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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_catB_Selection(list_se, **param):
"""
SE0/SE1 - Hit-Or-Miss
SE2/3 - Dilate (SE0)
SE2/3 - Erode (SE0)
SE4/5 - Dilate (SE1)
SE4/5 - Erode (SE1)
"""
sz = np.random.randint(2, 4)
# Select 1/2 pixels and dilate by SE0
base_img1 = np.zeros((param['img_size'], param['img_size']), dtype=np.int32)
idx1 = np.random.randint(0, param['img_size']//2, size=sz)
idx2 = np.random.randint(0, param['img_size']//2, size=sz)
base_img1[idx1, idx2] = 1
base_img1 = binary_dilation(base_img1, list_se_3x3[list_se[0]])
# Select 1/2 pixels and dilate by SE1
base_img2 = np.zeros((param['img_size'], param['img_size']), dtype=np.int32)
idx1 = np.random.randint(param['img_size']//2, param['img_size'], size=sz)
idx2 = np.random.randint(param['img_size']//2, param['img_size'], size=sz)
base_img2[idx1, idx2] = 1
base_img2 = binary_dilation(base_img2, list_se_3x3[list_se[1]])
# Combine the above images to get the base image.
base_img = np.logical_or(base_img1, base_img2)*1
# Copy the base_img for input/output
inp_img = np.array(base_img*1, copy=True)
out_img = np.array(base_img*1, copy=True)
# Next we have a hit_or_miss which selects a pixel and adds another color
tmp_img = binary_hit_or_miss(out_img, list_se_3x3[list_se[0]])
out_img[tmp_img] = 2 # Add another color
out_img = Process(out_img, num_colors=2)
# First color will be processed differently
out_img[:, :, 0] = binary_dilation(out_img[:, :, 0], list_se_3x3[list_se[2]])
out_img[:, :, 0] = binary_dilation(out_img[:, :, 0], list_se_3x3[list_se[3]])
out_img[:, :, 0] = binary_erosion(out_img[:, :, 0], list_se_3x3[list_se[2]])
out_img[:, :, 0] = binary_erosion(out_img[:, :, 0], list_se_3x3[list_se[3]])
# Second color will be processed differently
out_img[:, :, 1] = binary_dilation(out_img[:, :, 1], list_se_3x3[list_se[0]])
out_img[:, :, 1] = binary_dilation(out_img[:, :, 1], list_se_3x3[list_se[4]])
out_img[:, :, 1] = binary_dilation(out_img[:, :, 1], list_se_3x3[list_se[5]])
out_img[:, :, 1] = binary_erosion(out_img[:, :, 1], list_se_3x3[list_se[4]])
out_img[:, :, 1] = binary_erosion(out_img[:, :, 1], list_se_3x3[list_se[5]])
# Resolve the color by the rule
rule = np.array([[0, 0, 0], [0, 1, 2], [1, 0, 1], [1, 1, 2]], dtype=np.int32)
out_img = Change_Colour(out_img, rule)
return inp_img, out_img
def generate_one_task_CatB_Selection(**param):
"""
"""
k_example = 0
list_se_idx = np.random.randint(0, 8, size=6)
data = []
while k_example < param['no_examples_per_task']:
inp_img, out_img = generate_inp_out_catB_Selection(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:
# If trivial regenerate the list of se's
# And reset all variables!!
data = []
list_se_idx = np.random.randint(0, 8, size=6)
k_example = -1
else:
data.append((inp_img, out_img))
# Increment k_example
k_example += 1
return data, list_se_idx
def write_dict_json_CatB_Selection(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_CatB_Selection(list_se_idx, fname):
"""
"""
color_rule = np.array([[0, 0, 0], [0, 1, 2], [1, 0, 1], [1, 1, 2]], dtype=np.int32)
with open(fname, 'w') as f:
f.write("Hit-Or-Miss SE{} \n".format(list_se_idx[0]))
f.write("Band 1 - Dilation SE{} \n".format(list_se_idx[2]+1))
f.write("Band 1 - Dilation SE{} \n".format(list_se_idx[3]+1))
f.write("Band 1 - Erosion SE{} \n".format(list_se_idx[2]+1))
f.write("Band 1 - Erosion SE{} \n".format(list_se_idx[3]+1))
f.write("Band 2 - Dilation SE{} \n".format(list_se_idx[0]+1))
f.write("Band 2 - Dilation SE{} \n".format(list_se_idx[4]+1))
f.write("Band 2 - Dilation SE{} \n".format(list_se_idx[5]+1))
f.write("Band 2 - Erosion SE{} \n".format(list_se_idx[4]+1))
f.write("Band 2 - Erosion SE{} \n".format(list_se_idx[5]+1))
f.write("Color rule : {}".format(json.dumps([[int(y) for y in x] for x in color_rule])))
f.write("\n")
def write_solution_CatB_Selection_json(list_se_idx, fname):
"""
"""
color_rule = np.array([[0, 0, 0], [0, 1, 2], [1, 0, 1], [1, 1, 2]], dtype=np.int32)
data = []
data.append((None, "Hit-Or-Miss", "SE{}".format(list_se_idx[0]+1)))
data.append((1, "Dilation", "SE{}".format(list_se_idx[2]+1)))
data.append((1, "Dilation", "SE{}".format(list_se_idx[3]+1)))
data.append((1, "Erosion", "SE{}".format(list_se_idx[2]+1)))
data.append((1, "Erosion", "SE{}".format(list_se_idx[3]+1)))
data.append((2, "Dilation", "SE{}".format(list_se_idx[0]+1)))
data.append((2, "Dilation", "SE{}".format(list_se_idx[4]+1)))
data.append((2, "Dilation", "SE{}".format(list_se_idx[5]+1)))
data.append((2, "Erosion", "SE{}".format(list_se_idx[4]+1)))
data.append((2, "Erosion", "SE{}".format(list_se_idx[5]+1)))
data.append((None, "change_color", [[int(y) for y in x] for x in color_rule]))
with open(fname, "w") as f:
f.write(json.dumps(data))
def generate_100_tasks_CatB_Selection(seed, **param):
"""
"""
np.random.seed(seed)
os.makedirs("./Dataset/CatB_Selection", exist_ok=True)
for task_no in range(100):
data, list_se_idx = generate_one_task_CatB_Selection(**param)
fname = './Dataset/CatB_Selection/Task{:03d}.json'.format(task_no)
write_dict_json_CatB_Selection(data, fname)
fname = './Dataset/CatB_Selection/Task{:03d}_soln.txt'.format(task_no)
write_solution_CatB_Selection(list_se_idx, fname)
fname = './Dataset/CatB_Selection/Task{:03d}_soln.json'.format(task_no)
write_solution_CatB_Selection_json(list_se_idx, fname)
if __name__ == "__main__":
param = {}
param['img_size'] = 15
param['se_size'] = 3 # 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_CatB_Selection(32, **param)
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