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import numpy as np
import cv2 as cv
from time import time
def compress_jpg(image, quality):
"""Compress image using JPEG compression."""
encode_param = [int(cv.IMWRITE_JPEG_QUALITY), quality]
_, buffer = cv.imencode('.jpg', image, encode_param)
return cv.imdecode(buffer, cv.IMREAD_COLOR)
def desaturate(image):
"""Convert image to grayscale."""
return cv.cvtColor(image, cv.COLOR_BGR2GRAY)
def create_lut(contrast, brightness):
"""Create lookup table for contrast and brightness adjustment."""
lut = np.arange(256, dtype=np.uint8)
lut = cv.LUT(lut, lut)
lut = cv.convertScaleAbs(lut, None, contrast/128, brightness)
return lut
def elapsed_time(start):
"""Calculate elapsed time since start."""
return f"{time() - start:.3f}s"
def genELA(img, quality=75, scale=50, contrast=20, linear=False, grayscale=False):
"""
Perform Error Level Analysis on an image.
Args:
img: Input image (numpy array)
quality: JPEG compression quality (1-100)
scale: Output multiplicative gain (1-100)
contrast: Output tonality compression (0-100)
linear: Whether to use linear difference
grayscale: Whether to output grayscale image
Returns:
Processed ELA image
"""
# Convert image to float32 and normalize
original = img.astype(np.float32) / 255
# Compress image
compressed = compress_jpg(img, quality)
compressed = compressed.astype(np.float32) / 255
# Calculate difference based on mode
if not linear:
difference = cv.absdiff(original, compressed)
ela = cv.convertScaleAbs(cv.sqrt(difference) * 255, None, scale / 20)
else:
ela = cv.convertScaleAbs(cv.subtract(compressed, img), None, scale)
# Apply contrast adjustment
contrast_value = int(contrast / 100 * 128)
ela = cv.LUT(ela, create_lut(contrast_value, contrast_value))
# Convert to grayscale if requested
if grayscale:
ela = desaturate(ela)
return ela |