Download IPARC_ChallengeV2/TestPredicate.py from Alignment-Lab-AI/ARC-stuff: direct link, hf CLI and curl.
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- Download file 2.75 kB
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https://huggingface.co/datasets/Alignment-Lab-AI/ARC-stuff/resolve/ae779ad6ae2b063e5ea32e44356fa02ed2af6b42/IPARC_ChallengeV2/TestPredicate.py
- Command line
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hf download hf://datasets/Alignment-Lab-AI/ARC-stuff@ae779ad6ae2b063e5ea32e44356fa02ed2af6b42/IPARC_ChallengeV2/TestPredicate.py
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curl -L -o TestPredicate.py https://huggingface.co/datasets/Alignment-Lab-AI/ARC-stuff/resolve/ae779ad6ae2b063e5ea32e44356fa02ed2af6b42/IPARC_ChallengeV2/TestPredicate.py
2.75 kB
| #!/usr/bin/python | |
| """ | |
| """ | |
| import os.path | |
| import sys | |
| import numpy as np | |
| import json | |
| import pdb | |
| from matplotlib import pyplot as plt | |
| import os | |
| from ast import literal_eval | |
| # 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 write_output_json(data): | |
| """ | |
| """ | |
| dict_data = [] | |
| for tmp in data: | |
| if len(tmp.shape) == 2: | |
| out = [[int(y) for y in x] for x in tmp] | |
| elif len(tmp.shape) == 3: | |
| out = [[[int(x3) for x3 in x2] for x2 in x1] for x1 in tmp] | |
| dict_data.append({"input": out}) | |
| print(json.dumps(dict_data)) | |
| def perform_op(fname, list_ops): | |
| def _perform_task(img, k_iterate, op, se): | |
| for _ in range(k_iterate): | |
| list_se = ['SE1', 'SE2', 'SE3', 'SE4', 'SE5', 'SE6', 'SE7', 'SE8'] | |
| if op == 'Dilation': | |
| list_se_idx = list_se.index(se) | |
| img = binary_dilation(img, list_se_3x3[list_se_idx]) | |
| elif op == 'Erosion': | |
| list_se_idx = list_se.index(se) | |
| img = binary_erosion(img, list_se_3x3[list_se_idx]) | |
| elif op == 'HitOrMiss': | |
| list_se_idx = list_se.index(se) | |
| tmp_img = binary_hit_or_miss(img, list_se_3x3[list_se_idx]) | |
| img[tmp_img] = 2 # Add another color | |
| img = Process(img, num_colors=2) | |
| elif op == 'ChangeColor': | |
| img = Change_Colour(img, np.array(se, dtype=np.int32)) | |
| return img | |
| # Load the dataset. | |
| with open(fname, 'r') as f: | |
| data = json.load(f) | |
| out = [] | |
| for d in data: | |
| img = np.array(d['input'], dtype=np.int32) | |
| if len(img.shape) == 2: | |
| sx, sy = img.shape | |
| img = img.reshape((sx, sy, 1)) | |
| for band, k_iterate, op, se in list_ops: | |
| if op != 'HitOrMiss' and op != 'ChangeColor': | |
| img[:, :, band-1] = _perform_task(img[:, :, band-1], k_iterate, op, se) | |
| elif op == 'HitOrMiss': | |
| img = _perform_task(img[:, :, band-1], k_iterate, op, se) | |
| else: | |
| img = _perform_task(img, k_iterate, op, se) | |
| img = img*1 | |
| out.append(img) | |
| write_output_json(out) | |
| if __name__ == '__main__': | |
| fname = sys.argv[1] | |
| list_ops = [] | |
| for arg in sys.argv[2:]: | |
| band, op, se, k_iterate = str(arg).split("-") | |
| band = int(band) | |
| k_iterate = int(k_iterate) | |
| if op == 'ChangeColor': | |
| se = np.array(literal_eval(se)) | |
| list_ops.append((band, k_iterate, op, se)) | |
| perform_op(fname, list_ops) | |