import os

import numpy as np

from scipy.ndimage import maximum_filter, minimum_filter, binary_fill_holes

import skimage as ski

from skimage import io, filter, color, exposure, morphology, feature, draw, measure, transform

#figsize(16, 10)

l = os.listdir("../data")

for f in l :

    if not f.endswith(".jpg") :

        l.remove(f)

qstain = np.array([[.26451728, .5205347, .81183386], [.9199094, .29797825, .25489032], [.28947765, .80015373, .5253158]])

for im in l :

    print im

    A = transform.rescale(io.imread("../data/" + im), 0.25)

    deconv = ski.img_as_float(color.separate_stains(A, np.linalg.inv(qstain)))

    subveins1 = \

    morphology.remove_small_objects(

        filter.threshold_adaptive(

            filter.gaussian_filter(

                deconv[:, :, 2] / deconv[:, :, 0],

                11),

            250, offset = -0.13),

        60)

    subveins2 = \

    morphology.remove_small_objects(

        filter.threshold_adaptive(

            filter.gaussian_filter(

                    maximum_filter(

                    deconv[:, :, 2] / deconv[:, :, 0],

                    5),

                11),

            250, offset = -0.13),

        60)

    veins = \

    maximum_filter(

        morphology.remove_small_objects(

            binary_fill_holes(

                morphology.binary_closing(

                    np.logical_or(subveins1, subveins2),

                    morphology.disk(25)),

                ),

            250),

        27)

    inflammation = \

    maximum_filter(

        morphology.remove_small_objects(

            filter.threshold_adaptive(

                exposure.adjust_sigmoid(

                    filter.gaussian_filter(

                        exposure.equalize_adapthist(

                            exposure.rescale_intensity(

                                deconv[:, :, 1],

                                out_range = (0, 1)),

                            ntiles_y = 1),

                            5),

                    cutoff = 0.6),

                    75, offset = -0.12),

            250),

        29)

    # Labelled

    total = np.zeros_like(A)

    #total[:, :, 0] = blood

    total[:, :, 1] = veins

    total[:, :, 2] = inflammation

    io.imsave("__1.gif", A)

    io.imsave("__2.gif", total)

    os.system("gifsicle --delay=80 --loop __*.gif > %s.gif" % im)