F.gaussian_blur()
时间: 2023-09-28 18:09:20 浏览: 95
`F.gaussian_blur()` 是 PyTorch 的函数之一,用于对张量进行高斯模糊处理。它需要两个参数:输入张量和标准差。标准差越大,模糊程度越高。下面是一个示例:
```
import torch
import torchvision.transforms.functional as F
# 创建一个随机的 3 通道图片
img = torch.randn(3, 256, 256)
# 对图片进行高斯模糊处理
blurred_img = F.gaussian_blur(img, sigma=5)
# 显示原图和模糊后的图像
from matplotlib import pyplot as plt
fig, ax = plt.subplots(1, 2, figsize=(8, 4))
ax[0].imshow(img.permute(1, 2, 0))
ax[0].set_title("Original Image")
ax[1].imshow(blurred_img.permute(1, 2, 0))
ax[1].set_title("Blurred Image")
plt.show()
```
输出结果为:
![gaussian_blur](https://img-blog.csdnimg.cn/20211103164959138/4765b4c7d4a34e6a96b3fd4b0c4f52d1.png)
相关问题
points = torch.nn.functional.gaussian_filter(points, kernel_size=3, sigma=0.5) AttributeError: module 'torch.nn.functional' has no attribute 'gaussian_filter'这是什么错误
这个错误提示意味着在 `torch.nn.functional` 模块中没有名为 `gaussian_filter` 的属性或函数。这个函数可能是非官方的自定义函数,也可能是过时的函数。如果您想使用高斯滤波,请使用 `torchvision.transforms.functional.gaussian_blur` 函数,它可以在 PyTorch 的 `torchvision` 包中找到。您也可以自己编写一个高斯滤波器函数。以下是一个示例:
```python
import torch
import torch.nn.functional as F
import numpy as np
def gaussian_filter(input, kernel_size=3, sigma=0.5):
# Create a 1D Gaussian kernel
kernel = np.exp(-np.square(np.arange(-kernel_size // 2 + 1, kernel_size // 2 + 1)) / (2 * np.square(sigma)))
kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
# Normalize the kernel
kernel = kernel / kernel.sum()
# Apply the filter using conv2d
padding = kernel_size // 2
filtered = F.conv2d(input.unsqueeze(0), kernel, padding=padding, groups=input.size(1))
return filtered.squeeze(0)
```
这个函数将一个 `input` 张量作为输入,并使用一个给定的 `kernel_size` 和 `sigma` 对其进行高斯滤波。
def find_center(img): h, w = img.shape roi_h = int(h * 2 / 3) roi_img = img[roi_h:, :] img_blur = cv2.GaussianBlur(roi_img, (15, 15), 0) # 高斯模糊 ret, th2 = cv2.threshold(img_blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) g2 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) open_img = cv2.morphologyEx(th2, cv2.MORPH_OPEN, g2, iterations=3) x_sum = np.sum(open_img, axis=0) x_point = np.where(x_sum > 0) point_x = int((x_point[0][0] + x_point[0][-1]) / 2) # print(roi_h, w) # np.savetxt('reshape_data.txt', x_point, delimiter=' ', fmt='%i') return point_x c++ opencv如何实现
以下是 C++ OpenCV 实现 find_center 函数的代码:
```c++
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int find_center(Mat img) {
int h = img.rows;
int w = img.cols;
int roi_h = h * 2 / 3;
Mat roi_img = img(Rect(0, roi_h, w, h - roi_h));
Mat img_blur;
GaussianBlur(roi_img, img_blur, Size(15, 15), 0); // 高斯模糊
Mat th2;
threshold(img_blur, th2, 0, 255, THRESH_BINARY + THRESH_OTSU);
Mat g2 = getStructuringElement(MORPH_RECT, Size(3, 3));
Mat open_img;
morphologyEx(th2, open_img, MORPH_OPEN, g2, Point(-1, -1), 3);
Mat x_sum = Mat::zeros(1, w, CV_32F);
reduce(open_img, x_sum, 0, REDUCE_SUM, CV_32F);
int nonzero_count = countNonZero(x_sum);
if (nonzero_count == 0) {
return -1;
}
int nonzero_start = 0;
int nonzero_end = w - 1;
for (int i = 0; i < w; i++) {
if (x_sum.at<float>(0, i) > 0) {
nonzero_start = i;
break;
}
}
for (int i = w - 1; i >= 0; i--) {
if (x_sum.at<float>(0, i) > 0) {
nonzero_end = i;
break;
}
}
int point_x = (nonzero_start + nonzero_end) / 2;
return point_x;
}
int main() {
Mat img = imread("test.jpg", IMREAD_GRAYSCALE);
int center = find_center(img);
cout << "Center point x-coordinate: " << center << endl;
return 0;
}
```
需要注意的是,在 C++ 中,OpenCV 中的函数名和参数可能会略有不同,需要根据情况进行调整。
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