def get_prediction(classifier, img_path, debug=False):
img_width, img_height = 224, 224 # Default input size for VGG16
conv_base = VGGFace(include_top=False, input_shape=(img_width, img_height, 3),model='resnet50',pooling=None)
# print(conv_base.summary())
#conv_base.layers.pop() # Get rid of the classification layer
#conv_base.layers.pop() # Get rid of the dropout layer
#conv_base.outputs = [conv_base.layers[-1].output]
#conv_base.layers[-1].outbound_nodes = []
# conv_base.layers.add(MaxPooling2D(pool_size=(2, 2)))
conv_base.layers.pop()
conv_base.outputs = [conv_base.layers[-1].output]
conv_base.layers[-1].outbound_nodes =[]
#print(conv_base.summary())
# Get picture
if not img_path:
print("no image passed in, using default image from 26-30")
truth = '26-30'
base_dir = '../'
img_path = path.join(base_dir, 'maleTest/26-30', '28_0_3_20170105175516710.jpg.chip.jpg')
else:
truth = img_path.split('/')[-2]
if debug:
print("image is:", img_path)
print("truth for this image is:", truth)
img = image.load_img(img_path, target_size=(img_width, img_height))
img_tensor = image.img_to_array(img) # Image data encoded as integers in the 0–255 range
img_tensor /= 255. # Normalize to [0,1] for plt.imshow application
# Extract features
features = conv_base.predict(img_tensor.reshape(1,img_width, img_height, 3))
print(features.shape)
# Make prediction
try:
prediction = classifier.predict(features)
except:
prediction = classifier.predict(features.reshape(1, 7*7*2048))
# Show picture
#plt.imshow(img_tensor)
#plt.show()
# Write prediction
[prediction] = prediction
labels = ['1-2', '14-16', '17-20', '21-25', '26-30', '3-5', '31-35', '36-40', '41-45', '46-50', '51-55', '56-60', '6-8', '61-70', '9-13']
oneOff= {
'1-2': ['1-2','3-5'],
'3-5': ['1-2','3-5','6-8'],
'6-8': ['3-5','6-8','9-13'],
'9-13':['6-8','9-13','14-16'],
'14-16':['9-13','14-16','17-20'],
'17-20':['14-16','17-20','21-25'],
'21-25':['17-20','21-25','26-30'],
'26-30':['21-25','26-30','31-35'],
'31-35':['26-30','31-35','36-40'],
'36-40':['31-35','36-40','41-45'],
'41-45':['36-40','41-45','46-50'],
'46-50':['41-45','46-50','51-55'],
'51-55':['46-50','51-55','56-60'],
'56-60':['51-55','56-60','61-70'],
'61-70':['56-60','61-70']
}
predictionList=[]
predictionText = labels[int(prediction)]
if predictionText != truth:
if debug:
print("PREDICTION WAS WRONG!!!!")
predictionList.append(0)
else:
predictionList.append(1)
if predictionText in oneOff[truth]:
predictionList.append(1)
else:
predictionList.append(0)
return predictionList
frame = frame[x:x + w, y:y + h]
frame = np.array(frame.resize((224, 224))).reshape((1, 224, 224, 3))
frame = np.expand_dims(frame, axis=0)
frame = preprocess_input(frame)
return vgg_features.predict(frame[0]), frame[0]
##############################
# Preparing Model
##############################
from keras_vggface.vggface import VGGFace
vgg_features = VGGFace(include_top=True, input_shape=(224, 224, 3))
vgg_features.layers.pop()
vgg_features.layers.pop()
vgg_features.outputs = [vgg_features.layers[-1].output]
vgg_features.layers[-1].outbound_nodes = []
########################################
# Preprocessing data to Extract Features
#########################################
data_points = os.listdir("zippedFaces/unzippedFaces"
) ## GETTING DATA POINTS THAT IS NAME OF CELEBRTIES.
no_of_record_per_image = 3 ## NUMBER OF TIMES EVERY CELEBRITIES (DIFFERENT) IMAGES IS TO BE STORED
face_feature = np.zeros((len(data_points) * no_of_record_per_image,
2048)) ### ARRAY THAT WILL STORE THE FACE FEATURE
if not ("Face_Feature" in os.listdir()): ### MAKING DIR
os.mkdir("Face_Feature")
os.mkdir("Face_Feature/Faces")
def build_model(neurons=[32, 8],
droprate=0.5,
activation=LeakyReLU(),
optimizer=Adam(lr=0.0001),
feature_model_type='resnet50',
n_hidden_layers=2,
n_nodes_output_cat=3,
bmomentum=0.99,
bepsilon=0.001,
reg_val=0.001):
# ------------------------------------
# The first part of the model consists
# of the pre-trained VGG face model
# ------------------------------------
# Grab relavent part of network
if feature_model_type == 'vgg16':
vgg_model = VGGFace(model='vgg16')
n_lyrs = 22
for l in range(4):
vgg_model.layers.pop()
vgg_model.outputs = [vgg_model.layers[-1].output]
vgg_model.layers[-1].outbound_nodes = []
elif feature_model_type == 'resnet50':
vgg_model = VGGFace(model='resnet50')
out = vgg_model.get_layer('flatten_1').output
n_lyrs = 175
vgg_model.layers.pop()
vgg_model.outputs = [vgg_model.layers[-1].output]
vgg_model.layers[-1].outbound_nodes = []
# Add input layer for age and gender
age_gender_input = Input(shape=(2, ), name='age_gender_input')
vgg_model = Model(inputs=vgg_model.inputs, outputs=vgg_model.outputs)
# Merge auxilary input to end of pretrained model
x = concatenate([vgg_model.output, age_gender_input])
# Add Batch Normalization. Auxillary inputs are on a different scale as VGG outputs
x = BatchNormalization(momentum=bmomentum, epsilon=bepsilon)(x)
# First new layer
if reg_val is None:
x_cat = Dense(neurons[0])(x)
x_cont = Dense(neurons[0])(x_cont)
else:
x_cat = Dense(neurons[0], kernel_regularizer=l2(reg_val))(x)
x_cont = Dense(neurons[0], kernel_regularizer=l2(reg_val))(x)
x_cat = activation(x_cat)
x_cat = Dropout(droprate)(x_cat)
x_cont = activation(x_cont)
x_cont = Dropout(droprate)(x_cont)
# Create split layers for classification and for regression
for n in range(1, n_hidden_layers):
if reg_val is None:
x_cat = Dense(neurons[n])(x_cat)
x_cont = Dense(neurons[n])(x_cont)
else:
x_cat = Dense(neurons[n], kernel_regularizer=l2(reg_val))(x_cat)
x_cont = Dense(neurons[n], kernel_regularizer=l2(reg_val))(x_cont)
x_cat = activation(x_cat)
x_cat = Dropout(droprate)(x_cat)
x_cont = activation(x_cont)
x_cont = Dropout(droprate)(x_cont)
# Add output layers for classification and regression tasks
x_cat = Dense(n_nodes_output_cat, activation='softmax',
name='cat_out')(x_cat)
x_cont = Dense(1, activation='linear', name='cont_out')(x_cont)
model = Model(inputs=[vgg_model.input, age_gender_input],
outputs=[x_cat, x_cont])
# Freeze layers associated with VGGFace model
for layer in model.layers[:n_lyrs]:
layer.trainable = False
model.compile(loss={
'cat_out': 'categorical_crossentropy',
'cont_out': 'mean_squared_error'
},
optimizer=optimizer,
metrics={
'cat_out': ['acc'],
'cont_out': ['mse']
})
return model
