def calculateTestEmbeddings():
graph = load_graph('src/20180402-114759/20180402-114759.pb')
faceList = []
for imagePath in glob.glob('data/library/test2/*'):
# loading cropped,RGBscale,aligned (160,160)sized faces as reqd by FaceNet
faceList.append(
np.expand_dims(cv2.resize(pre.getFaceColor(imagePath), (160, 160)),
axis=0))
with tf.Session(graph=graph) as sess:
images_placeholder = graph.get_tensor_by_name("import/input:0")
embeddings = graph.get_tensor_by_name("import/embeddings:0")
phase_train_placeholder = graph.get_tensor_by_name(
"import/phase_train:0")
faceListInput = np.concatenate(faceList, axis=0)
#normalizing the input
faceListInput = np.float32(faceListInput) / 255.0
feedDict = {
phase_train_placeholder: False,
images_placeholder: faceListInput
}
values = sess.run(embeddings, feedDict)
# save embedding values
np.save('src/cstmrEmbeddings', values)
tf.reset_default_graph()
import matplotlib.pyplot as plt
import pre_processing2 as pre
# load trained images embeddings
empEmbeddings = np.load('src/empEmbeddings.npy')
print empEmbeddings.shape
# load test image embeddings
cstmrEmbeddings = np.load('src/cstmrEmbeddings.npy')
print cstmrEmbeddings.shape
faceListTrain = []
faceListTest = []
for imagePath in glob.glob('data/library/train2/*'):
faceListTrain.append(cv2.resize(pre.getFaceColor(imagePath),(160,160)))
for imagePath in glob.glob('data/library/test2/*'):
faceListTest.append(cv2.resize(pre.getFaceColor(imagePath),(160,160)))
plt.subplot2grid((1,4),(0,0))
plt.imshow(faceListTrain[0])
# calculate L2 norm of test image versusal of the training images
for i in range(0,len(empEmbeddings)):
for j in range(0,len(cstmrEmbeddings)):
plt.subplot2grid((1,4),(0,j+1))
plt.imshow(faceListTest[j])
plt.title(np.linalg.norm(empEmbeddings[i] - cstmrEmbeddings[j]))
values = sess.run(embeddings, feedDict)
# save embedding values
np.save('src/cstmrEmbeddings', values)
tf.reset_default_graph()
if __name__ == '__main__':
graph = load_graph('src/20180402-114759/20180402-114759.pb')
faceList = []
for imagePath in glob.glob('data/library/test2/*'):
# loading cropped,RGBscale,aligned (160,160)sized faces as reqd by FaceNet
faceList.append(
np.expand_dims(cv2.resize(pre.getFaceColor(imagePath), (160, 160)),
axis=0))
with tf.Session(graph=graph) as sess:
images_placeholder = graph.get_tensor_by_name("import/input:0")
embeddings = graph.get_tensor_by_name("import/embeddings:0")
phase_train_placeholder = graph.get_tensor_by_name(
"import/phase_train:0")
faceListInput = np.concatenate(faceList, axis=0)
#normalizing the input
faceListInput = np.float32(faceListInput) / 255.0
print(faceListInput.shape)
feedDict = {
phase_train_placeholder: False,
