First commit

gifcreator.py

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+#!/usr/bin/python
+# -*- coding: utf-8 -*- 
+
+__author__ = "Aleksey Lobanov"
+__copyright__ = "Copyright 2016, Aleksey Lobanov"
+__credits__ = ["Aleksey Lobanov"]
+__license__ = "MIT"
+__maintainer__ = "Aleksey Lobanov"
+__email__ = "i@likemath.ru"
+
+import sys
+from copy import deepcopy
+
+import numpy as np
+
+import keras
+from keras.models import Sequential
+from keras.layers import Dense
+
+import imageio  # for gifs
+
+from PIL import Image, ImageDraw
+
+from neurallife import nextGen
+
+
+"""
+# from original article
+start_pos = [
+    [0,0,0,0,1,0,1,0,0],
+    [0,1,0,0,1,0,0,1,0],
+    [0,1,1,0,1,1,0,1,0],
+    [1,0,0,1,1,0,0,0,0],
+    [0,1,1,1,0,1,0,1,0],
+    [0,0,1,0,1,0,0,0,0],
+    [0,0,1,1,0,0,1,0,0],
+    [0,1,1,0,1,1,0,0,0],
+    [0,0,0,0,0,0,0,0,0]
+]
+"""
+
+# about 27 original positions
+start_pos = [
+    [0, 0, 0, 0, 0, 0, 0, 1, 0],
+    [0, 0, 0, 0, 0, 0, 1, 1, 0],
+    [0, 0, 1, 0, 0, 1, 1, 1, 0],
+    [1, 0, 1, 0, 1, 0, 0, 0, 0],
+    [1, 0, 1, 0, 1, 1, 0, 0, 0],
+    [0, 1, 0, 0, 0, 0, 0, 0, 0],
+    [0, 0, 0, 0, 0, 0, 0, 0, 0],
+    [0, 0, 0, 0, 0, 1, 0, 0, 0],
+    [0, 0, 0, 0, 0, 1, 1, 0, 0]
+]
+
+
+"""
+Code for good, long start positions:
+
+def getBest(max_cnt):
+    cur_max = 0
+    cur_best = None
+    for i in range(max_cnt):
+        cur_field = np.random.randint(2, size=(9,9)).tolist()
+        cur_cnt = getCnt(cur_field)
+        if cur_cnt > cur_max:
+            cur_max = getCnt(cur_field, 200)
+            cur_best = cur_field
+    return (cur_best,cur_max)
+
+
+def getCnt(pos, max_cnt=100):
+    cnt = 0
+    ker_pred = pos
+    cur_pos = pos
+    while True:
+        cnt += 1
+        ker_pred = nn.predict(np.asarray(ker_pred).reshape((1,1,9,9)))
+        ker_pred = np.rint(ker_pred).astype("int").reshape((9,9)).tolist()
+
+        old_cur_pos = deepcopy(cur_pos)
+        next_gen(cur_pos, len(cur_pos))
+        if np.asarray(old_cur_pos).sum() == np.asarray(cur_pos).sum():
+            break
+        if ker_pred != cur_pos:
+            break
+        if cnt > max_cnt:
+            return 0
+    return cnt
+"""
+
+LINE_SIZE = 2
+SQUARE_SIZE = 18
+FRAME_COUNT = 30
+FRAME_DELAY = 0.3  # in seconds
+
+
+def loadKeras(path):
+    model = keras.models.model_from_json(open(path + '.json').read())
+    model.load_weights(path + '.h5')
+    model.compile(loss='MSE', optimizer='nadam', metrics=[ ])
+    return model
+
+
+def imageFromList(l):
+    global LINE_SIZE, SQUARE_SIZE
+    height = LINE_SIZE * (len(l) + 1) + SQUARE_SIZE * len(l)  # =height
+    width = LINE_SIZE * (len(l[0]) + 1) + SQUARE_SIZE * len(l[0])
+    tmp_img = Image.new('RGB', (width, height), (0, 0, 0))
+    pil_draw = ImageDraw.Draw(tmp_img)
+    for y in range(len(l)):
+        for x in range(len(l[0])):
+            if l[y][x] == 0:
+                pil_draw.rectangle((
+                    x * (LINE_SIZE + SQUARE_SIZE) + LINE_SIZE,
+                    y * (LINE_SIZE + SQUARE_SIZE) + LINE_SIZE,
+                    (x + 1) * (LINE_SIZE + SQUARE_SIZE)-1,
+                    (y + 1) * (LINE_SIZE + SQUARE_SIZE)-1,
+                ), fill=(255, 255, 255))
+    return tmp_img
+
+N = 9  # board size
+
+if __name__ == '__main__':
+    nn = loadKeras(sys.argv[1])
+    nn_frames = []
+    real_frames = []
+    ker_pred = cur_pos = start_pos
+    
+    for i in range(FRAME_COUNT):
+        #ker_pred = cur_pos
+        
+        nn_frames.append(imageFromList(ker_pred))
+        ker_pred = nn.predict(np.asarray(ker_pred).reshape((1, N**2)))
+        ker_pred = np.rint(ker_pred).astype("int").reshape((N, N)).tolist()
+        
+        real_frames.append(imageFromList(cur_pos))
+        old_pos = deepcopy(cur_pos)
+        nextGen(cur_pos, len(start_pos))
+        
+        # because need some pause at end
+        #if cur_pos == old_pos:
+        #    break
+    width, height = nn_frames[0].size
+    
+    imageio.mimsave(
+        'gif_neural.gif',
+        [np.asarray(img.getdata()).reshape((width, height, 3)) for img in nn_frames],
+        fps=1/FRAME_DELAY
+        )
+    imageio.mimsave(
+        'gif_real.gif',
+        [np.asarray(img.getdata()).reshape((width, height, 3)) for img in real_frames],
+        fps=1/FRAME_DELAY
+        )
+
+    
+