使用计算机视觉和深度学习完成车牌检测的代码

好的，下面我将分别给出使用计算机视觉和深度学习完成车牌检测的代码。

1. 计算机视觉实现车牌检测的代码

这里我们使用Python和OpenCV库实现车牌检测。代码如下：

import cv2

# 图像预处理
def preprocess(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    thresh = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 2)
    return thresh

# 车牌定位
def locate_plate(img):
    contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    for cnt in contours:
        x, y, w, h = cv2.boundingRect(cnt)
        area = cv2.contourArea(cnt)
        if area > 1000 and area < 5000 and w > h and w / h > 2:
            plate_img = img[y:y+h, x:x+w]
            return plate_img
    return None

# 字符分割
def segment_chars(img):
    contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    chars = []
    for cnt in contours:
        x, y, w, h = cv2.boundingRect(cnt)
        if w > 10 and h > 10:
            char_img = img[y:y+h, x:x+w]
            chars.append(char_img)
    return chars

# 字符识别
def recognize_chars(img):
    pass  # 这里可以使用OCR技术实现字符识别

# 车牌检测
def detect_plate(img):
    thresh = preprocess(img)
    plate_img = locate_plate(thresh)
    if plate_img is None:
        return None
    chars = segment_chars(plate_img)
    plate_chars = []
    for char in chars:
        char = cv2.resize(char, (20, 20))
        char = cv2.copyMakeBorder(char, 4, 4, 4, 4, cv2.BORDER_CONSTANT, value=(0, 0, 0))
        plate_chars.append(char)
    plate_chars = recognize_chars(plate_chars)
    plate_chars = ''.join(plate_chars)
    return plate_chars

# 测试代码
if __name__ == '__main__':
    img = cv2.imread('test.jpg')
    plate_chars = detect_plate(img)
    print(plate_chars)

2. 深度学习实现车牌检测的代码

这里我们使用Python和TensorFlow库实现车牌检测。代码如下：

import tensorflow as tf
import cv2

# 网络设计
def create_model(input_shape):
    model = tf.keras.models.Sequential([
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
        tf.keras.layers.MaxPooling2D((2, 2)),
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
        tf.keras.layers.MaxPooling2D((2, 2)),
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model

# 数据准备
def prepare_data():
    pass  # 这里需要收集车牌图像数据，进行标注和分类

# 训练网络
def train_model():
    X_train, y_train, X_test, y_test = prepare_data()
    model = create_model(X_train[0].shape)
    model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10)

# 车牌检测
def detect_plate(img):
    model = tf.keras.models.load_model('model.h5')
    plate_img = cv2.resize(img, (100, 50))
    plate_img = plate_img / 255.0
    plate_img = plate_img.reshape((1, 50, 100, 3))
    result = model.predict(plate_img)
    if result > 0.5:
        return 'plate number'
    else:
        return None

# 测试代码
if __name__ == '__main__':
    img = cv2.imread('test.jpg')
    plate_chars = detect_plate(img)
    print(plate_chars)

这里只是简单地介绍了两种方法的代码实现，并没有完整的数据和模型训练。如果需要实际应用，需要根据实际情况进行数据和模型的准备和训练。