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import cv2
import numpy as np
from typing import Union
from PIL import Image
from io import BytesIO
import imghdr
from fastapi import HTTPException
from app.utils.file_utils import get_file_content
def preprocess_image(image: Union[str, np.ndarray, Image.Image], hash_size: int = 32) -> np.ndarray:
if isinstance(image, str):
content = get_file_content(image)
img = Image.open(BytesIO(content))
img = strip_metadata(img)
image = np.array(img)
elif isinstance(image, Image.Image):
image = strip_metadata(image)
image = np.array(image)
if len(image.shape) == 3:
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
image = cv2.resize(image, (hash_size, hash_size), interpolation=cv2.INTER_AREA)
image = cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX)
return image
def strip_metadata(img: Image.Image) -> Image.Image:
data = list(img.getdata())
img_without_exif = Image.new(img.mode, img.size)
img_without_exif.putdata(data)
return img_without_exif
def perceptual_image_hash(image: Union[str, np.ndarray, Image.Image], hash_size: int = 32) -> str:
processed_image = preprocess_image(image, hash_size)
dct = cv2.dct(np.float32(processed_image))
dct_low = dct[:8, :8]
median = np.median(dct_low[1:])
hash_value = ''
for i in range(8):
for j in range(8):
hash_value += '1' if dct_low[i, j] > median else '0'
return hash_value
def hamming_distance(hash1: str, hash2: str) -> int:
return sum(c1 != c2 for c1, c2 in zip(hash1, hash2))
def are_images_similar(hash1: str, hash2: str, threshold: int = 5) -> bool:
distance = hamming_distance(hash1, hash2)
return distance <= threshold
def process_image(filename: str):
try:
content = get_file_content(filename)
img = Image.open(BytesIO(content))
image_hash = perceptual_image_hash(img)
return image_hash
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
def compare_images(filename1: str, filename2: str):
try:
content1 = get_file_content(filename1)
content2 = get_file_content(filename2)
img1 = Image.open(BytesIO(content1))
img2 = Image.open(BytesIO(content2))
hash1 = perceptual_image_hash(img1)
hash2 = perceptual_image_hash(img2)
are_similar = are_images_similar(hash1, hash2)
distance = hamming_distance(hash1, hash2)
return {
"image1_hash": hash1,
"image2_hash": hash2,
"are_similar": are_similar,
"hamming_distance": distance
}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error comparing images: {str(e)}")
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