def RandomCircle(x_min=-100,
x_max=100,
y_min=-100,
y_max=100,
r_min=0,
r_max=20):
x = Random(x_min, x_max)
y = Random(y_min, y_max)
r = Random(r_min, r_max)
return F.circle([x, y, r])
def RandomCircle(x_min=-100,
x_max=100,
y_min=-100,
y_max=100,
r_min=0,
r_max=20):
""" ランダムな円生成 """
x = Random(x_min, x_max)
y = Random(y_min, y_max)
r = Random(r_min, r_max)
return F2.circle([x, y, r])
def MakeSign2D(sign_type, scale):
"""
いろんな種類の標識作成
sign_type:
0: 半径0.3mの円
1: 1辺0.8mの正三角形
2: 1辺0.9mの正方形
3. 1辺0.45mのひし形(てか正方形)
4. 1辺が0.05～1のどれかの長方形
scale: sign_typeのスケールを標準とした倍率
"""
# 標識作成
if sign_type == 0:
r = 0.3 * scale
fig = F2.circle([0, 0, r])
fig_type = 0
AABB = np.array([-0.35, 0.35, -0.35, 0.35]) * scale
elif sign_type == 1:
r = 0.8 / np.sqrt(3) * scale
fig = F2.tri([0, 0, r, Random(0, np.pi * 2 / 3)])
fig_type = 1
AABB = np.array([-0.45, 0.45, -0.45, 0.45]) * scale
elif sign_type == 2:
r = 0.9 * scale
fig = F2.rect([0, 0, r, r, Random(0, np.pi / 2)])
fig_type = 2
l = 0.9 * np.sqrt(2) / 2
AABB = np.array([-l * 1.1, l * 1.1, -l * 1.1, l * 1.1]) * scale
elif sign_type == 3:
r = 0.45 * scale
fig = F2.rect([0, 0, r, r, Random(0, np.pi / 2)])
fig_type = 2
l = 0.45 * np.sqrt(2) / 2
AABB = np.array([-l * 1.1, l * 1.1, -l * 1.1, l * 1.1]) * scale
else:
w = Random(0.5, 2)
h = Random(0.5, 2)
fig = F2.rect([0, 0, w, h, Random(0, np.pi / 2)])
fig_type = 2
l = np.sqrt(w**2 + h**2) * np.sqrt(2) / 2
AABB = np.array([-l * 1.1, l * 1.1, -l * 1.1, l * 1.1]) * scale
return fig, fig_type, AABB
def Crossover2(parents, fig, max_p, min_p, l, cross_rate):
if np.random.rand() >= cross_rate:
return np.random.choice(parents)
# n: パラメータの数, x: n+1人の親のパラメータのリスト
n = len(parents[0].figure.p)
x = np.array([parents[i].figure.p for i in range(n + 1)])
# g: xの重心
g = np.sum(x, axis=0) / n
alpha = np.sqrt(n + 2)
# p, cを定義
p, c = np.empty((0, n)), np.empty((0, n))
p = np.append(p, [g + alpha * (x[0] - g)], axis=0)
c = np.append(c, [[0 for i in range(n)]], axis=0)
for i in range(1, n + 1):
r = Random(0, 1)**(1 / i)
p = np.append(p, [g + alpha * (x[i] - g)], axis=0)
c = np.append(c, [r * (p[i - 1] - p[i] + c[i - 1])], axis=0)
#print(r, p[i], c[i])
# 子のパラメータはp[n]+c[n]となる
child = p[n] + c[n]
# パラメータが範囲外ならやり直し
# if CheckIB(child, fig, max_p, min_p, l):
# break
# パラメータが範囲外なら子は生成しない
# if not CheckIB(child, fig, max_p, min_p, l):
# return None
# パラメータをpersonクラスに代入する
if fig == 0:
figure = F.circle(child)
elif fig == 1:
figure = F.tri(child)
elif fig == 2:
figure = F.rect(child)
return person(fig, figure)
def CircleDict(points):
n = points.shape[0]
#N = 5000
# ランダムに3点ずつN組抽出
#index = np.array([np.random.choice(n, 3, replace=False) for i in range(N)])
index = np.random.choice(n, size=(int((n - n % 3) / 3), 3), replace=False)
points_set = points[index, :]
num = points_set.shape[0]
# 省略
DET = lambda v1, v2: np.linalg.det(np.stack([v1, v2]))
DOT = lambda v1, v2: np.dot(v1, v2)
c_list = lambda p1, p2, p3: np.array(
[DOT(norm(p1 - p3), (p1 + p3) / 2),
