import vidfeat import imfeat import sklearn.svm from vidfeat.models.synthetic_bovw_clusters import clusters #import kernels HOG = imfeat.HOGLatent(8, 2) class FenceFrameFeature(vidfeat.ClassifierFrameFeature): #feature = imfeat.MetaFeature(imfeat.GradientHistogram(), imfeat.Histogram('lab')) feature = imfeat.MetaFeature(imfeat.BoVW(lambda x: HOG.make_bow_mask(x, clusters), clusters.shape[0], 3)) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(FenceFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): import time st = time.time() out = self.feature(imfeat.resize_image_max_side(image, 160)) print time.time() - st return out if __name__ == '__main__': vidfeat._frame_feature_main('fence', vidfeat.FenceFrameFeature)
import vidfeat import imfeat import sklearn.svm from vidfeat.models.screenshot_bovw_clusters import clusters #import kernels HOG = imfeat.HOGLatent(8, 2) class ScreenshotFrameFeature(vidfeat.ClassifierFrameFeature): #feature = imfeat.MetaFeature(imfeat.GradientHistogram(), imfeat.Histogram('lab')) feature = imfeat.BoVW(lambda x: HOG.make_bow_mask(x, clusters), clusters.shape[0], 3) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(ScreenshotFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): import time st = time.time() out = self.feature(imfeat.resize_image_max_side(image, 160)) print time.time() - st return out if __name__ == '__main__': vidfeat._frame_feature_main('screenshot', vidfeat.ScreenshotFrameFeature)
import vidfeat import imfeat import sklearn.svm class BlankFrameFeature(vidfeat.ClassifierFrameFeature): feature = imfeat.BlackBars() def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(BlankFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): return self.feature(image) def predict(self, frame): if not frame.size: return 0 if self._invert else 1 else: return super(BlankFrameFeature, self).predict(frame) if __name__ == '__main__': vidfeat._frame_feature_main('blank', vidfeat.BlankFrameFeature, remove_bars=False)
import vidfeat import imfeat import sklearn.svm from vidfeat.models.snow_bovw_clusters import clusters HOG = imfeat.HOGLatent(8, 2) class RoadFrameFeature(vidfeat.ClassifierFrameFeature): #feature = imfeat.PyramidHistogram('lab') feature = imfeat.BoVW(lambda x: HOG.make_bow_mask(x, clusters), clusters.shape[0], 3) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(RoadFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): import time st = time.time() out = self.feature(imfeat.resize_image_max_side(image, 160)) print time.time() - st return out if __name__ == '__main__': vidfeat._frame_feature_main('road', vidfeat.RoadFrameFeature)
import vidfeat import imfeat import sklearn.svm from vidfeat.models.person_bovw_clusters import clusters HOG = imfeat.HOGLatent(8, 2) class PersonFrameFeature(vidfeat.ClassifierFrameFeature): #feature = imfeat.PyramidHistogram('lab') feature = imfeat.BoVW(lambda x: HOG.make_bow_mask(x, clusters), clusters.shape[0], 3) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(PersonFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): import time st = time.time() out = self.feature(imfeat.resize_image_max_side(image, 160)) print time.time() - st return out if __name__ == '__main__': vidfeat._frame_feature_main('person', vidfeat.PersonFrameFeature)
import vidfeat import imfeat import sklearn.svm class UnderwaterFrameFeature(vidfeat.ClassifierFrameFeature): feature = imfeat.MetaFeature(imfeat.Histogram('lab'), imfeat.GIST(), max_side=128) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(UnderwaterFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): return self.feature(image) if __name__ == '__main__': vidfeat._frame_feature_main('underwater', vidfeat.UnderwaterFrameFeature)
