Opencv 高斯金字塔|极客教程

在这里我们求出原图像 $\frac{1}{2}$ , $\frac{1}{4}$ , $\frac{1}{8}$ , $\frac{1}{16}$ , $\frac{1}{32}$ 大小的图像。

像这样把原图像缩小之后（像金字塔一样）重叠起来的就被称为高斯金字塔。

这种高斯金字塔的方法现在仍然有效。高斯金字塔的方法也用于提高图像清晰度的超分辨率成像（Super-Resolution ）深度学习方法。
python实现：

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

# Grayscale
def BGR2GRAY(img):
    # Grayscale
    gray = 0.2126 * img[..., 2] + 0.7152 * img[..., 1] + 0.0722 * img[..., 0]
    return gray

# Bi-Linear interpolation
def bl_interpolate(img, ax=1., ay=1.):
    if len(img.shape) > 2:
        H, W, C = img.shape
    else:
        H, W = img.shape
        C = 1

    aH = int(ay * H)
    aW = int(ax * W)

    # get position of resized image
    y = np.arange(aH).repeat(aW).reshape(aW, -1)
    x = np.tile(np.arange(aW), (aH, 1))

    # get position of original position
    y = (y / ay)
    x = (x / ax)

    ix = np.floor(x).astype(np.int)
    iy = np.floor(y).astype(np.int)

    ix = np.minimum(ix, W-2)
    iy = np.minimum(iy, H-2)

    # get distance 
    dx = x - ix
    dy = y - iy

    if C > 1:
        dx = np.repeat(np.expand_dims(dx, axis=-1), C, axis=-1)
        dy = np.repeat(np.expand_dims(dy, axis=-1), C, axis=-1)

    # interpolation
    out = (1-dx) * (1-dy) * img[iy, ix] + dx * (1 - dy) * img[iy, ix+1] + (1 - dx) * dy * img[iy+1, ix] + dx * dy * img[iy+1, ix+1]

    out = np.clip(out, 0, 255)
    out = out.astype(np.uint8)

    return out

# make image pyramid
def make_pyramid(gray):
    # first element
    pyramid = [gray]
    # each scale
    for i in range(1, 6):
        # define scale
        a = 2. ** i

        # down scale
        p = bl_interpolate(gray, ax=1./a, ay=1. / a)

        # add pyramid list
        pyramid.append(p)

    return pyramid

# Read image
img = cv2.imread("imori.jpg").astype(np.float)

gray = BGR2GRAY(img)

# pyramid
pyramid = make_pyramid(gray)

for i in range(6):
    cv2.imwrite("out_{}.jpg".format(2**i), pyramid[i].astype(np.uint8))
    plt.subplot(1, 6, i+1)
    plt.imshow(pyramid[i], cmap='gray')
    plt.axis('off')
    plt.xticks(color="None")
    plt.yticks(color="None")

plt.show()