DOT(norm(p2 - p3), (p2 + p3) / 2)])
center = lambda p1, p2, p3: \
np.array([DET(c_list(p1,p2,p3), norm(p2-p3)) / DET(norm(p1-p3), norm(p2-p3)),\
DET(norm(p1-p3), c_list(p1,p2,p3)) / DET(norm(p1-p3), norm(p2-p3))])
radius = lambda p1, c: np.linalg.norm(p1 - c)
c = np.array([
center(points_set[i][0], points_set[i][1], points_set[i][2])
for i in range(num)
])
r = np.array([radius(points_set[i][0], c[i]) for i in range(num)])
# パラメータ
# p = [x0, y0, z0, r]
r = np.reshape(r, (num, 1))
p = np.concatenate([c, r], axis=1)
# 円生成
Circles = [F.circle(p[i]) for i in range(num)]
# フィットしている点の数を数える
Scores = [
MarkPoints(Circles[i], points, epsilon=0.01)[0] for i in range(num)
]
print(p[Scores.index(max(Scores))], max(Scores))
return p[Scores.index(max(Scores))], Circles[Scores.index(max(Scores))]
def CreateRandomPerson(fig_type, max_p, min_p, l):
""" 個体をランダム生成 """
# 円
if fig_type == 0:
#print("円")
# min_p < x,y < max_p
x = Random(min_p[0], max_p[0])
y = Random(min_p[1], max_p[1])
# 0 < r < 2/3*l
r = Random(0.2 * l, 2 / 3 * l)
figure = F.circle([x, y, r])
# 正三角形
elif fig_type == 1:
#print("正三角形")
# min_p < x,y < max_p
x = Random(min_p[0], max_p[0])
y = Random(min_p[1], max_p[1])
# 0 < r < 2/3*l
r = Random(0.2 * l, 2 / 3 * l)
# 0 < t < pi*2/3
t = Random(0, np.pi * 2 / 3)
figure = F.tri([x, y, r, t])
# 長方形
elif fig_type == 2:
#print("長方形")
# min_p < x,y < max_p
x = Random(min_p[0], max_p[0])
y = Random(min_p[1], max_p[1])
# 0 < w,h < l
w = Random(0.2 * l, l)
h = Random(0.2 * l, l)
# 0 < t < pi
t = Random(0, np.pi)
figure = F.rect([x, y, w, h, t])
return person(fig_type, figure)
def Crossover(parents, fig, max_p, min_p, l, cross_rate):
"""
交叉を実装
シンプレックス(SPX)交叉を採用
"""
if np.random.rand() >= cross_rate:
return np.random.choice(parents)
# n: パラメータの数, x: n+1人の親のパラメータのリスト
n = len(parents[0].figure.p)
x = np.array([parents[i].figure.p for i in range(n + 1)])
# g: xの重心
g = np.sum(x, axis=0) / n
alpha = np.sqrt(n + 2)
# p, cを定義
p, c = np.empty((0, n)), np.empty((0, n))
p = np.append(p, [g + alpha * (x[0] - g)], axis=0)
c = np.append(c, [[0 for i in range(n)]], axis=0)
for i in range(1, n + 1):
r = Random(0, 1)**(1 / i)
p = np.append(p, [g + alpha * (x[i] - g)], axis=0)
c = np.append(c, [r * (p[i - 1] - p[i] + c[i - 1])], axis=0)
# 子のパラメータはp[n]+c[n]となる
child = p[n] + c[n]
# パラメータをpersonクラスに代入する
if fig == 0:
figure = F.circle(child)
elif fig == 1:
figure = F.tri(child)
elif fig == 2:
figure = F.rect(child)
return person(fig, figure)
def MakeSign2D(sign_type, scale):
# 標識作成
if sign_type == 0:
r = 0.3 * scale
fig = F.circle([0, 0, r])
fig_type = 0
AABB = np.array([-0.35, 0.35, -0.35, 0.35]) * scale
elif sign_type == 1:
r = 0.8 / np.sqrt(3) * scale
fig = F.tri([0, 0, r, Random(0, np.pi * 2 / 3)])
fig_type = 1
AABB = np.array([-0.45, 0.45, -0.45, 0.45]) * scale
elif sign_type == 2:
r = 0.9 * scale
fig = F.rect([0, 0, r, r, Random(0, np.pi / 2)])
fig_type = 2
l = 0.9 * np.sqrt(2) / 2
AABB = np.array([-l * 1.1, l * 1.1, -l * 1.1, l * 1.1]) * scale
