import numpy as np from numpy import int64 def bilinear_interp_vectorized(a: np.ndarray, b: np.ndarray) -> np.ndarray: """ This is the vectorized implementation of bilinear interpolation. - a is a ND array with shape [N, H1, W1, C], dtype = int64 - b is a ND array with shape [N, H2, W2, 2], dtype = float64 - return a ND array with shape [N, H2, W2, C], dtype = int64 """ # Get axis size from ndarray shape N, H1, W1, C = a.shape N1, H2, W2, _ = b.shape assert N == N1 # Calculate the indices for interpolation x_idx = np.floor(b[:, :, :, 0]).astype(int) y_idx = np.floor(b[:, :, :, 1]).astype(int) x = b[:, :, :, 0] - x_idx y = b[:, :, :, 1] - y_idx # Calculate the interpolation values res = a[:, x_idx, y_idx] * (1 - x) * (1 - y) + a[:, x_idx + 1, y_idx] * x * (1 - y) + a[:, x_idx, y_idx + 1] * (1 - x) * y + a[:, x_idx + 1, y_idx + 1] * x * y return res.astype(int64) 在res = a[:, x_idx, y_idx] * (1 - x) * (1 - y) + a[:, x_idx + 1, y_idx] * x * (1 - y) + a[:, x_idx, y_idx + 1] * (1 - x) * y + a[:, x_idx + 1, y_idx + 1] * x * y发生错误operands could not be broadcast together with shapes (8,8,720,1280,4) (8,720,1280)

这个错误是由于形状不匹配导致的。你可以在计算 `res` 之前，先调整 `x_idx` 和 `y_idx` 的形状，使其与 `a` 的形状相匹配。可以使用 `np.expand_dims` 函数来添加额外的维度。修改代码如下：

x_idx = np.expand_dims(x_idx, axis=-1)
y_idx = np.expand_dims(y_idx, axis=-1)

然后再重新计算 `res`。

希望这可以解决你的问题！如果还有其他问题，请随时提问。

相关问题

import numpy as np from numpy import int64 def bilinear_interp_baseline(a: np.ndarray, b: np.ndarray) -> np.ndarray: """ This is the baseline implementation of bilinear interpolation without vectorization. - a is a ND array with shape [N, H1, W1, C], dtype = int64 - b is a ND array with shape [N, H2, W2, 2], dtype = float64 - return a ND array with shape [N, H2, W2, C], dtype = int64 """ # Get axis size from ndarray shape N, H1, W1, C = a.shape N1, H2, W2, _ = b.shape assert N == N1 # Do iteration res = np.empty((N, H2, W2, C), dtype=int64) for n in range(N): for i in range(H2): for j in range(W2): x, y = b[n, i, j] x_idx, y_idx = int(np.floor(x)), int(np.floor(y)) _x, _y = x - x_idx, y - y_idx # For simplicity, we assume all x are in [0, H1 - 1), all y are in [0, W1 - 1) res[n, i, j] = a[n, x_idx, y_idx] * (1 - _x) * (1 - _y) + a[n, x_idx + 1, y_idx] * _x * (1 - _y) + \ a[n, x_idx, y_idx + 1] * (1 - _x) * _y + a[n, x_idx + 1, y_idx + 1] * _x * _y return res 将这段代码改成向量化的方式

下面是将代码改写为向量化方式的版本：

import numpy as np
from numpy import int64


def bilinear_interp_vectorized(a: np.ndarray, b: np.ndarray) -> np.ndarray:
    """
    This is the vectorized implementation of bilinear interpolation.
    - a is a ND array with shape [N, H1, W1, C], dtype = int64
    - b is a ND array with shape [N, H2, W2, 2], dtype = float64
    - return a ND array with shape [N, H2, W2, C], dtype = int64
    """
    # Get axis size from ndarray shape
    N, H1, W1, C = a.shape
    N1, H2, W2, _ = b.shape
    assert N == N1

    # Calculate the indices for interpolation
    x_idx = np.floor(b[:, :, :, 0]).astype(int)
    y_idx = np.floor(b[:, :, :, 1]).astype(int)
    x = b[:, :, :, 0] - x_idx
    y = b[:, :, :, 1] - y_idx

    # Calculate the interpolation values
    res = a[:, x_idx, y_idx] * (1 - x) * (1 - y) + a[:, x_idx + 1, y_idx] * x * (1 - y) + \
          a[:, x_idx, y_idx + 1] * (1 - x) * y + a[:, x_idx + 1, y_idx + 1] * x * y

    return res.astype(int64)

这个向量化的版本通过使用数组切片和广播操作，避免了显式的循环，从而提高了代码的执行效率。

def getDepthMapWithQ(disparityMap: np.ndarray, Q: np.ndarray) -> np.ndarray:

这是一个Python函数，用于从视差图(disparity map)和投影矩阵(Q)计算深度图(depth map)。

函数输入：

- disparityMap: 表示视差图的NumPy数组。
- Q: 表示投影矩阵的NumPy数组。

函数输出：

- 返回一个NumPy数组，表示深度图。

函数实现：

具体实现过程需要使用OpenCV库中的cv2.reprojectImageTo3D()函数，该函数可以根据视差图和投影矩阵计算深度图。具体实现代码如下：

import cv2
import numpy as np

def getDepthMapWithQ(disparityMap: np.ndarray, Q: np.ndarray) -> np.ndarray:
    points3D = cv2.reprojectImageTo3D(disparityMap, Q)
    depthMap = points3D[:, :, 2]
    return depthMap

其中，cv2.reprojectImageTo3D()函数的第一个参数是视差图，第二个参数是投影矩阵。函数返回值是一个三维点云数组，其中每个点的坐标为(x, y, z)，表示该点在相机坐标系下的三维坐标。因此，我们可以通过取点云数组的第三个维度，得到深度图。