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)