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| # Copyright (c) 2021 Anish Athalye. Released under the MIT license. | |
| import numpy as np | |
| import onnx | |
| from onnx_tf.backend import prepare | |
| from PIL import Image | |
| def load_model(path): | |
| onnx_model = onnx.load(path) | |
| model = prepare(onnx_model, training_mode=True) | |
| return model | |
| def load_seed(path): | |
| seed = open(path, 'rb').read()[128:] | |
| seed = np.frombuffer(seed, dtype=np.float32) | |
| seed = seed.reshape([96, 128]) | |
| return seed | |
| def load_image(path): | |
| im = Image.open(path).convert('RGB') | |
| im = im.resize([360, 360]) | |
| arr = np.array(im).astype(np.float32) / 255.0 | |
| arr = arr * 2.0 - 1.0 | |
| arr = arr.transpose(2, 0, 1).reshape([1, 3, 360, 360]) | |
| return arr | |
| def save_image(arr, path): | |
| arr = arr.reshape([3, 360, 360]).transpose(1, 2, 0) | |
| arr = (arr + 1.0) * (255.0 / 2.0) | |
| arr = arr.astype(np.uint8) | |
| im = Image.fromarray(arr) | |
| im.save(path) | |
| def hash_from_hex(hex_repr): | |
| n = int(hex_repr, 16) | |
| h = np.zeros(96) | |
| for i in range(96): | |
| h[i] = (n >> (95 - i)) & 1 | |
| return h | |
| def hash_to_hex(h): | |
| bits = ''.join(['1' if i >= 0.5 else '0' for i in h]) | |
| return '{:0{}x}'.format(int(bits, 2), len(bits) // 4) | |