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license_plate_detection.py
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license_plate_detection.py
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import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# Load and preprocess the dataset
# This code assumes you have a dataset of images with bounding box annotations for the license plates
# in Pascal VOC format. If your dataset is in a different format, you will need to modify this code.
data_dir = '/path/to/your/dataset'
datagen = ImageDataGenerator(rescale=1./255,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,
fill_mode='nearest',
validation_split=0.2) # reserve 20% of images for validation
train_generator = datagen.flow_from_directory(
data_dir,
target_size=(224, 224),
batch_size=32,
class_mode='categorical',
subset='training')
validation_generator = datagen.flow_from_directory(
data_dir,
target_size=(224, 224),
batch_size=32,
class_mode='categorical',
subset='validation')
# Load a pre-trained model
base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
# Freeze the base model
base_model.trainable = False
# Add your head on top
x = Flatten()(base_model.output)
x = Dense(1024, activation='relu')(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)
x = Dense(1, activation='sigmoid')(x)
model = Model(base_model.input, x)
# Compile the model
model.compile(optimizer=Adam(lr=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
# Train the model
history = model.fit(
train_generator,
steps_per_epoch = train_generator.samples // 32,
validation_data = validation_generator,
validation_steps = validation_generator.samples // 32,
epochs = 20)
# Save the trained model
model.save('license_plate_model.h5')