RAID-Algorithm/Optimized_distribution.py at main · tloureaux/RAID-Algorithm · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
#!/usr/bin/env python
# coding: utf-8

# In[ ]:


from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from skimage import segmentation
from sklearn.cluster import KMeans
from skimage import measure
import cv2 as cv
from shapely.geometry import Polygon
import rasterio.features
import matplotlib.patches as mpatches
import matplotlib.image as mpimg
import os

def mycontrolpointsopti(ncontrol_points,input_image,donnee):
    original_image = plt.imread(input_image)
    img = cv.imread(input_image,0)

    if img[0,0] > 128 :
        img = cv.bitwise_not(img)

    boundary = 100
    binary = np.where(img>boundary,True,False)

    contour = measure.find_contours(binary)[0]
    deja_fait = []
    for i in contour:
        deja_fait.append([i[1],i[0]])

    deja_faitx = [p[0] for p in deja_fait]
    deja_faity = [p[1] for p in deja_fait]

    #Find the uniform distribution of points
    ncontrol = 2
    tour = 0

    control_pointsx = [deja_faitx[tour]]
    control_pointsy = [deja_faity[tour]]

    for i in range(1,ncontrol):
        tour = i*(len(deja_faitx)//ncontrol)
        control_pointsx.append(deja_faitx[tour])
        control_pointsy.append(deja_faity[tour])

    #Affinement du séquençage

    while (ncontrol != ncontrol_points):
        ncontrol = ncontrol + 1

        black_pixels = []
        white_pixels = []

        all_pointsx = np.append(control_pointsx,control_pointsx[0])
        all_pointsy = np.append(control_pointsy,control_pointsy[0])

        for i in range(0,len(all_pointsx)-1):
            point_1 = [all_pointsx[i],all_pointsy[i]]
            point_2 = [all_pointsx[i+1],all_pointsy[i+1]]

            if deja_fait.index(point_1) < (deja_fait.index(point_2)+1):
                sous_liste = deja_fait[deja_fait.index(point_1):deja_fait.index(point_2)+1]

            else:
                fin = deja_fait[0:(deja_fait.index(point_2)+1)]
                debut = deja_fait[deja_fait.index(point_1):len(deja_fait)-2]
                debut.extend(fin)
                sous_liste = debut

            sous_listex = [p[0] for p in sous_liste]
            sous_listey = [p[1] for p in sous_liste]

            sous_poly = Polygon(list(zip(sous_listex,sous_listey)))
            sous_mask = rasterio.features.rasterize([sous_poly], out_shape=(len(binary), len(binary[0])))

            black_pixels.append(np.sum((binary==False)&(sous_mask==1)))
            white_pixels.append(np.sum((binary==True)&(sous_mask==1)))

        black_pixels = black_pixels/np.sum(black_pixels)
        white_pixels = white_pixels/np.sum(white_pixels)
        black_irr = [i for i,v in enumerate(black_pixels) if v == max(black_pixels)]
        white_irr = [i for i,v in enumerate(white_pixels) if v == max(white_pixels)]

        if not (black_irr == [] and white_irr == []):

            if max(max(black_pixels),max(white_pixels))==max(black_pixels):
                p1 = [all_pointsx[black_irr[0]],all_pointsy[black_irr[0]]]
                p2 = [all_pointsx[black_irr[0]+1],all_pointsy[black_irr[0]+1]]

            else:
                p1 = [all_pointsx[white_irr[0]],all_pointsy[white_irr[0]]]
                p2 = [all_pointsx[white_irr[0]+1],all_pointsy[white_irr[0]+1]]

            if deja_fait.index(p1) < (deja_fait.index(p2)+1):
                sous_liste = deja_fait[deja_fait.index(p1):deja_fait.index(p2)+1]

            else:
                fin = deja_fait[0:(deja_fait.index(p2)+1)]
                debut = deja_fait[deja_fait.index(p1):len(deja_fait)-2]
                debut.extend(fin)
                sous_liste = debut

            distdroite = []

            if p1[0] == p2[0]:

                for j in range(0,len(sous_liste)-1):
                    truedist = (sous_liste[j][0]-p1[0])**2
                    distdroite.append(truedist)

            elif p1[1] == p2[1]:

                for j in range(0,len(sous_liste)-1):
                    truedist = (sous_liste[j][1]-p1[1])**2
                    distdroite.append(truedist)

            else:
                adroite = (p2[1]-p1[1])/(p2[0]-p1[0])
                bdroite = p1[1]-adroite*p1[0]

                for j in range(0,len(sous_liste)-1):
                    xdroite = (sous_liste[j][1]-bdroite)/adroite
                    ydroite = adroite*sous_liste[j][0]+bdroite
                    truedist = min((sous_liste[j][1]-ydroite)**2,(sous_liste[j][0]-xdroite)**2)
                    distdroite.append(truedist)

            ncontrol_point = sous_liste[distdroite.index(np.amax(distdroite))]

            list_x = [i for i in range(0,len(control_pointsx)) if control_pointsx[i]==p1[0]]
            list_y = [i for i in range(0,len(control_pointsy)) if control_pointsy[i]==p1[1]]
            common = [element for element in list_x if element in list_y]

        control_pointsx.insert(common[0]+1,ncontrol_point[0])
        control_pointsy.insert(common[0]+1,ncontrol_point[1])

    poly_test = Polygon(list(zip(control_pointsx,control_pointsy)))
    mask_test = rasterio.features.rasterize([poly_test], out_shape=(len(binary), len(binary[0])))

    nblack_pixels = (np.sum((binary==False)&(mask_test==1)))
    nwhite_pixels = (np.sum((binary==True)&(mask_test==0)))
    error = nblack_pixels+nwhite_pixels

    if donnee == 'image':
        plt.figure(figsize = (15,15))
        plt.imshow(original_image)
        plt.plot(control_pointsx, control_pointsy, 'ro',lw=8,markersize=12)
        plt.plot(np.append(control_pointsx,control_pointsx[0]), np.append(control_pointsy,control_pointsy[0]), 'r',linewidth = 3.0)
        plt.xticks([])
        plt.yticks([])
        plt.show()

    elif donnee == 'error':
        return(error)