39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import numpy as np
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import pytest
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from PIL import Image
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from image_prediction.estimator.preprocessor.preprocessors.basic import BasicPreprocessor
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from image_prediction.estimator.preprocessor.preprocessors.identity import IdentityPreprocessor
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from image_prediction.estimator.preprocessor.utils import image_to_normalized_tensor, images_to_batch_tensor
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def image_conversion_is_correct(image):
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tensor = image_to_normalized_tensor(image)
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image_re = Image.fromarray(np.uint8(tensor * 255), mode="RGB")
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return image == image_re and tensor.ndim == 3
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def images_conversion_is_correct(images, tensor):
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if not (images or tensor.size > 0):
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return True
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return all([isinstance(tensor, np.ndarray), tensor.ndim == 4, tensor.shape[0] == len(images)])
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def test_image_to_tensor(images):
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assert all(map(image_conversion_is_correct, images))
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def test_images_to_batch_tensor(images):
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tensor = images_to_batch_tensor(images)
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assert images_conversion_is_correct(images, tensor)
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def test_basic_preprocessor(images):
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tensor = BasicPreprocessor()(images)
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assert images_conversion_is_correct(images, tensor)
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def test_identity_preprocessor(images):
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images_preprocessed = IdentityPreprocessor()(images)
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assert images_preprocessed == images
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