Opencv 缩小和放大|极客教程

]将imori.jpg进行灰度化处理之后，先缩小至原来的 $0.5$ 倍，再放大两倍吧。这样做的话，会得到模糊的图像。

放大缩小的时候使用双线性插值。如果将双线性插值方法编写成函数的话，编程会变得简洁一些。

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


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

gray = BGR2GRAY(img)

# Bilinear interpolation
out = bl_interpolate(gray.astype(np.float32), ax=0.5, ay=0.5)

# Bilinear interpolation
out = bl_interpolate(out, ax=2., ay=2.)

out = out.astype(np.uint8)

# Save result
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.imwrite("out.jpg", out)

输入(imori.jpg)：

Opencv 缩小和放大