Opencv the image pyramid - Image Fusion

Image pyramid

Usually, we used in the past is a constant size of the image. However, in some cases, we need to use different resolutions (same) image. For example, when searching for something (such as faces) in the image, we were not sure what size the object will be displayed in the image. In this case, we will need to create a set of the same images with different resolutions, and search for objects in all images. The set of images having different resolutions referred to as an "image pyramid" (because when they are stacked on the bottom, the highest resolution image is located at the top of the image, is located at the top of the lowest resolution, looks like a pyramid).

Gaussian pyramid: downsampling (narrow)

Gaussian pyramid: up-sampling (amplified)

Laplacian pyramid

For example - Image Fusion

Claim

The right half of the left half of the apple and oranges fused to get the new "breed."

Code

1, first, apples and oranges read images, and, since the layer 6 do downsampling, so that the adjustment of their length and width dimensions are an integer multiple of 64 (or fractional sampling will occur).

img1 = cv.imread('Apple.png', 3)
A = cv.resize(img1, (448, 448), interpolation=cv.INTER_CUBIC)  # 因为要做6层，所以图像尺寸的大小必须是64的整数倍
img2 = cv.imread('Orange.png', 3)
B = cv.resize(img2, (448, 448), interpolation=cv.INTER_CUBIC)
print(A.shape)
print(B.shape)

2, Apple generated image of a Gaussian pyramid.

G = A.copy()
gpA = [G]
plt.subplot(231), plt.imshow(cv.cvtColor(gpA[0], cv.COLOR_BGR2RGB))
for i in range(5):
    G = cv.pyrDown(G)
    gpA.append(G)
    plt.subplot(2, 3, i+2), plt.imshow(cv.cvtColor(gpA[i+1], cv.COLOR_BGR2RGB))

plt.show()

Get results:
3, Orange generated image of a Gaussian pyramid.

G = B.copy()
gpB = [G]
plt.subplot(231), plt.imshow(cv.cvtColor(gpB[0], cv.COLOR_BGR2RGB))
for i in range(5):
    G = cv.pyrDown(G)
    gpB.append(G)
    plt.subplot(2, 3, i+2), plt.imshow(cv.cvtColor(gpB[i+1], cv.COLOR_BGR2RGB))

plt.show()

Get results:
4, Apple generated image Laplacian pyramid.

lpA = [gpA[5]]
plt.subplot(231), plt.imshow(cv.cvtColor(lpA[0], cv.COLOR_BGR2RGB))
for i in range(5, 0, -1):
    GE = cv.pyrUp(gpA[i])
    L = cv.subtract(gpA[i-1], GE)
    lpA.append(L)
    plt.subplot(2, 3, 6-i+1), plt.imshow(cv.cvtColor(L, cv.COLOR_BGR2RGB))
plt.show()

Get results:
5 generate oranges image Laplacian pyramid.

lpB = [gpB[5]]
plt.subplot(231), plt.imshow(cv.cvtColor(lpB[0], cv.COLOR_BGR2RGB))
for i in range(5, 0, -1):
    GE = cv.pyrUp(gpB[i])
    L = cv.subtract(gpB[i-1], GE)
    lpB.append(L)
    plt.subplot(2, 3, 6-i+1), plt.imshow(cv.cvtColor(L, cv.COLOR_BGR2RGB))
plt.show()

Get results:
6 stitching.

LS = []
i = 1
for la, lb in zip(lpA, lpB):
    print(la.shape)
    rows, cols, dpt = la.shape
    ls = np.hstack((la[:, 0: int(cols/2)], lb[:, int(cols/2) :]))
    LS.append(ls)
    plt.subplot(2, 3, i), plt.imshow(cv.cvtColor(ls, cv.COLOR_BGR2RGB))
    i += 1
plt.show()

Get results:
7 reconstruction.

ls_ = LS[0]  # 取出最模糊的那张图片
plt.subplot(2, 3, 1), plt.imshow(cv.cvtColor(ls_, cv.COLOR_BGR2RGB))
for i in range(1, 6):
    ls_ = cv.pyrUp(ls_)  # 加零，高斯模糊
    ls_ = cv.add(ls_, LS[i])  # 加差值，提高分辨率
    plt.subplot(2, 3, i+1), plt.imshow(cv.cvtColor(ls_, cv.COLOR_BGR2RGB))
plt.show()

Results obtained:
8, comparative results.

# 对比直接拼接的效果
real = np.hstack((A[:, :int(cols/2)], B[:, int(cols/2):]))
plt.subplot(1, 2, 1), plt.imshow(cv.cvtColor(real, cv.COLOR_BGR2RGB))
plt.subplot(1, 2, 2), plt.imshow(cv.cvtColor(ls_, cv.COLOR_BGR2RGB))

got the answer: