Вопрос по algorithm – Какой алгоритм можно использовать, чтобы определить, являются ли изображения «одинаковыми» или похожими, независимо от размера?

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Error: User Rate Limit ExceededError: User Rate Limit Exceeded

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Error: User Rate Limit ExceededError: User Rate Limit ExceededError: User Rate Limit Exceeded

Error: User Rate Limit Exceeded mmcdole
Error: User Rate Limit Exceeded Zack The Human

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Error: User Rate Limit ExceededError: User Rate Limit ExceededError: User Rate Limit ExceededError: User Rate Limit ExceededError: User Rate Limit Exceeded

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Error: User Rate Limit Exceeded

Error: User Rate Limit Exceeded

Error: User Rate Limit Exceeded

Error: User Rate Limit Exceeded

Error: User Rate Limit Exceeded

Error: User Rate Limit ExceededError: User Rate Limit Exceeded

Error: User Rate Limit ExceededError: User Rate Limit Exceeded

Error: User Rate Limit ExceededError: User Rate Limit Exceeded

Error: User Rate Limit Exceeded
Error: User Rate Limit Exceeded mmcdole
Error: User Rate Limit Exceeded
Error: User Rate Limit Exceeded
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Error: User Rate Limit Exceeded

Error: User Rate Limit Exceeded

Error: User Rate Limit Exceeded
Error: User Rate Limit Exceeded
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Error: User Rate Limit ExceededError: User Rate Limit ExceededError: User Rate Limit Exceeded

Error: User Rate Limit Exceeded

Error: User Rate Limit Exceeded
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aHash (also called Average Hash or Mean Hash). This approach crushes the image into a grayscale 8x8 image and sets the 64 bits in the hash based on whether the pixel's value is greater than the average color for the image.

pHash (also called "Perceptive Hash"). This algorithm is similar to aHash but use a discrete cosine transform (DCT) and compares based on frequencies rather than color values.

dHash Like aHash and pHash, dHash is pretty simple to implement and is far more accurate than it has any right to be. As an implementation, dHash is nearly identical to aHash but it performs much better. While aHash focuses on average values and pHash evaluates frequency patterns, dHash tracks gradients.

Error: User Rate Limit Exceededfullstackml.com/2016/07/02/wavelet-image-hash-in-python

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