def measure_accuracy(self, nregistered=40, nunregistered=40):
s = System()
registered = sample(list(DataLoader.get_img_data('registered')),
nregistered)
unregistered = sample(list(DataLoader.get_img_data('unregistered')),
nunregistered)
self._test_identification(s, registered)
self._test_unregistered(s, unregistered)
def _create_database(self):
self._images = dict()
for key, img in DataLoader.get_img_data():
if key not in self._images.keys():
self._images[key] = img
self._graphs = {
k: Graph(i).get_stats()
for k, i in self._images.items()
}
def _create_database(self):
self._images = dict()
for key, img in DataLoader.get_img_data():
if key not in self._images.keys():
self._images[key] = img
self._graphs = {
k: Graph(i).get_stats()
for k, i in self._images.items()
}
def verify(self, target, id):
ids, imgs = zip(*list(self._images.items()))
imgs = np.array(imgs).reshape((len(ids), *IMG_SHAPE))
r = np.repeat(target, len(ids)).reshape((len(ids), *IMG_SHAPE))
probs = self._imagenet.net.predict([r, imgs]).flatten()
min_prob = np.min(probs)
print(min_prob)
if str(id) in ids: print(probs[ids.index(str(id))])
if min_prob < .8:
return False, None
return True, ids[np.argmin(probs)]
if __name__ == '__main__':
s = System()
for (id, img), _ in zip(DataLoader.get_img_data(), range(6)):
print(id)
print(s.verify(img, id))
