Pytorch 结合Sobel滤波的卷积操作来提取图像的轮廓图！

在pytorch中实现将sobel算子和卷积层结合来提取图像中物体的边缘轮廓图，如下代码是卷积执行soble边缘检测算子的实现：

import torch
import numpy as np
from torch import nn
from PIL import Image
from torch.autograd import Variable
import torch.nn.functional as F
import cv2

def edge_conv2d(im):
    # 用nn.Conv2d定义卷积操作
    conv_op = nn.Conv2d(3, 3, kernel_size=3, padding=1, bias=False)
    # 定义sobel算子参数
    sobel_kernel = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype='float32')
    # 将sobel算子转换为适配卷积操作的卷积核
    sobel_kernel = sobel_kernel.reshape((1, 1, 3, 3))
    # 卷积输出通道，这里我设置为3
    sobel_kernel = np.repeat(sobel_kernel, 3, axis=1)
    # 输入图的通道，这里我设置为3
    sobel_kernel = np.repeat(sobel_kernel, 3, axis=0)

    conv_op.weight.data = torch.from_numpy(sobel_kernel)
    # print(conv_op.weight.size())
    # print(conv_op, '\n')

    edge_detect = conv_op(im)
    print(torch.max(edge_detect))
    # 将输出转换为图片格式
    edge_detect = edge_detect.squeeze().detach().numpy()
    return edge_detect

def edge_extraction():
    im = cv2.imread('./cat.jpg', flags=1)
    im = np.transpose(im, (2, 0, 1))
    # 添加一个维度，对应于pytorch模型张量(B, N, W, H)中的batch_size
    im = im[np.newaxis, :]
    im = torch.Tensor(im)
    edge_detect = edge_conv2d(im)
    edge_detect = np.transpose(edge_detect, (1, 2, 0))
    # cv2.imshow('edge.jpg', edge_detect)
    # cv2.waitKey(0)
    cv2.imwrite('edge-2.jpg', edge_detect)

if __name__ == "__main__":
    edge_extraction()

结果如下图所示：

本文地址：https://blog.csdn.net/qq_37760750/article/details/107228529