113 lines
3.6 KiB
Python
113 lines
3.6 KiB
Python
from typing import Any
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import numpy as np
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import tensorflow as tf
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class EasyDict(dict):
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"""Convenience class that behaves like a dict but allows access with the attribute syntax."""
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def __getattr__(self, name: str) -> Any:
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try:
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return self[name]
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except KeyError:
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raise AttributeError(name)
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def __setattr__(self, name: str, value: Any) -> None:
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self[name] = value
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def __delattr__(self, name: str) -> None:
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del self[name]
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def lerp(a, b, t):
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out = a + (b - a) * t
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return out
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def lerp_clip(a, b, t):
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out = a + (b - a) * tf.clip_by_value(t, 0.0, 1.0)
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return out
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def adjust_dynamic_range(images, range_in, range_out, out_dtype):
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scale = (range_out[1] - range_out[0]) / (range_in[1] - range_in[0])
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bias = range_out[0] - range_in[0] * scale
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images = images * scale + bias
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images = tf.clip_by_value(images, range_out[0], range_out[1])
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images = tf.cast(images, dtype=out_dtype)
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return images
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def random_flip_left_right_nchw(images):
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s = tf.shape(images)
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mask = tf.random.uniform([s[0], 1, 1, 1], 0.0, 1.0)
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mask = tf.tile(mask, [1, s[1], s[2], s[3]])
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images = tf.where(mask < 0.5, images, tf.reverse(images, axis=[3]))
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return images
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def preprocess_fit_train_image(images, res):
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images = adjust_dynamic_range(images, range_in=(0.0, 255.0), range_out=(-1.0, 1.0), out_dtype=tf.dtypes.float32)
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images = random_flip_left_right_nchw(images)
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images.set_shape([None, 3, res, res])
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return images
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def postprocess_images(images):
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images = adjust_dynamic_range(images, range_in=(-1.0, 1.0), range_out=(0.0, 255.0), out_dtype=tf.dtypes.float32)
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images = tf.transpose(images, [0, 2, 3, 1])
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images = tf.cast(images, dtype=tf.dtypes.uint8)
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return images
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def merge_batch_images(images, res, rows, cols):
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batch_size = images.shape[0]
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assert rows * cols == batch_size
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canvas = np.zeros(shape=[res * rows, res * cols, 3], dtype=np.uint8)
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for row in range(rows):
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y_start = row * res
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for col in range(cols):
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x_start = col * res
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index = col + row * cols
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canvas[y_start:y_start + res, x_start:x_start + res, :] = images[index, :, :, :]
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return canvas
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def main():
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# out_dtype = tf.dtypes.float32
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# range_in = (0.0, 255.0)
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# range_out = (-1.0, 1.0)
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# # images = np.ones((2, 2), dtype=np.float32) * 255.0
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# images1 = np.ones((2, 2), dtype=np.float32) * 0.0
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# images2 = np.ones((2, 2), dtype=np.float32) * 127.5
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# images3 = np.ones((2, 2), dtype=np.float32) * 255.0
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# adjusted1 = adjust_dynamic_range(images1, range_in, range_out, out_dtype)
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# adjusted2 = adjust_dynamic_range(images2, range_in, range_out, out_dtype)
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# adjusted3 = adjust_dynamic_range(images3, range_in, range_out, out_dtype)
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# print(adjusted1)
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# print(adjusted2)
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# print(adjusted3)
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#
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# out_dtype = tf.dtypes.uint8
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# range_in = (-1.0, 1.0)
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# range_out = (0.0, 255.0)
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# images1 = np.ones((2, 2), dtype=np.float32) * -1.0
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# images2 = np.ones((2, 2), dtype=np.float32) * 0.0
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# images3 = np.ones((2, 2), dtype=np.float32) * 1.0
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# adjusted1 = adjust_dynamic_range(images1, range_in, range_out, out_dtype)
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# adjusted2 = adjust_dynamic_range(images2, range_in, range_out, out_dtype)
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# adjusted3 = adjust_dynamic_range(images3, range_in, range_out, out_dtype)
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# print(adjusted1)
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# print(adjusted2)
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# print(adjusted3)
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batch_size = 8
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res = 128
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fake_images = np.ones(shape=(batch_size, res, res, 3), dtype=np.uint8)
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merge_batch_images(fake_images, res, rows=4, cols=2)
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return
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if __name__ == '__main__':
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main()
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