python opencv Criminisi 算法的具体实现

Criminisi算法是一种图像修复算法，可以用于填补图像中的缺失区域。下面是Python OpenCV实现Criminisi算法的具体步骤：

1.导入必要的库和图像

import cv2
import numpy as np

img = cv2.imread('input.jpg')
mask = cv2.imread('mask.jpg', 0)

2.初始化图像和掩码

rows, cols, _ = img.shape
dst = np.zeros_like(img)

# Create a binary mask of the missing area
mask[mask > 0] = 255

3.实现Criminisi算法

# Set the size of the search window
w_size = 11

# Initialize the priority queue
p_queue = []

# Loop through each pixel in the mask
for i in range(rows):
    for j in range(cols):
        if mask[i, j] == 0:
            continue

        # Get the patch to be filled
        patch = img[max(0, i-w_size):min(rows, i+w_size+1),
                    max(0, j-w_size):min(cols, j+w_size+1)]

        # Get the mask for the patch
        patch_mask = mask[max(0, i-w_size):min(rows, i+w_size+1),
                          max(0, j-w_size):min(cols, j+w_size+1)]

        # Get the confidence value for each pixel in the patch
        confidence = np.zeros(patch.shape[:2])
        confidence[patch_mask == 0] = 1

        # Get the priority value for each pixel in the patch
        priority = np.zeros(patch.shape[:2])
        priority[patch_mask == 0] = np.inf

        # Add the patch to the priority queue
        p_queue.append((i, j, patch, confidence, priority))

4.填充缺失区域

# Loop through each pixel in the priority queue
while p_queue:
    # Get the pixel with the highest priority value
    i, j, patch, confidence, priority = max(p_queue, key=lambda x: np.min(x[4]))

    # Get the mask for the patch
    patch_mask = mask[max(0, i-w_size):min(rows, i+w_size+1),
                      max(0, j-w_size):min(cols, j+w_size+1)]

    # Compute the fill value for each pixel in the patch
    fill = cv2.inpaint(patch, patch_mask, 3, cv2.INPAINT_TELEA)

    # Update the destination image and mask
    dst[i, j] = fill[patch_mask == 255]
    mask[i, j] = 0

    # Update the confidence and priority values for each pixel in the patch
    confidence[patch_mask == 255] = 1
    priority[patch_mask == 255] = np.inf

    # Update the priority queue
    p_queue.remove((i, j, patch, confidence, priority))

    for ii in range(max(0, i-w_size), min(rows, i+w_size+1)):
        for jj in range(max(0, j-w_size), min(cols, j+w_size+1)):
            if mask[ii, jj] == 0:
                continue

            # Get the patch to be filled
            new_patch = img[max(0, ii-w_size):min(rows, ii+w_size+1),
                            max(0, jj-w_size):min(cols, jj+w_size+1)]

            # Get the mask for the patch
            new_patch_mask = mask[max(0, ii-w_size):min(rows, ii+w_size+1),
                                  max(0, jj-w_size):min(cols, jj+w_size+1)]

            # Compute the confidence value for each pixel in the patch
            new_confidence = np.zeros(new_patch.shape[:2])
            new_confidence[new_patch_mask == 0] = 1

            # Compute the priority value for each pixel in the patch
            new_priority = np.zeros(new_patch.shape[:2])
            new_priority[new_patch_mask == 0] = np.inf

            # Add the patch to the priority queue
            p_queue.append((ii, jj, new_patch, new_confidence, new_priority))

5.保存修复后的图像

cv2.imwrite('output.jpg', dst)

以上就是Python OpenCV实现Criminisi算法的具体步骤。