笑脸识别（加入空间注意机制）

笑脸识别（加入空间注意机制）

import os, shutilfrom tensorflow.keras import layersfrom tensorflow.keras import modelsfrom tensorflow.keras import optimizers, regularizersfrom tensorflow.keras.preprocessing.image import ImageDataGeneratorimport matplotlib.pyplot as pltimport cv2from tensorflow.keras.preprocessing import imagefrom tensorflow.keras.models import load_modelimport numpy as npimport globoriginal_dataset_dir = 'smiledataset'base_dir = 'smiledataset/smile_and_nosmile'person_test_path = 'smiledataset/smile_and_nosmile/validation/smile'def dataCollection_create_model(): train_dir = os.path.join(base_dir, 'train') # os.mkdir(train_dir) validation_dir = os.path.join(base_dir, 'validation') # os.mkdir(validation_dir) test_dir = os.path.join(base_dir, 'test') # os.mkdir(test_dir) train_smile_dir = os.path.join(train_dir, 'smile') # os.mkdir(train_smile_dir) train_nosmile_dir = os.path.join(train_dir, 'nosmile') # os.mkdir(train_nosmile_dir) validation_smile_dir = os.path.join(validation_dir, 'smile') # os.mkdir(validation_smile_dir) validation_nosmile_dir = os.path.join(validation_dir, 'nosmile') # os.mkdir(validation_nosmile_dir) test_smile_dir = os.path.join(test_dir, 'smile') # os.mkdir(test_smile_dir) test_nosmile_dir = os.path.join(test_dir, 'nosmile') # os.mkdir(test_nosmile_dir) model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu', kernel_regularizer=regularizers.l2(0.001))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu', kernel_regularizer=regularizers.l2(0.001))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu', kernel_regularizer=regularizers.l2(0.001))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Flatten()) model.add(layers.Dense(512, activation='relu')) model.add(layers.Dense(1, activation='sigmoid')) model.summary() model.compile(loss='binary_crossentropy', optimizer=optimizers.RMSprop(lr=1e-4), metrics=['acc']) train_datagen = ImageDataGenerator( rescale=1. / 255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, ) # Note that the validation data should not be augmented! test_datagen = ImageDataGenerator(rescale=1. / 255) train_generator = train_datagen.flow_from_directory( # This is the target directory train_dir, # All images will be resized to 150x150 target_size=(150, 150), batch_size=32, # Since we use binary_crossentropy loss, we need binary labels class_mode='binary') validation_generator = test_datagen.flow_from_directory( validation_dir, target_size=(150, 150), batch_size=32, class_mode='binary') history = model.fit_generator( train_generator, steps_per_epoch=1024, epochs=100, validation_data=validation_generator, validation_steps=50) model.save("smiledataset/smile.h5") acc = history.history['acc'] val_acc = history.history['val_acc'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(len(acc)) plt.plot(epochs, acc, 'bo', label='Training acc') plt.plot(epochs, val_acc, 'b', label='Validation acc') plt.title('Training and validation accuracy') plt.legend() plt.figure() plt.plot(epochs, loss, 'bo', label='Training loss') plt.plot(epochs, val_loss, 'b', label='Validation loss') plt.title('Training and validation loss') plt.legend() plt.show() datagen = ImageDataGenerator( rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') # This is module with image preprocessing utilities fnames = [os.path.join(train_smile_dir, fname) for fname in os.listdir(train_smile_dir)] # We pick one image to "augment" img_path = fnames[3] # Read the image and resize it img = image.load_img(img_path, target_size=(150, 150)) # Convert it to a Numpy array with shape (150, 150, 3) x = image.img_to_array(img) # Reshape it to (1, 150, 150, 3) x = x.reshape((1,) + x.shape) # The .flow() command below generates batches of randomly transformed images. # It will loop indefinitely, so we need to `break` the loop at some point! i = 0 for batch in datagen.flow(x, batch_size=1): plt.figure(i) imgplot = plt.imshow(image.array_to_img(batch[0])) i += 1 if i % 4 == 0: break plt.show()def read_list(): image_list= glob.glob(os.path.join(person_test_path, '*.jpg')) replace_list = [] for value in image_list: replace_list.append(value.replace("\\\\", "/")) return replace_listdef use_model(): model = load_model('smiledataset/smile.h5') img_path_list = read_list() count_smile = 0 count_nosmile = 0 for img_path in img_path_list: img2 = cv2.imread(img_path) font = cv2.FONT_HERSHEY_COMPLEX img = image.load_img(img_path, target_size=(150, 150)) img_tensor = image.img_to_array(img) / 255.0 img_tensor = np.expand_dims(img_tensor, axis=0) prediction = model.predict(img_tensor) print(prediction) if prediction[0][0] > 0.5: result = 'smile' count_smile +=1 cv2.putText(img2,'smile',(50,150),font,1,(0,0,255),3) else: result = 'nosmile' count_nosmile +=1 cv2.putText(img2,'nosmile',(50,150),font,1,(0, 0, 255),3) # cv2.imshow('wname',img2) # cv2.waitKey(0) # cv2.imwrite("smiledataset/result") (path, filename) = os.path.split(img_path) cv2.imwrite("smiledataset/result/smile/"+filename, img2, [int(cv2.IMWRITE_JPEG_QUALITY), 100]) print("总数："+(str)(len(img_path_list))+'\\n'+"smile 数量:"+(str)(count_smile)+'\\n'+"nosmile 数量："+(str)(count_nosmile))if __name__ =='__main__': dataCollection_create_model() use_model() # image_list = read_list()

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