opencv--图像金字塔

一，高斯金字塔--图片经过高斯+下采样

"""
高斯金字塔
"""
def gauss_pyramid():
    img = cv2.imread('./data/img4.png')
    lower_reso = cv2.pyrDown(img)
    lower_reso2 = cv2.pyrDown(lower_reso)
    plt.subplot(131), plt.imshow(img)
    plt.title('Input Image')
    plt.subplot(132), plt.imshow(lower_reso)
    plt.title('lower_1')
    plt.subplot(133), plt.imshow(lower_reso2)
    plt.title('lower_1')
    plt.show()

经过两次下采样的还原回去

与原图相比更加的模糊，因为丢失了信息。

二，拉普拉斯金字塔

由于高频细节信息在卷积和下采样中丢失，其保留所有层所丢失的高频信息,用于图像恢复,故可以将拉普拉斯保存的高频信息与高斯金字塔的放大图片融合

"""
图像融合
"""
def blend_image():
    A = cv2.imread('./data/apple.png')
    B = cv2.imread('./data/orange.png')

    b, g, r = cv2.split(A)
    A = cv2.merge([r, g, b])

    b, g, r = cv2.split(B)
    B= cv2.merge([r, g, b])

    A=cv2.resize(A,(240,240))
    B = cv2.resize(B, (240, 240))
    # print(A.shape)
    # print(B.shape)
    # generate Gaussian pyramid for A
    G = A.copy()
    gpA = [G]
    for i in range(6):
        G = cv2.pyrDown(G)
        gpA.append(G)
    # generate Gaussian pyramid for B
    G = B.copy()
    gpB = [G]
    for i in range(6):
        G = cv2.pyrDown(G)
        gpB.append(G)
    # generate Laplacian Pyramid for A
    lpA = [gpA[5]]
    # print(np.array(lpA).shape)
    for i in range(5, 0, -1):
        size = (gpA[i - 1].shape[1], gpA[i - 1].shape[0])
        GE = cv2.pyrUp(gpA[i],dstsize=size)
        L = cv2.subtract(gpA[i - 1], GE)
        lpA.append(L)
    # generate Laplacian Pyramid for B
    lpB = [gpB[5]]
    for i in range(5, 0, -1):
        size = (gpB[i - 1].shape[1], gpB[i - 1].shape[0])
        GE = cv2.pyrUp(gpB[i],dstsize=size)
        L = cv2.subtract(gpB[i - 1], GE)
        lpB.append(L)
    # Now add left and right halves of images in each level
    LS = []
    for la, lb in zip(lpA, lpB):
        rows, cols, dpt = la.shape
        ls = np.hstack((la[:, 0:cols // 2], lb[:, cols // 2:]))
        LS.append(ls)
    # now reconstruct
    ls_ = LS[0]
    for i in range(1, 6):
        size = (LS[i].shape[1], LS[i].shape[0])
        ls_ = cv2.pyrUp(ls_,dstsize=size)
        ls_ = cv2.add(ls_, LS[i])
    # image with direct connecting each half
    real = np.hstack((A[:, :cols // 2], B[:, cols // 2:]))
    plt.subplot(121)
    plt.imshow(ls_)
    plt.subplot(122)
    plt.imshow(real)
    plt.show()

打印结果：

左图是经过拉普拉斯金字塔的融合，右图是直接融合的。

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opencv--图像金字塔

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