How can I convert an imagehash to a NumPy array and derive transformed pHashes for rotated/flipped images?

Below is an explanation addressing your two questions:

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1. Converting the ImageHash to/from a NumPy Array

The imagehash library’s pHash objects store their hash as a NumPy array in the hash attribute. For example, if you have a pHash object:

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import imagehash
from PIL import Image

# Compute pHash
img = Image.open("your_image.jpg")
phash = imagehash.phash(img)

You can access its NumPy array representation like this:

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import numpy as np

# Direct access to the numpy array (dtype=bool)
hash_array = phash.hash

# Optionally, convert the boolean values to integers if preferred
hash_int_array = hash_array.astype(int)

To convert a NumPy array back into a pHash object, use the ImageHash constructor:

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from imagehash import ImageHash

# Reconstruct the ImageHash object from a numpy array
new_phash = ImageHash(hash_array)

Now, new_phash should represent the same hash as your original phash.

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2. Detecting Rotated/Flipped Images Directly from the pHash

The pHash (perceptual hash) is built upon a Discrete Cosine Transform (DCT) and was not designed to be reversible under geometric transformations. That is, there isn’t a simple “lookup” function for how a given pHash will change when the image is rotated or flipped. Here are some points and alternatives:

• Direct Derivation Limitations:
  Converting the hash to an array and applying a NumPy flip or rotation to that array does not reliably correspond to the pHash of an image that has been rotated or flipped. This is because the pHash extraction involves DCT and quantization steps that do not transform linearly under geometric operations.

• Better Approaches:

  • Recompute the pHash After Transformation:
    The most straightforward approach is to perform the geometric transform (e.g., rotate or flip the image) and then recompute the pHash. While you want to avoid this extra computation, the pHash algorithm is relatively fast compared to many other image processing tasks.

    You might pre-compute a set of hashes for the expected transformations (e.g., the original, rotated by 90°, 180°, 270° and horizontally/vertically flipped) and then compare the query hash against all of these.

  • Use Rotation-Invariant Hashes:
    Consider using alternative hashing algorithms that are designed to be invariant to rotation and reflection. There are several approaches (e.g., using the Radon transform) that can yield more invariant results if your application demands matching rotated copies.

  • Local Feature Methods:
    For robust detection across transformations, local feature approaches (like SIFT, ORB, or SURF) are often more effective. They explicitly match keypoints between images, regardless of rotation or reflection.

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Summary

• Conversion:
  Use the .hash attribute of an ImageHash object to obtain the NumPy array. Convert back by using ImageHash(np_array).

• Rotated/Flipped Detection:
  There isn’t a direct method to “rotate” a pHash without recomputing it or using a hash method designed to be invariant. For best results, either recompute the hash on the transformed image, precompute hashes for expected orientations, or use a rotation-invariant hashing method or local feature matching.

This approach balances efficiency with the limitations of the pHash algorithm for detecting geometric transformations.