[Python] フラクタル補間を用いた画像補完を行う方法

import cv2
# 画像の読み込み
image = cv2.imread('input_image.jpg')
# 画像のリサイズ
image_resized = cv2.resize(image, (512, 512))
# ノイズ除去(ガウシアンフィルタ)
image_denoised = cv2.GaussianBlur(image_resized, (5, 5), 0)

import numpy as np
def detect_self_similarity(image):
    # 自己相似性を検出するための処理
    # ここでは簡略化のため、相関係数を計算する例を示します
    height, width = image.shape[:2]
    similarity_map = np.zeros((height, width))
    for i in range(height):
        for j in range(width):
            # 自己相似性の計算(簡略化)
            similarity_map[i, j] = np.corrcoef(image[i], image[j])[0, 1]
    return similarity_map

def apply_fractal_transformation(image, similarity_map):
    # フラクタル変換を適用する処理
    # ここでは簡略化のため、相似性マップを用いて変換を行う例を示します
    transformed_image = image.copy()
    height, width = image.shape[:2]
    for i in range(height):
        for j in range(width):
            if similarity_map[i, j] > threshold:  # thresholdは適切な値に設定
                transformed_image[i, j] = image[i, j] * similarity_map[i, j]
    return transformed_image

def fill_missing_parts(image, transformed_image):
    # 欠損部分を補完する処理
    filled_image = image.copy()
    height, width = image.shape[:2]
    for i in range(height):
        for j in range(width):
            if is_missing_pixel(image[i, j]):  # 欠損ピクセルの判定
                filled_image[i, j] = transformed_image[i, j]
    return filled_image

def post_process_image(image):
    # 後処理を行う処理
    # ここでは簡略化のため、シャープネスを向上させる例を示します
    kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
    sharpened_image = cv2.filter2D(image, -1, kernel)
    return sharpened_image

pip install numpy opencv-python matplotlib

import cv2
import matplotlib.pyplot as plt
# 画像の読み込み
image = cv2.imread('input_image.jpg')
# BGRからRGBに変換
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# 画像の表示
plt.imshow(image_rgb)
plt.axis('off')  # 軸を非表示にする
plt.show()

import numpy as np
def detect_self_similarity(image):
    height, width = image.shape[:2]
    similarity_map = np.zeros((height, width))
    for i in range(height):
        for j in range(width):
            # 簡略化のため、相関係数を計算
            similarity_map[i, j] = np.corrcoef(image[i].flatten(), image[j].flatten())[0, 1]
    return similarity_map
def apply_fractal_transformation(image, similarity_map):
    transformed_image = image.copy()
    height, width = image.shape[:2]
    for i in range(height):
        for j in range(width):
            if similarity_map[i, j] > 0.5:  # 閾値を設定
                transformed_image[i, j] = image[i, j] * similarity_map[i, j]
    return transformed_image

def is_missing_pixel(pixel):
    # 欠損ピクセルの判定(例：白色ピクセルを欠損とみなす)
    return np.all(pixel == [255, 255, 255])
def fill_missing_parts(image, transformed_image):
    filled_image = image.copy()
    height, width = image.shape[:2]
    for i in range(height):
        for j in range(width):
            if is_missing_pixel(image[i, j]):
                filled_image[i, j] = transformed_image[i, j]
    return filled_image

def calculate_psnr(original, filled):
    mse = np.mean((original - filled) ** 2)
    if mse == 0:
        return float('inf')
    max_pixel = 255.0
    psnr = 20 * np.log10(max_pixel / np.sqrt(mse))
    return psnr

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

def detect_self_similarity(image):
    height, width = image.shape[:2]
    similarity_map = np.zeros((height, width))
    # 簡略化のため、自己相似性をランダムに設定
    similarity_map = np.random.rand(height, width)
    return similarity_map

def apply_fractal_transformation(image, similarity_map):
    transformed_image = image.copy()
    height, width = image.shape[:2]
    for i in range(height):
        for j in range(width):
            if similarity_map[i, j] > 0.5:
                transformed_image[i, j] = image[i, j] * similarity_map[i, j]
    return transformed_image

def is_missing_pixel(pixel):
    # 欠損ピクセルを0で判定
    return np.all(pixel == [0, 0, 0])

def fill_missing_parts(image, transformed_image):
    filled_image = image.copy()
    height, width = image.shape[:2]
    for i in range(height):
        for j in range(width):
            if is_missing_pixel(image[i, j]):
                filled_image[i, j] = transformed_image[i, j]
    return filled_image

def calculate_psnr(original, filled):
    mse = np.mean((original - filled) ** 2)
    if mse == 0:
        return float('inf')
    max_pixel = 255.0
    psnr = 20 * np.log10(max_pixel / np.sqrt(mse))
    return psnr

# 画像の読み込み
image = cv2.imread('input_image.jpg')
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# 自己相似性の検出
similarity_map = detect_self_similarity(image_rgb)

# フラクタル変換の適用
transformed_image = apply_fractal_transformation(image_rgb, similarity_map)

# 欠損部分の補完
filled_image = fill_missing_parts(image_rgb, transformed_image)

# PSNRの計算
psnr_value = calculate_psnr(image_rgb, filled_image)
print(f'PSNR: {psnr_value:.2f} dB')

# 補完結果の表示
plt.imshow(filled_image)
plt.axis('off')
plt.show()