用Python代码实现cv2.resize源码

好的，以下是用Python代码实现cv2.resize源码的示例：

import numpy as np

def resize(src, dsize=None, fx=None, fy=None, interpolation=None):
    # 获取原始图像的行数和列数
    h, w = src.shape[:2]
    
    # 如果指定了输出图像的大小，则计算缩放比例
    if dsize is not None:
        fx = dsize[0] / w
        fy = dsize[1] / h
    
    # 如果指定了缩放比例，则计算输出图像的大小
    if fx is not None and fy is not None:
        w = int(w * fx + 0.5)
        h = int(h * fy + 0.5)
    
    # 创建输出图像
    dst = np.zeros((h, w, src.shape[2]), dtype=src.dtype)
    
    # 根据指定的插值方法进行缩放
    if interpolation == cv2.INTER_NEAREST:
        for i in range(h):
            for j in range(w):
                x = int(j / fx + 0.5)
                y = int(i / fy + 0.5)
                dst[i, j] = src[y, x]
    elif interpolation == cv2.INTER_LINEAR:
        for i in range(h):
            for j in range(w):
                x = j / fx
                y = i / fy
                x1 = int(x)
                y1 = int(y)
                x2 = min(x1 + 1, src.shape[1] - 1)
                y2 = min(y1 + 1, src.shape[0] - 1)
                dx = x - x1
                dy = y - y1
                dst[i, j] = (1 - dx) * (1 - dy) * src[y1, x1] + dx * (1 - dy) * src[y1, x2] + (1 - dx) * dy * src[y2, x1] + dx * dy * src[y2, x2]
    elif interpolation == cv2.INTER_AREA:
        for i in range(h):
            for j in range(w):
                x1 = int(j / fx)
                y1 = int(i / fy)
                x2 = min(x1 + 1, src.shape[1] - 1)
                y2 = min(y1 + 1, src.shape[0] - 1)
                dst[i, j] = np.mean(src[y1:y2+1, x1:x2+1], axis=(0, 1))
    elif interpolation == cv2.INTER_CUBIC:
        for i in range(h):
            for j in range(w):
                x = j / fx
                y = i / fy
                x1 = int(x) - 1
                y1 = int(y) - 1
                x2 = min(x1 + 4, src.shape[1] - 1)
                y2 = min(y1 + 4, src.shape[0] - 1)
                dx = x - x1
                dy = y - y1
                A = np.array([[1, x1, x1**2, x1**3],
                              [1, x2, x2**2, x2**3],
                              [0, 1, 2*x1, 3*x1**2],
                              [0, 1, 2*x2, 3*x2**2]])
                b = np.array([src[y1, x1], src[y2, x2], (src[y1, x2] - src[y1, x1]) / (x2 - x1), (src[y2, x2] - src[y2, x1]) / (x2 - x1)])
                c = np.linalg.solve(A, b)
                dst[i, j] = c[0] + c[1]*x + c[2]*x**2 + c[3]*x**3
    else:
        raise ValueError("Invalid interpolation method.")
    
    return dst

注意：这里的代码仅是示例，具体实现可能与cv2.resize源码有所不同。