给出python的仿射尺度算法，势减少算法，路径跟踪发

以下是基于Python的仿射尺度算法示例代码，其中使用了DoG (Difference of Gaussian) 算法来检测关键点，使用SIFT (Scale-Invariant Feature Transform) 算法来计算关键点的特征描述子：

import cv2 as cv
import numpy as np

# 定义初始尺度、尺度步长和尺度数
INITIAL_SCALE = 1.0
SCALE_STEP = 1.25
NUM_SCALES = 8

# 定义 DoG 检测相关参数
DOG_SIGMA = 1.6
DOG_K = np.sqrt(2)
DOG_THRESH = 0.01

# 定义 SIFT 特征计算相关参数
SIFT_SIGMA = 1.5
SIFT_ORI_HIST_BINS = 36
SIFT_DESC_HIST_BINS = 8

# 加载图像
img = cv.imread("image.jpg", cv.IMREAD_GRAYSCALE)

# 构建尺度空间
scales = [INITIAL_SCALE * (SCALE_STEP ** i) for i in range(NUM_SCALES)]
pyramid = [cv.GaussianBlur(img, (0, 0), sigma) for sigma in scales]

# 计算 DoG 图像
dog_pyramid = [pyramid[i + 1] - pyramid[i] for i in range(NUM_SCALES - 1)]

# 检测关键点
keypoints = []
for i in range(1, NUM_SCALES - 1):
    dog_curr = dog_pyramid[i]
    dog_prev = cv.resize(dog_pyramid[i - 1], dog_curr.shape[::-1], interpolation=cv.INTER_LINEAR)
    dog_next = cv.resize(dog_pyramid[i + 1], dog_curr.shape[::-1], interpolation=cv.INTER_LINEAR)

    # 寻找极值点
    rows, cols = dog_curr.shape
    for r in range(1, rows - 1):
        for c in range(1, cols - 1):
            patch_curr = dog_curr[r - 1:r + 2, c - 1:c + 2]
            patch_prev = dog_prev[r - 1:r + 2, c - 1:c + 2]
            patch_next = dog_next[r - 1:r + 2, c - 1:c + 2]
            extremum_value = patch_curr[1, 1]
            if np.abs(extremum_value) > DOG_THRESH:
                # 检查极值点是否为局部极值
                if extremum_value > 0:
                    if np.all(patch_curr > extremum_value) and \
                       np.all(patch_prev > extremum_value) and \
                       np.all(patch_next > extremum_value):
                        keypoints.append((i, r, c))
                else:
                    if np.all(patch_curr < extremum_value) and \
                       np.all(patch_prev < extremum_value) and \
                       np.all(patch_next < extremum_value):
                        keypoints.append((i, r, c))

# 计算关键点的尺度和方向
keypoints_with_scale = []
for octave, row, col in keypoints:
    scale = scales[octave]
    dog_image = dog_pyramid[octave]
    dx = (dog_image[row, col + 1] - dog_image[row, col - 1]) / 2.0
    dy = (dog_image[row + 1, col] - dog_image[row - 1, col]) / 2.0
    magnitude = np.sqrt(dx ** 2 + dy ** 2)
    orientation = np.rad2deg(np.arctan2(dy, dx))
    keypoints_with_scale.append((col, row, scale, orientation))

# 计算关键点的 SIFT 描述子
descriptors = []
for x, y, scale, angle in keypoints_with_scale:
    # 构建关键点周围的小区域
    image_patch = cv.getRectSubPix(img, (16 * scale, 16 * scale), (x, y))
    image_patch = cv.rotate(image_patch, angle)

    # 计算 SIFT 描述子
    sift = cv.xfeatures2d.SIFT_create(sigma=SIFT_SIGMA)
    keypoint = cv.KeyPoint(8 * scale, 8 * scale, _size=16 * scale)
    _, descriptor = sift.compute(image_patch, [keypoint])
    descriptor = np.squeeze(descriptor)
    descriptor /= np.linalg.norm(descriptor)
    descriptor[descriptor > 0.2] = 0.2
    descriptor /= np.linalg.norm(descriptor)

    descriptors.append(descriptor)

# 输出关键点和描述子
for i in range(len(keypoints)):
    print("Keypoint: ", keypoints_with_scale[i], " Descriptor: ", descriptors[i])

势减少算法和路径跟踪发不是仿射尺度算法的一部分，可能需要更多上下文信息才能回答这些问题。