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refactor: use opencv to calculate image phash

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283375 2023-10-03 16:38:11 +08:00
parent d5ccbd5a01
commit c009a28f92
Signed by: 283375
SSH Key Fingerprint: SHA256:UcX0qg6ZOSDOeieKPGokA5h7soykG61nz2uxuQgVLSk
1 changed files with 31 additions and 7 deletions
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38
src/arcaea_offline_ocr/phash_db.py
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@ -1,8 +1,32 @@
import sqlite3 import sqlite3
import imagehash import cv2
import numpy as np import numpy as np
from PIL import Image
def phash_opencv(img_gray, hash_size=8, highfreq_factor=4):
# type: (cv2.Mat | np.ndarray, int, int) -> np.ndarray
"""
Perceptual Hash computation.
Implementation follows http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
Adapted from `imagehash.phash`, pure opencv implementation
The result is slightly different from `imagehash.phash`.
"""
if hash_size < 2:
raise ValueError("Hash size must be greater than or equal to 2")
img_size = hash_size * highfreq_factor
image = cv2.resize(img_gray, (img_size, img_size), interpolation=cv2.INTER_LANCZOS4)
image = np.float32(image)
dct = cv2.dct(image, flags=cv2.DCT_ROWS)
dct = cv2.dct(image)
dctlowfreq = dct[:hash_size, :hash_size]
med = np.median(dctlowfreq)
diff = dctlowfreq > med
return diff
def hamming_distance_sql_function(user_input, db_entry) -> int: def hamming_distance_sql_function(user_input, db_entry) -> int:
@ -46,8 +70,8 @@ class ImagePHashDatabase:
self.hashes_head = [h[: self.hashes_slice_size] for h in self.hashes] self.hashes_head = [h[: self.hashes_slice_size] for h in self.hashes]
self.hashes_tail = [h[-self.hashes_slice_size :] for h in self.hashes] self.hashes_tail = [h[-self.hashes_slice_size :] for h in self.hashes]
def lookup_hash(self, image_hash: imagehash.ImageHash, *, limit: int = 5): def lookup_hash(self, image_hash: np.ndarray, *, limit: int = 5):
image_hash = image_hash.hash.flatten() image_hash = image_hash.flatten()
# image_hash_head = image_hash[: self.hashes_slice_size] # image_hash_head = image_hash[: self.hashes_slice_size]
# image_hash_tail = image_hash[-self.hashes_slice_size :] # image_hash_tail = image_hash[-self.hashes_slice_size :]
# head_xor_results = [image_hash_head ^ h for h in self.hashes] # head_xor_results = [image_hash_head ^ h for h in self.hashes]
@ -58,8 +82,8 @@ class ImagePHashDatabase:
] ]
return sorted(xor_results, key=lambda r: r[1])[:limit] return sorted(xor_results, key=lambda r: r[1])[:limit]
def lookup_image(self, pil_image: Image.Image): def lookup_image(self, img_gray: cv2.Mat):
image_hash = imagehash.phash( image_hash = phash_opencv(
pil_image, hash_size=self.hash_size, highfreq_factor=self.highfreq_factor img_gray, hash_size=self.hash_size, highfreq_factor=self.highfreq_factor
) )
return self.lookup_hash(image_hash)[0] return self.lookup_hash(image_hash)[0]