def __init__(self, haar_filters, data, labels, num_chosen_wc, num_bins,
visualizer, num_cores, style, save_dir):
self.filters = haar_filters
self.data = data
self.labels = labels
self.num_chosen_wc = num_chosen_wc
self.num_bins = num_bins
self.visualizer = visualizer
self.num_cores = num_cores
self.style = style
self.chosen_wcs = None
if style == 'Ada':
self.weak_classifiers = [Ada_Weak_Classifier(i, filt[0], filt[1], self.num_bins) \
for i, filt in enumerate(self.filters)]
elif style == 'Real':
if save_dir is not None and os.path.exists(save_dir):
print(
'[Loading chosen weak classifiers from Adaboost, %s loading...]'
% save_dir)
self.load_trained_wcs(save_dir)
else:
print("Chosen classifiers not found")
return
self.chosen_classifiers_from_AB = np.array(self.chosen_wcs)[:, 1]
self.weak_classifiers = [
Real_Weak_Classifier(
i, self.chosen_classifiers_from_AB[i].plus_rects,
self.chosen_classifiers_from_AB[i].minus_rects,
self.num_bins)
for i in range(len(self.chosen_classifiers_from_AB))
]
def __init__(self, haar_filters, data, labels, num_chosen_wc, num_bins,
visualizer, num_cores, style):
self.filters = haar_filters
self.data = data
self.labels = labels
self.num_chosen_wc = num_chosen_wc
self.num_bins = num_bins
self.visualizer = visualizer
self.num_cores = num_cores
self.style = style
self.chosen_wcs = []
if style == 'Ada':
self.weak_classifiers = [Ada_Weak_Classifier(i, filt[0], filt[1], self.num_bins)\
for i, filt in enumerate(self.filters)]
elif style == 'Real':
self.weak_classifiers = [Real_Weak_Classifier(i, filt[0], filt[1], self.num_bins)\
for i, filt in enumerate(self.filters)]