| import numpy as np |
| import json |
| import pdb |
| from matplotlib import pyplot as plt |
| import os |
|
|
| |
| 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_Iteration(list_se_idx, k_iterate, **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 |
|
|
| 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(2): |
| out_img = binary_dilation(out_img, list_se_3x3[list_se_idx[idx]]) |
|
|
| for idx in range(2): |
| out_img = binary_erosion(out_img, list_se_3x3[list_se_idx[idx]]) |
|
|
| for idx in range(k_iterate): |
| out_img = binary_dilation(out_img, list_se_3x3[list_se_idx[-1]]) |
|
|
| for idx in range(k_iterate): |
| out_img = binary_erosion(out_img, list_se_3x3[list_se_idx[-1]]) |
|
|
| return inp_img, out_img |
|
|
|
|
| def generate_one_task_CatB_Iteration(**param): |
| """ |
| """ |
| number_subtasks = 3 |
| list_se_idx = np.random.randint(0, 8, 3) |
| k_iterate = np.random.randint(2, 5) |
|
|
| data_tot = [] |
| list_se_tot = [] |
| k_subtask = 0 |
| while k_subtask < number_subtasks: |
| data_subtask = [] |
| k_example = 0 |
| list_se_subtask = np.array(list_se_idx, copy=True) |
| for idx in [0, 1]: |
| idx_tmp = np.random.randint(0, 8) |
| list_se_subtask[idx] = idx_tmp |
|
|
| while k_example < param['no_examples_per_task']: |
| inp_img, out_img = generate_inp_out_catB_Iteration(list_se_subtask, k_iterate, **param) |
|
|
| |
| 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: |
| |
| |
| data_subtask = [] |
| k_example = -1 |
| list_se_subtask = np.array(list_se_idx, copy=True) |
| for idx in [0, 1]: |
| idx_tmp = np.random.randint(0, 8) |
| list_se_subtask[idx] = idx_tmp |
| else: |
| |
| data_subtask.append((inp_img, out_img, k_subtask)) |
| k_example += 1 |
|
|
| data_tot += data_subtask |
| list_se_tot.append(list_se_subtask) |
| k_subtask += 1 |
|
|
| return data_tot, list_se_tot, k_iterate |
|
|
|
|
| def write_dict_json_CatB_Iteration(data, fname): |
| """ |
| """ |
| dict_data = [] |
| for (inp, out, subtask) 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, "subtask": subtask}) |
|
|
| with open(fname, "w") as f: |
| f.write(json.dumps(dict_data)) |
|
|
|
|
| def write_solution_CatB_Iteration(list_se_idx, k_iterate, fname): |
| """ |
| """ |
| with open(fname, 'w') as f: |
| for list_se_idx_subtask in list_se_idx: |
| f.write("Subtask \n") |
| f.write("-------- \n") |
| i = 0 |
| while i < 2: |
| f.write("Dilation SE{}\n".format(list_se_idx_subtask[i]+1)) |
| i += 1 |
| i = 0 |
| while i < 2: |
| f.write(" Erosion SE{}\n".format(list_se_idx_subtask[i]+1)) |
| i += 1 |
| i = 2 |
| f.write("Iterate {} Dilation SE{}\n".format(k_iterate, list_se_idx_subtask[i]+1)) |
| f.write("Iterate {} Erosion SE{}\n".format(k_iterate, list_se_idx_subtask[i]+1)) |
| f.write("\n") |
|
|
|
|
| def write_solution_CatB_Iteration_json(list_se_idx, k_iterate, fname): |
| """ |
| Solution written in format: |
| subtask - |
| n_iterate - |
| op - Dilation/Erosion/Color_Change |
| SE = SE0-SE7 |
| """ |
| data = [] |
| subtask = 0 |
| for list_se_idx_subtask in list_se_idx: |
| i = 0 |
| while i < 2: |
| data.append((subtask, 1, 'Dilation', 'SE{}'.format(list_se_idx_subtask[i]+1))) |
| i += 1 |
| i = 0 |
| while i < 2: |
| data.append((subtask, 1, 'Erosion', 'SE{}'.format(list_se_idx_subtask[i]+1))) |
| i += 1 |
| data.append((subtask, k_iterate, 'Dilation', 'SE{}'.format(list_se_idx_subtask[i]+1))) |
| data.append((subtask, k_iterate, 'Erosion', 'SE{}'.format(list_se_idx_subtask[i]+1))) |
| subtask += 1 |
|
|
| with open(fname, "w") as f: |
| f.write(json.dumps(data)) |
|
|
|
|
| def generate_100_tasks_CatB_Iteration(seed, **param): |
| """ |
| """ |
| np.random.seed(seed) |
|
|
| os.makedirs("./Dataset/CatB_Iteration", exist_ok=True) |
|
|
| for task_no in range(100): |
| data, list_se_idx, k_iterate = generate_one_task_CatB_Iteration(**param) |
| fname = './Dataset/CatB_Iteration/Task{:03d}.json'.format(task_no) |
| write_dict_json_CatB_Iteration(data, fname) |
|
|
| fname = './Dataset/CatB_Iteration/Task{:03d}_soln.txt'.format(task_no) |
| write_solution_CatB_Iteration(list_se_idx, k_iterate, fname) |
|
|
| fname = './Dataset/CatB_Iteration/Task{:03d}_soln.json'.format(task_no) |
| write_solution_CatB_Iteration_json(list_se_idx, k_iterate, fname) |
|
|
|
|
| if __name__ == "__main__": |
| param = {} |
| param['img_size'] = 15 |
| param['se_size'] = 5 |
| param['seq_length'] = 4 |
| param['no_examples_per_task'] = 4 |
| param['no_colors'] = 3 |
|
|
| generate_100_tasks_CatB_Iteration(32, **param) |
|
|