Update preprocessor.py
Browse files- preprocessor.py +8 -7
preprocessor.py
CHANGED
@@ -241,20 +241,21 @@ def resize_longside(
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def select_best_resolution(original_size: tuple, possible_resolutions: list) -> tuple:
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"""
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Selects the best-fit resolution from a list of possible resolutions based on the original image size.
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This function, adapted from LLaVA-Next
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(https://github.com/huggingface/transformers/blob/v4.40.2/src/transformers/models/llava_next/image_processing_llava_next.py),
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evaluates each resolution by computing its effective and wasted area compared to the original size.
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The optimal resolution is the one that maximizes the effective area while minimizing unused (wasted) space.
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Args:
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original_size (tuple): The original image size in the format (height, width).
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possible_resolutions (list): A list of candidate resolutions in the format [(height1, width1), (height2, width2), ...].
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Returns:
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tuple: The best-fit resolution in the format (height, width).
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"""
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original_height, original_width = original_size
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best_fit = None
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def select_best_resolution(original_size: tuple, possible_resolutions: list) -> tuple:
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"""
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Selects the best-fit resolution from a list of possible resolutions based on the original image size.
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This function evaluates each resolution by computing its effective and wasted area compared to the original size.
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The optimal resolution is the one that maximizes the effective area while minimizing unused (wasted) space.
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Args:
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original_size (tuple): The original image size in the format (height, width).
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possible_resolutions (list): A list of candidate resolutions in the format [(height1, width1), (height2, width2), ...].
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Returns:
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tuple: The best-fit resolution in the format (height, width).
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This function includes code adapted from the file image_processing_llava_next.py in the LLaVA-Next
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project(https://github.com/huggingface/transformers/blob/v4.40.2/src/transformers/models/llava_next/image_processing_llava_next.py),
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which is licensed under apache-2.0.
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"""
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original_height, original_width = original_size
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best_fit = None
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