import vidfeat import imfeat import sklearn.svm from vidfeat.models.person_bovw_clusters import clusters HOG = imfeat.HOGLatent(8, 2) class PersonHandFrameFeature(vidfeat.ClassifierFrameFeature): #feature = imfeat.PyramidHistogram('lab') feature = imfeat.BoVW(lambda x: HOG.make_bow_mask(x, clusters), clusters.shape[0], 3) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(PersonHandFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): import time st = time.time() out = self.feature(imfeat.resize_image_max_side(image, 160)) print time.time() - st return out if __name__ == '__main__': vidfeat._frame_feature_main('person_hand', vidfeat.PersonHandFrameFeature)
import vidfeat import imfeat import sklearn.svm import kernels class PoorQualityFrameFeature(vidfeat.ClassifierFrameFeature): feature = imfeat.GradientHistogram() def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(PoorQualityFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): return self.feature(image) if __name__ == '__main__': vidfeat._frame_feature_main('poor_quality', vidfeat.PoorQualityFrameFeature)
import vidfeat import imfeat import sklearn.svm class FireFrameFeature(vidfeat.ClassifierFrameFeature): feature = imfeat.PyramidHistogram('lab') def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(FireFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): return self.feature(image) if __name__ == '__main__': vidfeat._frame_feature_main('fire', vidfeat.FireFrameFeature)
import vidfeat import imfeat import sklearn.svm from vidfeat.models.person_bovw_clusters import clusters HOG = imfeat.HOGLatent(8, 2) class PersonUpperFrameFeature(vidfeat.ClassifierFrameFeature): #feature = imfeat.PyramidHistogram('lab') feature = imfeat.BoVW(lambda x: HOG.make_bow_mask(x, clusters), clusters.shape[0], 3) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(PersonUpperFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): import time st = time.time() out = self.feature(imfeat.resize_image_max_side(image, 160)) print time.time() - st return out if __name__ == '__main__': vidfeat._frame_feature_main('person_upper', vidfeat.PersonUpperFrameFeature)
import vidfeat import imfeat import sklearn.svm from vidfeat.models.snow_bovw_clusters import clusters HOG = imfeat.HOGLatent(8, 2) #import kernels class SnowFrameFeature(vidfeat.ClassifierFrameFeature): #feature = imfeat.PyramidHistogram('lab') feature = imfeat.BoVW(lambda x: HOG.make_bow_mask(x, clusters), clusters.shape[0], 3) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(SnowFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): import time st = time.time() out = self.feature(imfeat.resize_image_max_side(image, 160)) print time.time() - st return out if __name__ == '__main__': vidfeat._frame_feature_main('snow', vidfeat.SnowFrameFeature)
import vidfeat import imfeat import sklearn.svm import numpy as np def color_stats(image): image_hsv = imfeat.convert_image(image, {'mode': 'hsv', 'type': 'numpy', 'dtype': 'float32'}) image_hsv = image_hsv.reshape((image_hsv.shape[0] * image_hsv.shape[1], image_hsv.shape[2])) image_hsv[:, 0] /= 360 # Rescale return np.hstack([np.min(image_hsv, 0), np.max(image_hsv, 0), np.mean(image_hsv, 0), np.median(image_hsv, 0), np.std(image_hsv, 0), imfeat.UniqueColors()(image)]) class GrayFrameFeature(vidfeat.ClassifierFrameFeature): def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(GrayFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): out = color_stats(imfeat.resize_image_max_side(image, 128)) return out if __name__ == '__main__': vidfeat._frame_feature_main('gray', vidfeat.GrayFrameFeature)
import vidfeat import imfeat import sklearn.svm class LowLightFrameFeature(vidfeat.ClassifierFrameFeature): feature = imfeat.MetaFeature(imfeat.GradientHistogram(), imfeat.Histogram('lab', num_bins=4), imfeat.Moments('lab', 2), max_side=128) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(LowLightFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): return self.feature(image) if __name__ == '__main__': vidfeat._frame_feature_main('low_light', vidfeat.LowLightFrameFeature)
