SIFT图像拼接：让照片无缝连接的魔法

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

def feather_blend(image1, image2_transformed, width, height):
    # 创建羽化掩码
    # 计算重叠区域宽度
    overlap_width = image2_transformed.shape[1] - width
    if overlap_width <= 0:
        return image2_transformed

    # 创建线性渐变掩码
    mask = np.ones((height, width + overlap_width, 3), dtype=np.float32)
    for col in range(width, width + overlap_width):
        alpha = (col - width) / overlap_width
        mask[:, col, :] = 1 - alpha

    # 应用掩码
    image2_transformed[0:height, 0:width] = image1 * mask[0:height, 0:width] + \
                                            image2_transformed[0:height, 0:width] * (1 - mask[0:height, 0:width])
    return image2_transformed

def sift_image_stitching(image_path1, image_path2, output_path='./images/panorama_result.jpg'):
    # 读取两张待拼接的图像
    print(f'读取图像: {image_path1} 和 {image_path2}')
    image1 = cv2.imread(image_path1)
    image2 = cv2.imread(image_path2)

    if image1 is None or image2 is None:
        print('错误：无法读取图像，请检查文件路径')
        return None

    # 转换为RGB格式（因为OpenCV默认读取的是BGR格式）
    image1_rgb = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)
    image2_rgb = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)

    # 初始化SIFT检测器
    print('初始化SIFT检测器...')
    sift = cv2.SIFT_create()

    # 检测SIFT特征点和计算描述符
    print('检测SIFT特征点...')
    kp1, des1 = sift.detectAndCompute(image1, None)
    kp2, des2 = sift.detectAndCompute(image2, None)

    # 使用FLANN匹配器匹配特征点
    print('匹配特征点...')
    FLANN_INDEX_KDTREE = 1
    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
    search_params = dict(checks=50)
    flann = cv2.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(des1, des2, k=2)

    # 筛选优质匹配点（根据Lowe的比率测试）
    good_matches = []
    for m, n in matches:
        if m.distance < 0.7 * n.distance:
            good_matches.append(m)

    print(f'找到 {len(good_matches)} 个优质匹配点')

    # 绘制匹配点并保存
    matched_image = cv2.drawMatches(image1_rgb, kp1, image2_rgb, kp2, good_matches, None, flags=2)
    matched_image_path = './images/sift_matching_result.png'
    cv2.imwrite(matched_image_path, cv2.cvtColor(matched_image, cv2.COLOR_RGB2BGR))
    print(f'匹配点图像已保存至: {matched_image_path}')

    # 获取匹配点的坐标
    points1 = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
    points2 = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)

    # 计算单应性矩阵（homography matrix）使用RANSAC算法
    print('计算单应性矩阵...')
    H, mask = cv2.findHomography(points2, points1, cv2.RANSAC, 5.0)

    # 使用单应性矩阵变换图像2
    height, width = image1.shape[:2]
    # 计算输出图像的大小，确保能够容纳变换后的图像2和原图像1
    stitched_width = width + image2.shape[1]
    stitched_height = max(height, image2.shape[0])

    print('变换图像...')
    stitched_image = cv2.warpPerspective(image2, H, (stitched_width, stitched_height))

    # 使用羽化过渡融合
    print('应用羽化过渡融合...')
    stitched_image = feather_blend(image1, stitched_image, width, height)

    # 优化：裁剪黑边
    print('裁剪黑边...')
    # 寻找非黑色区域的边界
    gray = cv2.cvtColor(stitched_image, cv2.COLOR_BGR2GRAY)
    _, thresh = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)
    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    x, y, w, h = cv2.boundingRect(contours[0])
    stitched_image_cropped = stitched_image[y:y+h, x:x+w]

    # 转换为RGB格式以便显示
    stitched_image_rgb = cv2.cvtColor(stitched_image_cropped, cv2.COLOR_BGR2RGB)

    # 保存拼接后的图像
    cv2.imwrite(output_path, cv2.cvtColor(stitched_image_cropped, cv2.COLOR_RGB2BGR))
    print(f'拼接图像已保存至: {output_path}')

    # 显示结果
    plt.figure(figsize=(18, 10))

    plt.subplot(221)
    plt.imshow(image1_rgb)
    plt.title('图像1')
    plt.axis('off')

    plt.subplot(222)
    plt.imshow(image2_rgb)
    plt.title('图像2')
    plt.axis('off')

    plt.subplot(223)
    plt.imshow(matched_image)
    plt.title('特征点匹配')
    plt.axis('off')

    plt.subplot(224)
    plt.imshow(stitched_image_rgb)
    plt.title('拼接结果')
    plt.axis('off')

    plt.tight_layout()
    plt.show()

    return stitched_image_rgb

if __name__ == '__main__':
    # 输入图像路径
    image_path1 = './images/image_stitching_1.jpg'
    image_path2 = './images/image_stitching_2.jpg'

    # 执行图像拼接
    print('开始图像拼接...')
    result = sift_image_stitching(image_path1, image_path2)

    if result is not None:
        print('图像拼接完成！')
    else:
        print('图像拼接失败。')