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import numpy as np
from PIL import Image, ImageDraw
def prepare_image(image: Image):
# convert image
width, height = image.size
width = width // 8 * 8
height = height // 8 * 8
image = image.crop((0, 0, width, height))
image = image.convert('L')
image_array = []
# image to arrays
for x in range(width):
for y in range(height):
crop = image.crop((x, y, x + 8, y + 8))
image_array.append(np.reshape(np.asarray(crop) / 255, (1, 64)))
# save image_array
image_array = np.asarray(image_array)
return image_array
def draw_image(map, size):
# size
step = 10
width, height = size
new_width = width // 8 * 8 * step
new_height = height // 8 * 8 * step
# create canvas
image = Image.new('RGB', (new_width, new_height), (255, 255, 255))
draw = ImageDraw.Draw(image)
iter = 0
# drawing
for x in range(0, new_width, step):
for y in range(0, new_height, step):
if map[iter] == 1:
xn, yn = x, y + 8
elif map[iter] == 2:
xn, yn = x + 8, y
elif map[iter] == 3:
xn, yn = x + 8, y - 8
elif map[iter] == 4:
xn, yn = x + 8, y + 8
else:
iter += 1
continue
draw.line(xy=[(x, y), (xn, yn)], fill='black')
iter += 1
image = image.resize((width, height), Image.Resampling.LANCZOS)
return image
def create_map(image_array):
# Load synapses
synapses = np.load('./final_synapses.npz')
W1 = synapses['arr_0']
b1 = synapses['arr_1']
W2 = synapses['arr_2']
b2 = synapses['arr_3']
W3 = synapses['arr_4']
b3 = synapses['arr_5']
def predict(x):
def relu(t):
return np.maximum(t, 0)
def softmax(t):
out = np.exp(t)
return out / np.sum(out)
# Calculate
t1 = x @ W1 + b1
h1 = relu(t1)
t2 = h1 @ W2 + b2
h2 = relu(t2)
t3 = h2 @ W3 + b3
z = softmax(t3)
return z
# Form map
map = []
for x in image_array:
z = predict(x)
y_pred = np.argmax(z)
map.append(y_pred)
return map
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