elif sign_type == 3:
r = 0.45 * scale
fig = F.rect([0, 0, r, r, Random(0, np.pi / 2)])
fig_type = 2
l = 0.45 * np.sqrt(2) / 2
AABB = np.array([-l * 1.1, l * 1.1, -l * 1.1, l * 1.1]) * scale
else:
w = Random(0.5, 2)
h = Random(0.5, 2)
fig = F.rect([0, 0, w, h, Random(0, np.pi / 2)])
fig_type = 2
l = np.sqrt(w**2 + h**2) * np.sqrt(2) / 2
AABB = np.array([-l * 1.1, l * 1.1, -l * 1.1, l * 1.1]) * scale
# X, Y = Disassemble2d(sign_points)
# plt.plot(X, Y, marker=".", linestyle='None', color="red")
# plt.show()
return fig, fig_type, AABB
def check_exam(fig_type,
i,
dir_path="data/dataset/tri/",
out_path="data/GAtest/tri/"):
# データセット読み込み
fig_list = np.load(dir_path + "fig.npy")
AABB_list = np.load(dir_path + "AABB.npy")
outArea_list = np.load(dir_path + "outArea.npy")
print("fig:{}".format(np.array(fig_list).shape))
print("AABB:{}".format(np.array(AABB_list).shape))
print("outArea:{}".format(np.array(outArea_list).shape))
# points, outPointsはまずパスを読み込み
points_paths = sorted(glob(dir_path + "points/**.npy"),\
key=lambda s: int(re.findall(r'\d+', s)[len(re.findall(r'\d+', s))-1]))
outPoints_paths = sorted(glob(dir_path + "outPoints/**.npy"),\
key=lambda s: int(re.findall(r'\d+', s)[len(re.findall(r'\d+', s))-1]))
print("epoch:{}".format(i))
# points, outPoints読み込み
points = np.load(points_paths[i])
outPoints = np.load(outPoints_paths[i])
# 他も参照
fig_p = fig_list[i]
AABB = AABB_list[i]
outArea = outArea_list[i]
if fig_type == 0:
fig = F.circle(fig_p)
elif fig_type == 1:
fig = F.tri(fig_p)
else:
fig = F.rect(fig_p)
def calc_score1_2(points, out_contour, out_area, figure, flag=False):
# AABB内にあるのかチェック
max_p, min_p, _, l, AABB_area = buildAABB2d(points)
# print(max_p, min_p)
if len(figure.p) == 3:
fig = 0
elif len(figure.p) == 4:
fig = 1
else:
fig = 2
if not CheckIB2(figure.p, fig, max_p, min_p, l):
return -100
########################################################
# figureの2倍の図形を作成し、それを外枠とする
if fig == 0:
out_p = figure.p[:]
out_p[2] *= 1.2
out_figure = F.circle(out_p)
elif fig == 1:
out_p = figure.p[:]
out_p[2] *= 1.2
out_figure = F.tri(out_p)
else:
out_p = figure.p[:]
out_p[2] *= 1.2
out_p[3] *= 1.2
out_figure = F.rect(out_p)
# W >= 0(-図形の内部)のインデックスを取り出し、
# その数をCinとして保存
X, Y = Disassemble2d(points)
W = figure.f_rep(X, Y)
in_index = np.where(W >= 0)
Cin = len(in_index[0])
# Ain = 推定図形の面積
Ain = figure.CalcArea()
# Cout = outの点群数
# (外枠内の点群数 - inの点群数)
X2 = np.delete(X, in_index)
Y2 = np.delete(Y, in_index)
W2 = out_figure.f_rep(X2, Y2)
out_index = np.where(W2 >= 0)
Cout = len(out_index[0])
# Aout = outの面積 - Ain
Aout = out_figure.CalcArea() - Ain
if flag == True:
X = X[in_index]
Y = Y[in_index]
plt.plot(X, Y, marker=".", linestyle="None", color="blue")
X2 = X2[out_index]
Y2 = Y2[out_index]
plt.plot(X2, Y2, marker=".", linestyle="None", color="red")
fig1 = ContourPoints(figure.f_rep,
AABB=AABB,
grid_step=1000,
epsilon=0.01)
Xin, Yin = Disassemble2d(fig1)
plt.plot(Xin, Yin, marker=".", linestyle="None", color="blue")
