76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
from pathlib import Path
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from typing import Union
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import cv2
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import numpy as np
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from numpy.typing import NDArray
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from scipy import spatial
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from modules.database import col_photos
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def hash_array_to_hash_hex(hash_array):
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# convert hash array of 0 or 1 to hash string in hex
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hash_array = np.array(hash_array, dtype=np.uint8)
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hash_str = "".join(str(i) for i in 1 * hash_array.flatten())
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return hex(int(hash_str, 2))
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def hash_hex_to_hash_array(hash_hex) -> NDArray:
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# convert hash string in hex to hash values of 0 or 1
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hash_str = int(hash_hex, 16)
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array_str = bin(hash_str)[2:]
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return np.array(list(array_str), dtype=np.float32)
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def get_duplicates_cache(album: str) -> dict:
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return {
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photo["filename"]: [photo["_id"].__str__(), photo["hash"]]
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for photo in col_photos.find({"album": album})
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}
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async def get_phash(filepath: Union[str, Path]) -> str:
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img = cv2.imread(str(filepath))
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# resize image and convert to gray scale
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img = cv2.resize(img, (64, 64))
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img = np.array(img, dtype=np.float32)
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# calculate dct of image
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dct = cv2.dct(img)
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# to reduce hash length take only 8*8 top-left block
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# as this block has more information than the rest
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dct_block = dct[:8, :8]
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# caclulate mean of dct block excluding first term i.e, dct(0, 0)
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dct_average = (dct_block.mean() * dct_block.size - dct_block[0, 0]) / (
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dct_block.size - 1
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)
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# convert dct block to binary values based on dct_average
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dct_block[dct_block < dct_average] = 0.0
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dct_block[dct_block != 0] = 1.0
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# store hash value
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return hash_array_to_hash_hex(dct_block.flatten())
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async def get_duplicates(hash_string: str, album: str) -> list:
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duplicates = []
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cache = get_duplicates_cache(album)
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for image_name, image_object in cache.items():
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try:
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distance = spatial.distance.hamming(
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hash_hex_to_hash_array(cache[image_name][1]),
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hash_hex_to_hash_array(hash_string),
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)
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except ValueError:
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continue
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# print("{0:<30} {1}".format(image_name, distance), flush=True)
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if distance <= 0.1:
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duplicates.append(
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{
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"id": cache[image_name][0],
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"filename": image_name,
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"difference": distance,
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}
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)
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return duplicates
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