classification.py

from nbnn import *
from pascal import *
import logging,logging.config
from memuse import *
import os,sys, ConfigParser, shutil

def parse_cfg(cfg_file):
    with open(cfg_file) as cfg:
        test_parameters = dict()
        data_args = dict()
        descriptors = []
        flann_args = dict()
        
        config = ConfigParser.RawConfigParser()
        config.readfp(cfg)
        sections = config.sections()
        for section in sections:
            if section == 'Test':
                for name, value in config.items(section):
                    if name == 'batch_size':
                        if value == 'None':
                            test_parameters[name] = None
                        else:
                            test_parameters[name] = int(value)
                    else:
                        test_parameters[name] = value
            elif section == 'Dataset':
                for name, value in config.items(section):
                    if name == 'classes':
                        data_args[name] = value.split(',')
                    else:
                        data_args[name] = value
            elif section == 'Flann':
                for name, value in config.items(section):
                    if name in ['k','checks','trees']:
                        flann_args[name] = int(value)
                    else:
                        flann_args[name] = value
            else:
                d = [section, dict()]
                for name, value in config.items(section):
                    if name=='alpha':
                        d[1][name] = float(value)
                    elif name=='verbose':
                        d[1][name] = value=='True'
                    else:
                        d[1][name] = value
                descriptors.append(d)
    return test_parameters, data_args, descriptors, flann_args

if __name__ == "__main__":
    
    if len(sys.argv) < 2:
        raise Exception("Please give a config file as command line argument")
    
    configfile = sys.argv[1]
    test_params, data_args, descriptor_args, flann_args = parse_cfg(configfile)
    
    logging.config.fileConfig(test_params['log_config'],disable_existing_loggers=False)
    log = logging.getLogger('')
    f = MemuseFilter()
    log.handlers[0].addFilter(f)

    log.info('===================VOC CLASSIFICATION ===================')
    log.info('=========================================================')
    
    # classes =['aeroplane','bicycle','bird','boat','bottle','bus','car','cat',\
    #     'chair','cow','diningtable','dog','horse','motorbike','person',\
    #     'pottedplant','sheep','sofa','train','tvmonitor']
    log.info('===================INIT VOCCLAS DATASET===================')
    dataset = VOCClassification(**data_args)
    log.debug(dataset.train_set)
    log.info("===================INIT RESULTSHANDLER===================")
    vrh = VOCClassificationResultsHandler(dataset,test_params['result_path'],th=1)
    log.info('=====================INIT DESCRIPTOR=====================')
    descriptors = [eval(d)(**kwargs) for d,kwargs in descriptor_args]
    log.info('=====================INIT ESTIMATOR =====================')
    estimators = [NBNNEstimator.from_dataset(test_params['temp_path'], dataset, \
        descriptor, **flann_args) for descriptor in descriptors]
    log.info("======================STARTING TEST======================")
    dataset.toggle_training()
    run_test(dataset, descriptors, estimators, vrh.set_results, \
        batch_size=test_params['batch_size'], output_function=ranked_classify)
    #vrh.print_results()
    log.info("=====================SAVING RESULTS =====================")
    vrh.save_to_files()
    log.info("=======================CLEANING UP=======================")
    shutil.rmtree(test_params['temp_path'])