from pprint import pprint from Detector import Detector __author__ = 'Rik Smit ([email protected])' projectDirectory = '/home/rik-target/owncloud-rixmit/Studie/Droneproject/dataset/cows/' datasetIds = [ 'DJI_0005_cut_233-244', 'DJI_0007_cut_22-65', 'DJI_0081' ] detector = Detector(projectDirectory, datasetIds) detector.featureType = 'HSV-HIST' detector.classifierType = 'SVM-RBF' testtype = 'interset' # or crossset parameterOptions = { # lists of different parameter values that should be tested for the feature descriptors 'channelsSet': [[0, 1, 2], [1], [0, 1]], 'histSizeSet': [32, 8], 'orientationsSet': [1, 2, 4, 8], 'ppcSet': [8, 16, 32], 'cpbSet': [2, 4, 8] } datasetIdx = 0 # used for interset (which set is tested on classifierParameters = { # single paramters that are used for the classifier 'C': [1], 'gamma': [0.1],
from pprint import pprint from Detector import Detector __author__ = 'Rik Smit ([email protected])' projectDirectory = '/home/rik-target/owncloud-rixmit/Studie/Droneproject/dataset/cows/' datasetIds = ['DJI_0005_cut_233-244', 'DJI_0007_cut_22-65', 'DJI_0081'] detector = Detector(projectDirectory, datasetIds) detector.featureType = 'HSV-HIST' detector.classifierType = 'SVM-RBF' runs = 20 for active in [True, False]: print("Testing (active=%s)" % active) results = None for run in range(runs): result = detector.activeLearningTest( active, datasetIdx=1) # use dataset DJI_0007_cut_22-65 as it is big enough if not results: results = result else: results = [x + y for x, y in zip(results, result)] pprint([x / runs for x in results])
