import os
import cv2
import fawn
import tensorflow as tf
import numpy as np
import face_detect_vectorize as fdv
IMAGE_SZ = 160
MODEL_PATH = '20180402-114759/model-20180402-114759'
client = fawn.Fawn('http://127.0.0.1:8000')
model = fdv.Model(path=MODEL_PATH, image_size=IMAGE_SZ)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
print("Load faceNet =>>>")
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
model.loader(sess)
print("Done!\n")
face = fdv.Face('sample_input.png', None, 30)
face.detect_face()
face.write_clip(write_to_file=False)
face.vectorize(sess=sess,
model=model,
model_input_size=(IMAGE_SZ, IMAGE_SZ))
for feature in face.face_features:
res = client.search(feature.reshape(512), K=5)
path = os.path.join('data', barcode[4:6], barcode[6:8], barcode)
info = fdv.read_pickle(path, "info")
if len(info['faces']) == 0:
return faces
else:
features = np.load(path + '/features.npy')
for face in info['faces']:
faces[face['ID']] = features[int(face['ID'])]
return faces
# log file for errors
logf = open(sys.argv[2], 'w')
# The server url of image database
client = fawn.Fawn('http://127.0.0.1:8888')
# insert the image information into the database
tasks = []
with open(BARCODE_URLs_FILE, 'r') as IMAGES:
for line in IMAGES:
key, thumb, _ = line.strip().split(',')
tasks.append((key, thumb))
for task in tqdm(tasks):
try:
faceFeatures = extract_features(barcode=task[0])
thumbnail = task[1]
except Exception as ex:
logf.write('%s: %s \n' % (barcode, ex))