Why am I getting this error in facenet?
I am trying to run facematch(facenet) on my Virtual Machine (Google Cloud Platform). At first, things were running smoothly and it was embedding the points of the faces, but then out of the blue, my code stopped working.
The first code, you can see the imports are there
For the second code, you can see the imports are there.
This is the ls commands, so you can see that all the directories/modules are there and see the errors I'm getting
Anyone can share some insight on what I'm doing wrong?
Face_match_demo code:
import tensorflow as tf
import numpy as np
import facenet
from align import detect_face
import cv2
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--img1", type = str, required=True)
parser.add_argument("--img2", type = str, required=True)
args = parser.parse_args()
# some constants kept as default from facenet
minsize = 20
threshold = [0.6, 0.7, 0.7]
factor = 0.709
margin = 44
input_image_size = 160
sess = tf.Session()
# read pnet, rnet, onet models from align directory and files are det1.npy, det2.npy, det3.npy
pnet, rnet, onet = detect_face.create_mtcnn(sess, 'align')
# read 20170512-110547 model file downloaded from https://drive.google.com/file/d/0B5MzpY9kBtDVZ2RpVDYwWmxoSUk
facenet.load_model("20170512-110547/20170512-110547.pb")
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
def getFace(img):
faces = []
img_size = np.asarray(img.shape)[0:2]
bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
if not len(bounding_boxes) == 0:
for face in bounding_boxes:
if face[4] > 0.50:
det = np.squeeze(face[0:4])
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - margin / 2, 0)
bb[1] = np.maximum(det[1] - margin / 2, 0)
bb[2] = np.minimum(det[2] + margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + margin / 2, img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
resized = cv2.resize(cropped, (input_image_size,input_image_size),interpolation=cv2.INTER_CUBIC)
prewhitened = facenet.prewhiten(resized)
faces.append({'face':resized,'rect':[bb[0],bb[1],bb[2],bb[3]],'embedding':getEmbedding(prewhitened)})
return faces
def getEmbedding(resized):
reshaped = resized.reshape(-1,input_image_size,input_image_size,3)
feed_dict = {images_placeholder: reshaped, phase_train_placeholder: False}
embedding = sess.run(embeddings, feed_dict=feed_dict)
return embedding
def compare2face(img1,img2):
face1 = getFace(img1)
face2 = getFace(img2)
if face1 and face2:
# calculate Euclidean distance
dist = np.sqrt(np.sum(np.square(np.subtract(face1[0]['embedding'], face2[0]['embedding']))))
return dist
return -1
img1 = cv2.imread(args.img1)
img2 = cv2.imread(args.img2)
distance = compare2face(img1, img2)
threshold = 1.10 # set yourself to meet your requirement
print("distance = "+str(distance))
face_embeddings_demo code:
import tensorflow as tf
from align import detect_face
import facenet
import cv2
import imutils
import numpy as np
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--img", type = str, required=True)
args = parser.parse_args()
# some constants kept as default from facenet
minsize = 20
threshold = [0.6, 0.7, 0.7]
factor = 0.709
margin = 44
input_image_size = 160
sess = tf.Session()
# read pnet, rnet, onet models from align directory and files are det1.npy, det2.npy, det3.npy
pnet, rnet, onet = detect_face.create_mtcnn(sess, 'align')
# read 20170512-110547 model file downloaded from https://drive.google.com/file/d/0B5MzpY9kBtDVZ2RpVDYwWmxoSUk
facenet.load_model("20170512-110547/20170512-110547.pb")
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embedding_size = embeddings.get_shape()[1]
def getFace(img):
faces = []
img_size = np.asarray(img.shape)[0:2]
bounding_boxes, points = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
if not len(bounding_boxes) == 0:
for face in bounding_boxes:
if face[4] > 0.50:
det = np.squeeze(face[0:4])
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - margin / 2, 0)
bb[1] = np.maximum(det[1] - margin / 2, 0)
bb[2] = np.minimum(det[2] + margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + margin / 2, img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
resized = cv2.resize(cropped, (input_image_size,input_image_size),interpolation=cv2.INTER_CUBIC)
prewhitened = facenet.prewhiten(resized)
faces.append({'face':resized,'rect':[bb[0],bb[1],bb[2],bb[3]],'embedding':getEmbedding(prewhitened)})
return faces
def getEmbedding(resized):
reshaped = resized.reshape(-1,input_image_size,input_image_size,3)
feed_dict = {images_placeholder: reshaped, phase_train_placeholder: False}
# print(feed_dict)
embedding = sess.run(embeddings, feed_dict=feed_dict)
return embedding
img = cv2.imread(args.img)
img = imutils.resize(img,width=1000)
faces = getFace(img)
for face in faces:
print("Embeddings = "+str(face['embedding']))
cv2.waitKey(0)
cv2.destroyAllWindows()
You have to have the __init__.py in the package directory to be recognized as a package. It can be an empty file, but it has to be present. You don't have this in the align directory.
From the documentation:
The __init__.py files are required to make Python treat the directories as containing packages
From your comment, the error
usage: face_match_demo.py [-h] --img1 IMG1 --img2 IMG2 face_match_demo.py: error: ambiguous option: --img=images/faces.jpg could match --img2, --img1
means that face_match_demo.py is actually a utility to match two images, to say whether they contain the same face or not. So you have to provide two images to it, and it will tell if the face is the same. And you need to use the --img1 and --img2 options to do that like this:
python face_match_demo.py --img1 images/faces.jpg --img2 [[another face image]]