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sep_3.py
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sep_3.py
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from heapq import *
import cv2
import numpy as np
import random
import copy
import matplotlib.pyplot as plt
from pathfinding.core.diagonal_movement import DiagonalMovement
from pathfinding.core.grid import Grid
from pathfinding.finder.a_star import AStarFinder
import multiprocessing
import tsp
import ast
global path_adj
global image_name
def astar(array, start, goal):
start = list(start)
goal = list(goal)
grid = Grid(matrix=array)
finder = AStarFinder(diagonal_movement=DiagonalMovement.always)
path, runs = finder.find_path(grid.node(start[1], start[0]), grid.node(goal[1], goal[0]), grid)
return path
def calc_path(img, X, Y):
path = []
for x in X:
for y in Y:
data = astar(img, tuple(x), tuple(y))
path.append(len(data))
return np.array(path)
def init_adj(X, img_name):
img = cv2.imread(img_name, 2)
path_adj = [[0 for col in range(len(X))] for row in range(len(X))]
# make adj list
for i in range(len(X)):
for j in range(i, len(X)):
path_adj[i][j] = len(astar(img, X[i], X[j]))
for i in range(len(X)):
for j in range(i, len(X)):
path_adj[j][i] = path_adj[i][j]
return path_adj
# init
def init_kmean(K, X, img):
image_name = 'pointed_map.png'
mean = set()
X_label = []
path_adj = init_adj(X, image_name)
# 초기 점 설정
# 첫번째 점은 랜덤
point = random.randint(0, len(X) - 1)
# 이전에 선택된 점과의 거리를 기준으로 가장 먼 점이 높은 확률로 선정되게 한다
while len(mean) < K:
sample_rate = []
# 0~1까지의 확률을 가진 실수 리스트
total = sum(path_adj[point])
for i in range(len(X)):
sample_rate.append(path_adj[i][point] / total)
# 선택
select = random.random()
index = 0
for i in sample_rate:
select -= i
if select <= 0:
break
index += 1
mean.add(point)
point = index
mean = list(mean)
mean.sort()
# 초기점에따라 군집화
cnt = 0
for x_i in range(len(X)):
if cnt in mean:
X_label.append(mean.index(cnt))
cnt += 1
continue
s = 9999
label = 0
for i in mean:
length = path_adj[x_i][i]
# length = len(astar(img, X[x_i], X[i]))
if s > length:
s = length
label = i
X_label.append(mean.index(label))
cnt += 1
return X_label
# calc G
def calc_G(K, X, img, X_label):
mean = []
label_cnt = []
for i in range(K):
mean.append([0, 0])
label_cnt.append(0)
mean = np.int_(mean)
label_cnt = np.array(label_cnt)
for i in range(len(X)):
mean[X_label[i]] += X[i]
label_cnt[X_label[i]] += 1
for i in range(len(mean)):
mean[i] = is_wall(img, mean[i] / label_cnt[i])
return mean
def is_wall(img, p):
row, col = p
height, width = img.shape
# print(row, col)
row = int(row)
col = int(col)
flag = 0
if img[row][col] == 0: # 중심점이 벽일경우
while True:
if row - flag > 0:
if img[row - flag][col] != 0: # 위쪽이 검은색X
return np.array([row - flag, col])
if row + flag < width:
if img[row + flag][col] != 0: # 아래쪽이 검은색X
return np.array([row + flag, col])
if col - flag > 0:
if img[row][col - flag] != 0: # 왼쪽이 검은색X
return np.array([row, col - flag])
if col + flag < width:
if img[row][col + flag] != 0: # 오른쪽이 검은색X
return np.array([row, col + flag])
flag = flag + 1
else:
return np.array([row, col])
def write_to_img(img_name, X, X_label):
img = cv2.imread(img_name)
