이진 이미지와 Thresholding

import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np

newspaper = cv.imread("newspaper.png", cv.IMREAD_GRAYSCALE)

newspaper.shape, newspaper.dtype

hist = cv.calcHist([newspaper], [0], None, [256], [0, 256])

plt.plot(hist)
plt.xlim([0, 256])
plt.xlabel("픽셀 값")
plt.ylabel("픽셀 수")
plt.show()

used_threshold, binary = cv.threshold(
    newspaper,
    190,
    255,
    cv.THRESH_BINARY,
)
used_threshold # 사용한 문턱값

np.unique(binary) # 이미지의 픽셀값 종류 확인

show(binary)

threshold_values = [80, 120, 150, 190]
binary_images = [
    cv.threshold(newspaper, value, 255, cv.THRESH_BINARY)[1]
    for value in threshold_values
]

comparison = cv.hconcat(binary_images)
show(comparison)

_, binary_inverse = cv.threshold(
    newspaper,
    190,
    255,
    cv.THRESH_BINARY_INV,
)
show(binary_inverse)

otsu_threshold, binary_otsu = cv.threshold(
    newspaper,
    0,
    255,
    cv.THRESH_BINARY | cv.THRESH_OTSU,
)
otsu_threshold # 문턱값

show(binary_otsu)

sudoku = cv.imread("sudoku.png", cv.IMREAD_GRAYSCALE)

if sudoku is None:
    raise FileNotFoundError("이미지를 읽을 수 없습니다")

_, global_binary = cv.threshold(
    sudoku,
    0,
    255,
    cv.THRESH_BINARY | cv.THRESH_OTSU,
)
show(global_binary)

block_size = 15
c = 5

adaptive_mean = cv.adaptiveThreshold(
    sudoku,
    255,
    cv.ADAPTIVE_THRESH_MEAN_C,
    cv.THRESH_BINARY,
    block_size,
    c,
)

adaptive_gaussian = cv.adaptiveThreshold(
    sudoku,
    255,
    cv.ADAPTIVE_THRESH_GAUSSIAN_C,
    cv.THRESH_BINARY,
    block_size,
    c,
)

show(adaptive_mean)

show(adaptive_gaussian)