上述LIFT算法四张图块制作代码

以下是使用Python实现LIFT算法四张图块制作的示例代码：

import cv2
import numpy as np

# 定义子块的大小
block_size = 16

# 读取输入图像
img = cv2.imread('input.jpg')

# 划分子块
h, w, _ = img.shape
h_blocks = h // block_size
w_blocks = w // block_size
img_blocks = np.zeros((h_blocks, w_blocks, block_size, block_size, 3), dtype=np.uint8)
for i in range(h_blocks):
    for j in range(w_blocks):
        img_blocks[i, j] = img[i*block_size:(i+1)*block_size, j*block_size:(j+1)*block_size]

# 提取SIFT、DoG和GLOH特征
sift = cv2.xfeatures2d.SIFT_create()
dog = cv2.xfeatures2d.DOG_create()
gloh = cv2.xfeatures2d.GLPH_create()
sift_blocks = []
dog_blocks = []
gloh_blocks = []
for i in range(h_blocks):
    sift_row = []
    dog_row = []
    gloh_row = []
    for j in range(w_blocks):
        gray = cv2.cvtColor(img_blocks[i, j], cv2.COLOR_BGR2GRAY)
        kps = sift.detect(gray, None)
        kps, des = sift.compute(gray, kps)
        sift_row.append(des)
        
        kps = dog.detect(gray, None)
        kps, des = dog.compute(gray, kps)
        dog_row.append(des)
        
        kps = gloh.detect(gray, None)
        kps, des = gloh.compute(gray, kps)
        gloh_row.append(des)
        
    sift_blocks.append(sift_row)
    dog_blocks.append(dog_row)
    gloh_blocks.append(gloh_row)

# 将SIFT、DoG和GLOH特征合并
sift_list = [des for row in sift_blocks for des in row]
dog_list = [des for row in dog_blocks for des in row]
gloh_list = [des for row in gloh_blocks for des in row]
sift_block = np.concatenate(sift_list, axis=0)
dog_block = np.concatenate(dog_list, axis=0)
gloh_block = np.concatenate(gloh_list, axis=0)

# 提取颜色信息
color_block = np.zeros((h_blocks, w_blocks, block_size, block_size, 3), dtype=np.uint8)
for i in range(h_blocks):
    for j in range(w_blocks):
        color_block[i, j] = img_blocks[i, j]

# 将特征图块转换为四张图块
sift_img = np.array(sift_block, dtype=np.float32).reshape(-1, 128)
dog_img = np.array(dog_block, dtype=np.float32).reshape(-1, 128)
gloh_img = np.array(gloh_block, dtype=np.float32).reshape(-1, 144)
color_img = np.array(color_block, dtype=np.float32).reshape(-1, 3)

# 将四张图块保存到文件
np.savetxt('sift.txt', sift_img)
np.savetxt('dog.txt', dog_img)
np.savetxt('gloh.txt', gloh_img)
np.savetxt('color.txt', color_img)

上述代码将输入图像按照给定的子块大小划分成多个子块，并分别提取SIFT、DoG、GLOH和颜色信息。然后将这些特征组合成四张图块，并将它们保存到文件中供后续使用。需要注意的是，这里的代码只是一个示例，实际应用中需要根据具体情况进行修改和调整。