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
 
 
def nn_conv2d(im):
  # 用nn.Conv2d定义卷积操作
  conv_op = nn.Conv2d(1, 1, 3, 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))
  # 给卷积操作的卷积核赋值
  conv_op.weight.data = torch.from_numpy(sobel_kernel)
  # 对图像进行卷积操作
  edge_detect = conv_op(Variable(im))
  # 将输出转换为图片格式
  edge_detect = edge_detect.squeeze().detach().numpy()
  return edge_detect
 
def functional_conv2d(im):
  sobel_kernel = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype='float32') #
  sobel_kernel = sobel_kernel.reshape((1, 1, 3, 3))
  weight = Variable(torch.from_numpy(sobel_kernel))
  edge_detect = F.conv2d(Variable(im), weight)
  edge_detect = edge_detect.squeeze().detach().numpy()
  return edge_detect
 
def main():
  # 读入一张图片，并转换为灰度图
  im = Image.open('./cat.jpg').convert('L')
  # 将图片数据转换为矩阵
  im = np.array(im, dtype='float32')
  # 将图片矩阵转换为pytorch tensor,并适配卷积输入的要求
  im = torch.from_numpy(im.reshape((1, 1, im.shape[0], im.shape[1])))
  # 边缘检测操作
  # edge_detect = nn_conv2d(im)
  edge_detect = functional_conv2d(im)
  # 将array数据转换为image
  im = Image.fromarray(edge_detect)
  # image数据转换为灰度模式
  im = im.convert('L')
  # 保存图片
  im.save('edge.jpg', quality=95)
 
if __name__ == "__main__":
  main()