def __init__(self, port):
util_root = '/works/demon_11st/utils'
sys.path.insert(0, util_root)
from demon_utils import demon_utils
app.demon_utils = demon_utils('/storage/enroll')
vsm_root = '/works/demon_11st/vsm'
sys.path.insert(0, vsm_root)
from vsm import vsm
#import pdb; pdb.set_trace()
image_sentence_filename = '/storage/attribute/PBrain_all.csv.image_sentence.txt'
app.vsm = vsm(image_sentence_filename)
agent_detector_root = '/works/demon_11st/agent/detection'
sys.path.insert(0, agent_detector_root)
import _init_paths
from conf import conf
from agent_detector import agent_detector
yaml_file = '/storage/product/detection/11st_All/cfg/faster_rcnn_end2end_test.yml'
conf = conf(yaml_file, 0)
app.agent_detector = agent_detector()
#import pdb; pdb.set_trace()
agent_attribute_root = '/works/demon_11st/agent/attribute'
sys.path.insert(0, agent_attribute_root)
from agent_attribute import agent_attribute
attribute_demon_host_ip = host_ip
attribute_demon_port= 8080
app.agent_attribute = agent_attribute( \
attribute_demon_host_ip, attribute_demon_port)
app.result_dic = init_result_dic()
web_server.__init__(self, app, port)
def trainClassifiers(self):
"""
Function pre creates objects for all classifiers, so that prediction is fast.
An instance dict of those models is created so that they can be indexed easily.
arguments: none
return: none
"""
self.which = {0:vsm(),1:nb(),2:svm()}
for i in self.which.keys():
self.which[i].fit()
host_ip = '10.202.4.219'
feature_demon_port = 8080
port = 8081
html_filename = 'index_11st.html'
html_filename_vsm = 'index_vsm.html'
sentense_filename = \
'/storage/coco/COCO_trainval_test_sentenses.ResCept_epoch35_embedding2048_hidden384_layer2.txt'
#'/storage/coco/COCO_trainval_sentense.inception7_lstm2_embedding384.txt'
#'/works/VSM/documents/COCO_sentense.txt'
url_prefix = 'http://%(host_ip)s:%(port)d/lua_wrapper_request_handler/?url=%%s' % \
{'host_ip': host_ip, 'port': feature_demon_port}
exifutils = exifutil()
vsm = vsm(sentense_filename)
# global the flask app object
app = flask.Flask(__name__)
def gen(camera):
while True:
frame = camera.get_frame()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
@app.route('/vsm_request_handler', methods=['GET'])
@crossdomain(origin='*')
from vsm import vsm
host_ip = '10.202.4.219'
feature_demon_port = 8080
port = 8081
html_filename = 'index_11st.html'
html_filename_vsm = 'index_vsm.html'
sentense_filename = \
'/storage/coco/COCO_trainval_test_sentenses.ResCept_epoch35_embedding2048_hidden384_layer2.txt'
#'/storage/coco/COCO_trainval_sentense.inception7_lstm2_embedding384.txt'
#'/works/VSM/documents/COCO_sentense.txt'
url_prefix = 'http://%(host_ip)s:%(port)d/lua_wrapper_request_handler/?url=%%s' % \
{'host_ip': host_ip, 'port': feature_demon_port}
exifutils = exifutil()
vsm = vsm(sentense_filename)
# global the flask app object
app = flask.Flask(__name__)
def gen(camera):
while True:
frame = camera.get_frame()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
@app.route('/vsm_request_handler', methods=['GET'])
@crossdomain(origin='*')
def vsm_request_handler():
else:
shutil.copyfile(
os.path.join(path, filename),
os.path.join('data/20news-test/' + folder, filename))
i += 1
if __name__ == '__main__':
# 将数据划分train set 与 test set
# devide()
# 输入train set
train_X, train_Y = vsm.input_data(base_path='data/20news-train',
out1='data/knn-out/train_X.csv',
out2='data/knn-out/train_Y.csv')
# 生成词典
dictionary = vsm.f_dictionary(train_X, 'data/knn-out/dictionary.csv')
# tf-idf 获取train_X 的 vector space
vsm_train = vsm.vsm(train_X, dictionary)
del train_X
gc.collect()
# 输入test set
test_X, test_Y = vsm.input_data(base_path='data/20news-test',
out1='data/knn-out/test_X.csv',
out2='data/knn-out/test_Y.csv')
# tf-idf 获取test_X 的 vector space
vsm_test = vsm.vsm(test_X, dictionary)
del test_X
gc.collect()
# knn分类
knn(vsm_train, train_Y, vsm_test, test_Y)
