我添加了一个带有烧瓶的安静界面来对齐图像。 如果使用gunicorn运行代码：
out = pnet(img_y)
调用align.detect_face.detect_face来计算图像的bounding_box时，无法获取pnet的值。运行时停止功能
python faceapi.py但如果我使用def create_mtcnn(sess, model_path):
if not model_path:
model_path,_ = os.path.split(os.path.realpath(__file__))
with tf.variable_scope('pnet'):
data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
pnet = PNet({'data':data})
pnet.load(os.path.join(model_path, 'det1.npy'), sess)
with tf.variable_scope('rnet'):
data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
rnet = RNet({'data':data})
rnet.load(os.path.join(model_path, 'det2.npy'), sess)
with tf.variable_scope('onet'):
data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
onet = ONet({'data':data})
onet.load(os.path.join(model_path, 'det3.npy'), sess)
pnet_fun = lambda img : sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0':img})
rnet_fun = lambda img : sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0':img})
onet_fun = lambda img : sess.run(('onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0'), feed_dict={'onet/input:0':img})
return pnet_fun, rnet_fun, onet_fun
，它将获得正确的结果。但我想使用多线程来响应每个请求。如何使用线程池进行响应？
pnet在align.detect_face.create_mtcnn中使用以下代码：
#faceapi.py
from flask import Flask, jsonify, request
import tensorflow as tf
import align.detect_face
import facenet
from scipy import misc
import numpy as np
import cv2
import base64
app = Flask(__name__)
model_dir = "./20170512-110547"
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
# Load mtcnn model
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
def alignimage(image_files, margin, image_size, minsize, pnet, rnet, onet, threshold, factor):
nrof_samples = len(image_files)
img_list = [None] * nrof_samples
for i in range(nrof_samples):
tmp_image = base64.b64decode(image_files[i])
imgarray = np.fromstring(tmp_image, dtype=np.uint8)
imgbgra = cv2.imdecode(imgarray, 1)
img = cv2.cvtColor(imgbgra, cv2.COLOR_BGR2RGB)
img_size = np.asarray(img.shape)[0:2]
bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
det = np.squeeze(bounding_boxes[0, 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], :]
aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
prewhitened = facenet.prewhiten(aligned)
img_list[i] = prewhitened
@app.route('/api/facecompare', methods=['POST'])
def facecompare():
img_list = [None] * 2
img_list[0] = request.json['imageA']
img_list[1] = request.json['imageB']
alignimage(img_list, 44, 160, minsize, pnet, rnet, onet, threshold, factor)
if __name__ == '__main__':
app.run()
restful接口如下：
JSON.parse