opencv python cuda 实例
时间: 2023-10-15 21:05:18 浏览: 94
这里提供一个简单的 OpenCV Python CUDA 实例,它将使用 GPU 加速来进行图像处理。该示例将使用 Canny 边缘检测算法来检测图像中的边缘。
首先,您需要确保您的计算机具有 NVIDIA GPU 并且已安装了 CUDA 工具包。然后,您需要安装 `opencv-python` 和 `opencv-contrib-python` 库。您可以使用以下命令来安装它们:
```
pip install opencv-python
pip install opencv-contrib-python
```
接下来,您需要导入所需的库:
```python
import cv2
import numpy as np
from numba import cuda
```
然后,您需要将图像加载到内存中:
```python
img = cv2.imread('image.jpg')
```
接下来,您需要将图像转换为灰度图像:
```python
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
```
然后,您需要将图像分配到 GPU 上:
```python
d_img = cuda.to_device(gray_img)
```
接下来,您需要定义一个 CUDA 内核函数来执行 Canny 边缘检测算法。以下是一个简单的内核函数示例:
```python
@cuda.jit
def canny_kernel(d_img, d_edges):
x, y = cuda.grid(2)
if x > 0 and y > 0 and x < d_img.shape[0]-1 and y < d_img.shape[1]-1:
dx = (d_img[x+1, y+1] + 2*d_img[x+1, y] + d_img[x+1, y-1]) - (d_img[x-1, y+1] + 2*d_img[x-1, y] + d_img[x-1, y-1])
dy = (d_img[x-1, y-1] + 2*d_img[x, y-1] + d_img[x+1, y-1]) - (d_img[x-1, y+1] + 2*d_img[x, y+1] + d_img[x+1, y+1])
mag = np.sqrt(dx**2 + dy**2)
angle = np.arctan2(dy, dx)
if angle < 0:
angle += np.pi
angle = angle * 180 / np.pi
if angle > 22.5 and angle <= 67.5:
if mag > d_img[x-1, y-1] and mag > d_img[x+1, y+1]:
d_edges[x, y] = mag
elif angle > 67.5 and angle <= 112.5:
if mag > d_img[x, y-1] and mag > d_img[x, y+1]:
d_edges[x, y] = mag
elif angle > 112.5 and angle <= 157.5:
if mag > d_img[x-1, y+1] and mag > d_img[x+1, y-1]:
d_edges[x, y] = mag
else:
if mag > d_img[x-1, y] and mag > d_img[x+1, y]:
d_edges[x, y] = mag
```
该内核函数接受一个输入图像和一个输出图像,并在 GPU 上执行 Canny 边缘检测算法。然后,您需要定义一个 GPU 线程块和网格来执行内核函数:
```python
threads_per_block = (32, 32)
blocks_per_grid_x = np.ceil(gray_img.shape[0] / threads_per_block[0]).astype(np.int32)
blocks_per_grid_y = np.ceil(gray_img.shape[1] / threads_per_block[1]).astype(np.int32)
blocks_per_grid = (blocks_per_grid_x, blocks_per_grid_y)
```
最后,您可以在 GPU 上调用内核函数:
```python
d_edges = cuda.device_array_like(gray_img)
canny_kernel[blocks_per_grid, threads_per_block](d_img, d_edges)
```
最后,您可以将处理后的图像从 GPU 复制回到主机内存中:
```python
edges = d_edges.copy_to_host()
```
您可以使用以下代码保存处理后的图像:
```python
cv2.imwrite('edges.jpg', edges)
```
完成!现在您已经学会了如何使用 OpenCV Python CUDA 在 GPU 上加速图像处理了。
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