# -*- coding: utf-8 -*-

"""

Created on Thu Feb 25 20:53:17 2016

@author: George Oliveira Barros, george_gob@hotmail.com.

PGCA - Universidade Estadual de Feira de Santana (UEFS)

PathoSpoter-K Feature Extraction: Version 1.0

"""

import numpy as np

import matplotlib.pyplot as plt

import ia636 as ia

import pymorph as morph

from skimage.feature import blob_log

from pylab import uint8

from skimage.filter import threshold_otsu

from skimage import img_as_ubyte #Conversão

from PIL import Image

from skimage.morphology import disk

from skimage.color import rgb2hed

from skimage.measure import label, regionprops

print __doc__

glom_rgb = Image.open('s3.jpg')

glom_gl = glom_rgb.convert('L') #Gray Level

glom_hed = rgb2hed(glom_rgb) #hed

glom_h = glom_hed[:, :, 0] #hematoxylim

glom_h = ia.ianormalize(glom_h)

selem = disk(10) #elemento estruturante

glom_h = np.array(glom_h)

glom_h = 255 - uint8(glom_h)

#Segmentation

glom_by_reconsTopHat = morph.closerecth(glom_h,selem) #reconstrução morfológicas de fechamento

global_thresh = threshold_otsu(glom_by_reconsTopHat) #Otsu

glom_bin = glom_by_reconsTopHat > global_thresh + global_thresh*0.1

glom_bin = img_as_ubyte(glom_bin)

selem = disk(3)

glom_seg = morph.open(glom_bin, selem)

glom_seg = morph.close(glom_seg, selem) #Fechamento final

#Mostra as etapas

fig, axes = plt.subplots(2, 3, figsize=(14, 10))

fig.suptitle('Preprocessing, segmentation')

ax1, ax2, ax3, ax4, ax5, ax6 = axes.ravel()

ax1.imshow(glom_rgb, vmin=0, vmax=255, cmap=plt.cm.gray); ax1.set_title("RGB")

ax2.imshow(glom_hed, vmin=0, vmax=255, cmap=plt.cm.gray); ax2.set_title("HED")

ax3.imshow(glom_h, cmap=plt.cm.gray); ax3.set_title("255 - H (Hematoxylin)")

ax4.imshow(glom_by_reconsTopHat, vmin=0, vmax=255, cmap=plt.cm.gray); ax4.set_title("Closing-by-reconstruction top-hat")

ax5.imshow(glom_bin, vmin=0, vmax=255, cmap=plt.cm.gray); ax5.set_title("Otsu Thresholding")

ax6.imshow(glom_seg, vmin=0, vmax=255, cmap=plt.cm.gray); ax6.set_title("Opening")

#------------------------------------------------------------------------------------------------

#Feature Extraction

print "Feature Extraction:"

label_img = label(glom_seg) #Identifica formas através da vizinhaça 4 E 8

regions = regionprops(label_img) #Lista de propriedades das regiões selecionadas

print "Quantidade de regioes de nucleos", len(regions)

regionBlob = img_as_ubyte(glom_seg)

blobs = blob_log(regionBlob, 20, threshold=.01)

print "Quantidade de aglomerados de nucleos", len(blobs)

plt.show()