from dect import calc_shape_corners
import math
from scipy import ndimage
from skimage import filters
import numpy as np
import matplotlib.pyplot as plt

img = ndimage.imread("/home/tpolgrabia/Pobrane/CSFID/tracks_cropped/00003.jpg")
(n,m) = img.shape
otsu_lvl = filters.threshold_otsu(img)
m = img <= otsu_lvl
img_label, nb_labels = ndimage.label(m)
sizes = ndimage.sum(m, img_label, range(0, nb_labels+1))
min_seg_size = 100
to_be_removed_segs = sizes < min_seg_size
remove_mask = to_be_removed_segs[img_label]
img_label[remove_mask] = 0
labels = np.unique(img_label)
nb_labels = len(labels)
img_label = np.searchsorted(labels, img_label)
labels = np.unique(img_label)
centers = ndimage.center_of_mass(m, img_label, labels)
centers = np.array(centers, dtype=np.float)

(nc,who_cares) = centers.shape

def distance_point(m1, m2):
    df = m1-m2
    df2 = df*df
    sd2 = np.sum(df2)
    d = math.sqrt(sd2)
    return d

# TODO check if generates all rates of distance pairs
for i in range(0, nc):
    for j in range(0, i):
        for k in range(0, j):
            m1 = centers[i,:]
            m2 = centers[j,:]
            m3 = centers[k,:]
            d1 = distance_point(m1, m2)
            d2 = distance_point(m2, m3)
            d = d1 / d2
            print("{}-{}/{}-{}: {}".format(i, j, j, k, d))

corners = np.zeros(img.shape, dtype=np.bool)

for i in range(0, nb_labels):
    mseg = img_label == i
#     plt.imshow(mseg, cmap="Greys_r")
    seg_corner = calc_shape_corners(mseg)
    corners = corners + seg_corner

(n,m) = img.shape
mat_x = np.tile(np.arange(0, m),n).reshape((n,m))
mat_y = np.tile(np.arange(0, n),m).reshape((m,n)).T

xs = mat_x[corners]
ys = mat_y[corners]

plt.imshow(img, cmap="Greys_r")
plt.plot(xs, ys, "b+")
plt.show()