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creates koffka adelson automatically for different ppd and returns th…
…e stimulus and mask
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makohl2020me
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Apr 12, 2022
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stimuli/illusions/koffka_adelson_auto_including_mask.py
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import numpy as np | ||
from stimuli.utils import resize_array, write_array_to_image | ||
import PIL as pil | ||
import matplotlib.pyplot as plt | ||
import matplotlib.image as mpimg | ||
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# choose values by hand | ||
value_1 = 255 | ||
value_2 = 200 | ||
value_3 = 165 | ||
value_4 = 130 | ||
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# choose ppd | ||
ppd = 64 | ||
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def ini_matrix(ppd): | ||
a = [] | ||
for i in range(ppd): | ||
a.append(np.zeros(ppd)) | ||
return(a) | ||
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def get_mask(arr_list, ppd): | ||
mask = np.array(ini_matrix(ppd)) | ||
target1 = arr_list[0] | ||
target2 = arr_list[1] | ||
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for i in range(int(ppd/16)): | ||
y_diff = target1[2] - target1[0] +1 | ||
for j in range(y_diff): | ||
y = target1[0] | ||
x = target1[1] | ||
mask[y+j][x+i] = 1 | ||
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for i in range(int(ppd/16)): | ||
y_diff = target2[2] - target2[0] +1 | ||
for j in range(y_diff): | ||
y = target2[0] | ||
x = target2[3] | ||
mask[y+j][x+i] = 2 | ||
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plt.imshow(mask) | ||
return(mask) | ||
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def get_array(num, row): | ||
arr = np.array([row]) | ||
for i in range(num): | ||
arr_add = np.array([row]) | ||
arr = (np.concatenate((arr,arr_add))) | ||
return arr | ||
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def koffka_mask(): | ||
arr_1 = np.array([int(ppd/2),int(ppd *(3/4)),int(ppd *(3/4)),int(ppd *(3/4)) ]) | ||
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arr_2 = np.array([int(ppd *(5/16)),int(ppd *(5/16)),int(ppd *(9/16)),int(ppd *(5/16))]) | ||
arr_list = [arr_1,arr_2] | ||
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mask = get_mask(arr_list,ppd) | ||
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return(mask) | ||
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def koffka_adelson_auto(): | ||
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a = np.ones((ppd,1), dtype = int)*value_4 | ||
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b = np.ones((ppd,1), dtype = int)*value_4 | ||
x = int(ppd/8) | ||
y = int(ppd*(11/16)) | ||
b[x:y] = value_2 | ||
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c = np.ones((ppd,1), dtype = int)*value_4 | ||
x1 = int(ppd/8) | ||
y1 = int(ppd*(5/16)) | ||
c[x1:y1] = value_2 | ||
x2 = int(ppd/2) | ||
y2 = int(ppd*(11/16)) | ||
c[x2:y2] = value_2 | ||
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d = np.ones((ppd,1), dtype = int)*value_1 | ||
x1 = int(ppd*(5/16)) | ||
y1 = int(ppd/2) | ||
d[x1:y1] = value_3 | ||
x2 = int(ppd*(11/16)) | ||
y2 = int(ppd*(7/8)) | ||
d[x2:y2] = value_3 | ||
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e = np.ones((ppd,1), dtype = int)*value_1 | ||
x1 = x1 | ||
y1 = y2 | ||
e[x1:y1] = value_3 | ||
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f = np.ones((ppd,1), dtype = int)*value_1 | ||
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arr = np.concatenate((get_array(int(ppd*(3/16)),a), | ||
get_array(int(ppd*(3/16)),b), | ||
get_array(int(ppd*(2/16)),c), | ||
get_array(int(ppd*(2/16)),d), | ||
get_array(int(ppd*(3/16)),e), | ||
get_array(int(ppd*(3/16)),f))) | ||
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arr = np.fliplr(np.rot90((arr),3)) | ||
return(arr) | ||
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if __name__ == '__main__': | ||
#test | ||
#plt.imshow(koffka_adelson_auto()) | ||
#koffka_mask() | ||
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