tkinterにおける跳ね返り処理

前提・実現したいこと

Pythonを使ってBOIDモデルを実装しています．

発生している問題・エラーメッセージ

Python標準のtkinterを使っているのですが，birdが複数いる時の跳ね返りの処理が上手く動作せず，右下の角に収束してしまいます．

該当のソースコード

Python
1import math
2import random
3from statistics import mean
4from tkinter import *
5
6
7class Coordinate():
8    def __init__(self):
9        self.x = 0
10        self.y = 0
11
12    def reset(self):
13        self.x = 0
14        self.y = 0
15
16
17class Bird():
18    """
19    Bird Class
20    """
21    def __init__(self, x, y, vx, vy, identifier, field_info, others, view):
22        """Bird Class Constructor
23
24        BOID用のエージェントです
25        :param x: x coordinate
26        :param y: y coordinate
27        :param vx: x方向の移動量
28        :param vy:　y方向の移動量
29        :param id: 識別用id
30        :param field_info: (field_width, field_height, fish_radius, fish_speed)
31        :param others: other fish
32        :param view: 視野の広さ(半径)
33        """
34        self.x = x
35        self.y = y
36        self.vx = vx
37        self.vy = vy
38        self.identifier = identifier
39        self.others = others
40        self.view = view
41        self.field_info = field_info
42        self.inverse_x = self.field_info[0] - self.x
43        self.inverse_y = self.field_info[1] - self.y
44        self.r1 = 2.0 # cohesion coefficient
45        self.r2 = 0.5 # separation coefficient
46        self.r3 = 1.0 # alignment coefficient
47        self.neighbors = []  # 近くにいるエージェント
48
49        self.v1 = Coordinate()
50        self.v2 = Coordinate()
51        self.v3 = Coordinate()
52
53    def set_others(self, others):
54        self.others = others
55        self.find_neighbors()
56
57    # 結合
58    def cohesion(self):
59        center_pull_coeff = 10
60        if len(self.neighbors) == 0:
61            return
62        self.v1.x = mean([agent.x for agent in self.neighbors if not agent.identifier == self.identifier])
63        self.v1.y = mean([agent.y for agent in self.neighbors if not agent.identifier == self.identifier])
64        self.v1.x = (self.v1.x - self.x) / center_pull_coeff
65        self.v1.y = (self.v1.y - self.y) / center_pull_coeff
66
67    # 離脱
68    def separation(self):
69        personal_space = 50
70        for agent in self.neighbors:
71            if self.identifier == agent.identifier:
72                continue
73
74            dist = self.calc_distance(agent, self)
75            if dist <= personal_space:
76                self.v2.x -= (agent.x - self.x) / dist
77                self.v2.y -= (agent.y - self.y) / dist
78
79    def _separation(self):
80        if len(self.neighbors) == 0:
81            return
82        self.v2.x = mean([agent.x for agent in self.neighbors if not agent.identifier == self.identifier]) - self.x
83        self.v2.y = mean([agent.y for agent in self.neighbors if not agent.identifier == self.identifier]) - self.y
84
85    # 距離の計算
86    def calc_distance(self, a, b):
87        return min(math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2),
88                   math.sqrt((a.inverse_x - b.x) ** 2 + (a.inverse_y - b.y) ** 2))
89
90    # 整列
91    def alignment(self):
92        if len(self.neighbors) == 0:
93            return
94        self.v3.x = mean([agent.x for agent in self.neighbors if not self.identifier == agent.identifier])
95        self.v3.y = mean([agent.y for agent in self.neighbors if not self.identifier == agent.identifier])
96        self.v3.x = (self.v3.x - self.vx) / 2
97        self.v3.y = (self.v3.y - self.vy) / 2
98
99    def _collision_detection(self):
100        if self.x - self.field_info[2] <= 0:
101            self.vx *= -1
102            self.x = self.field_info[2]
103        if self.x + self.field_info[2] >= self.field_info[0]:
104            self.vx *= -1
105            self.x = self.field_info[0] - self.field_info[2]
106
107        if self.y - self.field_info[2] <= 0:
108            self.vy *= -1
109            self.y = self.field_info[2]
110        if self.y + self.field_info[2] >= self.field_info[1]:
111            self.vy *= -1
112            self.y = self.field_info[1] - self.field_info[2]
113
114    def step(self):
115        self.cohesion()
116        self.separation()
117        self.alignment()
118
119    def find_neighbors(self):
120        self.neighbors = [neighbor for neighbor in self.others
121                          if self.calc_distance(self, neighbor) <= self.view and
122                          not self.identifier == neighbor.identifier]
123
124    def update(self):
125        self.vx += self.r1 * self.v1.x + self.r2 * self.v2.x + self.r3 * self.v3.x
126        self.vy += self.r1 * self.v1.y + self.r2 * self.v2.y + self.r3 * self.v3.y
127
128        distance = math.sqrt(self.vx ** 2 + self.vy ** 2)
129        if distance > self.field_info[3]:
130            self.vx = (self.vx / distance) * self.field_info[3]
131            self.vy = (self.vy / distance) * self.field_info[3]
132
133        self.x += int(self.vx)
134        self.y += int(self.vy)
135
136        self._collision_detection()
137        print(self.vx, self.vy)
138
139        self.inverse_x = self.field_info[0] - self.x
140        self.inverse_y = self.field_info[1] - self.y
141
142    def clear_movement(self):
143        self.v1.reset()
144        self.v2.reset()
145        self.v3.reset()
146
147    def draw(self):
148        self.clear_movement()
149        self.step()
150        self.update()
151        return self.x, self.y, self.vx, self.vy
152
153root = Tk()
154
155WIDTH = 1000
156HEIGHT = 1000
157
158fish_radian = 10
159bird_num = 3
160SPEED = 5
161VIEW = 50
162
163c = Canvas(root, width=WIDTH, height=HEIGHT)
164c.pack()
165
166
167fish_list = [Bird(random.randint(0, WIDTH), random.randint(0, HEIGHT), random.randint(-SPEED, SPEED),
168                  random.randint(-SPEED, SPEED), i, (WIDTH, HEIGHT, fish_radian, SPEED), [], VIEW) for i in range(bird_num)]
169[fish.set_others(fish_list) for fish in fish_list]
170
171
172def animate():
173    c.delete("all")
174    for fish in fish_list:
175        fish.draw()
176        c.create_oval(fish.x - fish_radian, fish.y - fish_radian, fish.x+fish_radian, fish.y+fish_radian)
177        c.create_line(fish.x, fish.y, fish.x + fish.vx * 3, fish.y + fish.vy * 3)
178    [fish.set_others(fish_list) for fish in fish_list]
179    root.after(20, animate)
180
181animate()
182root.mainloop()
183
184

