File size: 3,450 Bytes
d5bfab8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
import os
import numpy as np
import json
import pdb
from matplotlib import pyplot as plt
# from skimage.morphology import binary_dilation, binary_erosion
from scipy.ndimage import binary_dilation, binary_erosion, binary_hit_or_miss
import random
from ListSelEm import *
from Utils import Process, Change_Colour
def generate_inp_out_catA_Simple(list_se_idx, **param):
"""
"""
base_img = np.zeros((param['img_size'], param['img_size']), dtype=np.int32)
sz = np.random.randint(3, 6)
idx1 = np.random.randint(0, param['img_size'], size=sz)
idx2 = np.random.randint(0, param['img_size'], size=sz)
base_img[idx1, idx2] = 1
# Select a random SE to dilate the base image
# This way the inputs would have some structure but still remain random.
for _ in range(2):
idx = np.random.randint(0, 8)
base_img = binary_dilation(base_img, list_se_3x3[idx])
inp_img = np.array(base_img, copy=True)
out_img = np.array(base_img, copy=True)
for idx in range(len(list_se_idx)):
out_img = binary_dilation(out_img, list_se_3x3[list_se_idx[idx]])
for idx in range(len(list_se_idx)):
out_img = binary_erosion(out_img, list_se_3x3[list_se_idx[idx]])
return inp_img, out_img
def generate_one_task_CatA_Simple(**param):
"""
"""
list_se_idx = np.random.randint(0, 8, 4)
data = []
k = 0
while k < param['no_examples_per_task']:
inp_img, out_img = generate_inp_out_catA_Simple(list_se_idx, **param)
# Check if both input and output images are non-trivial
FLAG = False
if np.all(inp_img*1 == 1) or np.all(inp_img*1 == 0):
FLAG = True
elif np.all(out_img*1 == 1) or np.all(out_img*1 == 0):
FLAG = True
if FLAG:
list_se_idx = np.random.randint(0, 8, 4)
data = []
k = -1
else:
# If not trivial proceed.
data.append((inp_img, out_img))
k += 1
return data, list_se_idx
def write_dict_json_CatA_Simple(data, fname):
"""
"""
dict_data = []
for (inp, out) in data:
inp = [[int(y) for y in x] for x in inp]
out = [[int(y) for y in x] for x in out]
dict_data.append({"input": inp, "output": out})
with open(fname, "w") as f:
f.write(json.dumps(dict_data))
def write_solution_CatA_Simple(list_se_idx, fname):
"""
"""
with open(fname, 'w') as f:
for idx in list_se_idx:
f.write("Dilation SE{}\n".format(idx+1))
for idx in list_se_idx:
f.write("Erosion SE{}\n".format(idx+1))
def generate_100_tasks_CatA_Simple(seed, **param):
"""
"""
np.random.seed(seed)
os.makedirs("./Dataset/CatA_Simple", exist_ok=True)
for task_no in range(100):
data, list_se_idx = generate_one_task_CatA_Simple(**param)
fname = './Dataset/CatA_Simple/Task{:03d}.json'.format(task_no)
write_dict_json_CatA_Simple(data, fname)
fname = './Dataset/CatA_Simple/Task{:03d}_soln.txt'.format(task_no)
write_solution_CatA_Simple(list_se_idx, fname)
if __name__ == "__main__":
param = {}
param['img_size'] = 15
param['se_size'] = 5 # Size of the structuring element
param['seq_length'] = 4 # Number of primitives would be 2*param['seq_length']
param['no_examples_per_task'] = 4
param['no_colors'] = 3
generate_100_tasks_CatA_Simple(32, **param)
|