二值图像利用bp神经网络进行图像边缘提取的python代码

以下是基于BP神经网络的图像边缘提取代码示例：

import numpy as np
import cv2

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# BP神经网络类
class BPNeuralNetwork:
    def __init__(self, d_in, d_hidden, d_out):
        self.d_in = d_in # 输入层节点数
        self.d_hidden = d_hidden # 隐藏层节点数
        self.d_out = d_out # 输出层节点数

        self.W1 = np.random.randn(self.d_in, self.d_hidden) # 输入层到隐藏层的连接权值
        self.W2 = np.random.randn(self.d_hidden, self.d_out) # 隐藏层到输出层的连接权值

    # 前向传播
    def forward(self, X):
        # 隐藏层输入
        self.z2 = np.dot(X, self.W1)
        # 隐藏层输出
        self.a2 = sigmoid(self.z2)
        # 输出层输入
        self.z3 = np.dot(self.a2, self.W2)
        # 输出层输出
        self.y_hat = sigmoid(self.z3)

    # 根据误差反向传播更新权值
    def backward(self, X, y, learning_rate):
        # 输出层误差
        delta3 = (y - self.y_hat) * self.y_hat * (1 - self.y_hat)
        # 隐藏层误差
        delta2 = np.dot(delta3, self.W2.T) * self.a2 * (1 - self.a2)

        # 更新权值
        self.W2 += learning_rate * np.dot(self.a2.T, delta3)
        self.W1 += learning_rate * np.dot(X.T, delta2)

    # 训练网络
    def train(self, X, y, epochs, learning_rate):
        for i in range(epochs):
            self.forward(X)
            self.backward(X, y, learning_rate)

# 加载图像
img_gray = cv2.imread('test.png', cv2.IMREAD_GRAYSCALE)

# 二值化
img_bw = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)[1]

# 提取图像边缘
kernel = np.ones((5, 5), np.uint8)
img_edge = cv2.dilate(img_bw, kernel) - cv2.erode(img_bw, kernel)

# 构造训练样本
X = img_bw.reshape(-1, 1) / 255.0
y = img_edge.reshape(-1, 1) / 255.0

# 构建BP神经网络
bp_nn = BPNeuralNetwork(d_in=1, d_hidden=16, d_out=1)

# 训练网络
bp_nn.train(X=X, y=y, epochs=1000, learning_rate=0.1)

# 进行边缘检测
img_edge_bp = bp_nn.y_hat.reshape(img_bw.shape) * 255.0

# 显示结果
cv2.imshow('Original Image', img_gray)
cv2.imshow('Binary Image', img_bw)
cv2.imshow('Edge Image (BP)', img_edge_bp.astype(np.uint8))
cv2.waitKey(0)
cv2.destroyAllWindows()

注意：这只是示例代码，可能需要根据具体需求进行适当修改。