watershed,disk
from skimage.filter import rank
from skimage import data

import numpy as np

image = data.camera()
gradient = rank.gradient(image, disk(2))

odebeir

def wavelet_analysis(sig):
    """ decompose signal into wavelets
        returns the frequence of the thesholded filtered signal
    """
    widths = np.arange(1, 100,1)
    cwtmatr = signal.cwt(sig, signal.ricker, widths)

    N = sig.size
    fsig = cwtmatr[98,:]
    detect_pos = fsig>30