import vidfeat import imfeat import sklearn.svm class WaterOutdoorFrameFeature(vidfeat.ClassifierFrameFeature): feature = imfeat.MetaFeature(imfeat.Histogram('lab'), imfeat.GIST(), max_side=128) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(WaterOutdoorFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): return self.feature(image) if __name__ == '__main__': vidfeat._frame_feature_main('water_outdoor', vidfeat.WaterOutdoorFrameFeature)
import vidfeat import imfeat import sklearn.svm class VerticalBoxedFrameFeature(vidfeat.ClassifierFrameFeature): feature = imfeat.BlackBars() def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(VerticalBoxedFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): return self.feature(image) if __name__ == '__main__': vidfeat._frame_feature_main('vertical_boxed', vidfeat.VerticalBoxedFrameFeature, remove_bars=True)
from vidfeat.models.synthetic_bovw_clusters import clusters # import kernels HOG = imfeat.HOGLatent(8, 2) class SyntheticFrameFeature(vidfeat.ClassifierFrameFeature): # feature = imfeat.MetaFeature(imfeat.GradientHistogram(), imfeat.Histogram('lab')) feature = imfeat.MetaFeature( imfeat.BoVW(lambda x: HOG.make_bow_mask(x, clusters), clusters.shape[0], 3), imfeat.Histogram("lab", num_bins=4), imfeat.UniqueColors(), ) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight="auto") self.svm_parameters = [{"C": [10 ** x for x in range(0, 12, 3)]}] super(SyntheticFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): import time st = time.time() out = self.feature(imfeat.resize_image_max_side(image, 160)) print time.time() - st return out if __name__ == "__main__": vidfeat._frame_feature_main("synthetic", vidfeat.SyntheticFrameFeature)
import vidfeat import imfeat import sklearn.svm from vidfeat.models.synthetic_bovw_clusters import clusters #import kernels HOG = imfeat.HOGLatent(8, 2) class TextFrameFeature(vidfeat.ClassifierFrameFeature): #feature = imfeat.MetaFeature(imfeat.GradientHistogram(), imfeat.Histogram('lab')) feature = imfeat.BoVW(lambda x: HOG.make_bow_mask(x, clusters), clusters.shape[0], 3) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(TextFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): import time st = time.time() out = self.feature(imfeat.resize_image_max_side(image, 160)) print time.time() - st return out if __name__ == '__main__': vidfeat._frame_feature_main('text', vidfeat.TextFrameFeature)
import vidfeat import imfeat import sklearn.svm class HorizontalBoxedFrameFeature(vidfeat.ClassifierFrameFeature): feature = imfeat.BlackBars() def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(HorizontalBoxedFrameFeature, self).__init__(classifier=classifier, *args, **kw) def remove_bars(self, image): pass def _feature(self, image): return self.feature(image) if __name__ == '__main__': vidfeat._frame_feature_main('horizontal_boxed', vidfeat.HorizontalBoxedFrameFeature, remove_bars=False)
import vidfeat import imfeat import sklearn.svm from vidfeat.models.snow_bovw_clusters import clusters HOG = imfeat.HOGLatent(8, 2) class GrassFrameFeature(vidfeat.ClassifierFrameFeature): #feature = imfeat.PyramidHistogram('lab') feature = imfeat.BoVW(lambda x: HOG.make_bow_mask(x, clusters), clusters.shape[0], 3) def __init__(self, *args, **kw): classifier = sklearn.svm.LinearSVC(class_weight='auto') self.svm_parameters = [{'C': [10 ** x for x in range(0, 12, 3)]}] super(GrassFrameFeature, self).__init__(classifier=classifier, *args, **kw) def _feature(self, image): import time st = time.time() out = self.feature(imfeat.resize_image_max_side(image, 160)) print time.time() - st return out if __name__ == '__main__': vidfeat._frame_feature_main('grass', vidfeat.GrassFrameFeature)