用bp网络识别车牌源码

以下是基于BP神经网络的车牌识别源码示例，供参考：

import numpy as np
import cv2

# 读入训练集
data = np.loadtxt('train_data.txt', delimiter=',')
labels = np.loadtxt('train_label.txt', delimiter=',')

# 归一化数据
data = data / 255.0

# 定义神经网络结构
input_size = 2700
hidden_size = 100
output_size = 34

# 初始化权重和偏置
weights_input_hidden = np.random.randn(input_size, hidden_size)
bias_input_hidden = np.random.randn(hidden_size)
weights_hidden_output = np.random.randn(hidden_size, output_size)
bias_hidden_output = np.random.randn(output_size)

# 定义激活函数
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# 定义反向传播函数
def backpropagation(data, labels, weights_input_hidden, bias_input_hidden, weights_hidden_output, bias_hidden_output, learning_rate):
    # 前向传播计算输出
    hidden_layer_input = np.dot(data, weights_input_hidden) + bias_input_hidden
    hidden_layer_output = sigmoid(hidden_layer_input)
    output_layer_input = np.dot(hidden_layer_output, weights_hidden_output) + bias_hidden_output
    output_layer_output = sigmoid(output_layer_input)

    # 计算误差
    error = labels - output_layer_output

    # 反向传播计算梯度
    output_layer_gradient = error * output_layer_output * (1 - output_layer_output)
    hidden_layer_gradient = np.dot(output_layer_gradient, weights_hidden_output.T) * hidden_layer_output * (1 - hidden_layer_output)

    # 更新权重和偏置
    weights_hidden_output += learning_rate * np.dot(hidden_layer_output.T, output_layer_gradient)
    bias_hidden_output += learning_rate * np.sum(output_layer_gradient, axis=0)
    weights_input_hidden += learning_rate * np.dot(data.T, hidden_layer_gradient)
    bias_input_hidden += learning_rate * np.sum(hidden_layer_gradient, axis=0)

    # 返回更新后的权重和偏置
    return weights_input_hidden, bias_input_hidden, weights_hidden_output, bias_hidden_output

# 训练神经网络
num_epochs = 5000
learning_rate = 0.1

for epoch in range(num_epochs):
    # 随机抽取一个样本进行训练
    index = np.random.randint(len(data))
    sample = data[index]
    label = labels[index]

    # 进行反向传播更新权重和偏置
    weights_input_hidden, bias_input_hidden, weights_hidden_output, bias_hidden_output = backpropagation(sample, label, weights_input_hidden, bias_input_hidden, weights_hidden_output, bias_hidden_output, learning_rate)

    # 每隔100次迭代输出一次损失
    if epoch % 100 == 0:
        hidden_layer_input = np.dot(data, weights_input_hidden) + bias_input_hidden
        hidden_layer_output = sigmoid(hidden_layer_input)
        output_layer_input = np.dot(hidden_layer_output, weights_hidden_output) + bias_hidden_output
        output_layer_output = sigmoid(output_layer_input)
        loss = np.mean(np.square(labels - output_layer_output))
        print('Epoch %d Loss %f' % (epoch, loss))

# 读入测试图片
img = cv2.imread('test.jpg')

# 转换为灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 进行二值化
_, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

# 进行形态学操作，去除噪点
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)

# 查找轮廓
_, contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 对每个轮廓进行识别
for contour in contours:
    # 获取轮廓的外接矩形
    x, y, w, h = cv2.boundingRect(contour)

    # 提取车牌区域
    plate = gray[y:y+h, x:x+w]

    # 对车牌区域进行处理，将其转换为神经网络输入的格式
    plate = cv2.resize(plate, (60, 45)).reshape(1, -1) / 255.0

    # 进行前向传播预测结果
    hidden_layer_input = np.dot(plate, weights_input_hidden) + bias_input_hidden
    hidden_layer_output = sigmoid(hidden_layer_input)
    output_layer_input = np.dot(hidden_layer_output, weights_hidden_output) + bias_hidden_output
    output_layer_output = sigmoid(output_layer_input)

    # 获取预测结果
    result = np.argmax(output_layer_output)

    # 绘制车牌区域和识别结果
    cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
    cv2.putText(img, chr(result + ord('A')), (x, y-5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)

# 显示结果
cv2.imshow('result', img)
cv2.waitKey(0)

需要注意的是，以上示例仅为基于BP神经网络的车牌识别的一种实现方式，具体效果与准确率取决于训练数据的质量和数量等因素。