color_list = []
while len(color_list) <= max(X_label) + 1:
color = list(np.random.random(size=3) * 255)
while color in color_list:
color = list(np.random.random(size=3) * 255)
color_list.append(color)
for i in range(len(X)):
row = X[i][0]
col = X[i][1]
img[row][col] = color_list[X_label[i]]
'''
cv2.imshow('result', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
'''
cv2.imwrite('result.png', img)
def write_to_imgf(img_name, X, out_name):
img = cv2.imread(img_name)
color_list = []
while len(color_list) <= len(X):
color = list(np.random.random(size=3) * 255)
while color in color_list:
color = list(np.random.random(size=3) * 255)
color_list.append(color)
for x in X:
color = color_list.pop()
for f in x:
row = f[0]
col = f[1]
img[row][col] = color
cv2.imwrite(out_name, img)
def run_kmeans(args):
K = args[0]
X = args[1]
img_name = args[2]
img = cv2.imread(img_name, 2)
# X = np.array([[[40,1], [42, 10]], [[10, 34], [21,16], [2, 23]], [[37, 26], [39, 37], [37, 41], [47, 48], [48,12]]])
# X = np.array([[40,1], [42, 10], [10, 34], [21,16], [2, 23], [37, 26], [39, 37], [37, 41], [47, 48], [48,12]])
X_label = init_kmean(K, X, img)
mean = calc_G(K, X, img, X_label)
prev = []
# print("check")
# run kmean
while True:
temp_label = []
density = []
for i in range(K):
density.append(0)
cnt = 0
for x in X:
s = 9999
label = 0
for i in range(len(mean)):
length = len(astar(img, x, list(mean[i])))
if s > length:
s = length
label = i
density[label] += s ** 2
temp_label.append(label)
cnt += 1
if X_label == temp_label:
break
if mean in prev:
break
X_label = copy.deepcopy(temp_label)
mean = calc_G(K, X, img, temp_label)
prev.append(mean)
return X_label, density
def elbow(inertias):
opt = []
for i in range(1, len(inertias) - 1):
opt.append((inertias[i - 1] - inertias[i]) / (inertias[i] - inertias[i + 1]))
return opt.index(max(opt)) + 3
def display_tsp(img_name):
img_origin = cv2.imread(img_name, 2)
'''
list_tsp=[]
for i in range(K):
list_tsp.append([])
for i in range(len(X)):
list_tsp[X_label[i]].append(X[i])
for i in range(K):
print(i,"번째: ",list_tsp[i],len(list_tsp[i]))
'''
f1 = open("saved_data_k.txt", 'r')
f2 = open("saved_data_sep.txt", 'r')
K = int(f1.readline())
X_label = f2.read()
f1.close()
f2.close()
list_tsp = ast.literal_eval(X_label)
tsped_point = []
print(list_tsp)
route_file = open('route.txt', 'w')
for i in range(K):
temp = [];
img = cv2.imread(img_name)
color = list(np.random.random(size=3) * 255)
tsp_path_adj = init_adj(list_tsp[i], img_name)
'''for j in range(len(tsp_path_adj)):
print(j,":",tsp_path_adj[j])'''
r = range(len(tsp_path_adj))
# Dictionary of distance
dist = {(m, j): tsp_path_adj[m][j] for m in r for j in r}
path_len, path = tsp.tsp(r, dist)
print(path)
for j in range(len(path)):
print(j, "번째:", path[j], list_tsp[i][j])
temp.append(list_tsp[i][j])
print(temp)
tsped_point.append(temp)
for j in range(len(path) - 1):
print(path[j], path[j + 1])
route = (astar(img_origin, tuple(list_tsp[i][path[j]]), tuple(list_tsp[i][path[j + 1]])))
print("route:", route)
for l, k in route:
img[k][l] = color
cv2.imwrite('route%d.png' % (i), img)
print('출력: '+ str(tsped_point))
route_file.write(str(tsped_point))
