73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
import cv2
|
|
import numpy as np
|
|
from numpy.typing import NDArray
|
|
from scipy import spatial
|
|
|
|
from modules.database import col_photos
|
|
|
|
|
|
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():
|
|
try:
|
|
distance = spatial.distance.hamming(
|
|
hash_hex_to_hash_array(cache[image_name][1]),
|
|
hash_hex_to_hash_array(hash),
|
|
)
|
|
except ValueError:
|
|
continue
|
|
# print("{0:<30} {1}".format(image_name, distance), flush=True)
|
|
if distance <= 0.1:
|
|
duplicates.append(
|
|
{
|
|
"id": cache[image_name][0],
|
|
"filename": image_name,
|
|
"difference": distance,
|
|
}
|
|
)
|
|
return duplicates
|