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genHighpass.py
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genHighpass.py
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from typing import Literal
import cv2
import numpy as np
def main(
image: np.ndarray,
filter: Literal["Gaussian", "box", "median", "max", "min"],
ksize: int,
diff_mode: Literal["subtract", "divide", "bitwise_and"],
):
"""Generalised highpass filter.
Args:
image (np.ndarray): Input image.
filter (Literal["Gaussian", "box", "median", "max", "min"]): Filter type.
ksize (int): Kernel size.
diff_mode (Literal["subtract", "divide", "bitwise_and"]): Difference method.
Returns:
np.ndarray: Highpass image.
"""
if filter == "Gaussian":
blur = cv2.GaussianBlur(image, (ksize, ksize), 0)
elif filter == "box":
blur = cv2.blur(image, (ksize, ksize))
elif filter == "median":
blur = cv2.medianBlur(image, ksize)
elif filter == "max":
kernel = np.ones((ksize, ksize), dtype=np.uint8)
blur = cv2.dilate(image, kernel)
elif filter == "min":
kernel = np.ones((ksize, ksize), dtype=np.uint8)
blur = cv2.erode(image, kernel)
else:
raise Exception("Unsupported filter type")
if diff_mode == "subtract":
dtype = cv2.CV_16S
highpass_img = cv2.subtract(image, blur, dtype=dtype)
highpass_img = cv2.add(highpass_img, 128, dtype=cv2.CV_8U)
elif diff_mode == "divide":
dtype = cv2.CV_64F
highpass_img = cv2.divide(image, blur, dtype=dtype)
highpass_img = cv2.multiply(highpass_img, 128, dtype=cv2.CV_8U)
elif diff_mode == "bitwise_and":
highpass_img = cv2.bitwise_and(image, blur)
else:
raise Exception("Unsupported diff mode")
return highpass_img