fig2 = ContourPoints(out_figure.f_rep,
AABB=AABB,
grid_step=1000,
epsilon=0.01)
Xout, Yout = Disassemble2d(fig2)
plt.plot(Xout, Yout, marker=".", linestyle="None", color="red")
plt.show()
print(points.shape)
print("{}/{} - {}/{}".format(Cin, Ain, Cout, Aout))
return Cin / Ain - Cout / Aout
r = np.array([radius(points_set[i][0], c[i]) for i in range(num)])
# パラメータ
# p = [x0, y0, z0, r]
r = np.reshape(r, (num, 1))
p = np.concatenate([c, r], axis=1)
# 円生成
Circles = [F.circle(p[i]) for i in range(num)]
# フィットしている点の数を数える
Scores = [
MarkPoints(Circles[i], points, epsilon=0.01)[0] for i in range(num)
]
print(p[Scores.index(max(Scores))], max(Scores))
return p[Scores.index(max(Scores))], Circles[Scores.index(max(Scores))]
C1 = F.circle([0, 0, 1])
points1 = ContourPoints(C1.f_rep, grid_step=450, epsilon=0.01, down_rate=0.5)
print("points:{}".format(points1.shape[0]))
X, Y = Disassemble2d(points1)
plt.plot(X, Y, marker=".", linestyle="None", color="blue")
x, figure = CircleDict(points1)
plot_implicit2d(figure.f_rep, points1)
plt.show()
def RandomCircle2(r, x_min=-100, x_max=100, y_min=-100, y_max=100):
x = Random(x_min, x_max)
y = Random(y_min, y_max)
return F.circle([x, y, r])
def use_dataset(fig_type,
num,
dir_path="data/dataset/tri/",
out_path="data/GAtest/tri/"):
# データセット読み込み
fig_list = np.load(dir_path + "fig.npy")
AABB_list = np.load(dir_path + "AABB.npy")
outArea_list = np.load(dir_path + "outArea.npy")
print("fig:{}".format(np.array(fig_list).shape))
print("AABB:{}".format(np.array(AABB_list).shape))
print("outArea:{}".format(np.array(outArea_list).shape))
# points, outPointsはまずパスを読み込み
points_paths = sorted(glob(dir_path + "points/**.npy"),\
key=lambda s: int(re.findall(r'\d+', s)[len(re.findall(r'\d+', s))-1]))
outPoints_paths = sorted(glob(dir_path + "outPoints/**.npy"),\
key=lambda s: int(re.findall(r'\d+', s)[len(re.findall(r'\d+', s))-1]))
# print(points_paths)
# print(outPoints_paths)
for i in range(num):
print("epoch:{}".format(i))
# points, outPoints読み込み
points = np.load(points_paths[i])
outPoints = np.load(outPoints_paths[i])
# print("points{}:{}".format(i, points.shape))
# print("outPoints{}:{}".format(i, outPoints.shape))
# 他も参照
fig_p = fig_list[i]
AABB = AABB_list[i]
outArea = outArea_list[i]
# print("p:{}".format(fig_p))
# print("AABB:{}".format(AABB))
# print("outArea:{}".format(outArea))
if fig_type == 0:
fig = F.circle(fig_p)
elif fig_type == 1:
fig = F.tri(fig_p)
else:
fig = F.rect(fig_p)
# GAにより最適パラメータ出力
# step1
# best0 = EntireGA(points, outPoints, outArea, CalcIoU0, out_path+"score0/"+str(i)+".png")
# best1 = EntireGA(points, outPoints, outArea, CalcIoU1, out_path+"score1/"+str(i)+".png")
# best2 = EntireGA(points, outPoints, outArea, CalcIoU2, out_path+"score2/"+str(i)+".png")
# best3 = EntireGA(points, outPoints, outArea, CalcIoU3, out_path+"score3/"+str(i)+".png")
# step1.5
#best0, n = EntireGA(points, outPoints, outArea, CalcIoU3, out_path+str(i)+".png")
