def judge_gender_by_face_bak(face):
model = "../model/cnn_age_gender_models_and_data.0.0.2/gender_net.caffemodel"
net_def = "../model/cnn_age_gender_models_and_data.0.0.2/deploy_gender.prototxt"
caffe.set_mode_gpu()
net = caffe.Net(net_def, model, caffe.TEST)
data = []
img = face
X = np.empty((1, 3, 227, 227))
img = cv2.resize(img, (227, 227))
image = np.array(img, dtype=np.float32)
X[0, 0, :, :] = image[:, :, 0]
X[0, 1, :, :] = image[:, :, 1]
X[0, 2, :, :] = image[:, :, 2]
net.blobs['data'].data[...] = X
net.forward()
prob = net.blobs['prob'].data[0]
index = 1 if prob[1] > prob[0] else 0
print("gender is ", gender_list[index], "confidence is ", prob[index])
print("gender is not", gender_list[~index], "confidence is ", prob[~index])
data = {
"index": index,
"gender": gender_list[index],
"confidence": str(prob[index])
}
return data
def judge_age_by_face_bak(face):
model = "../model/cnn_age_gender_models_and_data.0.0.2/age_net.caffemodel"
net_def = "../model/cnn_age_gender_models_and_data.0.0.2/deploy_age.prototxt"
caffe.set_mode_gpu()
#load mean
#mean_filename='../model/cnn_age_gender_models_and_data.0.0.2/mean.binaryproto'
#proto_data = open(mean_filename, "rb").read()
#a = caffe.io.caffe_pb2.BlobProto.FromString(proto_data)
#mean = caffe.io.blobproto_to_array(a)[0]
age_net_pretrained = model
age_net_model_file = net_def
age_net = caffe.Classifier(age_net_model_file, age_net_pretrained,\
#mean=mean,\#mean`s dims is not equal 227*227
channel_swap=(2,1,0),\
raw_scale=255,\
image_dims=(227, 227))
prediction = age_net.predict([face])
print 'predicted age:', age_list[prediction[0].argmax()]
print("confidence is :", prediction[0][prediction[0].argmax()])
index = prediction[0].argmax()
data = {
"index": index,
"age": age_list[index],
"confidence": str(prediction[0][index])
}
return data
def judge_gender_by_face(face):
model = "../model/cnn_age_gender_models_and_data.0.0.2/gender_net.caffemodel"
net_def = "../model/cnn_age_gender_models_and_data.0.0.2/deploy_gender.prototxt"
caffe.set_mode_gpu()
mean_filename='../model/cnn_age_gender_models_and_data.0.0.2/mean.binaryproto'
proto_data = open(mean_filename, "rb").read()
a = caffe.io.caffe_pb2.BlobProto.FromString(proto_data)
mean = caffe.io.blobproto_to_array(a)[0]
gender_net_pretrained=model
gender_net_model_file=net_def
gender_net = caffe.Classifier(gender_net_model_file, gender_net_pretrained,\
#mean=mean,\
channel_swap=(2,1,0),\
raw_scale=255,\
image_dims=(227, 227))
prediction = gender_net.predict([face])
print 'predicted gender:', gender_list[prediction[0].argmax()]
print ("confidence is :",prediction[0][prediction[0].argmax()])
index = prediction[0].argmax()
data = {
"index":index,
"gender":gender_list[index],
"confidence":str(prediction[0][index])
}
return data
# -*- coding: utf-8 -*-
from import_caffe import caffe
import numpy as np
import os
import cv2
import json
gender_list = ["male", "female"]
#load model globaly
model = "../model/cnn_age_gender_models_and_data.0.0.2/gender_net.caffemodel"
net_def = "../model/cnn_age_gender_models_and_data.0.0.2/deploy_gender.prototxt"
caffe.set_mode_gpu()
#load mean
#mean_filename='../model/cnn_age_gender_models_and_data.0.0.2/mean.binaryproto'
#proto_data = open(mean_filename, "rb").read()
#a = caffe.io.caffe_pb2.BlobProto.FromString(proto_data)
#mean = caffe.io.blobproto_to_array(a)[0]
gender_net_pretrained = model
gender_net_model_file = net_def
gender_net = caffe.Classifier(gender_net_model_file, gender_net_pretrained,\
#mean=mean,\#mean`s dims is not equal 227*227
channel_swap=(2,1,0),\
raw_scale=255,\
image_dims=(227, 227))