from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from skimage.filters import threshold_otsu
import glob
import sys

from skimage import feature
from skimage.transform import hough_line, hough_line_peaks, hough_circle, hough_circle_peaks
from skimage.morphology import disk, square, closing, opening
from skimage import measure

from scipy import ndimage as ndi
from copy import deepcopy

import visualPercepUtils as vpu

def testOtsu(im, params=None):
    nbins = 256
    th = threshold_otsu(im)
    hist = np.histogram(im.flatten(), bins=nbins, range=[0, 255])[0]
    # vpu.showInGrid([im,hist],m=2,n=1)
    # exit()
    return [th, im > th, hist]  # threshold, binarized image (using such threshold), and image histogram


def fillGaps(im, params=None):
    binIm = im > threshold_otsu(im)
    sElem = disk(params['closing-radius'])
    return [binIm, closing(binIm, sElem)]

def removeSmallRegions(im, params=None):
    binIm = im > threshold_otsu(im)
    sElem = disk(params['opening-radius'])
    return [binIm, opening(binIm, sElem)]

def fillGapsThenRemoveSmallRegions(im, params=None):
    out = fillGaps(im, params)  # first, fill gaps
    binIm, closeIm = out[0], out[1]
    sElem = disk(params['opening-radius'])
    return [binIm, opening(closeIm, sElem)]

def labelConnectedComponents(im, params=None):
    binIm = fillGapsThenRemoveSmallRegions(im, params)[1]
    return [binIm, measure.label(binIm, background=0)]

def ss(im):
    plt.imshow(im, cmap="gray")

def sc(im):
    plt.imshow(im, cmap="jet")


im = Image.open('imgs-P5/monedas.pgm').convert('L')
im1 = Image.open('imgs-P5/monedas1.pgm').convert('L')
im2 = Image.open('imgs-P5/monedas2.pgm').convert('L')


monedas = np.array(im)
monedas1 = np.array(im1)
monedas2 = np.array(im2)

ss(monedas)

labeled = labelConnectedComponents(monedas2, {'closing-radius': 2, 'opening-radius': 5})[1]

sc(labeled)

regions = measure.regionprops(labeled)
r = 1
for region in regions:
    print "Region", r
    print "\t area: ", region.area
    print "\t perimeter: ", region.perimeter
    r += 1

edges = feature.canny(labeled, sigma=0,  low_threshold=0, high_threshold=1, use_quantiles=False)
hough_radii = np.arange(18, 26, 1)
hough_res = hough_circle(edges, hough_radii)


ss(feature.canny(labeled, sigma=0,  low_threshold=0, high_threshold=1, use_quantiles=False))

accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,total_num_peaks=4)
for center_y, center_x, radius in zip(cy, cx, radii):
    print center_x, center_y
    print radius