python车牌识别系统开源代码_Python+Tensorflow+CNN实现车牌识别的示例代码

以下是一个基于Python+Tensorflow+CNN的车牌识别系统的示例代码，供您参考：

首先，需要安装必要的库：

pip install tensorflow-gpu==1.14.0
pip install opencv-python
pip install imutils

然后，我们需要下载数据集。此处我们使用的是CCPD数据集，可以从以下链接下载：https://github.com/detectRecog/CCPD。

接下来，我们定义一个函数`load_data`，用于加载数据集：

import os
import cv2
import numpy as np
 
def load_data(data_dir):
    data = []
    labels = []
 
    for root, dirs, files in os.walk(data_dir):
        for file in files:
            if file.endswith('.jpg'):
                image_path = os.path.join(root, file)
                image = cv2.imread(image_path)
                image = cv2.resize(image, (120, 32))
                data.append(image)
 
                label = os.path.basename(root)
                labels.append(label)
 
    data = np.array(data, dtype='float32')
    data /= 255.0
    labels = np.array(labels)
 
    return data, labels

接下来，我们定义一个函数`build_model`，用于构建模型：

from keras.models import Sequential
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.core import Dense, Dropout, Activation, Flatten
 
def build_model(input_shape, num_classes):
    model = Sequential()
 
    model.add(Conv2D(32, (3, 3), padding='same', input_shape=input_shape))
    model.add(Activation('relu'))
    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
 
    model.add(Conv2D(64, (3, 3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(64, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
 
    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes))
    model.add(Activation('softmax'))
 
    return model

接下来，我们加载数据集并构建模型：

data_dir = '/path/to/dataset'
input_shape = (32, 120, 3)
num_classes = 65
 
data, labels = load_data(data_dir)
 
model = build_model(input_shape, num_classes)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

接下来，我们训练模型：

from keras.utils import to_categorical
 
num_epochs = 10
batch_size = 32
 
labels = to_categorical(labels, num_classes=num_classes)
 
model.fit(data, labels, batch_size=batch_size, epochs=num_epochs, validation_split=0.1)

最后，我们可以使用训练好的模型对车牌进行识别：

import imutils
from keras.preprocessing.image import img_to_array
 
def recognize_plate(image, model):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (7, 7), 0)
    edged = cv2.Canny(gray, 50, 200)
 
    cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
 
    for c in cnts:
        x, y, w, h = cv2.boundingRect(c)
 
        if w / h > 4 and w > 100:
            roi = gray[y:y + h, x:x + w]
            thresh = cv2.threshold(roi, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
 
            thresh = cv2.resize(thresh, (120, 32))
            thresh = thresh.astype("float") / 255.0
            thresh = img_to_array(thresh)
            thresh = np.expand_dims(thresh, axis=0)
 
            preds = model.predict(thresh)
            label = chr(preds.argmax(axis=1)[0] + 48)
 
            cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
            cv2.putText(image, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 0), 2)
 
            break
 
    return image

使用示例：

image = cv2.imread('/path/to/image')
image = recognize_plate(image, model)
cv2.imshow('Image', image)
cv2.waitKey(0)

以上就是一个基于Python+Tensorflow+CNN的车牌识别系统的示例代码。