使用深度学习和OpenCV的早期火灾检测系统
重磅干货，第一时间送达
创建用于室内和室外火灾检测的定制InceptionV3和CNN架构。
创建定制的CNN架构
import tensorflow as tfimport keras_preprocessingfrom keras_preprocessing import imagefrom keras_preprocessing.image import ImageDataGeneratorTRAINING_DIR = "Train"training_datagen = ImageDataGenerator(rescale = 1./255, horizontal_flip=True, rotation_range=30, height_shift_range=0.2, fill_mode='nearest')VALIDATION_DIR = "Validation"validation_datagen = ImageDataGenerator(rescale = 1./255)train_generator = training_datagen.flow_from_directory(TRAINING_DIR, target_size=(224,224), class_mode='categorical', batch_size = 64)validation_generator = validation_datagen.flow_from_directory( VALIDATION_DIR, target_size=(224,224), class_mode='categorical', batch_size= 16)from tensorflow.keras.optimizers import Adammodel = tf.keras.models.Sequential([tf.keras.layers.Conv2D(96, (11,11), strides=(4,4), activation='relu', input_shape=(224, 224, 3)), tf.keras.layers.MaxPooling2D(pool_size = (3,3), strides=(2,2)),tf.keras.layers.Conv2D(256, (5,5), activation='relu'),tf.keras.layers.MaxPooling2D(pool_size = (3,3), strides=(2,2)),tf.keras.layers.Conv2D(384, (5,5), activation='relu'),tf.keras.layers.MaxPooling2D(pool_size = (3,3), strides=(2,2)),tf.keras.layers.Flatten(),tf.keras.layers.Dropout(0.2),tf.keras.layers.Dense(2048, activation='relu'),tf.keras.layers.Dropout(0.25),tf.keras.layers.Dense(1024, activation='relu'),tf.keras.layers.Dropout(0.2),tf.keras.layers.Dense(2, activation='softmax')])model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.0001),metrics=['acc'])history = model.fit(train_generator,steps_per_epoch = 15,epochs = 50,validation_data = validation_generator,validation_steps = 15)创建定制的InceptionV3模型
import tensorflow as tfimport keras_preprocessingfrom keras_preprocessing import imagefrom keras_preprocessing.image import ImageDataGeneratorTRAINING_DIR = "Train"training_datagen = ImageDataGenerator(rescale=1./255,zoom_range=0.15,horizontal_flip=True,fill_mode='nearest')VALIDATION_DIR = "/content/FIRE-SMOKE-DATASET/Test"validation_datagen = ImageDataGenerator(rescale = 1./255)train_generator = training_datagen.flow_from_directory(TRAINING_DIR,target_size=(224,224),shuffle = True,class_mode='categorical',batch_size = 128)validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,target_size=(224,224),class_mode='categorical',shuffle = True,batch_size= 14)from tensorflow.keras.applications.inception_v3 import InceptionV3from tensorflow.keras.preprocessing import imagefrom tensorflow.keras.models import Modelfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Input, Dropoutinput_tensor = Input(shape=(224, 224, 3))base_model = InceptionV3(input_tensor=input_tensor, weights='imagenet', include_top=False)x = base_model.outputx = GlobalAveragePooling2D()(x)x = Dense(2048, activation='relu')(x)x = Dropout(0.25)(x)x = Dense(1024, activation='relu')(x)x = Dropout(0.2)(x)predictions = Dense(2, activation='softmax')(x)model = Model(inputs=base_model.input, outputs=predictions)for layer in base_model.layers: layer.trainable = Falsemodel.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['acc'])history = model.fit(train_generator,steps_per_epoch = 14,epochs = 20,validation_data = validation_generator,validation_steps = 14)#To train the top 2 inception blocks, freeze the first 249 layers and unfreeze the rest.for layer in model.layers[:249]: layer.trainable = Falsefor layer in model.layers[249:]: layer.trainable = True#Recompile the model for these modifications to take effectfrom tensorflow.keras.optimizers import SGDmodel.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['acc'])history = model.fit(train_generator,steps_per_epoch = 14,epochs = 10,validation_data = validation_generator,validation_steps = 14)实时测试
import cv2import numpy as npfrom PIL import Imageimport tensorflow as tffrom keras.preprocessing import image#Load the saved modelmodel = tf.keras.models.load_model('InceptionV3.h5')video = cv2.VideoCapture(0)while True: _, frame = video.read()#Convert the captured frame into RGB im = Image.fromarray(frame, 'RGB')#Resizing into 224x224 because we trained the model with this image size. im = im.resize((224,224)) img_array = image.img_to_array(im) img_array = np.expand_dims(img_array, axis=0) / 255 probabilities = model.predict(img_array)[0] #Calling the predict method on model to predict 'fire' on the image prediction = np.argmax(probabilities) #if prediction is 0, which means there is fire in the frame. if prediction == 0: frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) print(probabilities[prediction])cv2.imshow("Capturing", frame) key=cv2.waitKey(1) if key == ord('q'): breakvideo.release()cv2.destroyAllWindows()结论
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