PhotosAPI/modules/hasher.py
2022-12-20 01:22:32 +01:00

55 lines
2.1 KiB
Python

from modules.database import col_photos
import numpy as np
from numpy.typing import NDArray
from scipy import spatial
import cv2
def hash_array_to_hash_hex(hash_array):
# convert hash array of 0 or 1 to hash string in hex
hash_array = np.array(hash_array, dtype = np.uint8)
hash_str = ''.join(str(i) for i in 1 * hash_array.flatten())
return (hex(int(hash_str, 2)))
def hash_hex_to_hash_array(hash_hex) -> NDArray:
# convert hash string in hex to hash values of 0 or 1
hash_str = int(hash_hex, 16)
array_str = bin(hash_str)[2:]
return np.array([i for i in array_str], dtype = np.float32)
def get_duplicates_cache(album: str) -> dict:
output = {}
for photo in col_photos.find( {"album": album} ):
output[photo["filename"]] = [photo["_id"].__str__(), photo["hash"]]
return output
async def get_phash(filepath: str) -> str:
img = cv2.imread(filepath)
# resize image and convert to gray scale
img = cv2.resize(img, (64, 64))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = np.array(img, dtype = np.float32)
# calculate dct of image
dct = cv2.dct(img)
# to reduce hash length take only 8*8 top-left block
# as this block has more information than the rest
dct_block = dct[: 8, : 8]
# caclulate mean of dct block excluding first term i.e, dct(0, 0)
dct_average = (dct_block.mean() * dct_block.size - dct_block[0, 0]) / (dct_block.size - 1)
# convert dct block to binary values based on dct_average
dct_block[dct_block < dct_average] = 0.0
dct_block[dct_block != 0] = 1.0
# store hash value
return hash_array_to_hash_hex(dct_block.flatten())
async def get_duplicates(hash: str, album: str) -> list:
duplicates = []
cache = get_duplicates_cache(album)
for image_name in cache.keys():
distance = spatial.distance.hamming(
hash_hex_to_hash_array(cache[image_name][1]),
hash_hex_to_hash_array(hash)
)
print("{0:<30} {1}".format(image_name, distance), flush=True)
if distance <= 0.25:
duplicates.append({"id": cache[image_name][0], "filename": image_name, "difference": distance})
return duplicates