img_pkl_folder = str(sys.argv[1])
feature_pkl_folder = str(sys.argv[2])
pickle_file_counter = 0
batch_size = int(sys.argv[3])
# set display defaults
#plt.rcParams['figure.figsize'] = (10, 10) # large images
#plt.rcParams['image.interpolation'] = 'nearest' # don't interpolate: show square pixels
#plt.rcParams['image.cmap'] = 'gray' # use grayscale output rather than a (potentially misleading) color heatmap
caffe.set_mode_cpu()
model_def = 'vgg_face_caffe/vgg_face_caffe/VGG_FACE_deploy.prototxt'
model_weights = 'vgg_face_caffe/vgg_face_caffe/VGG_FACE.caffemodel'
#img_pkl_folder = 'fr_train_data_faceIndexPkl/'
img_pkl_paths = get_files(img_pkl_folder)
img_path_vec, img_label_vec = load_data_xy(img_pkl_paths)
img_feature_vec = []
#create net
net = caffe.Net(model_def, model_weights, caffe.TEST)
#mu = np.load('vgg_face_caffe/vgg_face_caffe/ilsvrc_2012_mean.npy')
#mu = mu.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel values
#print 'mean-subtracted values:', zip('BGR', mu)
#mu=[129.1863, 104.7624, 93.5940]
mu=np.array([93.5940, 104.7624, 129.1863])
mu = mu.reshape((3,1,1))
transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1)) # move image channels to outermost dimension
#transformer.set_mean('data', mu) # subtract the dataset-mean value in each channel
#sotmax
keras_model.add_node(layer=Dense(1, activation='sigmoid'), name='dense4', input='dense3')
keras_model.add_output('output', input='dense4')
#add soft max or min_max or maxpooling
keras_model.compile('adadelta', {'output': 'mean_squared_error'})
return keras_model
#history = keras_model.fit({'input1':X_train, 'input2':X2_train, 'output':y_train}, nb_epoch=10)
#predictions = graph.predict({'input1':X_test, 'input2':X2_test}) # {'output':...}
if __name__ == "__main__":
train1_data_path = str(sys.argv[1])
train2_data_path = str(sys.argv[2])
#read train1_data
train1_data_path_vec = get_files(train1_data_path)
train1_data_vec, train1_label_vec = load_data_xy(train1_data_path_vec)
#read train2_data
train2_data_path_vec = get_files(train2_data_path)
train2_data_vec, train2_label_vec = load_data_xy(train2_data_path_vec)
#genereate output label
train1_len = len(train1_label_vec)
train2_len = len(train2_label_vec)
assert train1_len == train2_len
train_label_vec = np.zeros(train1_len, dtype='float32')
for i in range(train1_len):
if train1_label_vec[i] == train2_label_vec[i]:#the same person
train_label_vec[i] = 1.
else:
train_label_vec[i] = 0.
name='dense4',
input='dense3')
keras_model.add_output('output', input='dense4')
#add soft max or min_max or maxpooling
keras_model.compile('adadelta', {'output': 'mean_squared_error'})
return keras_model
#history = keras_model.fit({'input1':X_train, 'input2':X2_train, 'output':y_train}, nb_epoch=10)
#predictions = graph.predict({'input1':X_test, 'input2':X2_test}) # {'output':...}
if __name__ == "__main__":
train1_data_path = str(sys.argv[1])
train2_data_path = str(sys.argv[2])
#read train1_data
train1_data_path_vec = get_files(train1_data_path)
train1_data_vec, train1_label_vec = load_data_xy(train1_data_path_vec)
#read train2_data
train2_data_path_vec = get_files(train2_data_path)
train2_data_vec, train2_label_vec = load_data_xy(train2_data_path_vec)
#genereate output label
train1_len = len(train1_label_vec)
train2_len = len(train2_label_vec)
assert train1_len == train2_len
train_label_vec = np.zeros(train1_len, dtype='float32')
for i in range(train1_len):
if train1_label_vec[i] == train2_label_vec[i]: #the same person
train_label_vec[i] = 1.
else:
train_label_vec[i] = 0.