route_file.close()
def get_opt_kmean(img_name, X, end):
# 인수 만들기
args = []
for i in range(2, end):
args.append((i, X, img_name))
# 멀티프로세싱
print("processing start")
pool = multiprocessing.Pool(processes=len(args)) # 현재 시스템에서 사용 할 프로세스 개수
result = pool.map(run_kmeans, args)
pool.close()
pool.join()
# 분산도 구하기
mean_dist = []
for density in result:
mean_dist.append(sum(density[1]))
e = elbow(mean_dist)
print("elbow : ", e)
X_label, density = result[e - 2] # 0번째의 K는 2이므로 elbow에서 2를 빼서 K를 찾음
sep = [[] for row in range(e)]
for i in range(len(X)):
sep[X_label[i]].append(list(X[i]))
return sep
if __name__ == '__main__':
image_name = 'pointed_map.png'
f2 = open("original_points.txt", 'r')
f1 = open("moved_points.txt", 'r')
X = ast.literal_eval(f1.read())
X_original = ast.literal_eval(f2.read())
f1.close()
f2.close()
# X = np.array(
# [[2, 3], [2, 12], [2, 18], [2, 23], [2, 32], [2, 33], [2, 34], [2, 42], [2, 71], [3, 10], [3, 47], [3, 68], [5, 24], [5, 29], [5, 35], [6, 2], [6, 18], [6, 51], [6, 56], [6, 59], [6, 63], [6, 73], [7, 5], [7, 13], [7, 42], [8, 5], [8, 18], [8, 42], [8, 73], [9, 5], [9, 56], [9, 58], [10, 26], [10, 27], [10, 28], [10, 29], [10, 35], [10, 36], [11, 2], [12, 53], [12, 67], [12, 73], [13, 10], [13, 19], [13, 24], [13, 33], [13, 34], [13, 44], [13, 47], [13, 73], [15, 55], [15, 62], [16, 50], [18, 2], [18, 7], [18, 24], [18, 32], [18, 38], [18, 40], [18, 45], [18, 50], [19, 57], [19, 64], [19, 66], [20, 50], [20, 73], [21, 3], [21, 14], [21, 15], [21, 18], [21, 26], [21, 28], [21, 37], [21, 38], [21, 63], [22, 45], [22, 68], [23, 20], [24, 20], [24, 30], [25, 73], [26, 2], [26, 3], [26, 6], [26, 8], [26, 15], [26, 16], [26, 25], [26, 35], [26, 40], [26, 55], [26, 60], [27, 30], [27, 55], [27, 60], [28, 50], [28, 55], [28, 68], [29, 25], [29, 50], [29, 55], [29, 73], [30, 35], [30, 45], [31, 2], [31, 7], [31, 8], [31, 17], [31, 35], [31, 63], [32, 40], [33, 2], [33, 50], [33, 60], [34, 15], [34, 30], [34, 45], [34, 77], [35, 35], [35, 50], [36, 2], [36, 5], [36, 25], [36, 57], [36, 64], [36, 65], [37, 2], [37, 10], [37, 30], [37, 35], [38, 15], [38, 20], [38, 47], [39, 56], [39, 60], [39, 68], [39, 76], [40, 2], [40, 10], [40, 27], [40, 29], [40, 38], [42, 5], [42, 15], [42, 20], [42, 55], [43, 10], [43, 27], [43, 31], [43, 35], [43, 41], [43, 44], [43, 46], [44, 48], [44, 57], [44, 61], [44, 69], [44, 75], [45, 23], [46, 5], [46, 10], [46, 23], [46, 48], [46, 77], [47, 2], [47, 15], [47, 53], [47, 57], [47, 58], [47, 66], [47, 70], [47, 71], [47, 73]])
sep = get_opt_kmean(image_name, X, 10)
write_to_imgf(image_name, sep, 'result1.png')
i = 0
while True:
if i >= len(sep):
break
if len(sep[i]) > 15:
'''
X_label, density = run_kmeans(4, sep[i], image_name)
temp_sep = [[] for row in range(4)]
for j in range(len(sep[i])):
temp_sep[X_label[j]].append(sep[i][j])
'''
temp_sep = get_opt_kmean(image_name, sep[i], 10)
sep.pop(i)
for t in temp_sep:
sep.append(t)
i += 1
write_to_imgf(image_name, sep, 'result2.png')
f = open('saved_data_k.txt', 'w')
f.write(str(len(sep)))
f.close()
f = open('saved_data_sep.txt', 'w')
f.write(str(sep))
f.close()
display_tsp(image_name)