# step2
# best0, alt0 = EntireGA(points, outPoints, outArea, CalcIoU1, out_path+"score0/"+str(i)+".png", fig_type)
# best1, alt1 = EntireGA(points, outPoints, outArea, CalcIoU1, out_path+"score1/"+str(i)+".png", fig_type,
# half_reset_num=15, all_reset_num=9)
# best2, alt2 = EntireGA(points, outPoints, outArea, CalcIoU1, out_path+"score2/"+str(i)+".png", fig_type,
# add_num=30)
start = time.time()
best3, alt3 = EntireGA(points,
outPoints,
outArea,
CalcIoU1,
out_path + "score3/" + str(i) + ".png",
fig_type,
tournament_size=25,
n_epoch=600,
add_num=30,
half_reset_num=15,
all_reset_num=9)
mid1 = time.time()
best4, alt4 = EntireGA(points,
outPoints,
outArea,
calc_score2,
out_path + "score4/" + str(i) + ".png",
fig_type,
tournament_size=25,
n_epoch=600,
add_num=30,
half_reset_num=15,
all_reset_num=9)
mid2 = time.time()
best5, alt5 = EntireGA(points,
outPoints,
outArea,
calc_score1_5,
out_path + "score5/" + str(i) + ".png",
fig_type,
tournament_size=25,
n_epoch=600,
add_num=30,
half_reset_num=15,
all_reset_num=9)
mid3 = time.time()
best6, alt6 = EntireGA(points,
outPoints,
outArea,
calc_score1_2,
out_path + "score6/" + str(i) + ".png",
fig_type,
tournament_size=25,
n_epoch=600,
add_num=30,
half_reset_num=15,
all_reset_num=9)
end = time.time()
print("score3:{}s, score4:{}s, score5:{}s, score6:{}s".format(
(mid1 - start), (mid2 - mid1), (mid3 - mid2), (end - mid3)))
rec_list = []
# IoU0 = LastIoU(fig, best0.figure, AABB, out_path+"IoU0/"+str(i)+".png")
# IoU1 = LastIoU(fig, best1.figure, AABB, out_path+"IoU1/"+str(i)+".png")
# IoU2 = LastIoU(fig, best2.figure, AABB, out_path+"IoU2/"+str(i)+".png")
IoU3 = LastIoU(fig, best3.figure, AABB,
out_path + "IoU3/" + str(i) + ".png")
IoU4 = LastIoU(fig, best4.figure, AABB,
out_path + "IoU4/" + str(i) + ".png")
IoU5 = LastIoU(fig, best5.figure, AABB,
out_path + "IoU5/" + str(i) + ".png")
IoU6 = LastIoU(fig, best6.figure, AABB,
out_path + "IoU6/" + str(i) + ".png")
# rec_list.append(IoU0)
# if IoU0 == -1:
# rec_list.append(LastIoU(fig, alt0.figure, AABB, out_path+"IoU0/"+str(i)+"re.png"))
# rec_list.append(IoU1)
# if IoU1 == -1:
# rec_list.append(LastIoU(fig, alt1.figure, AABB, out_path+"IoU1/"+str(i)+"re.png"))
# rec_list.append(IoU2)
# if IoU2 == -1:
# rec_list.append(LastIoU(fig, alt2.figure, AABB, out_path+"IoU2/"+str(i)+"re.png"))
rec_list.append(IoU3)
if IoU3 == -1:
rec_list.append(
LastIoU(fig, alt3.figure, AABB,
out_path + "IoU3/" + str(i) + "re.png"))
rec_list.append(IoU4)
if IoU4 == -1:
rec_list.append(
LastIoU(fig, alt4.figure, AABB,
out_path + "IoU4/" + str(i) + "re.png"))
rec_list.append(IoU5)
if IoU5 == -1:
rec_list.append(
LastIoU(fig, alt5.figure, AABB,
out_path + "IoU5/" + str(i) + "re.png"))
rec_list.append(IoU6)
if IoU5 == -1:
rec_list.append(
LastIoU(fig, alt6.figure, AABB,
out_path + "IoU6/" + str(i) + "re.png"))
print(rec_list)
with open(out_path + "circle_k.csv", 'a', newline="") as f:
writer = csv.writer(f)
writer.writerow(rec_list)