試したこと

条件式を色々変えて試しました．
ほぼ全ての条件で同様にウィンドウの右下に収束してしまいました．

補足情報（FW/ツールのバージョンなど）

MacOS Catalina
Python 3.6.8

行動規範の内容に同意します

回答1件

ベストアンサー

python
1        self.vx += self.r1 * self.v1.x + self.r2 * self.v2.x + self.r3 * self.v3.x
2        self.vy += self.r1 * self.v1.y + self.r2 * self.v2.y + self.r3 * self.v3.y

self.vx, self.vy がどんどん変化して、どんどん早くなっていくと思うのですが、意図した動作ですか？

ソースの理解を兼ねてリファクタリングしてみました。
問題そうな部分も修正してみました。

python
1import random
2from statistics import mean
3from tkinter import *
4
5
6class Field:
7    WIDTH = 300
8    HEIGHT = 200
9
10
11class Coordinate():
12
13    def __init__(self):
14        self.reset()
15
16    def reset(self):
17        self.x = 0
18        self.y = 0
19
20
21class Bird():
22    NUM = 3
23    RADIAN = 10
24    SPEED = 5
25    VIEW = 50
26
27    @staticmethod
28    def setup():
29        Bird.birds = [Bird() for _ in range(Bird.NUM)]
30
31    def __init__(self):
32        self.x = random.randint(0, Field.WIDTH)
33        self.y = random.randint(0, Field.HEIGHT)
34        self.vx = random.randint(-self.SPEED, self.SPEED)
35        self.vy = random.randint(-self.SPEED, self.SPEED)
36        self.view = self.VIEW
37        self.inverse_x = Field.WIDTH - self.x
38        self.inverse_y = Field.HEIGHT - self.y
39        self.r1 = 2.0 # cohesion coefficient
40        self.r2 = 0.5 # separation coefficient
41        self.r3 = 1.0 # alignment coefficient
42
43        self.v1 = Coordinate()
44        self.v2 = Coordinate()
45        self.v3 = Coordinate()
46
47    def neighbors(self):
48        return [agent for agent in self.birds if agent != self]
49
50    # 結合
51    def cohesion(self):
52        neighbors = self.neighbors()
53        if not neighbors:
54            return
55        center_pull_coeff = 10
56        self.v1.x = mean([agent.x for agent in neighbors])
57        self.v1.y = mean([agent.y for agent in neighbors])
58        self.v1.x = (self.v1.x - self.x) / center_pull_coeff
59        self.v1.y = (self.v1.y - self.y) / center_pull_coeff
60
61    # 離脱
62    def separation(self):
63        personal_space = 50
64        for agent in self.neighbors():
65            dist = self.calc_distance(agent)
66            if dist and dist <= personal_space:
67                self.v2.x -= (agent.x - self.x) / dist
68                self.v2.y -= (agent.y - self.y) / dist
69
70    def _separation(self):
71        neighbors = self.neighbors()
72        if not neighbors:
73            return
74        self.v2.x = mean([agent.x for agent in neighbords]) - self.x
75        self.v2.y = mean([agent.y for agent in neighbords]) - self.y
76
77    # 距離の計算
78    def calc_distance(self, target):
79        return ((target.x - self.x) ** 2 + (target.y - self.y) ** 2) ** 0.5
80
81    # 整列
82    def alignment(self):
83        neighbors = self.neighbors()
84        if not neighbors:
85            return
86        self.v3.x = mean([agent.x for agent in neighbors])
87        self.v3.y = mean([agent.y for agent in neighbors])
