ImageHash package - simplify hash

Python library to hash by different methods. Useful to build indexes / similarity search.

The different hashing techniques are

• Average hashing  - 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.
• Perceptual hashing - use a discrete cosine transform (DCT) and compares based on frequencies rather than color values
• Difference hashing - gradient hash, calculate the difference for each of the pixel and compares the difference with the average differences.
• Wavelet hashing - works in the frequency domain as pHash but it uses DWT instead of DCT.
• HSV color hashing (colorhash)
• Crop-resistant hashing

Ref - Link1, Link2

Ref - Link

Package Installation

File Upload

Create Hash Methods

Depending on usecases need to evaluate which suits the need or a combination of techniques.

	#package installation
	pip install ImageHash
	#upload images to googlecolab
	from google.colab import files
	filesdata = files.upload()
	from PIL import Image
	import imagehash
	avgimagehash = []
	#read and calculate hash
	#print length of each hash
	for fn in filesdata.keys():
	hash = imagehash.average_hash(Image.open(fn))
	avgimagehash.append(hash)
	print(len(avgimagehash[0]))
	print(avgimagehash[0])
	from PIL import Image
	import imagehash
	wimagehash = []
	#read and calculate hash
	#print length of each hash
	for fn in filesdata.keys():
	hash = imagehash.whash(Image.open(fn))
	wimagehash.append(hash)
	print(len(wimagehash[0]))
	print(wimagehash[0])
	from PIL import Image
	import imagehash
	cimagehash = []
	#read and calculate hash
	#print length of each hash
	for fn in filesdata.keys():
	hash = imagehash.colorhash(Image.open(fn))
	cimagehash.append(hash)
	print(len(cimagehash[0]))
	print(cimagehash[0])

view raw ImageHashexample.py hosted with ❤ by GitHub

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