88        self.v3.x = (self.v3.x - self.vx) / 2
89        self.v3.y = (self.v3.y - self.vy) / 2
90
91    def _collision_detection(self):
92        if self.x - self.RADIAN < 0:
93            self.vx *= -1
94            self.x = self.RADIAN
95        if self.x + self.RADIAN > Field.WIDTH:
96            self.vx *= -1
97            self.x = Field.WIDTH - self.RADIAN
98
99        if self.y - self.RADIAN < 0:
100            self.vy *= -1
101            self.y = self.RADIAN
102        if self.y + self.RADIAN > Field.HEIGHT:
103            self.vy *= -1
104            self.y = Field.HEIGHT - self.RADIAN
105
106    def step(self):
107        self.cohesion()
108        self.separation()
109        self.alignment()
110
111    def update(self):
112        dx = self.r1 * self.v1.x + self.r2 * self.v2.x + self.r3 * self.v3.x
113        dy = self.r1 * self.v1.y + self.r2 * self.v2.y + self.r3 * self.v3.y
114        self.vx += dx if self.vx >= 0 else -dx
115        self.vy += dy if self.vy >= 0 else -dy
116
117        distance = (self.vx ** 2 + self.vy ** 2) ** 0.5
118        if distance > self.SPEED:
119            self.vx = (self.vx / distance) * self.SPEED
120            self.vy = (self.vy / distance) * self.SPEED
121
122        self.x += int(self.vx)
123        self.y += int(self.vy)
124
125        self._collision_detection()
126        print(self.vx, self.vy)
127
128        self.inverse_x = Field.WIDTH - self.x
129        self.inverse_y = Field.HEIGHT - self.y
130
131    def clear_movement(self):
132        self.v1.reset()
133        self.v2.reset()
134        self.v3.reset()
135
136    def draw(self, drawer):
137        self.clear_movement()
138        self.step()
139        self.update()
140        drawer.create_oval(self.x - self.RADIAN, self.y - self.RADIAN, self.x+self.RADIAN, self.y+self.RADIAN)
141        drawer.create_line(self.x, self.y, self.x + self.vx * 3, self.y + self.vy * 3)
142
143def main():
144    Bird.setup()
145
146    root = Tk()
147
148    canvas = Canvas(root, width=Field.WIDTH, height=Field.HEIGHT)
149    canvas.pack()
150
151    def animate():
152        canvas.delete("all")
153        for bird in Bird.birds:
154            bird.draw(canvas)
155        root.after(20, animate)
156
157    animate()
158    root.mainloop()
159
160if __name__ == '__main__':
161    main()

投稿2019/12/01 11:59

編集2019/12/01 13:32

shiracamus

総合スコア5406

tkr1205

2019/12/01 12:47

shiracamusさん，ありがとうございます． https://qiita.com/takashi/items/9d684a6e3742a15e8726 こちらの記事を参考にして書いたコードなのですが，self.vx, self.vyに関しては，その下にあります条件式で，一定の値を超えた場合は調整するようにしております．

shiracamus

2019/12/01 13:18

それでピンときましたが、 dx = self.r1 * self.v1.x + self.r2 * self.v2.x + self.r3 * self.v3.x dy = self.r1 * self.v1.y + self.r2 * self.v2.y + self.r3 * self.v3.y self.vx += dx if self.vx >= 0 else -dx self.vy += dy if self.vy >= 0 else -dy でいかがでしょう？回答欄のソースコードも修正してみました。

tkr1205

2019/12/01 13:37

上手く壁で跳ね返ってくれるようになりました．ありがとうございます！まだ，BOIDとしての動きには問題があるので修正しようと思います．ありがとうございます．

行動規範の内容に同意します